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Pools R^2 of m repeated complete data models.
pool.r.squared(object, adjusted = FALSE)
An object of class 'mira', produced by lm.mids
or
with.mids
with lm
as modeling function.
A logical value. If adjusted=TRUE then the adjusted R^2 is calculated. The default value is FALSE.
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.
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.
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/
# 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
#
# }
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