mice (version 3.13.0)

# pool.r.squared: Pools R^2 of m models fitted to multiply-imputed data

## Description

The function pools the coefficients of determination R^2 or the adjusted coefficients of determination (R^2_a) obtained with the `lm` modeling function. For pooling it uses the Fisher z-transformation.

## Usage

`pool.r.squared(object, adjusted = FALSE)`

## Arguments

object

An object of class 'mira' or 'mipo', produced by `lm.mids`, `with.mids`, or `pool` with `lm` as modeling function.

A logical value. If adjusted=TRUE then the adjusted R^2 is calculated. The default value is FALSE.

## Value

Returns a 1x4 table with components. Component `est` is the pooled R^2 estimate. Component `lo95` is the 95 % lower bound of the pooled R^2. Component `hi95` is the 95 % upper bound of the pooled R^2. Component `fmi` is the fraction of missing information due to nonresponse.

## References

Harel, O (2009). The estimation of R^2 and adjusted R^2 in incomplete data sets using multiple imputation, Journal of Applied Statistics, 36:1109-1118.

Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. New York: John Wiley and Sons.

van Buuren S and Groothuis-Oudshoorn K (2011). `mice`: Multivariate Imputation by Chained Equations in `R`. Journal of Statistical Software, 45(3), 1-67. https://www.jstatsoft.org/v45/i03/

`pool`,`pool.scalar`

## Examples

Run this code
``````# NOT RUN {
imp <- mice(nhanes, print = FALSE, seed = 16117)
fit <- with(imp, lm(chl ~ age + hyp + bmi))

# input: mira object
pool.r.squared(fit)
pool.r.squared(fit, adjusted = TRUE)

# input: mipo object
est <- pool(fit)
pool.r.squared(est)
pool.r.squared(est, adjusted = TRUE)
# }
``````

Run the code above in your browser using DataCamp Workspace