| testModels {mitml} | R Documentation |
Test multiple parameters and compare nested models
Description
Performs multi-parameter hypothesis tests for a vector of statistical parameters and compares nested statistical models obtained from multiply imputed data sets.
Usage
testModels(model, null.model, method = c("D1", "D2", "D3", "D4"),
use = c("wald", "likelihood"), ariv = c("default", "positive", "robust"),
df.com = NULL, data = NULL)
Arguments
model |
A list of fitted statistical models (“full” model) as produced by |
null.model |
A list of fitted statistical models (“restricted” model) as produced by |
method |
A character string denoting the method by which the test is performed. Can be |
use |
A character string denoting Wald- or likelihood-based based tests. Can be either |
ariv |
A character string denoting how the ARIV is calculated. Can be |
df.com |
(optional) A number denoting the complete-data degrees of freedom for the hypothesis test. Only used if |
data |
(optional) A list of imputed data sets (see 'Details'). Only used if |
Details
This function compares two nested statistical models fitted to multiply imputed data sets by pooling Wald-like or likelihood-ratio tests.
Pooling methods for Wald-like tests of multiple parameters were introduced by Rubin (1987) and further developed by Li, Raghunathan and Rubin (1991).
The pooled Wald test is referred to as D_1 and can be used by setting method = "D1".
D_1 is the multi-parameter equivalent of testEstimates, that is, it tests multiple parameters simultaneously.
For D_1, the complete-data degrees of freedom are assumed to be infinite, but they can be adjusted for smaller samples by supplying df.com (Reiter, 2007).
An alternative method for Wald-like hypothesis tests was suggested by Li, Meng, Raghunathan and Rubin (1991).
The procedure is called D_2 and can be used by setting method = "D2".
D_2 calculates the Wald-test directly for each data set and then pools the resulting \chi^2 values.
The source of these values is specified by the use argument.
If use = "wald" (the default), then a Wald test similar to D_1 is performed.
If use = "likelihood", then the two models are compared with a likelihood-ratio test instead.
Pooling methods for likelihood-ration tests were suggested by Meng and Rubin (1992).
This procedure is referred to as D_3 and can be used by setting method = "D3".
D_3 compares the two models by pooling the likelihood-ratio test across multiply imputed data sets.
Finally, an improved method for pooling likelihood-ratio tests was recommended by Chan & Meng (2019).
This method is referred to as D_4 and can be used by setting method = "D4".
D_4 also compares models by pooling the likelihood-ratio test but does so in a more general and efficient manner.
The function supports different classes of statistical models depending on which method is chosen.
D_1 supports models that define coef and vcov methods (or similar) for extracting the parameter estimates and their estimated covariance matrix.
D_2 can be used for the same models (if use = "wald" and models that define a logLik method (if use = "likelihood").
D_3 supports linear models, linear mixed-effects models (see Laird, Lange, & Stram, 1987) with an arbitrary cluster structed if estimated with lme4 or a single cluster if estimated by nlme, and structural equation models estimated with lavaan (requires ML estimator, see 'Note').
Finally, D_4 supports models that define a logLik method but can fail if the data to which the model was fitted cannot be found.
In such a case, users can provide the list of imputed data sets directly by specifying the data argument or refit with the include.data argument in with.mitml.list.
Support for other statistical models may be added in future releases.
The D_4, D_3, and D_2 methods support different estimators of the relative increase in variance (ARIV), which can be specified with the ariv argument.
If ariv = "default", the default estimators are used.
If ariv = "positive", the default estimators are used but constrained to take on strictly positive values.
This is useful if the estimated ARIV is negative.
If ariv = "robust", which is available only for D_4, the "robust" estimator proposed by Chan & Meng (2019) is used.
This method should be used with caution, because it requires much stronger assumptions and may result in liberal inferences if these assumptions are violated.
Value
A list containing the results of the model comparison.
A print method is used for more readable output.
Note
The methods D_4, D_3, and the likelihood-based D_2 assume that models were fit using maximum likelihood (ML).
Models fit using REML are automatically refit using ML.
Models fit in 'lavaan' using the MLR estimator or similar techniques that require scaled chi^2 difference tests are currently not supported.
Author(s)
Simon Grund
References
Chan, K. W., & Meng, X.-L. (2019). Multiple improvements of multiple imputation likelihood ratio tests. ArXiv:1711.08822 [Math, Stat]. https://arxiv.org/abs/1711.08822
Laird, N., Lange, N., & Stram, D. (1987). Maximum likelihood computations with repeated measures: Application of the em algorithm. Journal of the American Statistical Association, 82, 97-105.
Li, K.-H., Meng, X.-L., Raghunathan, T. E., & Rubin, D. B. (1991). Significance levels from repeated p-values with multiply-imputed data. Statistica Sinica, 1, 65-92.
Li, K. H., Raghunathan, T. E., & Rubin, D. B. (1991). Large-sample significance levels from multiply imputed data using moment-based statistics and an F reference distribution. Journal of the American Statistical Association, 86, 1065-1073.
Meng, X.-L., & Rubin, D. B. (1992). Performing likelihood ratio tests with multiply-imputed data sets. Biometrika, 79, 103-111.
Reiter, J. P. (2007). Small-sample degrees of freedom for multi-component significance tests with multiple imputation for missing data. Biometrika, 94, 502-508.
Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. Hoboken, NJ: Wiley.
See Also
testEstimates, testConstraints, with.mitml.list, anova.mitml.result
Examples
data(studentratings)
fml <- ReadDis + SES ~ ReadAchiev + (1|ID)
imp <- panImpute(studentratings, formula = fml, n.burn = 1000, n.iter = 100, m = 5)
implist <- mitmlComplete(imp)
# * Example 1: multiparameter hypothesis test for 'ReadDis' and 'SES'
# This tests the hypothesis that both effects are zero.
require(lme4)
fit0 <- with(implist, lmer(ReadAchiev ~ (1|ID), REML = FALSE))
fit1 <- with(implist, lmer(ReadAchiev ~ ReadDis + (1|ID), REML = FALSE))
# apply Rubin's rules
testEstimates(fit1)
# multiparameter hypothesis test using D1 (default)
testModels(fit1, fit0)
# ... adjusting for finite samples
testModels(fit1, fit0, df.com = 47)
# ... using D2 ("wald", using estimates and covariance-matrix)
testModels(fit1, fit0, method = "D2")
# ... using D2 ("likelihood", using likelihood-ratio test)
testModels(fit1, fit0, method = "D2", use = "likelihood")
# ... using D3 (likelihood-ratio test, requires ML fit)
testModels(fit1, fit0, method = "D3")
# ... using D4 (likelihood-ratio test, requires ML fit)
testModels(fit1, fit0, method = "D4")