mice (version 3.3.0)

pool.r.squared: Pooling: R squared

Description

Pools R^2 of m repeated complete data models.

Usage

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

Arguments

object

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

adjusted

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.

Details

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.

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/

See Also

pool,pool.scalar

Examples

# NOT RUN {

imp<-mice(nhanes)

fit<-lm.mids(chl~age+hyp+bmi,imp)
pool.r.squared(fit)
pool.r.squared(fit,adjusted=TRUE)

#fit<-lm.mids(chl~age+hyp+bmi,imp)
#
#> pool.r.squared(fit)
#          est     lo 95     hi 95       fmi
#R^2 0.5108041 0.1479687 0.7791927 0.3024413
#
#> pool.r.squared(fit,adjusted=TRUE)
#          est      lo 95    hi 95       fmi
#adj R^2 0.4398066 0.08251427 0.743172 0.3404165
#


# }