best_modelR Documentation

best_model

Description

Returns the best input models. The coefficient of the best model can be found with the poly_model function

Usage

best_model(
  inpt_datf,
  Degree,
  Coeff_v = NA,
  Powers = NA,
  Mth_symb,
  Numrtr_v = NA
)

Arguments

inpt_datf

is the input dataframe, first column for the x values and second column for the y values

Degree

is a vector containing all the degrees. Each degree represents how many coefficients the model has.

Coeff_v

is a list containing the vector containing the coefficients for each model. The first value of each coefficient vector is always the constant, so it is not linked to any math symbol

Powers

is a list containing all the values associated with the math symbols of mth_symb list for each model. Because you can have multiple models in the function, so Powers is separated with the "-" separator between the different powers values for each model like in the examples

Mth_symb

is a list containing the vector of the different math symbols linked to the coefficients from the second value

Numrtr_v

is a list containing the different numerator values for each math symbol for each model, see examples

Examples


print(best_model(inpt_datf=data.frame(mtcars$wt, mtcars$mpg), Degree=c(2, 2), Coeff_v=c("-", 32.5, -3, "-", 32.5, -5, "-"), Powers=c("-", 1, "-", 1, "-"), Mth_symb=c("-", "x", "-", "x", "-"), Numrtr_v=NA))

[1] 2

print(best_model(inpt_datf=data.frame(mtcars$wt, mtcars$mpg), Degree=c(2, 2), Coeff_v=c("-", c(32.5, -5), "-", c(32.5, -3), "-"), Powers=c("-", c(1), "-", c(1), "-"), Mth_symb=c("-", c("x"), "-", c("1/x"), "-"), Numrtr_v=NA))

[1] 1

print(best_model(inpt_datf=data.frame(mtcars$wt, mtcars$mpg), Degree=c(2, 2), Coeff_v=c("-", c(32.5, -5), "-", c(32.5, -3), "-"), Powers=c("-", c(1), "-", list(c(1:length(mtcars$wt))), "-"), Mth_symb=c("-", c("x"), "-", c("1/x"), "-"), Numrtr_v=NA))

[1] 1

print(best_model(inpt_datf=data.frame(mtcars$wt, mtcars$mpg), Degree=c(2, 2), Coeff_v=c("-", c(32.5, -5), "-", c(32.5, -3, 2), "-"), Powers=c("-", c(1), "-", list(c(1:length(mtcars$wt)), 2), "-"), Mth_symb=c("-", c("x"), "-", c("list/x", "x"), "-"), Numrtr_v=c("-", list(c(1:length(mtcars$wt))), "-")))

#' [1] 1
R: calcall
calcallR Documentation

calcall

Description

Takes a formula as a character as an input and makes the calculation. Accepts also variables, in this case the part of the formula that contains the variable wont be calculated, but the others part will be as usual.

Usage

calcall(inpt)

Arguments

inpt

is the input formula as a character

Examples


print(calcall(inpt="ze+(yu*((fgf)-(-12+8-Y+4-T+4+97+a)+tt))"))

[1] "ze+(yu*(fgf-(-4-Y+4-T+101+a)+tt))"

print(calcall(inpt="ze+(yu*((fgf)-(-12+8-7+3-67+4+97+1)+tt))"))

[1] "ze+(yu*(fgf-27+tt))"

print(calcall(inpt="ze+(yu*((fgf)+(12*3/2+4)+tt))"))

[1] "ze+(yu*(fgf+22+tt))"

print(calcall(inpt="1+3*2+(-2/-3*-3*((fgf)-(--12-6)+2))+5-3*5"))

[1] "7+(-2*(fgf-4))+20"

print(calcall(inpt="1+3*2+(-2/-3*-3*((fgf)-(--12-6)+2))+(-log_e_1_e_2+t+2^3)+m-log_e_1_e_2+2^3-m-6*2+(e_ii-2-6+log_im_4-67)+-6+2+(y-5+7)"))

[1] "7+(-2*(fgf-4))+(-2+t+8)+m+6-m-12+(e_ii-8+log_im_4-67)-4+(y+2)"

print(calcall("(6+4*-(4-5))+3/3"))

[1] "11" 

print(calcall(inpt="1+3*2+(-2/-3*-3*((fgf)-(--12-6)+2))+(-log_e_1_e_2+t+2^3)+m-log_e_1_e_2+2^3-m-6*2+-6+2"))

[1] "7+(-2*(fgf-4))+(-2+t+8)+m+6-m-16"

print(calcall(inpt="(log_5_Z-2-6+5)+-6+2"))

[1] "(log_5_Z-3)-4"

print(calcall(inpt="m--2+-5"))

[1] "m-3"

print(calcall(inpt="(-2-6)+-6+2"))

[1] "-12" 

print(calcall(inpt="m-6"))

[1] "m-6"

print(calcall(inpt="--6"))

[1] "6"
R: calcall_var
calcall_varR Documentation

calcall_var

Description

Does the same thing as calcall function but calculates the formula that have variables. The values of the variables have to be given in a list of vectors, see examples.

Usage

calcall_var(inpt, var_name_v, var_val_l)

Arguments

inpt

is the input formula, with the variables

var_name_v

is the vector that contains the variables name in the order of apparition in the formula. If the variable appears multiple times in the formula, it has to be specified in this vector, see examples.

var_val_l

is the list containing the vectors containing the values of each variable, for each point you want to calculate. The vectors has to be given in the same order has the variable in var_name_v.

Examples


print(calcall_var(inpt="(6+m*-(4-imp))+3/jp", var_name_v=c("m", "imp", "jp"), 
                  var_val_l=list(

                                 c(1:6), 

                                 c(3, 4, 2, 5, 6, 1),

                                 c(6:1))))

 [1] "5.5"  "6.6"  "0.75" "11"   "17.5" "-9"

print(calcall_var(inpt="(6+m*-(4-imp))+3/jp+jp", var_name_v=c("m", "imp", "jp", "jp"), 
                   var_val_l=list(
 
                                  c(1:6), 
 
                                  c(3, 4, 2, 5, 6, 1),
 
                                  c(6:1))))

[1] "11.5" "11.6" "4.75" "14" "19.5" "-8"
R: datf_folder
datf_folderR Documentation

datf_folder

Description

Folds a dataframe, see examples.

Usage

datf_folder(inpt_datf)

Arguments

inpt_datf

is the input dataframe

Examples


print(datf_folder(inpt_datf = iris))

    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
1            5.1         3.5          1.4         0.2     setosa
2            6.9         3.1          4.9         1.5 versicolor
3            4.7         3.2          1.3         0.2     setosa
4            5.1         3.5          1.4         0.3     setosa
5            7.2         3.0          5.8         1.6  virginica
6            5.8         2.7          5.1         1.9  virginica
7            5.4         3.0          4.5         1.5 versicolor
8            6.7         3.1          5.6         2.4  virginica
9            6.0         3.0          4.8         1.8  virginica
10           5.4         3.4          1.5         0.4     setosa
11           6.9         3.1          5.4         2.1  virginica
12           5.8         2.7          5.1         1.9  virginica
13           6.4         3.1          5.5         1.8  virginica
14           5.7         2.6          3.5         1.0 versicolor
15           5.4         3.9          1.7         0.4     setosa
16           5.7         2.8          4.1         1.3 versicolor
17           5.1         3.7          1.5         0.4     setosa
18           4.4         3.0          1.3         0.2     setosa
19           5.7         3.8          1.7         0.3     setosa
20           5.1         3.8          1.5         0.3     setosa
21           5.4         3.4          1.7         0.2     setosa
22           6.7         3.1          4.7         1.5 versicolor
23           6.0         3.4          4.5         1.6 versicolor
24           6.9         3.1          4.9         1.5 versicolor
25           4.8         3.4          1.9         0.2     setosa
26           5.8         2.7          5.1         1.9  virginica
27           5.0         3.4          1.6         0.4     setosa
28           5.8         2.8          5.1         2.4  virginica
29           6.3         2.3          4.4         1.3 versicolor
30           4.7         3.2          1.6         0.2     setosa
31           4.8         3.0          1.4         0.3     setosa
32           5.4         3.4          1.5         0.4     setosa
33           6.1         2.6          5.6         1.4  virginica
34           6.1         3.0          4.6         1.4 versicolor
35           6.0         2.2          4.0         1.0 versicolor
36           5.0         3.2          1.2         0.2     setosa
37           5.5         3.5          1.3         0.2     setosa
38           5.8         2.8          5.1         2.4  virginica
39           6.2         3.4          5.4         2.3  virginica
40           5.1         3.4          1.5         0.2     setosa
41           5.0         3.5          1.3         0.3     setosa
42           4.5         2.3          1.3         0.3     setosa
43           4.9         3.6          1.4         0.1     setosa
44           5.0         3.5          1.6         0.6     setosa
45           5.7         3.0          4.2         1.2 versicolor
46           6.4         2.8          5.6         2.1  virginica
47           6.2         3.4          5.4         2.3  virginica
48           4.6         3.2          1.4         0.2     setosa
49           6.4         3.2          5.3         2.3  virginica
50           5.5         4.2          1.4         0.2     setosa
51           7.7         3.0          6.1         2.3  virginica
52           5.9         3.0          4.2         1.5 versicolor
53           6.5         3.0          5.5         1.8  virginica
54           5.4         3.9          1.7         0.4     setosa
55           6.5         2.8          4.6         1.5 versicolor
56           5.8         2.6          4.0         1.2 versicolor
57           5.7         2.8          4.5         1.3 versicolor
58           4.9         2.4          3.3         1.0 versicolor
59           6.7         3.1          5.6         2.4  virginica
60           6.1         3.0          4.9         1.8  virginica
61           5.8         2.8          5.1         2.4  virginica
62           5.9         3.0          4.2         1.5 versicolor
63           5.2         4.1          1.5         0.1     setosa
64           6.9         3.1          4.9         1.5 versicolor
65           5.6         2.9          3.6         1.3 versicolor
66           5.4         3.4          1.7         0.2     setosa
67           5.6         3.0          4.5         1.5 versicolor
68           5.8         2.7          4.1         1.0 versicolor
69           6.2         2.2          4.5         1.5 versicolor
70           6.2         2.2          4.5         1.5 versicolor
71           5.9         3.2          4.8         1.8 versicolor
72           6.1         2.8          4.0         1.3 versicolor
73           6.3         2.5          4.9         1.5 versicolor
74           5.0         3.0          1.6         0.2     setosa
75           4.6         3.4          1.4         0.3     setosa
76           6.4         3.2          5.3         2.3  virginica
77           6.7         3.1          4.7         1.5 versicolor
78           5.5         4.2          1.4         0.2     setosa
79           6.0         2.9          4.5         1.5 versicolor
80           5.4         3.9          1.7         0.4     setosa
81           5.5         3.5          1.3         0.2     setosa
82           6.3         3.3          6.0         2.5  virginica
83           5.8         2.7          3.9         1.2 versicolor
84           6.0         2.7          5.1         1.6 versicolor
85           6.8         2.8          4.8         1.4 versicolor
86           6.1         3.0          4.6         1.4 versicolor
87           6.7         3.1          4.7         1.5 versicolor
88           5.1         3.8          1.6         0.2     setosa
89           6.8         2.8          4.8         1.4 versicolor
90           6.9         3.2          5.7         2.3  virginica
91           6.0         3.4          4.5         1.6 versicolor
92           6.1         3.0          4.6         1.4 versicolor
93           5.8         2.6          4.0         1.2 versicolor
94           5.0         2.3          3.3         1.0 versicolor
95           5.7         3.0          4.2         1.2 versicolor
96           5.7         3.0          4.2         1.2 versicolor
97           5.7         2.9          4.2         1.3 versicolor
98           6.4         2.8          5.6         2.2  virginica
99           5.1         3.4          1.5         0.2     setosa
100          5.7         2.8          4.1         1.3 versicolor
101          6.5         2.8          4.6         1.5 versicolor
102          4.8         3.4          1.9         0.2     setosa
103          4.4         2.9          1.4         0.2     setosa
104          5.1         2.5          3.0         1.1 versicolor
105          7.4         2.8          6.1         1.9  virginica
106          7.6         3.0          6.6         2.1  virginica
107          4.9         2.5          4.5         1.7  virginica
108          7.3         2.9          6.3         1.8  virginica
109          4.8         3.4          1.9         0.2     setosa
110          5.7         4.4          1.5         0.4     setosa
111          6.5         3.2          5.1         2.0  virginica
112          6.9         3.2          5.7         2.3  virginica
113          5.9         3.2          4.8         1.8 versicolor
114          7.1         3.0          5.9         2.1  virginica
115          5.8         2.8          5.1         2.4  virginica
116          4.8         3.4          1.9         0.2     setosa
117          4.3         3.0          1.1         0.1     setosa
118          6.6         2.9          4.6         1.3 versicolor
119          5.1         2.5          3.0         1.1 versicolor
120          6.0         2.2          5.0         1.5  virginica
121          5.1         3.4          1.5         0.2     setosa
122          6.3         2.7          4.9         1.8  virginica
123          6.7         3.3          5.7         2.1  virginica
124          6.1         2.6          5.6         1.4  virginica
125          5.0         3.3          1.4         0.2     setosa
126          7.2         3.2          6.0         1.8  virginica
127          6.2         2.8          4.8         1.8  virginica
128          6.1         3.0          4.9         1.8  virginica
129          5.0         3.4          1.6         0.4     setosa
130          6.2         2.2          4.5         1.5 versicolor
131          7.4         2.8          6.1         1.9  virginica
132          6.6         2.9          4.6         1.3 versicolor
133          6.7         3.3          5.7         2.1  virginica
134          6.3         3.3          4.7         1.6 versicolor
135          5.7         2.9          4.2         1.3 versicolor
136          7.2         3.6          6.1         2.5  virginica
137          6.5         3.0          5.5         1.8  virginica
138          6.4         3.1          5.5         1.8  virginica
139          5.5         4.2          1.4         0.2     setosa
140          5.8         2.7          5.1         1.9  virginica
141          5.0         2.0          3.5         1.0 versicolor
142          6.9         3.1          5.1         2.3  virginica
143          5.8         2.7          5.1         1.9  virginica
144          6.8         3.2          5.9         2.3  virginica
145          6.7         3.3          5.7         2.5  virginica
146          5.1         3.3          1.7         0.5     setosa
147          5.1         3.8          1.9         0.4     setosa
148          6.5         3.0          5.2         2.0  virginica
149          4.6         3.6          1.0         0.2     setosa
150          5.9         3.0          5.1         1.8  virginica
R: individual_cloning
individual_cloningR Documentation

individual_cloning

Description

Allow to generate individuals with the same label as those existig and having as values at variables, a value generated with a normal distribution having as parameters the mean for the variable A for the individual I and the same goes for the standard deviation, see examples.

Usage

individual_cloning(inpt_datf, col_vars = c(), label_var, hmn)

Arguments

inpt_datf

is the input dataset as a dataframe

col_vars

is a vector containing the colnames or the column numbers of the variables

label_var

is a either the colnames or the column number of the label variable

hmn

is how many of new individual from the same label will be generated

Examples


datf <- iris
datf[, 5] <- as.character(datf[, 5])
datf <- individual_cloning(inpt_datf = datf, col_vars = c(1:4), label_var = 5, hmn = 3)
print(datf)
nrow(datf)
nrow(iris)

     Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
1        5.100000    3.500000     1.400000   0.2000000     setosa
2        4.900000    3.000000     1.400000   0.2000000     setosa
3        4.700000    3.200000     1.300000   0.2000000     setosa
4        4.600000    3.100000     1.500000   0.2000000     setosa
5        5.000000    3.600000     1.400000   0.2000000     setosa
6        5.400000    3.900000     1.700000   0.4000000     setosa
7        4.600000    3.400000     1.400000   0.3000000     setosa
8        5.000000    3.400000     1.500000   0.2000000     setosa
9        4.400000    2.900000     1.400000   0.2000000     setosa
10       4.900000    3.100000     1.500000   0.1000000     setosa
11       5.400000    3.700000     1.500000   0.2000000     setosa
12       4.800000    3.400000     1.600000   0.2000000     setosa
13       4.800000    3.000000     1.400000   0.1000000     setosa
14       4.300000    3.000000     1.100000   0.1000000     setosa
15       5.800000    4.000000     1.200000   0.2000000     setosa
16       5.700000    4.400000     1.500000   0.4000000     setosa
17       5.400000    3.900000     1.300000   0.4000000     setosa
18       5.100000    3.500000     1.400000   0.3000000     setosa
19       5.700000    3.800000     1.700000   0.3000000     setosa
20       5.100000    3.800000     1.500000   0.3000000     setosa
21       5.400000    3.400000     1.700000   0.2000000     setosa
22       5.100000    3.700000     1.500000   0.4000000     setosa
23       4.600000    3.600000     1.000000   0.2000000     setosa
24       5.100000    3.300000     1.700000   0.5000000     setosa
25       4.800000    3.400000     1.900000   0.2000000     setosa
26       5.000000    3.000000     1.600000   0.2000000     setosa
27       5.000000    3.400000     1.600000   0.4000000     setosa
28       5.200000    3.500000     1.500000   0.2000000     setosa
29       5.200000    3.400000     1.400000   0.2000000     setosa
30       4.700000    3.200000     1.600000   0.2000000     setosa
31       4.800000    3.100000     1.600000   0.2000000     setosa
32       5.400000    3.400000     1.500000   0.4000000     setosa
33       5.200000    4.100000     1.500000   0.1000000     setosa
34       5.500000    4.200000     1.400000   0.2000000     setosa
35       4.900000    3.100000     1.500000   0.2000000     setosa
36       5.000000    3.200000     1.200000   0.2000000     setosa
37       5.500000    3.500000     1.300000   0.2000000     setosa
38       4.900000    3.600000     1.400000   0.1000000     setosa
39       4.400000    3.000000     1.300000   0.2000000     setosa
40       5.100000    3.400000     1.500000   0.2000000     setosa
41       5.000000    3.500000     1.300000   0.3000000     setosa
42       4.500000    2.300000     1.300000   0.3000000     setosa
43       4.400000    3.200000     1.300000   0.2000000     setosa
44       5.000000    3.500000     1.600000   0.6000000     setosa
45       5.100000    3.800000     1.900000   0.4000000     setosa
46       4.800000    3.000000     1.400000   0.3000000     setosa
47       5.100000    3.800000     1.600000   0.2000000     setosa
48       4.600000    3.200000     1.400000   0.2000000     setosa
49       5.300000    3.700000     1.500000   0.2000000     setosa
50       5.000000    3.300000     1.400000   0.2000000     setosa
51       7.000000    3.200000     4.700000   1.4000000 versicolor
52       6.400000    3.200000     4.500000   1.5000000 versicolor
53       6.900000    3.100000     4.900000   1.5000000 versicolor
54       5.500000    2.300000     4.000000   1.3000000 versicolor
55       6.500000    2.800000     4.600000   1.5000000 versicolor
56       5.700000    2.800000     4.500000   1.3000000 versicolor
57       6.300000    3.300000     4.700000   1.6000000 versicolor
58       4.900000    2.400000     3.300000   1.0000000 versicolor
59       6.600000    2.900000     4.600000   1.3000000 versicolor
60       5.200000    2.700000     3.900000   1.4000000 versicolor
61       5.000000    2.000000     3.500000   1.0000000 versicolor
62       5.900000    3.000000     4.200000   1.5000000 versicolor
63       6.000000    2.200000     4.000000   1.0000000 versicolor
64       6.100000    2.900000     4.700000   1.4000000 versicolor
65       5.600000    2.900000     3.600000   1.3000000 versicolor
66       6.700000    3.100000     4.400000   1.4000000 versicolor
67       5.600000    3.000000     4.500000   1.5000000 versicolor
68       5.800000    2.700000     4.100000   1.0000000 versicolor
69       6.200000    2.200000     4.500000   1.5000000 versicolor
70       5.600000    2.500000     3.900000   1.1000000 versicolor
71       5.900000    3.200000     4.800000   1.8000000 versicolor
72       6.100000    2.800000     4.000000   1.3000000 versicolor
73       6.300000    2.500000     4.900000   1.5000000 versicolor
74       6.100000    2.800000     4.700000   1.2000000 versicolor
75       6.400000    2.900000     4.300000   1.3000000 versicolor
76       6.600000    3.000000     4.400000   1.4000000 versicolor
77       6.800000    2.800000     4.800000   1.4000000 versicolor
78       6.700000    3.000000     5.000000   1.7000000 versicolor
79       6.000000    2.900000     4.500000   1.5000000 versicolor
80       5.700000    2.600000     3.500000   1.0000000 versicolor
81       5.500000    2.400000     3.800000   1.1000000 versicolor
82       5.500000    2.400000     3.700000   1.0000000 versicolor
83       5.800000    2.700000     3.900000   1.2000000 versicolor
84       6.000000    2.700000     5.100000   1.6000000 versicolor
85       5.400000    3.000000     4.500000   1.5000000 versicolor
86       6.000000    3.400000     4.500000   1.6000000 versicolor
87       6.700000    3.100000     4.700000   1.5000000 versicolor
88       6.300000    2.300000     4.400000   1.3000000 versicolor
89       5.600000    3.000000     4.100000   1.3000000 versicolor
90       5.500000    2.500000     4.000000   1.3000000 versicolor
91       5.500000    2.600000     4.400000   1.2000000 versicolor
92       6.100000    3.000000     4.600000   1.4000000 versicolor
93       5.800000    2.600000     4.000000   1.2000000 versicolor
94       5.000000    2.300000     3.300000   1.0000000 versicolor
95       5.600000    2.700000     4.200000   1.3000000 versicolor
96       5.700000    3.000000     4.200000   1.2000000 versicolor
97       5.700000    2.900000     4.200000   1.3000000 versicolor
98       6.200000    2.900000     4.300000   1.3000000 versicolor
99       5.100000    2.500000     3.000000   1.1000000 versicolor
100      5.700000    2.800000     4.100000   1.3000000 versicolor
101      6.300000    3.300000     6.000000   2.5000000  virginica
102      5.800000    2.700000     5.100000   1.9000000  virginica
103      7.100000    3.000000     5.900000   2.1000000  virginica
104      6.300000    2.900000     5.600000   1.8000000  virginica
105      6.500000    3.000000     5.800000   2.2000000  virginica
106      7.600000    3.000000     6.600000   2.1000000  virginica
107      4.900000    2.500000     4.500000   1.7000000  virginica
108      7.300000    2.900000     6.300000   1.8000000  virginica
109      6.700000    2.500000     5.800000   1.8000000  virginica
110      7.200000    3.600000     6.100000   2.5000000  virginica
111      6.500000    3.200000     5.100000   2.0000000  virginica
112      6.400000    2.700000     5.300000   1.9000000  virginica
113      6.800000    3.000000     5.500000   2.1000000  virginica
114      5.700000    2.500000     5.000000   2.0000000  virginica
115      5.800000    2.800000     5.100000   2.4000000  virginica
116      6.400000    3.200000     5.300000   2.3000000  virginica
117      6.500000    3.000000     5.500000   1.8000000  virginica
118      7.700000    3.800000     6.700000   2.2000000  virginica
119      7.700000    2.600000     6.900000   2.3000000  virginica
120      6.000000    2.200000     5.000000   1.5000000  virginica
121      6.900000    3.200000     5.700000   2.3000000  virginica
122      5.600000    2.800000     4.900000   2.0000000  virginica
123      7.700000    2.800000     6.700000   2.0000000  virginica
124      6.300000    2.700000     4.900000   1.8000000  virginica
125      6.700000    3.300000     5.700000   2.1000000  virginica
126      7.200000    3.200000     6.000000   1.8000000  virginica
127      6.200000    2.800000     4.800000   1.8000000  virginica
128      6.100000    3.000000     4.900000   1.8000000  virginica
129      6.400000    2.800000     5.600000   2.1000000  virginica
130      7.200000    3.000000     5.800000   1.6000000  virginica
131      7.400000    2.800000     6.100000   1.9000000  virginica
132      7.900000    3.800000     6.400000   2.0000000  virginica
133      6.400000    2.800000     5.600000   2.2000000  virginica
134      6.300000    2.800000     5.100000   1.5000000  virginica
135      6.100000    2.600000     5.600000   1.4000000  virginica
136      7.700000    3.000000     6.100000   2.3000000  virginica
137      6.300000    3.400000     5.600000   2.4000000  virginica
138      6.400000    3.100000     5.500000   1.8000000  virginica
139      6.000000    3.000000     4.800000   1.8000000  virginica
140      6.900000    3.100000     5.400000   2.1000000  virginica
141      6.700000    3.100000     5.600000   2.4000000  virginica
142      6.900000    3.100000     5.100000   2.3000000  virginica
143      5.800000    2.700000     5.100000   1.9000000  virginica
144      6.800000    3.200000     5.900000   2.3000000  virginica
145      6.700000    3.300000     5.700000   2.5000000  virginica
146      6.700000    3.000000     5.200000   2.3000000  virginica
147      6.300000    2.500000     5.000000   1.9000000  virginica
148      6.500000    3.000000     5.200000   2.0000000  virginica
149      6.200000    3.400000     5.400000   2.3000000  virginica
150      5.900000    3.000000     5.100000   1.8000000  virginica
151      4.601009    3.727368     1.268078   0.3122136     setosa
210      4.613076    3.989209     1.555392   0.2953775     setosa
310      4.722235    3.602591     1.479940   0.2471369     setosa
513      5.660667    2.449398     4.241485   1.5317590 versicolor
511      5.987887    3.016099     3.690411   1.5357972 versicolor
512      5.803584    2.828602     4.024589   1.2767213 versicolor
1013     6.851160    3.287923     5.157840   1.5365199  virginica
1011     7.119751    3.460045     4.990113   1.2895762  virginica
1012     7.370573    3.140464     5.680828   1.8674812  virginica
[1] 159
[1] 150
R: individual_equalizer_max
individual_equalizer_maxR Documentation

individual_equalizer_max

Description

Remove the individual that are in exess according to a given value, see examples

Usage

individual_equalizer_max(inpt_datf, label_var, hmn)

Examples


print(individual_equalizer_max(inpt_datf = datf, label_var = 5, hmn = 15))

   Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
1           5.0         3.2          1.2         0.2     setosa
2           5.5         3.5          1.3         0.2     setosa
3           4.9         3.6          1.4         0.1     setosa
4           4.4         3.0          1.3         0.2     setosa
5           5.1         3.4          1.5         0.2     setosa
6           5.0         3.5          1.3         0.3     setosa
7           4.5         2.3          1.3         0.3     setosa
8           4.4         3.2          1.3         0.2     setosa
9           5.0         3.5          1.6         0.6     setosa
10          5.1         3.8          1.9         0.4     setosa
11          4.8         3.0          1.4         0.3     setosa
12          5.1         3.8          1.6         0.2     setosa
13          4.6         3.2          1.4         0.2     setosa
14          5.3         3.7          1.5         0.2     setosa
15          5.0         3.3          1.4         0.2     setosa
16          6.0         3.4          4.5         1.6 versicolor
17          6.7         3.1          4.7         1.5 versicolor
18          6.3         2.3          4.4         1.3 versicolor
19          5.6         3.0          4.1         1.3 versicolor
20          5.5         2.5          4.0         1.3 versicolor
21          5.5         2.6          4.4         1.2 versicolor
22          6.1         3.0          4.6         1.4 versicolor
23          5.8         2.6          4.0         1.2 versicolor
24          5.0         2.3          3.3         1.0 versicolor
25          5.6         2.7          4.2         1.3 versicolor
26          5.7         3.0          4.2         1.2 versicolor
27          5.7         2.9          4.2         1.3 versicolor
28          6.2         2.9          4.3         1.3 versicolor
29          5.1         2.5          3.0         1.1 versicolor
30          5.7         2.8          4.1         1.3 versicolor
31          7.7         3.0          6.1         2.3  virginica
32          6.3         3.4          5.6         2.4  virginica
33          6.4         3.1          5.5         1.8  virginica
34          6.0         3.0          4.8         1.8  virginica
35          6.9         3.1          5.4         2.1  virginica
36          6.7         3.1          5.6         2.4  virginica
37          6.9         3.1          5.1         2.3  virginica
38          5.8         2.7          5.1         1.9  virginica
39          6.8         3.2          5.9         2.3  virginica
40          6.7         3.3          5.7         2.5  virginica
41          6.7         3.0          5.2         2.3  virginica
42          6.3         2.5          5.0         1.9  virginica
43          6.5         3.0          5.2         2.0  virginica
44          6.2         3.4          5.4         2.3  virginica
45          5.9         3.0          5.1         1.8  virginica
R: individual_equalizer_min
individual_equalizer_minR Documentation

individual_equalizer_min

Description

Allow to increase the number of inividual from any label to a certain point based on the individual_cloning function from the same package (Rmach)

Usage

individual_equalizer_min(inpt_datf, col_vars = c(), label_var, untl)

Arguments

inpt_datf

is the input dataset as a dataframe

col_vars

is a vector containing the colnames or the column numbers of the variables

label_var

is a either the colnames or the column number of the label variable

untl

is how many individual from the same label the dataset has to have, at minimum

Examples


datf <- iris
datf[, 5] <- as.character(datf[, 5])
datf <- individual_equalizer_min(inpt_datf = datf, col_vars = c(1:4), label_var = 5, untl = 120)
print(datf)
nrow(datf)
nrow(iris)

     Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
1       5.100000    3.500000     1.400000  0.20000000     setosa
2       4.900000    3.000000     1.400000  0.20000000     setosa
3       4.700000    3.200000     1.300000  0.20000000     setosa
4       4.600000    3.100000     1.500000  0.20000000     setosa
5       5.000000    3.600000     1.400000  0.20000000     setosa
6       5.400000    3.900000     1.700000  0.40000000     setosa
7       4.600000    3.400000     1.400000  0.30000000     setosa
8       5.000000    3.400000     1.500000  0.20000000     setosa
9       4.400000    2.900000     1.400000  0.20000000     setosa
10      4.900000    3.100000     1.500000  0.10000000     setosa
11      5.400000    3.700000     1.500000  0.20000000     setosa
12      4.800000    3.400000     1.600000  0.20000000     setosa
13      4.800000    3.000000     1.400000  0.10000000     setosa
14      4.300000    3.000000     1.100000  0.10000000     setosa
15      5.800000    4.000000     1.200000  0.20000000     setosa
16      5.700000    4.400000     1.500000  0.40000000     setosa
17      5.400000    3.900000     1.300000  0.40000000     setosa
18      5.100000    3.500000     1.400000  0.30000000     setosa
19      5.700000    3.800000     1.700000  0.30000000     setosa
20      5.100000    3.800000     1.500000  0.30000000     setosa
21      5.400000    3.400000     1.700000  0.20000000     setosa
22      5.100000    3.700000     1.500000  0.40000000     setosa
23      4.600000    3.600000     1.000000  0.20000000     setosa
24      5.100000    3.300000     1.700000  0.50000000     setosa
25      4.800000    3.400000     1.900000  0.20000000     setosa
26      5.000000    3.000000     1.600000  0.20000000     setosa
27      5.000000    3.400000     1.600000  0.40000000     setosa
28      5.200000    3.500000     1.500000  0.20000000     setosa
29      5.200000    3.400000     1.400000  0.20000000     setosa
30      4.700000    3.200000     1.600000  0.20000000     setosa
31      4.800000    3.100000     1.600000  0.20000000     setosa
32      5.400000    3.400000     1.500000  0.40000000     setosa
33      5.200000    4.100000     1.500000  0.10000000     setosa
34      5.500000    4.200000     1.400000  0.20000000     setosa
35      4.900000    3.100000     1.500000  0.20000000     setosa
36      5.000000    3.200000     1.200000  0.20000000     setosa
37      5.500000    3.500000     1.300000  0.20000000     setosa
38      4.900000    3.600000     1.400000  0.10000000     setosa
39      4.400000    3.000000     1.300000  0.20000000     setosa
40      5.100000    3.400000     1.500000  0.20000000     setosa
41      5.000000    3.500000     1.300000  0.30000000     setosa
42      4.500000    2.300000     1.300000  0.30000000     setosa
43      4.400000    3.200000     1.300000  0.20000000     setosa
44      5.000000    3.500000     1.600000  0.60000000     setosa
45      5.100000    3.800000     1.900000  0.40000000     setosa
46      4.800000    3.000000     1.400000  0.30000000     setosa
47      5.100000    3.800000     1.600000  0.20000000     setosa
48      4.600000    3.200000     1.400000  0.20000000     setosa
49      5.300000    3.700000     1.500000  0.20000000     setosa
50      5.000000    3.300000     1.400000  0.20000000     setosa
51      7.000000    3.200000     4.700000  1.40000000 versicolor
52      6.400000    3.200000     4.500000  1.50000000 versicolor
53      6.900000    3.100000     4.900000  1.50000000 versicolor
54      5.500000    2.300000     4.000000  1.30000000 versicolor
55      6.500000    2.800000     4.600000  1.50000000 versicolor
56      5.700000    2.800000     4.500000  1.30000000 versicolor
57      6.300000    3.300000     4.700000  1.60000000 versicolor
58      4.900000    2.400000     3.300000  1.00000000 versicolor
59      6.600000    2.900000     4.600000  1.30000000 versicolor
60      5.200000    2.700000     3.900000  1.40000000 versicolor
61      5.000000    2.000000     3.500000  1.00000000 versicolor
62      5.900000    3.000000     4.200000  1.50000000 versicolor
63      6.000000    2.200000     4.000000  1.00000000 versicolor
64      6.100000    2.900000     4.700000  1.40000000 versicolor
65      5.600000    2.900000     3.600000  1.30000000 versicolor
66      6.700000    3.100000     4.400000  1.40000000 versicolor
67      5.600000    3.000000     4.500000  1.50000000 versicolor
68      5.800000    2.700000     4.100000  1.00000000 versicolor
69      6.200000    2.200000     4.500000  1.50000000 versicolor
70      5.600000    2.500000     3.900000  1.10000000 versicolor
71      5.900000    3.200000     4.800000  1.80000000 versicolor
72      6.100000    2.800000     4.000000  1.30000000 versicolor
73      6.300000    2.500000     4.900000  1.50000000 versicolor
74      6.100000    2.800000     4.700000  1.20000000 versicolor
75      6.400000    2.900000     4.300000  1.30000000 versicolor
76      6.600000    3.000000     4.400000  1.40000000 versicolor
77      6.800000    2.800000     4.800000  1.40000000 versicolor
78      6.700000    3.000000     5.000000  1.70000000 versicolor
79      6.000000    2.900000     4.500000  1.50000000 versicolor
80      5.700000    2.600000     3.500000  1.00000000 versicolor
81      5.500000    2.400000     3.800000  1.10000000 versicolor
82      5.500000    2.400000     3.700000  1.00000000 versicolor
83      5.800000    2.700000     3.900000  1.20000000 versicolor
84      6.000000    2.700000     5.100000  1.60000000 versicolor
85      5.400000    3.000000     4.500000  1.50000000 versicolor
86      6.000000    3.400000     4.500000  1.60000000 versicolor
87      6.700000    3.100000     4.700000  1.50000000 versicolor
88      6.300000    2.300000     4.400000  1.30000000 versicolor
89      5.600000    3.000000     4.100000  1.30000000 versicolor
90      5.500000    2.500000     4.000000  1.30000000 versicolor
91      5.500000    2.600000     4.400000  1.20000000 versicolor
92      6.100000    3.000000     4.600000  1.40000000 versicolor
93      5.800000    2.600000     4.000000  1.20000000 versicolor
94      5.000000    2.300000     3.300000  1.00000000 versicolor
95      5.600000    2.700000     4.200000  1.30000000 versicolor
96      5.700000    3.000000     4.200000  1.20000000 versicolor
97      5.700000    2.900000     4.200000  1.30000000 versicolor
98      6.200000    2.900000     4.300000  1.30000000 versicolor
99      5.100000    2.500000     3.000000  1.10000000 versicolor
100     5.700000    2.800000     4.100000  1.30000000 versicolor
101     6.300000    3.300000     6.000000  2.50000000  virginica
102     5.800000    2.700000     5.100000  1.90000000  virginica
103     7.100000    3.000000     5.900000  2.10000000  virginica
104     6.300000    2.900000     5.600000  1.80000000  virginica
105     6.500000    3.000000     5.800000  2.20000000  virginica
106     7.600000    3.000000     6.600000  2.10000000  virginica
107     4.900000    2.500000     4.500000  1.70000000  virginica
108     7.300000    2.900000     6.300000  1.80000000  virginica
109     6.700000    2.500000     5.800000  1.80000000  virginica
110     7.200000    3.600000     6.100000  2.50000000  virginica
111     6.500000    3.200000     5.100000  2.00000000  virginica
112     6.400000    2.700000     5.300000  1.90000000  virginica
113     6.800000    3.000000     5.500000  2.10000000  virginica
114     5.700000    2.500000     5.000000  2.00000000  virginica
115     5.800000    2.800000     5.100000  2.40000000  virginica
116     6.400000    3.200000     5.300000  2.30000000  virginica
117     6.500000    3.000000     5.500000  1.80000000  virginica
118     7.700000    3.800000     6.700000  2.20000000  virginica
119     7.700000    2.600000     6.900000  2.30000000  virginica
120     6.000000    2.200000     5.000000  1.50000000  virginica
121     6.900000    3.200000     5.700000  2.30000000  virginica
122     5.600000    2.800000     4.900000  2.00000000  virginica
123     7.700000    2.800000     6.700000  2.00000000  virginica
124     6.300000    2.700000     4.900000  1.80000000  virginica
125     6.700000    3.300000     5.700000  2.10000000  virginica
126     7.200000    3.200000     6.000000  1.80000000  virginica
127     6.200000    2.800000     4.800000  1.80000000  virginica
128     6.100000    3.000000     4.900000  1.80000000  virginica
129     6.400000    2.800000     5.600000  2.10000000  virginica
130     7.200000    3.000000     5.800000  1.60000000  virginica
131     7.400000    2.800000     6.100000  1.90000000  virginica
132     7.900000    3.800000     6.400000  2.00000000  virginica
133     6.400000    2.800000     5.600000  2.20000000  virginica
134     6.300000    2.800000     5.100000  1.50000000  virginica
135     6.100000    2.600000     5.600000  1.40000000  virginica
136     7.700000    3.000000     6.100000  2.30000000  virginica
137     6.300000    3.400000     5.600000  2.40000000  virginica
138     6.400000    3.100000     5.500000  1.80000000  virginica
139     6.000000    3.000000     4.800000  1.80000000  virginica
140     6.900000    3.100000     5.400000  2.10000000  virginica
141     6.700000    3.100000     5.600000  2.40000000  virginica
142     6.900000    3.100000     5.100000  2.30000000  virginica
143     5.800000    2.700000     5.100000  1.90000000  virginica
144     6.800000    3.200000     5.900000  2.30000000  virginica
145     6.700000    3.300000     5.700000  2.50000000  virginica
146     6.700000    3.000000     5.200000  2.30000000  virginica
147     6.300000    2.500000     5.000000  1.90000000  virginica
148     6.500000    3.000000     5.200000  2.00000000  virginica
149     6.200000    3.400000     5.400000  2.30000000  virginica
150     5.900000    3.000000     5.100000  1.80000000  virginica
151     5.119546    3.240896     1.659373  0.25516050     setosa
152     4.902088    4.003746     1.228617  0.35778383     setosa
153     4.834331    3.698540     1.547812  0.33339113     setosa
154     5.134884    3.180819     1.588032  0.18761885     setosa
155     5.488401    3.298369     1.683031  0.18180736     setosa
156     4.758992    3.086108     1.434159  0.25348240     setosa
157     4.817610    3.052438     1.470246  0.18414810     setosa
158     5.372952    3.815612     1.344489  0.12705451     setosa
159     5.203751    3.331928     1.384586  0.26145797     setosa
160     5.154693    4.326639     1.585445  0.10767788     setosa
161     4.651867    2.915629     1.333128  0.24085761     setosa
162     4.703818    3.295307     1.524695  0.53200346     setosa
163     5.299254    3.127387     1.436154  0.32571756     setosa
164     4.576459    3.690579     1.500380  0.24860844     setosa
165     4.821700    3.891746     1.277726  0.34434218     setosa
166     5.195495    2.693142     1.518095  0.11628275     setosa
167     4.751171    4.076332     1.437831  0.29611751     setosa
168     4.895746    3.340168     1.505157  0.32204518     setosa
169     5.084452    2.649230     1.253577  0.34230634     setosa
170     4.994526    3.283612     1.466568  0.10785695     setosa
171     4.914249    3.713116     1.456736  0.13825711     setosa
172     5.168494    3.384539     1.391309  0.36352904     setosa
173     4.868237    3.608825     1.580430  0.16346689     setosa
174     4.922010    3.812630     1.385674  0.17966376     setosa
175     4.782539    3.520596     1.166369  0.19443475     setosa
176     4.999012    2.953373     1.276890  0.04813659     setosa
177     4.237476    3.501651     1.603897 -0.02137016     setosa
178     4.161835    2.900175     1.340508  0.31471652     setosa
179     5.326641    2.690628     1.367918  0.30229792     setosa
180     5.144879    2.889594     1.627228  0.29699450     setosa
181     5.032020    3.092995     1.262743  0.13014888     setosa
182     4.912576    4.102884     1.592814  0.46510333     setosa
183     4.886276    3.643501     1.362697  0.45850332     setosa
184     5.067843    3.644076     1.284018  0.11802271     setosa
185     4.870130    3.261045     1.387769  0.24945158     setosa
186     4.203276    3.532647     1.759381  0.22793382     setosa
187     5.147728    2.949748     1.344759  0.14613345     setosa
188     5.044451    3.821792     1.690910  0.27432788     setosa
189     5.144534    3.260319     1.486522  0.15193060     setosa
190     4.749463    3.242690     1.558031  0.29964703     setosa
191     5.012355    4.056773     1.568806  0.28175520     setosa
192     5.286178    3.657418     1.556329  0.25865612     setosa
193     4.739473    3.599081     1.361732  0.11096506     setosa
194     4.763743    3.719912     1.532282  0.23680057     setosa
195     4.352927    3.606171     1.443575  0.22201153     setosa
196     5.420318    3.234039     1.257110  0.29332868     setosa
197     5.032471    4.002458     1.149330  0.14118440     setosa
198     4.679526    3.634655     1.503754  0.19732104     setosa
199     4.655581    2.890624     1.538909  0.10855489     setosa
200     5.432263    3.587195     1.448039  0.15201721     setosa
201     5.030955    3.620666     1.379309  0.22296525     setosa
202     5.117052    3.640415     1.680914  0.22426164     setosa
203     4.206403    3.577511     1.579905  0.34627623     setosa
204     5.345245    3.207691     1.351151  0.10816533     setosa
205     5.287934    3.630390     1.494184  0.31610331     setosa
206     4.371540    3.674677     1.483436  0.12756906     setosa
207     4.458787    3.512193     1.499114  0.35598241     setosa
208     4.694526    4.189214     1.065203  0.32728599     setosa
209     5.199256    3.164026     1.523074 -0.00277085     setosa
210     4.857067    3.279462     1.431379  0.28051926     setosa
211     5.120333    3.079011     1.256199  0.26650341     setosa
212     5.526492    3.715932     1.385397  0.10935802     setosa
213     4.255062    3.442076     1.032584  0.22553491     setosa
214     5.547997    3.899931     1.805604  0.14245435     setosa
215     5.056086    3.556886     1.485842  0.25052054     setosa
216     4.602273    3.582194     1.637627  0.10750785     setosa
217     5.707143    3.272366     1.495331  0.24957136     setosa
218     4.529437    3.295707     1.370119  0.22733484     setosa
219     4.815724    3.274761     1.264803  0.19839835     setosa
220     5.219331    3.678528     1.534777  0.31111961     setosa
221     7.469393    3.164987     4.045576  1.29571858 versicolor
222     6.435686    2.759148     4.152976  1.38660991 versicolor
223     6.004909    2.617229     3.374965  1.50931533 versicolor
224     5.998960    2.889328     4.787927  0.99816956 versicolor
225     6.066878    2.738260     4.317450  1.35360632 versicolor
226     6.558577    3.004756     3.518091  1.10350572 versicolor
227     5.226591    2.937582     4.211646  1.82617395 versicolor
228     6.519901    3.085536     4.666132  1.47417398 versicolor
229     6.212108    2.297953     3.256134  1.57999643 versicolor
230     6.234065    2.904038     3.899946  1.61728632 versicolor
231     5.891353    2.871663     3.585063  1.15322879 versicolor
232     5.495659    2.332178     4.373762  1.40539853 versicolor
233     5.484945    3.186158     5.022759  1.03428734 versicolor
234     5.002003    2.716631     4.221475  1.13953629 versicolor
235     6.289043    3.017459     3.910062  1.38286708 versicolor
236     5.700736    3.131150     4.960207  1.14958223 versicolor
237     5.500216    3.190272     4.253273  1.18245190 versicolor
238     6.445503    2.960724     4.621510  1.21795268 versicolor
239     5.889688    2.752965     4.360846  1.21917725 versicolor
240     5.217994    2.727503     4.018054  1.19655177 versicolor
241     5.628761    2.782079     4.503714  1.21105694 versicolor
242     5.922639    2.647391     3.774616  1.48842237 versicolor
243     6.021925    2.565549     3.937052  1.45849084 versicolor
244     6.330301    2.687627     4.026615  0.94678432 versicolor
245     6.304311    2.635169     3.998727  1.45603553 versicolor
246     6.663896    3.297885     3.907486  1.16979322 versicolor
247     5.376404    2.885587     3.866554  1.05112744 versicolor
248     4.695327    2.578715     3.943357  1.16919180 versicolor
249     6.278448    3.381682     3.893139  1.31728551 versicolor
250     5.808922    2.342279     4.329488  1.36901786 versicolor
251     6.257850    3.299147     4.763327  1.45358673 versicolor
252     5.397398    2.181731     5.237967  1.63885805 versicolor
253     6.318406    3.370869     4.403785  1.71528585 versicolor
254     6.030213    2.934996     5.690094  1.18095022 versicolor
255     6.322254    2.643724     4.712019  1.30067547 versicolor
256     5.483814    3.540120     3.935919  1.36104088 versicolor
257     4.923149    2.834738     3.978205  1.09514320 versicolor
258     5.102353    3.275399     4.167623  1.69802624 versicolor
259     6.503755    2.772905     4.500401  1.10261134 versicolor
260     6.024940    2.379938     3.663719  1.24096925 versicolor
261     6.155505    2.960939     4.628437  1.63876689 versicolor
262     6.547596    2.753326     3.814345  1.50055748 versicolor
263     7.340028    3.049036     4.128880  1.43704378 versicolor
264     6.771703    2.744679     3.755760  1.35657812 versicolor
265     6.526113    3.315310     4.723554  1.13676188 versicolor
266     5.737681    2.732723     4.619607  1.20118401 versicolor
267     5.118896    3.053538     5.153921  1.24286955 versicolor
268     6.557536    2.506483     3.775426  1.25665234 versicolor
269     6.773637    3.056770     3.907444  1.48359009 versicolor
270     5.231083    2.716242     3.701491  1.43445828 versicolor
271     6.373044    2.810367     3.823155  1.48776176 versicolor
272     6.689764    2.329003     4.315204  1.20003129 versicolor
273     5.909787    2.877026     3.921463  1.44035219 versicolor
274     5.985060    3.408963     4.312826  1.14822888 versicolor
275     5.720711    3.047025     4.502301  1.30692891 versicolor
276     6.075586    2.625810     3.462166  1.13883320 versicolor
277     5.979742    3.037604     4.337108  1.17174718 versicolor
278     5.944742    3.187138     4.131605  1.40617115 versicolor
279     5.377366    2.850410     4.848731  1.31109047 versicolor
280     5.911520    2.601061     3.978657  1.19677413 versicolor
281     6.299276    3.083130     3.767828  1.21669672 versicolor
282     6.508117    2.717810     4.400327  1.15816277 versicolor
283     5.564065    2.991926     3.244794  0.97614826 versicolor
284     5.636803    3.041730     3.675623  1.52144698 versicolor
285     6.249670    2.545928     4.021866  1.48874150 versicolor
286     5.779178    3.126088     4.456842  1.35907598 versicolor
287     5.056560    3.158496     4.029340  1.09487926 versicolor
288     6.256082    2.754099     3.546839  1.10515518 versicolor
289     6.727157    3.127967     4.478930  1.36983039 versicolor
290     6.644075    2.156546     4.073352  1.24130902 versicolor
291     6.086009    2.661626     6.272420  1.43328200  virginica
292     6.415624    3.507285     4.970803  2.29244152  virginica
293     7.783730    3.194127     6.263952  2.12710505  virginica
294     6.714708    2.207256     4.695838  1.57280728  virginica
295     6.892027    3.146945     5.963832  2.03720894  virginica
296     6.384602    2.842640     5.424208  1.34455702  virginica
297     7.151880    2.761441     5.193842  2.65759524  virginica
298     7.000909    3.538284     5.949645  2.37981867  virginica
299     6.267784    3.471146     5.832588  1.97858577  virginica
300     6.684294    3.095409     5.918461  1.79584906  virginica
301     6.653542    3.193293     5.478747  2.02974253  virginica
302     6.932936    2.532998     5.398907  2.58686242  virginica
303     6.171339    3.401070     5.778270  2.14575174  virginica
304     6.321461    3.238482     5.728325  1.77370288  virginica
305     6.939597    3.105226     5.153168  2.30218152  virginica
306     4.983468    2.869016     5.249331  2.33602954  virginica
307     7.057275    3.000195     5.368063  2.29811745  virginica
308     5.648449    3.022504     4.670324  2.44199827  virginica
309     7.023223    3.038748     6.549980  1.74164740  virginica
310     6.621430    2.928325     4.114293  1.65060008  virginica
311     5.947210    2.572431     6.035025  1.67473550  virginica
312     6.720834    2.791217     4.373968  1.80139289  virginica
313     7.277691    3.013233     6.057093  2.41664038  virginica
314     6.036578    3.034487     5.680667  2.14347484  virginica
315     7.523033    2.906421     5.746571  2.19174990  virginica
316     6.148008    3.219150     5.385260  2.29487465  virginica
317     6.653134    3.286357     5.439343  2.01415643  virginica
318     7.665406    2.418833     4.912548  2.04701493  virginica
319     6.962181    3.122207     5.926113  2.14427668  virginica
320     6.968055    3.394053     5.176526  2.28774948  virginica
321     8.433217    3.190685     6.154875  1.86645175  virginica
322     5.865485    3.206422     6.182362  2.06380350  virginica
323     6.357587    3.105502     6.086674  2.22194560  virginica
324     7.000027    3.093890     5.694556  1.95490517  virginica
325     5.329756    3.313431     7.114499  1.82374316  virginica
326     7.063835    2.978432     6.702789  1.97846514  virginica
327     6.643032    3.331938     5.319034  1.98032475  virginica
328     5.812732    2.605752     4.698275  2.04751518  virginica
329     5.922603    2.951062     4.789723  1.86828922  virginica
330     6.534338    3.077621     4.735738  1.96590508  virginica
331     6.566409    2.869386     5.256565  2.30887779  virginica
332     5.873025    2.576689     5.399706  1.51365277  virginica
333     6.436762    2.807203     5.237271  1.70436243  virginica
334     6.700115    2.741499     6.361120  2.57743789  virginica
335     6.800498    2.964161     6.726096  2.01077453  virginica
336     6.817689    3.044292     5.651350  1.64623491  virginica
337     6.589657    2.978472     6.011304  2.51979646  virginica
338     8.263734    3.121411     5.285361  1.93618630  virginica
339     7.027356    2.891612     5.821978  1.92039311  virginica
340     4.943241    2.503378     5.732430  1.80385345  virginica
341     7.071175    2.628713     6.012994  2.06170238  virginica
342     6.074115    3.436504     5.791817  1.23968953  virginica
343     6.853310    2.681229     5.643604  1.21275207  virginica
344     6.254123    3.365158     5.832863  2.67274454  virginica
345     6.511558    2.738037     5.355683  1.85846301  virginica
346     5.842295    3.300082     4.540820  2.12329402  virginica
347     6.423004    3.294433     6.394560  1.76478497  virginica
348     5.833874    3.222916     5.861218  1.69319220  virginica
349     6.478021    3.028388     6.606609  2.06623919  virginica
350     7.784342    2.902471     5.142493  1.91602616  virginica
351     6.775815    3.445127     5.519265  2.13719655  virginica
352     7.014933    2.715428     6.798085  2.04147119  virginica
353     7.689606    2.506295     5.531764  1.88075834  virginica
354     7.506985    2.788839     5.837837  2.47057469  virginica
355     7.242421    2.782457     6.390016  1.66938074  virginica
356     6.400116    2.353697     4.388649  2.24717026  virginica
357     7.384851    3.077118     5.716925  2.36297064  virginica
358     6.892294    3.466955     4.959172  2.13813060  virginica
359     5.904443    3.286340     4.911794  1.90991134  virginica
360     6.292600    2.938076     5.710938  2.61396630  virginica
[1] 360
[1] 150
R: individual_route
individual_routeR Documentation

individual_route

Description

From a time serie, allow to get the most common route for each individual at a given depth (time - 1). Access the frequency value as an element from the output vector and the value itself (the path) as a name of its element, see examples.

Usage

individual_route(inpt_datf, col_target, id_col, untl_last = 2)

Arguments

inpt_datf

is the input time serie as a dataframe

col_target

is the column name or number that refers to the value of each individual

id_col

is the column name or number that refers to the individual (ids)

untl_last

is the depth value

Examples


datf_test <- data.frame("id" = c(1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 5, 5, 5),
                         "city" = c("A", "C", "B", "B", "A", "C", "A", "C", "A", "C", "B", "A", "A", "E"))

print(individual_route(inpt_datf = datf_test, 
                       col_target = "city", 
                       id_col = "id",
                       untl_last = 2))

AC CA BA 
 2  1  2 

print(individual_route(inpt_datf = datf_test, 
                       col_target = "city", 
                       id_col = "id",
                       untl_last = 3))

ACB  AC CAC  BA BAA 
  1   2   1   2   1
R: knn_Rmach_cross_validation_k
knn_Rmach_cross_validation_kR Documentation

knn_Rmach_cross_validation_k

Description

Allow to perform knn with cross validation for the optimal value of k neighbours used, see examples and parameters. The result outputed is a vector containing the ratio of correct label found divided by the total number of unique individuals in the current dataset where the training occurred. So, higher is better.

Usage

knn_Rmach_cross_validation_k(
  inpt_datf,
  train_prop,
  knn_v = c(),
  n_fold = 5,
  col_vars = c(),
  class_col
)

Arguments

inpt_datf

is the input dataset as a ddataframe

train_prop

is the training proportion

knn_v

is a vector containing the values of k neighbours to test

n_fold

is the number of fold used for each value of k, the higher this value is, he more accurate the result will be but the higher the amount of time it will takes

col_vars

is a vector containing the column names or numbers of the variables in the input dataframe

class_col

is the column names or number of the variable to predict in the input dataframe

Examples


iris[, 5] <- as.character(iris[, 5])
print(knn_Rmach_cross_validation_k(
        inpt_datf = iris,
        col_vars = c(1:4),
        n_fold = 5,
        knn_v = c(3, 5, 7, 9, 11),
        class_col = 5,
        train_prop = 0.7
))

[1] 0.9333333 0.9200000 0.9333333 0.9466667 0.9288889

# here the optimal k value is 9
R: knn_Rmach_cross_validation_train
knn_Rmach_cross_validation_trainR Documentation

knn_Rmach_cross_validation_train

Description

Allow to perform knn with cross validation for the optimal value of k neighbours used, see examples and parameters. The result outputed is a vector containing the ratio of correct label found divided by the total number of individuals in the current dataset where the training occurred. So, higher is better.

Usage

knn_Rmach_cross_validation_train(
  inpt_datf,
  train_prop_v = c(),
  k,
  n_fold = 5,
  col_vars = c(),
  class_col
)

Arguments

inpt_datf

is the input dataset as a ddataframe

n_fold

is the number of fold used for each value of k, the higher this value is, he more accurate the result will be but the higher the amount of time it will takes

col_vars

is a vector containing the column names or numbers of the variables in the input dataframe

class_col

is the column names or number of the variable to predict in the input dataframe

train_prop

is the training proportion

knn_v

is a vector containing the values of k neighbours to test

Examples


iris[, 5] <- as.character(iris[, 5])
print(knn_Rmach_cross_validation_train(
        inpt_datf = iris,
        col_vars = c(1:4),
        n_fold = 15,
        k = 7,
        class_col = 5,
        train_prop_v = c(0.7, 0.75, 0.8)
))

[1] 0.4057143 0.3273810 0.2400000

# here the optimal training proportion is 0.7
R: knn_Rmach
knn_RmachR Documentation

knn_Rmach

Description

KNN algorythm, see example

Usage

knn_Rmach(train, test, k, col_vars_train = c(), col_vars_test = c(), class_col)

Arguments

train

is a dataframe with the known individual and their variadbles and classification columns

test

is a dataframe with the new individuals with ich e do not know the class, only the variables

k

is the number of neighbours

col_vars_train

is a vector containing the column names or column numbers of the variables in train, if empty all column are considered as a variable apart from the last one that is considered as the classification column

col_vars_test

is a vector containing the column names or column numbers of the variables in test, if empty all column are considered as a variable

class_col

is the column name or column number of the classification column in train

Examples


cur_ids <- round(runif(n = 45, min = 1, max = 150))

vec <- knn_Rmach(train = iris[-cur_ids,], 
          test = iris[cur_ids, 1:4],
           col_vars_train = c(1:4),
          col_vars_test = c(1:4),
          class_col = 5,
          k = 3
       )

sum(vec == iris[cur_ids, 5]) / 45

[1] 0.9555556
R: lm_label_generation2
lm_label_generation2R Documentation

lm_label_generation2

Description

Same as lm_label_generation but limits the new individuals to min_hmn.

Usage

lm_label_generation2(inpt_datf, col_vars = c(), label_var, min_hmn, neighbrs)

Arguments

inpt_datf

is the input dataframe

col_vars

is a vector containing the column names or number of the variables

label_var

is the column name or number of the label variable

min_hmn

is the value from which a label is considered to appear enough times, so all individuals that have a label whose occurence is inferior will be cloned base on the method elaborated in the description of the function

neighbrs

is how many neighbours will be taken in count to calculate the local standard deviation

Examples


datf <- iris
datf <- datf[-c(101:137),]
datf[, 5] <- as.character(datf[, 5])
datf[datf[, 5] == "setosa", 5] <- 1
datf[datf[, 5] == "versicolor", 5] <- 2
datf[datf[, 5] == "virginica", 5] <- 3
datf[, 5] <- as.numeric(datf[, 5])
rownames(datf) <- c(1:nrow(datf))
print(datf)

    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1            5.1         3.5          1.4         0.2       1
2            4.9         3.0          1.4         0.2       1
3            4.7         3.2          1.3         0.2       1
4            4.6         3.1          1.5         0.2       1
5            5.0         3.6          1.4         0.2       1
6            5.4         3.9          1.7         0.4       1
7            4.6         3.4          1.4         0.3       1
8            5.0         3.4          1.5         0.2       1
9            4.4         2.9          1.4         0.2       1
10           4.9         3.1          1.5         0.1       1
11           5.4         3.7          1.5         0.2       1
12           4.8         3.4          1.6         0.2       1
13           4.8         3.0          1.4         0.1       1
14           4.3         3.0          1.1         0.1       1
15           5.8         4.0          1.2         0.2       1
16           5.7         4.4          1.5         0.4       1
17           5.4         3.9          1.3         0.4       1
18           5.1         3.5          1.4         0.3       1
19           5.7         3.8          1.7         0.3       1
20           5.1         3.8          1.5         0.3       1
21           5.4         3.4          1.7         0.2       1
22           5.1         3.7          1.5         0.4       1
23           4.6         3.6          1.0         0.2       1
24           5.1         3.3          1.7         0.5       1
25           4.8         3.4          1.9         0.2       1
26           5.0         3.0          1.6         0.2       1
27           5.0         3.4          1.6         0.4       1
28           5.2         3.5          1.5         0.2       1
29           5.2         3.4          1.4         0.2       1
30           4.7         3.2          1.6         0.2       1
31           4.8         3.1          1.6         0.2       1
32           5.4         3.4          1.5         0.4       1
33           5.2         4.1          1.5         0.1       1
34           5.5         4.2          1.4         0.2       1
35           4.9         3.1          1.5         0.2       1
36           5.0         3.2          1.2         0.2       1
37           5.5         3.5          1.3         0.2       1
38           4.9         3.6          1.4         0.1       1
39           4.4         3.0          1.3         0.2       1
40           5.1         3.4          1.5         0.2       1
41           5.0         3.5          1.3         0.3       1
42           4.5         2.3          1.3         0.3       1
43           4.4         3.2          1.3         0.2       1
44           5.0         3.5          1.6         0.6       1
45           5.1         3.8          1.9         0.4       1
46           4.8         3.0          1.4         0.3       1
47           5.1         3.8          1.6         0.2       1
48           4.6         3.2          1.4         0.2       1
49           5.3         3.7          1.5         0.2       1
50           5.0         3.3          1.4         0.2       1
51           7.0         3.2          4.7         1.4       2
52           6.4         3.2          4.5         1.5       2
53           6.9         3.1          4.9         1.5       2
54           5.5         2.3          4.0         1.3       2
55           6.5         2.8          4.6         1.5       2
56           5.7         2.8          4.5         1.3       2
57           6.3         3.3          4.7         1.6       2
58           4.9         2.4          3.3         1.0       2
59           6.6         2.9          4.6         1.3       2
60           5.2         2.7          3.9         1.4       2
61           5.0         2.0          3.5         1.0       2
62           5.9         3.0          4.2         1.5       2
63           6.0         2.2          4.0         1.0       2
64           6.1         2.9          4.7         1.4       2
65           5.6         2.9          3.6         1.3       2
66           6.7         3.1          4.4         1.4       2
67           5.6         3.0          4.5         1.5       2
68           5.8         2.7          4.1         1.0       2
69           6.2         2.2          4.5         1.5       2
70           5.6         2.5          3.9         1.1       2
71           5.9         3.2          4.8         1.8       2
72           6.1         2.8          4.0         1.3       2
73           6.3         2.5          4.9         1.5       2
74           6.1         2.8          4.7         1.2       2
75           6.4         2.9          4.3         1.3       2
76           6.6         3.0          4.4         1.4       2
77           6.8         2.8          4.8         1.4       2
78           6.7         3.0          5.0         1.7       2
79           6.0         2.9          4.5         1.5       2
80           5.7         2.6          3.5         1.0       2
81           5.5         2.4          3.8         1.1       2
82           5.5         2.4          3.7         1.0       2
83           5.8         2.7          3.9         1.2       2
84           6.0         2.7          5.1         1.6       2
85           5.4         3.0          4.5         1.5       2
86           6.0         3.4          4.5         1.6       2
87           6.7         3.1          4.7         1.5       2
88           6.3         2.3          4.4         1.3       2
89           5.6         3.0          4.1         1.3       2
90           5.5         2.5          4.0         1.3       2
91           5.5         2.6          4.4         1.2       2
92           6.1         3.0          4.6         1.4       2
93           5.8         2.6          4.0         1.2       2
94           5.0         2.3          3.3         1.0       2
95           5.6         2.7          4.2         1.3       2
96           5.7         3.0          4.2         1.2       2
97           5.7         2.9          4.2         1.3       2
98           6.2         2.9          4.3         1.3       2
99           5.1         2.5          3.0         1.1       2
100          5.7         2.8          4.1         1.3       2
101          6.4         3.1          5.5         1.8       3
102          6.0         3.0          4.8         1.8       3
103          6.9         3.1          5.4         2.1       3
104          6.7         3.1          5.6         2.4       3
105          6.9         3.1          5.1         2.3       3
106          5.8         2.7          5.1         1.9       3
107          6.8         3.2          5.9         2.3       3
108          6.7         3.3          5.7         2.5       3
109          6.7         3.0          5.2         2.3       3
110          6.3         2.5          5.0         1.9       3
111          6.5         3.0          5.2         2.0       3
112          6.2         3.4          5.4         2.3       3
113          5.9         3.0          5.1         1.8       3

print(lm_label_generation2(
                          inpt_datf = datf,
                          col_vars = c(1:4),
                          label_var = 5,
                          min_hmn = 50,
                          neighbrs = 3
                          )
)

    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1       5.100000    3.500000     1.400000    0.200000       1
2       4.900000    3.000000     1.400000    0.200000       1
3       4.700000    3.200000     1.300000    0.200000       1
4       4.600000    3.100000     1.500000    0.200000       1
5       5.000000    3.600000     1.400000    0.200000       1
6       5.400000    3.900000     1.700000    0.400000       1
7       4.600000    3.400000     1.400000    0.300000       1
8       5.000000    3.400000     1.500000    0.200000       1
9       4.400000    2.900000     1.400000    0.200000       1
10      4.900000    3.100000     1.500000    0.100000       1
11      5.400000    3.700000     1.500000    0.200000       1
12      4.800000    3.400000     1.600000    0.200000       1
13      4.800000    3.000000     1.400000    0.100000       1
14      4.300000    3.000000     1.100000    0.100000       1
15      5.800000    4.000000     1.200000    0.200000       1
16      5.700000    4.400000     1.500000    0.400000       1
17      5.400000    3.900000     1.300000    0.400000       1
18      5.100000    3.500000     1.400000    0.300000       1
19      5.700000    3.800000     1.700000    0.300000       1
20      5.100000    3.800000     1.500000    0.300000       1
21      5.400000    3.400000     1.700000    0.200000       1
22      5.100000    3.700000     1.500000    0.400000       1
23      4.600000    3.600000     1.000000    0.200000       1
24      5.100000    3.300000     1.700000    0.500000       1
25      4.800000    3.400000     1.900000    0.200000       1
26      5.000000    3.000000     1.600000    0.200000       1
27      5.000000    3.400000     1.600000    0.400000       1
28      5.200000    3.500000     1.500000    0.200000       1
29      5.200000    3.400000     1.400000    0.200000       1
30      4.700000    3.200000     1.600000    0.200000       1
31      4.800000    3.100000     1.600000    0.200000       1
32      5.400000    3.400000     1.500000    0.400000       1
33      5.200000    4.100000     1.500000    0.100000       1
34      5.500000    4.200000     1.400000    0.200000       1
35      4.900000    3.100000     1.500000    0.200000       1
36      5.000000    3.200000     1.200000    0.200000       1
37      5.500000    3.500000     1.300000    0.200000       1
38      4.900000    3.600000     1.400000    0.100000       1
39      4.400000    3.000000     1.300000    0.200000       1
40      5.100000    3.400000     1.500000    0.200000       1
41      5.000000    3.500000     1.300000    0.300000       1
42      4.500000    2.300000     1.300000    0.300000       1
43      4.400000    3.200000     1.300000    0.200000       1
44      5.000000    3.500000     1.600000    0.600000       1
45      5.100000    3.800000     1.900000    0.400000       1
46      4.800000    3.000000     1.400000    0.300000       1
47      5.100000    3.800000     1.600000    0.200000       1
48      4.600000    3.200000     1.400000    0.200000       1
49      5.300000    3.700000     1.500000    0.200000       1
50      5.000000    3.300000     1.400000    0.200000       1
51      7.000000    3.200000     4.700000    1.400000       2
52      6.400000    3.200000     4.500000    1.500000       2
53      6.900000    3.100000     4.900000    1.500000       2
54      5.500000    2.300000     4.000000    1.300000       2
55      6.500000    2.800000     4.600000    1.500000       2
56      5.700000    2.800000     4.500000    1.300000       2
57      6.300000    3.300000     4.700000    1.600000       2
58      4.900000    2.400000     3.300000    1.000000       2
59      6.600000    2.900000     4.600000    1.300000       2
60      5.200000    2.700000     3.900000    1.400000       2
61      5.000000    2.000000     3.500000    1.000000       2
62      5.900000    3.000000     4.200000    1.500000       2
63      6.000000    2.200000     4.000000    1.000000       2
64      6.100000    2.900000     4.700000    1.400000       2
65      5.600000    2.900000     3.600000    1.300000       2
66      6.700000    3.100000     4.400000    1.400000       2
67      5.600000    3.000000     4.500000    1.500000       2
68      5.800000    2.700000     4.100000    1.000000       2
69      6.200000    2.200000     4.500000    1.500000       2
70      5.600000    2.500000     3.900000    1.100000       2
71      5.900000    3.200000     4.800000    1.800000       2
72      6.100000    2.800000     4.000000    1.300000       2
73      6.300000    2.500000     4.900000    1.500000       2
74      6.100000    2.800000     4.700000    1.200000       2
75      6.400000    2.900000     4.300000    1.300000       2
76      6.600000    3.000000     4.400000    1.400000       2
77      6.800000    2.800000     4.800000    1.400000       2
78      6.700000    3.000000     5.000000    1.700000       2
79      6.000000    2.900000     4.500000    1.500000       2
80      5.700000    2.600000     3.500000    1.000000       2
81      5.500000    2.400000     3.800000    1.100000       2
82      5.500000    2.400000     3.700000    1.000000       2
83      5.800000    2.700000     3.900000    1.200000       2
84      6.000000    2.700000     5.100000    1.600000       2
85      5.400000    3.000000     4.500000    1.500000       2
86      6.000000    3.400000     4.500000    1.600000       2
87      6.700000    3.100000     4.700000    1.500000       2
88      6.300000    2.300000     4.400000    1.300000       2
89      5.600000    3.000000     4.100000    1.300000       2
90      5.500000    2.500000     4.000000    1.300000       2
91      5.500000    2.600000     4.400000    1.200000       2
92      6.100000    3.000000     4.600000    1.400000       2
93      5.800000    2.600000     4.000000    1.200000       2
94      5.000000    2.300000     3.300000    1.000000       2
95      5.600000    2.700000     4.200000    1.300000       2
96      5.700000    3.000000     4.200000    1.200000       2
97      5.700000    2.900000     4.200000    1.300000       2
98      6.200000    2.900000     4.300000    1.300000       2
99      5.100000    2.500000     3.000000    1.100000       2
100     5.700000    2.800000     4.100000    1.300000       2
101     6.400000    3.100000     5.500000    1.800000       3
102     6.000000    3.000000     4.800000    1.800000       3
103     6.900000    3.100000     5.400000    2.100000       3
104     6.700000    3.100000     5.600000    2.400000       3
105     6.900000    3.100000     5.100000    2.300000       3
106     5.800000    2.700000     5.100000    1.900000       3
107     6.800000    3.200000     5.900000    2.300000       3
108     6.700000    3.300000     5.700000    2.500000       3
109     6.700000    3.000000     5.200000    2.300000       3
110     6.300000    2.500000     5.000000    1.900000       3
111     6.500000    3.000000     5.200000    2.000000       3
112     6.200000    3.400000     5.400000    2.300000       3
113     5.900000    3.000000     5.100000    1.800000       3
114     3.262233   -2.836161     1.209831    3.756150       3
115     2.792928   -2.598126     1.903514    4.021822       3
116     2.660366   -3.253078     1.773561    3.552828       3
117     3.212474   -2.968186     1.299036    4.002004       3
118     3.304691   -3.120160     1.776056    3.421424       3
119     3.129013   -2.764565     1.084363    3.961070       3
120     3.278919   -2.962809     1.872274    3.862973       3
121     2.555308   -2.875592     1.808080    3.195783       3
122     3.466773   -3.325957     1.601554    3.675986       3
123     2.574830   -2.761658     1.151314    3.923658       3
124     3.324925   -3.055837     1.639577    3.913790       3
125     2.984227   -2.962149     1.721951    3.726171       3
126     3.149486   -2.664939     1.988111    4.036337       3
127     3.239279   -2.812136     1.961199    3.589471       3
128     3.124868   -2.825058     1.185327    3.550925       3
129     2.818810   -2.812702     1.497583    3.940851       3
130     2.947275   -3.162957     1.582021    3.577329       3
131     3.484623   -3.069765     1.952411    4.085512       3
132     2.461561   -2.772750     1.406235    3.823907       3
133     2.845755   -3.061859     2.278193    3.937838       3
134     3.382535   -3.103582     1.736065    4.025574       3
135     3.233128   -3.386703     1.333698    4.273769       3
136     3.189359   -2.823622     1.296134    4.140628       3
137     3.091862   -2.863102     1.080645    3.964312       3
138     2.741842   -2.929970     1.889022    3.713229       3
139     2.578026   -2.971548     1.597677    3.932410       3
140     2.925473   -3.323804     1.177113    3.551214       3
141     3.029594   -3.006599     1.350195    3.984042       3
142     2.755172   -2.698046     1.949463    3.811956       3
143     2.894695   -3.184067     1.605452    3.474205       3
144     3.260417   -2.908241     1.578760    3.691196       3
145     3.006636   -3.034710     1.628828    3.369206       3
146     3.352774   -2.915606     1.327263    3.699141       3
147     2.760372   -3.282009     1.762860    3.890322       3
148     3.545501   -3.080867     2.008176    3.930908       3
149     2.916121   -2.846311     1.822271    3.971336       3
150     2.827684   -3.028701     2.221580    3.957002       3
R: lm_label_generation
lm_label_generationR Documentation

lm_label_generation

Description

Allow to generate new individuals whose label are not present enough. It supposes that the variables and the label al have a linear relationship. This methods generates values of variables for new individuals based on a normal distribution whose mean is the value of the function found after a linear regression between a variable and the label, at the x value (label value). The standard deviation associated with the normal distribution is the local standard deviation with a given amount of neighbours. A neighbour is the set of individuals that share the same label. If the amount of neighbours exceeds the number of labels, so all labels will be considerd as a neighboor to calculate the local standard deviation.

Usage

lm_label_generation(inpt_datf, col_vars = c(), label_var, min_hmn, neighbrs)

Arguments

inpt_datf

is the input dataframe

col_vars

is a vector containing the column names or number of the variables

label_var

is the column name or number of the label variable

min_hmn

is the value from which a label is considered to appear enough times, so all individuals that have a label whose occurence is inferior will be cloned base on the method elaborated in the description of the function

neighbrs

is how many neighbours will be taken in count to calculate the local standard deviation

Examples


datf <- iris
datf <- datf[-c(101:137),]
datf[, 5] <- as.character(datf[, 5])
datf[datf[, 5] == "setosa", 5] <- 1
datf[datf[, 5] == "versicolor", 5] <- 2
datf[datf[, 5] == "virginica", 5] <- 3
datf[, 5] <- as.numeric(datf[, 5])
rownames(datf) <- c(1:nrow(datf))
print(datf)

    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1            5.1         3.5          1.4         0.2       1
2            4.9         3.0          1.4         0.2       1
3            4.7         3.2          1.3         0.2       1
4            4.6         3.1          1.5         0.2       1
5            5.0         3.6          1.4         0.2       1
6            5.4         3.9          1.7         0.4       1
7            4.6         3.4          1.4         0.3       1
8            5.0         3.4          1.5         0.2       1
9            4.4         2.9          1.4         0.2       1
10           4.9         3.1          1.5         0.1       1
11           5.4         3.7          1.5         0.2       1
12           4.8         3.4          1.6         0.2       1
13           4.8         3.0          1.4         0.1       1
14           4.3         3.0          1.1         0.1       1
15           5.8         4.0          1.2         0.2       1
16           5.7         4.4          1.5         0.4       1
17           5.4         3.9          1.3         0.4       1
18           5.1         3.5          1.4         0.3       1
19           5.7         3.8          1.7         0.3       1
20           5.1         3.8          1.5         0.3       1
21           5.4         3.4          1.7         0.2       1
22           5.1         3.7          1.5         0.4       1
23           4.6         3.6          1.0         0.2       1
24           5.1         3.3          1.7         0.5       1
25           4.8         3.4          1.9         0.2       1
26           5.0         3.0          1.6         0.2       1
27           5.0         3.4          1.6         0.4       1
28           5.2         3.5          1.5         0.2       1
29           5.2         3.4          1.4         0.2       1
30           4.7         3.2          1.6         0.2       1
31           4.8         3.1          1.6         0.2       1
32           5.4         3.4          1.5         0.4       1
33           5.2         4.1          1.5         0.1       1
34           5.5         4.2          1.4         0.2       1
35           4.9         3.1          1.5         0.2       1
36           5.0         3.2          1.2         0.2       1
37           5.5         3.5          1.3         0.2       1
38           4.9         3.6          1.4         0.1       1
39           4.4         3.0          1.3         0.2       1
40           5.1         3.4          1.5         0.2       1
41           5.0         3.5          1.3         0.3       1
42           4.5         2.3          1.3         0.3       1
43           4.4         3.2          1.3         0.2       1
44           5.0         3.5          1.6         0.6       1
45           5.1         3.8          1.9         0.4       1
46           4.8         3.0          1.4         0.3       1
47           5.1         3.8          1.6         0.2       1
48           4.6         3.2          1.4         0.2       1
49           5.3         3.7          1.5         0.2       1
50           5.0         3.3          1.4         0.2       1
51           7.0         3.2          4.7         1.4       2
52           6.4         3.2          4.5         1.5       2
53           6.9         3.1          4.9         1.5       2
54           5.5         2.3          4.0         1.3       2
55           6.5         2.8          4.6         1.5       2
56           5.7         2.8          4.5         1.3       2
57           6.3         3.3          4.7         1.6       2
58           4.9         2.4          3.3         1.0       2
59           6.6         2.9          4.6         1.3       2
60           5.2         2.7          3.9         1.4       2
61           5.0         2.0          3.5         1.0       2
62           5.9         3.0          4.2         1.5       2
63           6.0         2.2          4.0         1.0       2
64           6.1         2.9          4.7         1.4       2
65           5.6         2.9          3.6         1.3       2
66           6.7         3.1          4.4         1.4       2
67           5.6         3.0          4.5         1.5       2
68           5.8         2.7          4.1         1.0       2
69           6.2         2.2          4.5         1.5       2
70           5.6         2.5          3.9         1.1       2
71           5.9         3.2          4.8         1.8       2
72           6.1         2.8          4.0         1.3       2
73           6.3         2.5          4.9         1.5       2
74           6.1         2.8          4.7         1.2       2
75           6.4         2.9          4.3         1.3       2
76           6.6         3.0          4.4         1.4       2
77           6.8         2.8          4.8         1.4       2
78           6.7         3.0          5.0         1.7       2
79           6.0         2.9          4.5         1.5       2
80           5.7         2.6          3.5         1.0       2
81           5.5         2.4          3.8         1.1       2
82           5.5         2.4          3.7         1.0       2
83           5.8         2.7          3.9         1.2       2
84           6.0         2.7          5.1         1.6       2
85           5.4         3.0          4.5         1.5       2
86           6.0         3.4          4.5         1.6       2
87           6.7         3.1          4.7         1.5       2
88           6.3         2.3          4.4         1.3       2
89           5.6         3.0          4.1         1.3       2
90           5.5         2.5          4.0         1.3       2
91           5.5         2.6          4.4         1.2       2
92           6.1         3.0          4.6         1.4       2
93           5.8         2.6          4.0         1.2       2
94           5.0         2.3          3.3         1.0       2
95           5.6         2.7          4.2         1.3       2
96           5.7         3.0          4.2         1.2       2
97           5.7         2.9          4.2         1.3       2
98           6.2         2.9          4.3         1.3       2
99           5.1         2.5          3.0         1.1       2
100          5.7         2.8          4.1         1.3       2
101          6.4         3.1          5.5         1.8       3
102          6.0         3.0          4.8         1.8       3
103          6.9         3.1          5.4         2.1       3
104          6.7         3.1          5.6         2.4       3
105          6.9         3.1          5.1         2.3       3
106          5.8         2.7          5.1         1.9       3
107          6.8         3.2          5.9         2.3       3
108          6.7         3.3          5.7         2.5       3
109          6.7         3.0          5.2         2.3       3
110          6.3         2.5          5.0         1.9       3
111          6.5         3.0          5.2         2.0       3
112          6.2         3.4          5.4         2.3       3
113          5.9         3.0          5.1         1.8       3

print(lm_label_generation(
                          inpt_datf = datf,
                          col_vars = c(1:4),
                          label_var = 5,
                          min_hmn = 50,
                          neighbrs = 3
                          )
)

    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1       5.100000    3.500000    1.4000000    0.200000       1
2       4.900000    3.000000    1.4000000    0.200000       1
3       4.700000    3.200000    1.3000000    0.200000       1
4       4.600000    3.100000    1.5000000    0.200000       1
5       5.000000    3.600000    1.4000000    0.200000       1
6       5.400000    3.900000    1.7000000    0.400000       1
7       4.600000    3.400000    1.4000000    0.300000       1
8       5.000000    3.400000    1.5000000    0.200000       1
9       4.400000    2.900000    1.4000000    0.200000       1
10      4.900000    3.100000    1.5000000    0.100000       1
11      5.400000    3.700000    1.5000000    0.200000       1
12      4.800000    3.400000    1.6000000    0.200000       1
13      4.800000    3.000000    1.4000000    0.100000       1
14      4.300000    3.000000    1.1000000    0.100000       1
15      5.800000    4.000000    1.2000000    0.200000       1
16      5.700000    4.400000    1.5000000    0.400000       1
17      5.400000    3.900000    1.3000000    0.400000       1
18      5.100000    3.500000    1.4000000    0.300000       1
19      5.700000    3.800000    1.7000000    0.300000       1
20      5.100000    3.800000    1.5000000    0.300000       1
21      5.400000    3.400000    1.7000000    0.200000       1
22      5.100000    3.700000    1.5000000    0.400000       1
23      4.600000    3.600000    1.0000000    0.200000       1
24      5.100000    3.300000    1.7000000    0.500000       1
25      4.800000    3.400000    1.9000000    0.200000       1
26      5.000000    3.000000    1.6000000    0.200000       1
27      5.000000    3.400000    1.6000000    0.400000       1
28      5.200000    3.500000    1.5000000    0.200000       1
29      5.200000    3.400000    1.4000000    0.200000       1
30      4.700000    3.200000    1.6000000    0.200000       1
31      4.800000    3.100000    1.6000000    0.200000       1
32      5.400000    3.400000    1.5000000    0.400000       1
33      5.200000    4.100000    1.5000000    0.100000       1
34      5.500000    4.200000    1.4000000    0.200000       1
35      4.900000    3.100000    1.5000000    0.200000       1
36      5.000000    3.200000    1.2000000    0.200000       1
37      5.500000    3.500000    1.3000000    0.200000       1
38      4.900000    3.600000    1.4000000    0.100000       1
39      4.400000    3.000000    1.3000000    0.200000       1
40      5.100000    3.400000    1.5000000    0.200000       1
41      5.000000    3.500000    1.3000000    0.300000       1
42      4.500000    2.300000    1.3000000    0.300000       1
43      4.400000    3.200000    1.3000000    0.200000       1
44      5.000000    3.500000    1.6000000    0.600000       1
45      5.100000    3.800000    1.9000000    0.400000       1
46      4.800000    3.000000    1.4000000    0.300000       1
47      5.100000    3.800000    1.6000000    0.200000       1
48      4.600000    3.200000    1.4000000    0.200000       1
49      5.300000    3.700000    1.5000000    0.200000       1
50      5.000000    3.300000    1.4000000    0.200000       1
51      7.000000    3.200000    4.7000000    1.400000       2
52      6.400000    3.200000    4.5000000    1.500000       2
53      6.900000    3.100000    4.9000000    1.500000       2
54      5.500000    2.300000    4.0000000    1.300000       2
55      6.500000    2.800000    4.6000000    1.500000       2
56      5.700000    2.800000    4.5000000    1.300000       2
57      6.300000    3.300000    4.7000000    1.600000       2
58      4.900000    2.400000    3.3000000    1.000000       2
59      6.600000    2.900000    4.6000000    1.300000       2
60      5.200000    2.700000    3.9000000    1.400000       2
61      5.000000    2.000000    3.5000000    1.000000       2
62      5.900000    3.000000    4.2000000    1.500000       2
63      6.000000    2.200000    4.0000000    1.000000       2
64      6.100000    2.900000    4.7000000    1.400000       2
65      5.600000    2.900000    3.6000000    1.300000       2
66      6.700000    3.100000    4.4000000    1.400000       2
67      5.600000    3.000000    4.5000000    1.500000       2
68      5.800000    2.700000    4.1000000    1.000000       2
69      6.200000    2.200000    4.5000000    1.500000       2
70      5.600000    2.500000    3.9000000    1.100000       2
71      5.900000    3.200000    4.8000000    1.800000       2
72      6.100000    2.800000    4.0000000    1.300000       2
73      6.300000    2.500000    4.9000000    1.500000       2
74      6.100000    2.800000    4.7000000    1.200000       2
75      6.400000    2.900000    4.3000000    1.300000       2
76      6.600000    3.000000    4.4000000    1.400000       2
77      6.800000    2.800000    4.8000000    1.400000       2
78      6.700000    3.000000    5.0000000    1.700000       2
79      6.000000    2.900000    4.5000000    1.500000       2
80      5.700000    2.600000    3.5000000    1.000000       2
81      5.500000    2.400000    3.8000000    1.100000       2
82      5.500000    2.400000    3.7000000    1.000000       2
83      5.800000    2.700000    3.9000000    1.200000       2
84      6.000000    2.700000    5.1000000    1.600000       2
85      5.400000    3.000000    4.5000000    1.500000       2
86      6.000000    3.400000    4.5000000    1.600000       2
87      6.700000    3.100000    4.7000000    1.500000       2
88      6.300000    2.300000    4.4000000    1.300000       2
89      5.600000    3.000000    4.1000000    1.300000       2
90      5.500000    2.500000    4.0000000    1.300000       2
91      5.500000    2.600000    4.4000000    1.200000       2
92      6.100000    3.000000    4.6000000    1.400000       2
93      5.800000    2.600000    4.0000000    1.200000       2
94      5.000000    2.300000    3.3000000    1.000000       2
95      5.600000    2.700000    4.2000000    1.300000       2
96      5.700000    3.000000    4.2000000    1.200000       2
97      5.700000    2.900000    4.2000000    1.300000       2
98      6.200000    2.900000    4.3000000    1.300000       2
99      5.100000    2.500000    3.0000000    1.100000       2
100     5.700000    2.800000    4.1000000    1.300000       2
101     6.400000    3.100000    5.5000000    1.800000       3
102     6.000000    3.000000    4.8000000    1.800000       3
103     6.900000    3.100000    5.4000000    2.100000       3
104     6.700000    3.100000    5.6000000    2.400000       3
105     6.900000    3.100000    5.1000000    2.300000       3
106     5.800000    2.700000    5.1000000    1.900000       3
107     6.800000    3.200000    5.9000000    2.300000       3
108     6.700000    3.300000    5.7000000    2.500000       3
109     6.700000    3.000000    5.2000000    2.300000       3
110     6.300000    2.500000    5.0000000    1.900000       3
111     6.500000    3.000000    5.2000000    2.000000       3
112     6.200000    3.400000    5.4000000    2.300000       3
113     5.900000    3.000000    5.1000000    1.800000       3
114     3.337086   -3.010967    1.6118130    3.624331       3
115     2.411537   -2.886464    1.4126476    3.660604       3
116     3.210020   -2.775710    1.5750895    4.172761       3
117     2.929791   -3.155015    1.4007481    3.564110       3
118     3.308786   -3.254998    1.9936545    3.504558       3
119     3.289197   -2.825299    1.4533275    3.560123       3
120     3.182515   -3.192980    1.5196816    3.555298       3
121     3.559725   -3.018141    1.8414595    3.752408       3
122     2.611399   -2.798353    1.7837746    4.255737       3
123     3.031710   -2.905473    1.3845924    3.425221       3
124     2.905517   -2.759015    1.4464214    3.748606       3
125     2.356676   -2.929148    1.8148241    3.675484       3
126     3.186993   -2.748881    1.8868197    3.527604       3
127     3.332580   -2.814485    1.3231994    3.465241       3
128     3.029835   -3.217453    1.7912804    3.517651       3
129     3.073081   -3.306620    1.7800284    3.718459       3
130     3.283990   -3.215174    1.7407919    3.897852       3
131     3.087140   -2.990210    1.7520826    3.644679       3
132     3.272970   -3.056268    1.3994579    3.540260       3
133     2.450407   -2.841814    2.3437436    3.755238       3
134     3.545044   -3.040000    1.7329403    3.676300       3
135     2.839665   -3.111823    1.7736373    4.393735       3
136     2.875360   -3.351643    1.8670479    3.340322       3
137     2.850900   -3.282528    1.7458047    3.631666       3
138     2.492785   -3.131132    1.9228884    3.933619       3
139     2.479979   -3.049675    1.6795634    3.994376       3
140     2.382085   -2.973920    1.1220551    4.179145       3
141     2.924159   -2.872431    1.5889779    3.133596       3
142     3.431704   -3.225922    1.0053766    3.789316       3
143     2.878903   -2.750559    1.6468587    3.586866       3
144     3.053275   -3.085656    1.9171936    3.904307       3
145     2.604087   -2.627083    1.8233054    3.420803       3
146     2.601047   -2.965508    1.2239290    3.741698       3
147     3.348790   -3.394497    1.2943973    3.773516       3
148     4.017690   -3.020744    1.8265688    4.236493       3
149     2.351940   -3.299626    1.9135616    3.834306       3
150     2.927660   -2.947511    1.8911915    4.142867       3
151     2.948031   -2.898694    1.4692856    3.829044       3
152     3.244506   -3.159445    1.6976699    3.413799       3
153     2.863441   -3.034045    2.0560847    3.728603       3
154     3.348731   -3.217367    1.4792292    3.894735       3
155     3.383924   -3.285755    1.4741631    4.192404       3
156     3.110906   -3.351680    0.8286602    4.029459       3
157     3.238545   -2.820779    1.6919874    3.985022       3
158     3.468800   -2.888302    1.9049297    3.845491       3
159     3.277903   -3.219843    1.4328682    3.715557       3
160     3.178165   -3.178437    0.9839534    3.661058       3
161     3.272029   -3.334494    1.5386834    3.903441       3
162     2.950301   -2.956331    1.5906415    3.596755       3
163     3.413660   -3.506169    1.4874070    3.870051       3
R: multiple_groups
multiple_groupsR Documentation

multiple_groups

Description

Output all the possible combinations between elements within a vector for a group size, see examples

Usage

multiple_groups(inpt_v = c(), group_size = 2)

Arguments

inpt_v

is the input vector

group_size

is the group size

Examples


print(multiple_groups(inpt_v = c("Marc", "Sylvie", "Julien", "Christine", "Axel"), group_size = 2))

 [1] "Marc-Sylvie"      "Marc-Julien"      "Marc-Christine"   "Marc-Axel"       
 [5] "Sylvie-Julien"    "Sylvie-Christine" "Sylvie-Axel"      "Julien-Christine"
 [9] "Julien-Axel"      "Christine-Axel"  

print(multiple_groups(inpt_v = c("Marc", "Sylvie", "Julien", "Christine", "Axel"), group_size = 3))

 [1] "Marc-Sylvie-Julien"      "Marc-Sylvie-Christine"  
 [3] "Marc-Sylvie-Axel"        "Marc-Julien-Christine"  
 [5] "Marc-Julien-Axel"        "Marc-Christine-Axel"    
 [7] "Sylvie-Julien-Christine" "Sylvie-Julien-Axel"     
 [9] "Sylvie-Christine-Axel"   "Julien-Christine-Axel"  

print(multiple_groups(inpt_v = c("Marc", "Sylvie", "Julien", "Christine", "Axel"), group_size = 4))

[1] "Marc-Sylvie-Julien-Christine" "Marc-Sylvie-Julien-Axel"     
[3] "Marc-Sylvie-Christine-Axel"   "Marc-Julien-Christine-Axel"  
[5] "Sylvie-Julien-Christine-Axel"

print(multiple_groups(inpt_v = c("Marc", "Sylvie", "Julien", "Christine", "Axel"), group_size = 5))

[1] "Marc-Sylvie-Julien-Christine-Axel"
R: Rmach poly_model
poly_modelR Documentation

Rmach poly_model

Description

Take a datasets of x and y values and a function tha could fit all the data with the missing coefficients, and returns a list containing the coefficients that fit the best the data for a given function, as a vector for the first index, and at the second index, the actual sum of difference between each data point and the function at the same x values.

Usage

poly_model(
  inpt_datf,
  degree,
  twk_val = NA,
  sensi_val = twk_val,
  coeff_v = NA,
  powers = NA,
  mth_symb = c("x"),
  numrtr_v = NA
)

Arguments

inpt_datf

is the input data as a dataframe, first column is the x values and the second is the y values

degree

is how many coefficients will be involved (each coefficient multiplies either an x to the power of something, an exponential of something or a base something logarithm for a something value)

twk_val

is the value used for finding the best coefficients, it is directly linked to the accuracy of the coefficients, see the description for more information. Defaults to (max(yval) - min(yval)) / n

sensi_val

is the value from which two variations of a coefficient brings a so small accuracy contribution that the algorythm does not continue to find better coefficients. For example, if i set sensi_val = 0.001, so if coefficients alpha1 and beta1 brings a total difference between the function and the actual data of 10.8073 and then the algorythm find alpha2 and beta1 that brings a total difference equal to 10.8066, so the algorythm will stop running. But the coefficients returned will still be the best, that is alpha2 and beta1

coeff_v

is a vector containing the original coefficients for the function, so the closest those are from the best one, the fastest the algorythm will compute the best coefficients. The first value of coeff is always the constant.

powers

is a vector containing the exponent, or related value to mth_symb. powers can be a vector if those values are constants or it could be a list of vectors the length of observed individuals, if those values varies like in the examples. Notthat if you use variables in powers (list), each values of a vector from this list has to be at the exact same x coordinates of each observed individuals in the input dataframe. Ex: datf <- data.frame("x"=c(4, 4, 3, 2, 1, 1), "y"=c(1:6)), so vector(s) from powers that contain varying value must be of length 4. Also, the values are not ascendly sorted, don't worry values are ascendly sorted under the hood, so fill your powers vectors in the intuitive ascendly way

mth_symb

is a vector containing the elemnts linked to the coefficients from the second element. It can be x, e (exp(x)) or log-X (log(x)-base), and their reverse like 1/x. If the numerator varies the element should be entered like tis list/x, list/e or list/log-base. See numrtr_v for the values related to list

numrtr_v

is a vector containing the values for the numerator related to mth_symb if on element is like this: list/x or list/e

Examples


print(poly_model(inpt_datf=data.frame(mtcars$wt, mtcars$mpg), degree=2, coeff_v=c(32.5, -5), powers=c(1), mth_symb=c("x"),

                 numrtr_v=NA))

[[1]]
[1] 33.234375 -4.265625

[[2]]
[1] 74.78275

print(poly_model(inpt_datf=data.frame(mtcars$wt, mtcars$mpg), degree=2, coeff_v=c(32.5, -3), powers=c(1), mth_symb=c("x"),

                 numrtr_v=NA))

[[1]]
[1] 31.765625 -3.734375

[[2]]
[1] 80.36228

print(poly_model(inpt_datf=data.frame(mtcars$wt, mtcars$mpg), degree=2, coeff_v=c(32.5, -3), powers=list(c(1:length(mtcars$wt))), mth_symb=c("x"),

                 numrtr_v=NA))

[[1]]
[1] 32.5 -3.0

[[2]]
[1] 1.067436e+24

print(poly_model(inpt_datf=data.frame(mtcars$wt, mtcars$mpg), degree=2, coeff_v=c(32.5, -3), powers=list(c(1:length(mtcars$wt))), mth_symb=c("list/x"),

                 numrtr_v=list(c(length(mtcars$wt):1))))
[[1]]
[1] 19.28125 -0.06250

[[2]]
[1] 35839.44

print(poly_model(inpt_datf=data.frame(mtcars$wt, mtcars$mpg), degree=2, coeff_v=c(32.5, -3), powers=list(c(1:length(mtcars$wt))), mth_symb=c("1/x"),

                 numrtr_v=NA))

[[1]]
[1] 27.359375 -8.140625

[[2]]
[1] 160.2263

print(poly_model(inpt_datf=data.frame(mtcars$wt, mtcars$mpg), degree=1, coeff_v=c(32.5), powers=NA, mth_symb=NA,

                 numrtr_v=NA))

[[1]]
[1] 19.28125

[[2]]
[1] 148.7625

print(poly_model(inpt_datf=data.frame(mtcars$wt, mtcars$mpg), degree=2, coeff_v=c(32.5, -3, 2), powers=list(c(1:length(mtcars$wt)), 2), mth_symb=c("1/x", "x"),
 
                  numrtr_v=NA))

[[1]]
[1]  0.921875 -5.203125  2.000000

[[2]]
[1] 455.6017
R: Rmach_det
Rmach_detR Documentation

Rmach_det

Description

Calculates the determinant of any square matrix, see examples

Usage

Rmach_det(inpt_matr)

Arguments

inpt_matr

Examples


mtr_test2 <- matrix(nrow = 2, ncol = 2, data = c(4:6, 37))
mtr_test3 <- matrix(nrow = 3, ncol = 3, data = c(4:6, 37, 12, 33, 1, 2, 3))
mtr_test4 <- matrix(nrow = 4, ncol = 4, data = c(4:6, 37, 12, 33, 1, 2, 3, 8, 7, 8, 7, 11, 22, 21))
mtr_test5 <- matrix(nrow = 5, ncol = 5, data = c(1:25))
mtr_test6 <- matrix(nrow = 6, ncol = 6, data = c(1:36))
mtr_test7 <- matrix(nrow = 7, ncol = 7, data = c(1:49))
mtr_test8 <- matrix(nrow = 8, ncol = 8, data = c(1:64))
mtr_test9 <- matrix(nrow = 9, ncol = 9, data = c(1:81))

det(mtr_test2)
[1] 118
print(Rmach_det(inpt_matr = mtr_test2))
[1] 118

det(mtr_test3)
[1] -138 
print(Rmach_det(inpt_matr = mtr_test3))
[1] -138

det(mtr_test4)
[1] -20001
print(Rmach_det(inpt_matr = mtr_test4))
[1] -20001

det(mtr_test5)
[1] 0
print(Rmach_det(inpt_matr = mtr_test5))
[1] 0

det(mtr_test6)
[1] 0
print(Rmach_det(inpt_matr = mtr_test6))
[1] 0

det(mtr_test7)
[1] 0
print(Rmach_det(inpt_matr = mtr_test7))
[1] 0

det(mtr_test8)
[1] 0
print(Rmach_det(inpt_matr = mtr_test8))
[1] 0

det(mtr_test9)
[1] 0
print(Rmach_det(inpt_matr = mtr_test9))
[1] 0
R: v_Rmach_fold
sample_Rmach-classR Documentation

v_Rmach_fold

Description

Allow to create uniform sampling dataset for cross validation, train and test, see examples and variables

Arguments

inpt_datf

is the input dataframe

train_prop

is the training proportion

n_fold

is the number of distinc pair of training and test dataset that will be outputed

Examples


lst_test <- v_Rmach_fold(inpt_datf = iris[1:25,],
             train_prop = 0.7,
             n_fold = 4)

print(lst_test)

$sample1
An object of class "sample_Rmach"
Slot "train":
     Sepal.Length Sepal.Width Petal.Length Petal.Width Species test_status
24            5.1         3.3          1.7         0.5  setosa           0
18            5.1         3.5          1.4         0.3  setosa           0
12            4.8         3.4          1.6         0.2  setosa           0
19            5.7         3.8          1.7         0.3  setosa           0
20            5.1         3.8          1.5         0.3  setosa           0
5             5.0         3.6          1.4         0.2  setosa           0
4             4.6         3.1          1.5         0.2  setosa           0
23            4.6         3.6          1.0         0.2  setosa           0
18.1          5.1         3.5          1.4         0.3  setosa           0
1             5.1         3.5          1.4         0.2  setosa           0
7             4.6         3.4          1.4         0.3  setosa           0
14            4.3         3.0          1.1         0.1  setosa           0
7.1           4.6         3.4          1.4         0.3  setosa           0
4.1           4.6         3.1          1.5         0.2  setosa           0
19.1          5.7         3.8          1.7         0.3  setosa           0
9             4.4         2.9          1.4         0.2  setosa           0
8             5.0         3.4          1.5         0.2  setosa           0
16            5.7         4.4          1.5         0.4  setosa           0

Slot "test":
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species test_status
7           4.6         3.4          1.4         0.3  setosa           1
12          4.8         3.4          1.6         0.2  setosa           1
8           5.0         3.4          1.5         0.2  setosa           1
14          4.3         3.0          1.1         0.1  setosa           1
11          5.4         3.7          1.5         0.2  setosa           1
25          4.8         3.4          1.9         0.2  setosa           1
23          4.6         3.6          1.0         0.2  setosa           1

Slot "train_ids":
 [1] 24 18 12 19 20  5  4 23 18  1  7 14  7  4 19  9  8 16

Slot "test_ids":
[1]  7 12  8 14 11 25 23


$sample2
An object of class "sample_Rmach"
Slot "train":
     Sepal.Length Sepal.Width Petal.Length Petal.Width Species test_status
20            5.1         3.8          1.5         0.3  setosa           0
8             5.0         3.4          1.5         0.2  setosa           0
2             4.9         3.0          1.4         0.2  setosa           0
11            5.4         3.7          1.5         0.2  setosa           0
22            5.1         3.7          1.5         0.4  setosa           0
13            4.8         3.0          1.4         0.1  setosa           0
24            5.1         3.3          1.7         0.5  setosa           0
2.1           4.9         3.0          1.4         0.2  setosa           0
7             4.6         3.4          1.4         0.3  setosa           0
2.2           4.9         3.0          1.4         0.2  setosa           0
22.1          5.1         3.7          1.5         0.4  setosa           0
22.2          5.1         3.7          1.5         0.4  setosa           0
24.1          5.1         3.3          1.7         0.5  setosa           0
22.3          5.1         3.7          1.5         0.4  setosa           0
3             4.7         3.2          1.3         0.2  setosa           0
3.1           4.7         3.2          1.3         0.2  setosa           0
11.1          5.4         3.7          1.5         0.2  setosa           0
6             5.4         3.9          1.7         0.4  setosa           0

Slot "test":
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species test_status
8           5.0         3.4          1.5         0.2  setosa           1
12          4.8         3.4          1.6         0.2  setosa           1
1           5.1         3.5          1.4         0.2  setosa           1
11          5.4         3.7          1.5         0.2  setosa           1
2           4.9         3.0          1.4         0.2  setosa           1
18          5.1         3.5          1.4         0.3  setosa           1
20          5.1         3.8          1.5         0.3  setosa           1

Slot "train_ids":
 [1] 20  8  2 11 22 13 24  2  7  2 22 22 24 22  3  3 11  6

Slot "test_ids":
[1]  8 12  1 11  2 18 20


$sample3
An object of class "sample_Rmach"
Slot "train":
     Sepal.Length Sepal.Width Petal.Length Petal.Width Species test_status
5             5.0         3.6          1.4         0.2  setosa           0
14            4.3         3.0          1.1         0.1  setosa           0
16            5.7         4.4          1.5         0.4  setosa           0
4             4.6         3.1          1.5         0.2  setosa           0
16.1          5.7         4.4          1.5         0.4  setosa           0
15            5.8         4.0          1.2         0.2  setosa           0
3             4.7         3.2          1.3         0.2  setosa           0
18            5.1         3.5          1.4         0.3  setosa           0
25            4.8         3.4          1.9         0.2  setosa           0
23            4.6         3.6          1.0         0.2  setosa           0
4.1           4.6         3.1          1.5         0.2  setosa           0
24            5.1         3.3          1.7         0.5  setosa           0
20            5.1         3.8          1.5         0.3  setosa           0
7             4.6         3.4          1.4         0.3  setosa           0
19            5.7         3.8          1.7         0.3  setosa           0
21            5.4         3.4          1.7         0.2  setosa           0
23.1          4.6         3.6          1.0         0.2  setosa           0
11            5.4         3.7          1.5         0.2  setosa           0

Slot "test":
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species test_status
18          5.1         3.5          1.4         0.3  setosa           1
21          5.4         3.4          1.7         0.2  setosa           1
5           5.0         3.6          1.4         0.2  setosa           1
12          4.8         3.4          1.6         0.2  setosa           1
14          4.3         3.0          1.1         0.1  setosa           1
2           4.9         3.0          1.4         0.2  setosa           1
8           5.0         3.4          1.5         0.2  setosa           1

Slot "train_ids":
 [1]  5 14 16  4 16 15  3 18 25 23  4 24 20  7 19 21 23 11

Slot "test_ids":
[1] 18 21  5 12 14  2  8


$sample4
An object of class "sample_Rmach"
Slot "train":
     Sepal.Length Sepal.Width Petal.Length Petal.Width Species test_status
18            5.1         3.5          1.4         0.3  setosa           0
18.1          5.1         3.5          1.4         0.3  setosa           0
13            4.8         3.0          1.4         0.1  setosa           0
7             4.6         3.4          1.4         0.3  setosa           0
18.2          5.1         3.5          1.4         0.3  setosa           0
2             4.9         3.0          1.4         0.2  setosa           0
19            5.7         3.8          1.7         0.3  setosa           0
9             4.4         2.9          1.4         0.2  setosa           0
23            4.6         3.6          1.0         0.2  setosa           0
15            5.8         4.0          1.2         0.2  setosa           0
16            5.7         4.4          1.5         0.4  setosa           0
15.1          5.8         4.0          1.2         0.2  setosa           0
8             5.0         3.4          1.5         0.2  setosa           0
9.1           4.4         2.9          1.4         0.2  setosa           0
10            4.9         3.1          1.5         0.1  setosa           0
14            4.3         3.0          1.1         0.1  setosa           0
11            5.4         3.7          1.5         0.2  setosa           0
12            4.8         3.4          1.6         0.2  setosa           0

Slot "test":
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species test_status
9           4.4         2.9          1.4         0.2  setosa           1
13          4.8         3.0          1.4         0.1  setosa           1
4           4.6         3.1          1.5         0.2  setosa           1
19          5.7         3.8          1.7         0.3  setosa           1
22          5.1         3.7          1.5         0.4  setosa           1
11          5.4         3.7          1.5         0.2  setosa           1
5           5.0         3.6          1.4         0.2  setosa           1

Slot "train_ids":
 [1] 18 18 13  7 18  2 19  9 23 15 16 15  8  9 10 14 11 12

Slot "test_ids":
[1]  9 13  4 19 22 11  5