On 29-Oct-04 Avril Coghlan wrote:
Dear R help list,
I am trying to do a logistic regression where I have a categorical response variable Y and two numerical predictors X1 and X2. There are quite a lot of missing values for predictor X2. eg.,
Y X1 X2 red 0.6 0.2 * red 0.5 0.2 * red 0.5 NA red 0.5 NA green 0.2 0.1 * green 0.1 NA green 0.1 NA green 0.05 0.05 *
I am wondering can I combine X1 and X2 in a logistic regression to predict Y, using all the data for X1, even though there are NAs in the X2 data?
Or do I have to take only the cases for which there is data for both X1 and X2? (marked with *s above)
I don't know of any R routine directly aimed at logistic regression with missing values as you describe.
The aregImpute function in the Hmisc package can handle this, using predictive mean matching with weighted multinomial sampling of donor observations' binary covariate values.
. . ..
Ted.
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