wondim
Reputation: 727
tidyr unnest with purrr::map2 shows error Error: object 'model' not found
I got the error Error: object 'model' not found when running the code below. Is there any way of fixing the issue using another library or using the code differently?
The code below is runable until the error is shown. It is aimed at running k-fold validation.
K_folds_regression_function <- function(inputData, col_pos_Y, col_pos_Xn){
# ------------------------------- User Inputs -------------------------------------------------------------------------------------------------------
# inputFile <- 'Input_Data-MLR.csv' # <-- provide file name .csv
# col_pos_Y <- 11 # position of the Y variable
# col_pos_Xn <- 14 # position of LAST Xs
# ------------------------------- libraries ---------------------------------------------------------------------------------------------
library(modelr) ; library(dplyr) ; library(purrr) ; library(ggplot2)
library(tidyr) ; library(broom) ; library(tibble)
library(modelr)
library(broom)
my_y_x_data <- inputData[ , c(1,col_pos_Y:col_pos_Xn)]; head(my_y_x_data)
no_of_vars <- dim (my_y_x_data)[2] - 1 # including x and y and excluding the 1st ID column
no_of_xs <- no_of_vars - 1 # only Xs
set.seed(6)
colnames(my_y_x_data)[2] <- 'yy' # making sure that 2nd column which is a depedent variable is named 'yy'
k_folds_regr <- crossv_kfold(data = my_y_x_data, k = 10) # k is the no of folds, in this case 10 folds. around 7-11 folds will have minimum error, uually 10 is used
colnames(my_y_x_data)[2] <- 'yy' # making sure that 2nd column which is a dependent variable is named 'yy'
head(my_y_x_data)
library(tidyr)
# fitting models
k_folds_regr <- k_folds_regr %>% mutate(
model = map( train, ~ lm( yy ~ . , data = my_y_x_data[, -1]) ))
# k_fold_regr %>% mutate --> adds a new column # map (train, ) -> applys a function to each rows of train data
interim_regression_result_table <- k_folds_regr %>% unnest(
map2(model, test, ~augment(.x, newdata = .y) ) # Error happens here
)
}
Example Data:
Sites,SPM,Total_Rrs
WMS04,36,1.428096339
WMS05,36,1.468391179
WMS06,35,1.480664998
WMS07,30,1.563063147
WMS09,28.25,1.575149555
WMS13,29.5,1.527970637
WMS14,36.75,1.697142346
WMS15,54.5,1.701675753
WMS16,61.75,1.647421816
WMS17,46.5,1.734592117
WMS18,43.5,1.658748318
WMS19,44,1.56844909
WMS20,46.25,1.523165904
WMS22,46.5,1.727354533
WMS23,45.75,1.583000425
WMS24,43.5,1.679314139
WMS25,40.5,1.583000425
WMS26,16,1.722078046
WMS27,68.5,1.356190243
WMS29,44.25,1.65871572
WMS30,21.5,1.55141119
WMS32,49,1.549374723
WMS33,49.75,1.623284831
WMS34,47.5,1.716614557
WMS35,55.25,1.693683697
Usage:
inputFile = 'path/to/csv.csv'
inputData <- read.csv(inputFile, header=TRUE)
K_folds_regression_function(inputData, 2, 3)
Upvotes: 0
Views: 765
Answers (1)
Allan Cameron
Reputation: 173803
It seems that map2 didn't like the fact that it was being passed a list of lists. Base R's mapply is less fussy.
Note that it's not good practice to use library inside a function - it can lead to unintended side effects for users of your function, since packages are loaded that the user may not have wanted and thus some functions may have been overwritten. It is better to use the namespace qualifier package::function
You also had multiple calls to library for the same package, so I have tidied the function up to use namespace qualifiers instead of library calls:
K_folds_regression_function <- function(inputData, col_pos_Y, col_pos_Xn)
{
`%>%` <- dplyr::`%>%`
my_y_x_data <- inputData[ , c(1,col_pos_Y:col_pos_Xn)]
no_of_vars <- dim(my_y_x_data)[2] - 1
no_of_xs <- no_of_vars - 1
set.seed(6)
k_folds_regr <- modelr::crossv_kfold(data = my_y_x_data, k = 10)
colnames(my_y_x_data)[2] <- 'yy'
k_folds_regr <- k_folds_regr %>%
dplyr::mutate(model = purrr::map(train, ~ lm(yy~. ,data = my_y_x_data[, -1])))
mapply(function(.x, .y) broom::augment(.x, newdata = .y),
k_folds_regr$model, k_folds_regr$test, SIMPLIFY = FALSE)
}
Now when we run the function we get:
K_folds_regression_function(inputData, 2, 3)
#> $`1`
#> # A tibble: 3 x 5
#> Sites yy Total_Rrs .fitted .se.fit
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 WMS04 36 1.43 42.1 4.86
#> 2 WMS18 43.5 1.66 42.3 2.81
#> 3 WMS23 45.8 1.58 42.2 2.46
#>
#> $`2`
#> # A tibble: 3 x 5
#> Sites yy Total_Rrs .fitted .se.fit
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 WMS14 36.8 1.70 42.3 3.38
#> 2 WMS17 46.5 1.73 42.3 4.07
#> 3 WMS29 44.2 1.66 42.3 2.81
#>
#> $`3`
#> # A tibble: 3 x 5
#> Sites yy Total_Rrs .fitted .se.fit
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 WMS06 35 1.48 42.1 3.80
#> 2 WMS13 29.5 1.53 42.2 3.01
#> 3 WMS34 47.5 1.72 42.3 3.72
#>
#> $`4`
#> # A tibble: 3 x 5
#> Sites yy Total_Rrs .fitted .se.fit
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 WMS07 30 1.56 42.2 2.59
#> 2 WMS09 28.2 1.58 42.2 2.51
#> 3 WMS32 49 1.55 42.2 2.73
#>
#> $`5`
#> # A tibble: 3 x 5
#> Sites yy Total_Rrs .fitted .se.fit
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 WMS20 46.2 1.52 42.2 3.08
#> 2 WMS25 40.5 1.58 42.2 2.46
#> 3 WMS35 55.2 1.69 42.3 3.32
#>
#> $`6`
#> # A tibble: 2 x 5
#> Sites yy Total_Rrs .fitted .se.fit
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 WMS24 43.5 1.68 42.3 3.09
#> 2 WMS33 49.8 1.62 42.3 2.49
#>
#> $`7`
#> # A tibble: 2 x 5
#> Sites yy Total_Rrs .fitted .se.fit
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 WMS26 16 1.72 42.3 3.83
#> 2 WMS27 68.5 1.36 42.0 6.44
#>
#> $`8`
#> # A tibble: 2 x 5
#> Sites yy Total_Rrs .fitted .se.fit
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 WMS16 61.8 1.65 42.3 2.68
#> 2 WMS22 46.5 1.73 42.3 3.93
#>
#> $`9`
#> # A tibble: 2 x 5
#> Sites yy Total_Rrs .fitted .se.fit
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 WMS15 54.5 1.70 42.3 3.46
#> 2 WMS30 21.5 1.55 42.2 2.71
#>
#> $`10`
#> # A tibble: 2 x 5
#> Sites yy Total_Rrs .fitted .se.fit
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 WMS05 36 1.47 42.1 4.04
#> 2 WMS19 44 1.57 42.2 2.55
Upvotes: 1
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