CODEWITHSUNDEEP
r
Barry
Barry

Reputation: 105

Not losing observations when faced with missing data

I have a dataset where I've fitted a linear model and I've tried to use the step function on this linear model. I get an error message "saying number of rows in use has changed: remove missing values?".

I noticed that a few of the observations (not many) in my dataset had NA values for one variable. I've seen similar questions which suggest using na.omit(), but when I do this I lose the observations. I want to keep the observations however, because they contain useful information for the other variables. Is there a way to use step and avoid losing the observations?

Upvotes: 0

Views: 164

Answers (2)

SweepingsDemon
SweepingsDemon

Reputation: 149

You can call the nobs function to check that the number of observations is unchanged, and its use.fallback argument to potentially guess the missing values. The R documentation however recommends omitting the relevant data before running step.

Upvotes: 1

dinman
dinman

Reputation: 55

I would discourage you from simply omitting the missing values if they are indeed really missing. You can use multiple imputation via Amelia to impute the data such that you have a full dataset.

see here: https://cran.r-project.org/web/packages/Amelia/Amelia.pdf also I would recommend reviewing the book "Statistical Analysis With Missing Data" by R. Little and D.B. Rubin.

Upvotes: 0

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