R: Pooling: R squared

pool.r.squared {mice} | R Documentation

## 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.

### 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.

### 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.

### Author(s)

Karin Groothuis-Oudshoorn and Stef van Buuren, 2009

### 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

    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
    #

[Package mice version 3.8.0 Index]