I'm trying to fit a multiple regression model with pairwise deletion in the context of missing data.
lm() uses listwise deletion, which I'd prefer not to use in my case. I'd also prefer not to use multiple imputation or FIML. How can I do multiple regression with pairwise deletion in R?
I have tried the
mat.regress() function of the
psych package, which fits regression models to correlation/covariance matrices (which can be obtained from pairwise deletion), but the regression model does not appear to include an intercept parameter.
Here's what I've tried (small example):
set.seed(33333) y <- rnorm(1000) x1 <- y*2 + rnorm(1000, sd=.2) x2 <- y*5 + rnorm(1000, sd=.5) y[sample(1:1000, 10)] <- NA x1[sample(1:1000, 10)] <- NA x2[sample(1:1000, 10)] <- NA mydata <- data.frame(y, x1, x2) covMatrix <- cov(mydata, use="pairwise.complete.obs") #Listwise Deletion listwiseDeletion <- lm(y ~ x1 + x2, data=mydata) observations <- length(listwiseDeletion$na.action) #30 rows deleted due to listwise deletion coef(listwiseDeletion) (Intercept) x1 x2 0.001995527 0.245372245 0.100001989 #Pairwise Deletion --- but missing intercept pairwiseDeletion <- mat.regress(y="y", x=c("x1","x2"), data=covMatrix, n.obs=observations) pairwiseDeletion$beta y x1 0.1861277 x2 0.1251995 #Pairwise Deletion --- tried to add intercept, but received error when fitting model mydata$intercept <- 0 covMatrixWithIntercept <- cov(mydata, use="pairwise.complete.obs") pairwiseDeletionWithIntercept <- mat.regress(y="y", x=c("intercept","x1","x2"), data=covMatrixWithIntercept, n.obs=observations) Something is seriously wrong the correlation matrix. In smc, smcs were set to 1.0 Warning messages: 1: In cov2cor(C) : diag(.) had 0 or NA entries; non-finite result is doubtful 2: In cor.smooth(R) : I am sorry, there is something seriously wrong with the correlation matrix, cor.smooth failed to smooth it because some of the eigen values are NA. Are you sure you specified the data correctly?
So, how can I obtain an intercept parameter using
mat.regress, or how can I obtain parameter estimates from pairwise deletion using another method or package in R? I've seen matrix calculations to do this, but, ideally, there'd be a package that also outputs regression diagnostics, fit stats, etc. Also, preferably, the method would be able to fit interaction terms.