pool.compare {mice} | R Documentation |
Compares two nested models after m repeated complete data analysis
pool.compare(fit1, fit0, data=NULL, method="Wald")
fit1 |
An object of class 'mira', produced by with.mids() . |
fit0 |
An object of class 'mira', produced by with.mids() . The model in fit0
should be a submodel of fit1 . Moreover, the variables of the submodel
should be the first variables of the larger model and in the same order as in the submodel. |
data |
In case of method "likelihood" it is necessary to pass also the original mids
object to the data argument. Default value is NULL , in case of method="Wald". |
method |
A string describing the method to compare the two models. Two kind of comparisons are included so far: "Wald" and "likelihood". |
The function is based on the article of Meng and Rubin (1992). The Wald-method can be
found in paragraph 2.2 and the likelihoodmethod 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 obtaind with glm()
in
with.mids()
.
It is assumed that fit1 contains the larger model and the model in fit0
is fully contained in fit1
.
In case of method="Wald"
, the null hypothesis is tested that the extra parameters are all zero.
A list containing the elements:
call |
The call to the pool.compare function |
call11 |
The call that created fit1 |
call12 |
The call that created the imputations. |
call01 |
The call that created fit0 |
call02 |
The call that created the imputations. |
method |
The method used to compare two models: "Wald" or "likelihood" |
nmis |
The number of missing entries for each variable. |
m |
The number of imputations |
qhat1 |
A matrix, containing the estimated coeffients of the m repeated complete data analyses from fit1 |
qhat0 |
A matrix, containing the estimated coeffients of the m repeated complete data analyses from fit0 |
ubar1 |
The mean of the variances of object1, formula (3.1.3), Rubin (1987). |
ubar0 |
The mean of the variances of object0, formula (3.1.3), Rubin (1987). |
qbar1 |
The pooled estimate of object1, formula (3.1.2) Rubin (1987). |
qbar0 |
The pooled estimate of object0, formula (3.1.2) Rubin (1987). |
Dm |
The test statistic |
rm |
Relative increase in variance due to nonresponse, formula (3.1.7), Rubin (1987). |
df1 |
df1; Under the null hypothesis it is assumed that Dm has an F distribution with (df1,df2) degrees of freedom. |
df2 |
df2 |
pvalue |
P-value of testing whether the larger model is statistically different from the smaller submodel. |
Karin Groothuis-Oudshoorn and Stef van Buuren, 2009
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., Groothuis-Oudshoorn, K. (2009) MICE: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, forthcoming. http://www.stefvanbuuren.nl/publications/MICE in R - Draft.pdf
lm.mids
, glm.mids
, vcov
,
print.mira
, summary.mira
### To compare two linear models: imp <- mice(nhanes2) mi1 <- with(data=imp, expr=lm(bmi~age+hyp+chl)) mi0 <- with(data=imp, expr=lm(bmi~age+hyp)) pc <- pool.compare(mi1, mi0, method="Wald") pc$spvalue # [,1] #[1,] 0.000293631 # ### Comparison of two general linear models (logistic regression). imp <- mice(boys, maxit=2) fit0 <- with(imp, glm(gen>levels(gen)[1] ~ hgt+hc,family=binomial)) fit1 <- with(imp, glm(gen>levels(gen)[1] ~ hgt+hc+reg,family=binomial)) pool.compare(fit1, fit0, method="likelihood", data=imp)