pool.r.squared {mice} | R Documentation |
Pools R^2 of m repeated complete data models.
pool.r.squared(object,adjusted=FALSE)
object |
An object of class 'mira', produced by |
adjusted |
A logical value. If adjusted=TRUE then the adjusted R^2 is calculated. The default value is FALSE. |
The function pools the coefficients of determination R^2 or the adjusted coefficients
of determination (R^2_a) obtained with the lm
modelling function. For pooling it uses
the Fisher z-transformation.
Returns a 1x4 table with elements:
est |
The pooled R^2 estimate |
lo95 |
The 95 % lower bound of the pooled R^2. |
hi95 |
The 95 % upper bound of the pooled R^2. |
fmi |
The fraction of missing information due to nonresponse. |
Karin Groothuis-Oudshoorn and Stef van Buuren, 2009
Harel, O (2009). The estimation of R^2 and adjusted R^2 in incomplete data sets using multiple imputation, Journal of Applied Statistics (to appear).
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.
http://www.jstatsoft.org/v45/i03/
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 #