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I am using the mice package and lmer from lme4 for my analyses. However, pool.r.squared() won't work on this output. I am looking for suggestions on how to include the computation of the adjusted R squared in the following workflow.

require(lme4, mice)
imp <- mice(nhanes)
imp2 <- mice::complete(imp, "all") # This step is necessary in my analyses to include other variables/covariates following the multiple imputation
fit <- lapply(imp2, lme4::lmer, 
              formula = bmi ~ (1|age) + hyp + chl,
              REML = T)
est <- pool(fit)
summary(est)
Christine Blume
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1 Answers1

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You have two separate problems here.

First, there are several opinions about what an R-squared for multilevel/mixed-model regressions actually is. This is the reason why pool.r.squared does not work for you, as it does not accept results from anything other than lm(). I do not have an answer for you how to calculate something R-squared-ish for your model and since it is a statistics question – not a programming one – I am not going into detail. However, a quick search indicates that for some kinds of multilevel R-squares, there are functions available for R, e.g. mitml::multilevelR2.

Second, in order to pool a statistic across imputation samples, it should be normally distributed. Therefore, you have to transform R-squared into Fisher's Z and back-transform it after pooling. See https://stefvanbuuren.name/fimd/sec-pooling.html

In the following I assume that you have a way (or several options) to calculate your (adjusted) R-squared. Assuming that you use mitl::multilevelR2 and choose the method by LaHuis et al. (2014), you can compute and pool it across your imputations with the following steps:

# what you did before:
imp <- mice::mice(nhanes)
imp2 <- mice::complete(imp, "all")
fit_l <- lapply(imp2, lme4::lmer, 
              formula = bmi ~ (1|age) + hyp + chl,
              REML = T)

# get your R-squareds in a vector (replace `mitl::multilevelR2` with your preferred function for this)
Rsq <- lapply(fit_l, mitml::multilevelR2, print="MVP")
Rsq <- as.double(Rsq)

# convert the R-squareds into Fisher's Z-scores
Zrsq <- 1/2*log( (1+sqrt(Rsq)) / (1-sqrt(Rsq)) )

# get the variance of Fisher's Z (same for all imputation samples)
Var_z <- 1 / (nrow(imp2$`1`)-3)
Var_z <- rep(Var_z, imp$m)

# pool the Zs
Z_pool <- pool.scalar(Zrsq, Var_z, n=imp$n)$qbar

# back-transform pooled Z to Rsquared
Rsq_pool <- ( (exp(2*Z_pool) - 1) / (exp(2*Z_pool) + 1) )^2

Rsq_pool #done
benimwolfspelz
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