R feature selection with LASSO - r

Question

I have a small data set (37 observations x 23 features) and want to perform feature selection with LASSO regression in order to its reduce dimensionality. To achieve this, I designed the below code based on online tutorials
#Load the libraries
library(mlbench)
library(elasticnet)
library(caret)
#Initialize cross validation and train LASSO
cv_5 <- trainControl(method="cv", number=5)
lasso <- train( ColumnY ~., data=My_Data_Frame, method='lasso', trControl=cv_5)
#Filter out the variables whose coefficients have squeezed to 0
drop <-predict.enet(lasso$finalModel, type='coefficients', s=lasso$bestTune$fraction, mode='fraction')$coefficients
drop<-drop[drop==0]%>%names()
My_Data_Frame<- My_Data_Frame%>%select(-drop)
In most cases the code runs without errors but it occasionally throws the following:
Warning messages:
1: model fit failed for Fold2: fraction=0.9 Error in if (zmin < gamhat) { : missing value where TRUE/FALSE needed
2: In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, :
There were missing values in resampled performance measures.
I sense this happens because my data has few rows and some variables have low variance.
Is there a way I can bypass or fix this issue (e.g. setting a parameter in the flow)?

Answer 1

You have a low number of observations, so there's a good chance in some training set, that some of your columns will be all zero, or very low variance. For example:
library(caret)
set.seed(222)
df = data.frame(ColumnY = rnorm(37),matrix(rbinom(37*23,1,p=0.15),ncol=23))
cv_5 <- trainControl(method="cv", number=5)
lasso <- train( ColumnY ~., data=df, method='lasso', trControl=cv_5)
Warning messages:
1: model fit failed for Fold4: fraction=0.9 Error in elasticnet::enet(as.matrix(x), y, lambda = 0, ...) :
Some of the columns of x have zero variance
Before running below, check that for categorical columns, all of them don't have only 1 positive label..
One way is to increase the cv fold, if you set 5, you are using 80% of the data. Try 10 to use 90% of the data:
cv_10 <- trainControl(method="cv", number=10)
lasso <- train( ColumnY ~., data=df, method='lasso', trControl=cv_10)
And as you might have seen.. since the dataset is so small, cross-validation might not offer you that much advantage, you can also do leave one out cross-validation:
tr <- trainControl(method="LOOCV")
lasso <- train( ColumnY ~., data=df, method='lasso', trControl=tr)

Answer 2

You can use the FSinR package to perform feature selection. It is in R and accessible from CRAN. It has a wide variety of filter and wrapper methods that you can combine with search methods. The interface to generate the wrapper evaluator follows the caret interface. For example:
# Load the library
library(FSinR)
# Choose one of the search methods
searcher <- searchAlgorithm('sequentialForwardSelection')
# Choose one of the filter/wrapper evaluators (You can remove the fitting and resampling params if you want to make it simpler)(These are the parameters of the train and trainControl of caret)
resamplingParams <- list(method = "cv", number = 5)
fittingParams <- list(preProc = c("center", "scale"), metric="Accuracy", tuneGrid = expand.grid(k = c(1:20)))
evaluator <- wrapperEvaluator('knn', resamplingParams, fittingParams)
# You make the feature selection (returns the best features)
results <- featureSelection(My_Data_Frame, 'ColumnY', searcher, evaluator)