R: Compare two nested models fitted to imputed data

pool.compare {mice} | R Documentation

## Compare two nested models fitted to imputed data

### Description

This function is deprecated in V3. Use `D1` or `D3` instead.

### Usage

    pool.compare(fit1, fit0, method = c("wald", "likelihood"), data = NULL)

### Arguments

`fit1` | An object of class 'mira', produced by `with.mids()`.
`fit0` | An object of class 'mira', produced by `with.mids()`. The model in `fit0` is a nested fit0 of `fit1`.
`method` | Either `"wald"` or `"likelihood"` specifying the type of comparison. The default is `"wald"`.
`data` | No longer used.

### Details

Compares two nested models after m repeated complete data analysis

The function is based on the article of Meng and Rubin (1992). The Wald-method can be found in paragraph 2.2 and the likelihood method can be found in paragraph 3. One could use the Wald method for comparison of linear models obtained with e.g. `lm` (in `with.mids()`). The likelihood method should be used in case of logistic regression models obtained with `glm()` in `with.mids()`.

The function assumes that `fit1` is the larger model, and that model `fit0` is fully contained in `fit1`. In case of `method='wald'`, the null hypothesis is tested that the extra parameters are all zero.

### Value

A list containing several components. Component `call` is the call to the `pool.compare` function. Component `call11` is the call that created `fit1`. Component `call12` is the call that created the imputations. Component `call01` is the call that created `fit0`. Component `call02` is the call that created the imputations. Components `method` is the method used to compare two models: 'Wald' or 'likelihood'. Component `nmis` is the number of missing entries for each variable. Component `m` is the number of imputations. Component `qhat1` is a matrix, containing the estimated coefficients of the m repeated complete data analyses from `fit1`. Component `qhat0` is a matrix, containing the estimated coefficients of the m repeated complete data analyses from `fit0`. Component `ubar1` is the mean of the variances of `fit1`, formula (3.1.3), Rubin (1987). Component `ubar0` is the mean of the variances of `fit0`, formula (3.1.3), Rubin (1987). Component `qbar1` is the pooled estimate of `fit1`, formula (3.1.2) Rubin (1987). Component `qbar0` is the pooled estimate of `fit0`, formula (3.1.2) Rubin (1987). Component `Dm` is the test statistic. Component `rm` is the relative increase in variance due to nonresponse, formula (3.1.7), Rubin (1987). Component `df1`: df1 = under the null hypothesis it is assumed that `Dm` has an F distribution with (df1,df2) degrees of freedom. Component `df2`: df2. Component `pvalue` is the P-value of testing whether the model `fit1` is statistically different from the smaller `fit0`.

### Author(s)

Karin Groothuis-Oudshoorn and Stef van Buuren, 2009

### References

Li, K.H., Meng, X.L., Raghunathan, T.E. and Rubin, D. B. (1991). Significance levels from repeated p-values with multiply-imputed data. Statistica Sinica, 1, 65-92.

Meng, X.L. and Rubin, D.B. (1992). Performing likelihood ratio tests with multiple-imputed data sets. Biometrika, 79, 103-111.

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

`lm.mids`, `glm.mids`

[Package mice version 3.12.0 Index]