Unable to run elastic net logistic regression model

Machine Learning and Modeling
1

I have split my data into training and test data for both the predictor and response variables. My predictor variables have been converted to dummy variables in a data frame and are numeric. My response variable is a factor with two levels. The dimensions of each factor are correct and there are no errors in the labelling of predictors etc.

set.seed(1234)
en1 <- train(train.response.1~., train.predictors.1, method='glmnet', tuneGrid =expand.grid(alpha=seq(0,1,length=10), lambda = seq(0.0001,0.2,length=5)), trControl=custom.1)

I am getting the error:

Error in `[.data.frame`(data, , all.vars(Terms), drop = FALSE) : 
  undefined columns selected
2

Can you please provide a minimal reprex (reproducible example)? The goal of a reprex is to make it as easy as possible for me to recreate your problem so that I can fix it: please help me help you!

If you've never heard of a reprex before, start by reading "What is a reprex", and follow the advice further down that page.

3 
##This script follows on from script: LCA_LL_V1 due to implementation of posterior probabilities needed (line 42)


## Elastic Net Regression on environmental predictors (Fam_env_and_conflict_scale)

#Install packages and open library 

install.packages("caret")
#> Installing package into 'C:/Users/lukel/OneDrive/Documents/R/win-library/4.1'
#> (as 'lib' is unspecified)
#> package 'caret' successfully unpacked and MD5 sums checked
#> Warning: cannot remove prior installation of package 'caret'
#> Warning in file.copy(savedcopy, lib, recursive = TRUE):
#> problem copying C:\Users\lukel\OneDrive\Documents\R\win-
#> library\4.1\00LOCK\caret\libs\x64\caret.dll to C:
#> \Users\lukel\OneDrive\Documents\R\win-library\4.1\caret\libs\x64\caret.dll:
#> Permission denied
#> Warning: restored 'caret'
#> 
#> The downloaded binary packages are in
#>  C:\Users\lukel\AppData\Local\Temp\RtmpgtoA2j\downloaded_packages
library(caret)
#> Warning: package 'caret' was built under R version 4.1.3
#> Loading required package: ggplot2
#> Warning: package 'ggplot2' was built under R version 4.1.3
#> Loading required package: lattice
install.packages("glmnet")
#> Installing package into 'C:/Users/lukel/OneDrive/Documents/R/win-library/4.1'
#> (as 'lib' is unspecified)
#> package 'glmnet' successfully unpacked and MD5 sums checked
#> Warning: cannot remove prior installation of package 'glmnet'
#> Warning in file.copy(savedcopy, lib, recursive = TRUE):
#> problem copying C:\Users\lukel\OneDrive\Documents\R\win-
#> library\4.1\00LOCK\glmnet\libs\x64\glmnet.dll to C:
#> \Users\lukel\OneDrive\Documents\R\win-library\4.1\glmnet\libs\x64\glmnet.dll:
#> Permission denied
#> Warning: restored 'glmnet'
#> 
#> The downloaded binary packages are in
#>  C:\Users\lukel\AppData\Local\Temp\RtmpgtoA2j\downloaded_packages
library(glmnet)
#> Warning: package 'glmnet' was built under R version 4.1.3
#> Loading required package: Matrix
#> Loaded glmnet 4.1-6

#Set WD and Open CSV file

setwd("C:/Users/lukel/OneDrive/Desktop/University_Documents/PhD/Year_1/NIMH_Data/Predictors")
Fam.env.con <- read.csv("Family_env_and_conflict_scale.csv")

#####################################################################################
#Data wrangling on predictor variables
####################################################################################

#Filter year one data

Fam.env.con.1 <- filter(Fam.env.con, grepl("1_year_follow_up_y_arm_1", eventname))
#> Error in is.factor(x): object 'eventname' not found

#Remove column

Fam.env.con.1 <- Fam.env.con.1[ ,-c(11)]
#> Error in eval(expr, envir, enclos): object 'Fam.env.con.1' not found

#Change colnames

colnames(Fam.env.con.1) <- c("Key", "Fam_fight", "Fam_rarely_angry", "Fam_angry_throw_things", "Fam_lose_temper", "Fam_criticise", "Fam_hit", "Fam_keep_peace", "Fam_outdo", "Fam_raise")
#> Error in colnames(Fam.env.con.1) <- c("Key", "Fam_fight", "Fam_rarely_angry", : object 'Fam.env.con.1' not found

#Change columns to numeric for purpose of mutating values within columns (next step)

Fam.env.con.2 <- Fam.env.con.1 %>% mutate_at(c(2:10), as.numeric)
#> Error in Fam.env.con.1 %>% mutate_at(c(2:10), as.numeric): could not find function "%>%"

#Change data values in specific columns so all TRUE = 1 and all FALSE = 0

Fam.env.con.2[c(3,5,8,10)][Fam.env.con.2[c(3,5,8,10)] == 1] <- 3
#> Error in Fam.env.con.2[c(3, 5, 8, 10)][Fam.env.con.2[c(3, 5, 8, 10)] == : object 'Fam.env.con.2' not found
Fam.env.con.2[c(3,5,8,10)][Fam.env.con.2[c(3,5,8,10)] == 0] <- 1
#> Error in Fam.env.con.2[c(3, 5, 8, 10)][Fam.env.con.2[c(3, 5, 8, 10)] == : object 'Fam.env.con.2' not found
Fam.env.con.2[c(3,5,8,10)][Fam.env.con.2[c(3,5,8,10)] == 3] <- 0
#> Error in Fam.env.con.2[c(3, 5, 8, 10)][Fam.env.con.2[c(3, 5, 8, 10)] == : object 'Fam.env.con.2' not found

#Remove NA values

Fam.env.con.3 <- na.omit(Fam.env.con.2)
#> Error in na.omit(Fam.env.con.2): object 'Fam.env.con.2' not found

#####################################################################################
#######################################################################################

#Combine polca posterior states (class) and Fam_env_conflict data by 'Key'

Combined.data <- merge(poLCA_posterior.states, Fam.env.con.3, by = 'Key')
#> Error in merge(poLCA_posterior.states, Fam.env.con.3, by = "Key"): object 'poLCA_posterior.states' not found

#Remove columns from dataset

Combined.data.2 <- Combined.data[ ,-c(1,3:5,15)]
#> Error in eval(expr, envir, enclos): object 'Combined.data' not found

#Change colnames

colnames(Combined.data.2) <- c("state", "fam.fight", "fam.rarely.angry", "fam.angry.throw.things", "fam.lose.temper", "fam.criticise", "fam.hit", "fam.keep.peace", "fam.outdo", "fam.raise")
#> Error in colnames(Combined.data.2) <- c("state", "fam.fight", "fam.rarely.angry", : object 'Combined.data.2' not found

#Remove class 'one' (serious del) so y variable is binary 

EN1 <- subset(Combined.data.2, Combined.data.2[, 1] != 1)
#> Error in subset(Combined.data.2, Combined.data.2[, 1] != 1): object 'Combined.data.2' not found

#Drop levels on factor variable

EN1 <- droplevels(EN1)
#> Error in droplevels(EN1): object 'EN1' not found

#Create dummy variables required for glmnet

dummy.vars.1 <- model.matrix(state~., EN1)[,-1]
#> Error in terms.formula(object, data = data): object 'EN1' not found

#Create Y variable as factor 

response.1 <- EN1$state
#> Error in eval(expr, envir, enclos): object 'EN1' not found

#Split data into training and test data (80/20)

set.seed(123)

train.index.1 <- createDataPartition(response.1, p = 0.8, list = FALSE)
#> Error in createDataPartition(response.1, p = 0.8, list = FALSE): object 'response.1' not found

train.predictors.1 <- dummy.vars.1[train.index.1, ]
#> Error in eval(expr, envir, enclos): object 'dummy.vars.1' not found

train.response.1 <- response.1[train.index.1]
#> Error in eval(expr, envir, enclos): object 'response.1' not found

test.predictors.1 <- dummy.vars.1[-train.index.1, ]
#> Error in eval(expr, envir, enclos): object 'dummy.vars.1' not found

test.response.1 <- response.1[-train.index.1] 
#> Error in eval(expr, envir, enclos): object 'response.1' not found

#Change factor variable names so they are valid

levels(train.response.1) <- c("two", "three")
#> Error in levels(train.response.1) <- c("two", "three"): object 'train.response.1' not found

#Change factor variable names so they are valid

levels(test.response.1) <- c("two", "three")
#> Error in levels(test.response.1) <- c("two", "three"): object 'test.response.1' not found

#########################################################################
#first elastic net model using cross-validation sampling (class 2 vs 3)
#########################################################################

#Create parameters for cross-validation (testing model with random subsamples of training data)

custom.1 <- trainControl(method = "repeatedcv", number = 10, repeats = 5, verboseIter = TRUE, classProbs = TRUE, summaryFunction = twoClassSummary)

#Create model 

set.seed(1234)
EN1.model.cv <- train(train.response.1~.,  data = train.predictors.1, method='glmnet', tuneGrid =expand.grid(alpha=seq(0,1,length=10), lambda = seq(0.0001,0.2,length=5)), trControl=custom.1)
#> Error in eval(expr, p): object 'train.predictors.1' not found

Created on 2023-01-09 with reprex v2.0.2

4

That's helpful but not reproducible.

There are some obvious errors prior to modeling, so maybe work on getting that code working?

Can you make a small, fake version of your local data or something that shows the modeling error?

5

Hi Max,

I'm not sure why it's showing up with errors as when I run the code I have 0 errors until I run the model. Regarding a fake dataset, I'm not sure how I would do this as I'm very new to using R. I have ran this with the response variable (factor; class two, class three) and the predictors (numeric; 0 and 1) in the same data frame and it has worked. I'm not sure if this will cause further complications as it doesn't appear to be the 'usual' way of doing this, as I've seen people split the predictors and response variable separate.

6

You could just make the column names something like v1, v2, etc but I understand the concern.

Can you run str(Fam.env.con) right after you read it in and put that here?

8

Hi Max,

Thanks for your prompt response. I've changed the column names to see if this did anything but it still gave the same error. After combining two datasets and cleaning the data, this is what the dataset looked like when I started focusing on the elastic net (the code below looks a bit messy on my screen, but basically its a factor followed by numerical variables:

'data.frame':	11195 obs. of  10 variables:
 $ state                 : Factor w/ 3 levels "two","one","three": 3 3 2 1 2 2 3 2 3 3 ...
 $ Fam_fight             : num  0 0 1 0 0 0 0 0 0 0 ...
 $ Fam_rarely_angry      : num  0 0 0 1 1 1 1 0 1 1 ...
 $ Fam_angry_throw_things: num  0 0 0 0 0 0 0 0 0 0 ...
 $ Fam_lose_temper       : num  0 0 0 1 0 1 0 0 1 1 ...
 $ Fam_criticise         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ Fam_hit               : num  0 0 1 0 0 0 0 0 0 0 ...
 $ Fam_keep_peace        : num  0 1 1 0 1 1 1 1 1 1 ...
 $ Fam_outdo             : num  0 0 0 0 1 0 0 1 1 0 ...
 $ Fam_raise             : num  0 1 1 1 1 1 1 1 0 1 ...
9

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