?na.omit
??
But it is certainly true that omitting missing values first and
analyzing the remaining data can:
1. Leave you with no data to analyze
2. Result in biased and misleading conclusions if the missingness
mechanism is related to the covariates.
In general, handling data with missing va
Hi everyone,
I have a large dataset with missing values. I tried using glmnet, but it seems
that it cannot handle NA values in the design matrix. I also tried lars, but I
get an error too. Does anyone know of any package for computing the lasso
solution which handles NA values?
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