Are there missing values in your data? If so, try adding
the argument
na.action = na.exclude
to your original call to glm or lm. It is like the default
na.omit except that it records which rows were omitted
(because they contained missing values) and fills in
the corresponding entries in the p
Hi all,
Given a simple logistic regression on a training data set using glm,
the number of predicted values is less than the number of observations
in the training set:
> fit.train.pred <- predict(fit, type = "response")
> nrow(train)
[1] 62660
> length(fit.train.pred)
[1] 58152
>
As a relative
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