I've trained an elastic net model in R using glmnet and would like to use it to make predictions off of a new data set.
But I'm having trouble producing the matrix to use as an argument in the predict() method because some of my factor variables (dummy variables indicating the presence of comorbidities) in the new data set only have one level (the comorbidities were never observed), which means I can't use<blockquote>
model.matrix(RESPONSE ~ ., new_data)</blockquote>
because it gives me the (expected)<blockquote>
*tmp*, value = contr.funs[1 + isOF[nn]]) :
contrasts can be applied only to factors with 2 or more levels
I'm at a loss for how to get around this issue. Is there a way in R that I can construct an appropriate matrix for use in predict() in this situation, or do I need to prepare the matrix outside of R? In either case, how might I go about doing it?
Here is a toy example that reproduces the issue I'm having:
x1 <- rnorm(100) x2 <- as.factor(rbinom(100, 1, 0.6)) x3 <- as.factor(rbinom(100, 1, 0.4)) y <- rbinom(100, 1, 0.2) toy_data <- data.frame(x1, x2, x3, y) colnames(toy_data) = c("Continuous", "FactorA", "FactorB", "Outcome") mat1 <- model.matrix(Outcome ~ ., toy_data)[,-1] y1 <- toy_data$Outcome new_data <- toy_data new_data$FactorB <- as.factor(0) #summary(new_data) # Just to verify that FactorB now only contains one level mat2 <- model.matrix(Outcome ~ ., new_data)[,-1]Answer1:
You can set the
levels of your dataset to match the
levels of the complete dataset in your example. A factor can have values present in the
levels even when that value isn't present in the variable.
You can do this with the
levels argument in
new_data$FactorB <- factor(0, levels = levels(toy_data$FactorB))
Or by using the
levels() function with assignment:
levels(new_data$FactorB) <- levels(toy_data$FactorB)
Using either approach,
model.matrix() works properly once you have more than one level:
head( model.matrix(Outcome ~ ., new_data)[,-1] ) Continuous FactorA1 FactorB1 1 -1.91632972 0 0 2 1.11411267 0 0 3 -1.21333837 1 0 4 -0.06311276 0 0 5 1.31599915 0 0 6 0.36374591 1 0