I'm new to Lasso regression and am trying to get glmnet to work in preparation for lasso regression. Unfortunately, I hit a problem pretty early on. Could someone let me know what I've done wrong? Thank you!
df0<- read_csv("data_cleaned.csv") #Categorical variables set to factors df0$PL_Binary_Score<-as.factor(df0$PL_Binary_Score) df0$Gender<-as.factor(df0$Gender) df0$Ethnicity<-as.factor(df0$Ethnicity) df0$Age<-as.factor(df0$Age) df0$Location<-as.factor(df0$Location) df0$Income<-as.factor(df0$Income) df0$Education<-as.factor(df0$Education) df0$Working_From_Home<-as.factor(df0$Working_From_Home) #Omit all NAs PL<-na.omit(df0) fit <- PL[c("Social_Media_Time", "Ethnicity", "Age", "Location", "Income", "Education", "Working_From_Home", "Traditional_Time_Hrs", "Private_Use", "Anxiety_Diagnosed", "Gender", "Public_Use")] PL.2<- as.matrix(fit) fit = glmnet(PL.2, "PL_Binary_Score")
I get the following error:
number of observations in y (1) not equal to the number of rows of x (287).
I'm a bit confused because there are no missing values that could explain a difference in row length.