Fits a linear weighted mixed-effects model.
mixed.sdf(
formula,
data,
weightVars = NULL,
weightTransformation = TRUE,
recode = NULL,
defaultConditions = TRUE,
tolerance = 0.01,
nQuad = NULL,
verbose = 0,
family = NULL,
centerGroup = NULL,
centerGrand = NULL,
fast = FALSE,
...
)A mixedSdfResults object with the following elements:
the original call used in mixed.sdf
the formula used to fit the model
a vector of coefficient estimates
a vector with the standard error estimates of the coefficients and the standard error of the variance components
estimated variance components of the model
the number of levels in the model
the intraclass correlation coefficient of the model
the number of plausible values
a data.frame that includes the number of observations for each group
the number of observations in the original data
the number of observations used in the analysis
the data used in the model
If the formula does not involve plausible values, the function will return the following additional elements:
the likelihood function
the log-likelihood of the model
If the formula involves plausible values, the function will return the following additional elements:
the estimated variance from uncertainty in the scores
the estimated variance from sampling
a formula
for the multilevel regression or mixed model. See Examples and the vignette titled
Methods Used for Estimating Mixed-Effects Models in EdSurvey
for more details on how to specify a mixed model. If y is
left blank, the default subject scale or subscale variable
will be used. (You can find the default using
showPlausibleValues.) If y is a variable
for a subject scale or subscale (one of the names shown by
showPlausibleValues), then that subject scale
or subscale is used.
an edsurvey.data.frame or a light.edsurvey.data.frame
character vector indicating weight variables for
corresponding levels to use. The weightVar must be
the weights for the edsurvey.data.frame. The weight variables
must be in the order of level (from lowest to highest level).
a logical value to indicate whether the function
should standardize weights before using it in the
multilevel model. If set to TRUE, the
function will look up standard weight
transformation methods often used for a specific
survey. Weight transformation can be found in the vignette titled
Methods Used for Estimating Mixed-Effects Models in EdSurvey.
If set to FALSE or if the survey of the specified data does
not have a standard weight transformation method,
raw weights will be used.
a list of lists to recode variables. Defaults to NULL. Can be set as
recode=list(var1 = list(from= c("a", "b", "c"), to= "d")).
See Examples in lm.sdf.
a logical value. When set to the default value of
TRUE, uses the default conditions stored in
an edsurvey.data.frame to subset the data.
Use print on an edsurvey.data.frame
to see the default conditions.
depreciated, no effect
depreciated, no effect
an integer; when set to 1, it will print out
the brief progress of the function mix.sdf.
Users can use these traced messages for further diagnosis.
When set to 2, it will print
out the detailed progress, including temporary estimates
during the optimization. Defaults to 0, which
will run the function without output.
this argument is depreciated; please use the WeMix
package's mix function directly for binomial models.
a list in which the name of each element is the name of the aggregation level,
and the element is a formula of variable names to be group mean centered. For example, to group mean center
gender and age within the group student: list("student"= ~gender+age). Defaults to NULL, which means
predictors are not adjusted by group centering. See Examples in the WeMix function mix.
a formula of variable names to be grand mean centered. For example, to center the
variable education by overall mean of education: ~education. Defaults to NULL, which means predictors
are not adjusted by grand centering.
depreciated, no effect
other potential arguments to be used in mix
Paul Bailey, Trang Nguyen, and Claire Kelley
This function uses the mix call in the WeMix package to fit mixed models.
When the outcome does not have plausible values, the variance estimator directly from
the mix function is used; these account for covariance at the top level
of the model specified by the user.
When the outcome has plausible values, the coefficients are estimated in the same
way as in lm.sdf, that is, averaged across the plausible values.
In addition, the variance of the coefficients is estimated
as the sum of the variance estimate from the mix function and the
imputation variance. The formula for the imputation variance is, again, the same
as for lm.sdf,
with the same estimators as in the vignette titled
Statistical Methods Used in EdSurvey.
In the section
“Estimation of Standard Errors of Weighted Means When Plausible Values Are Present, Using the Jackknife Method”
in the formula for \(V_{imp}\), the variance
and estimates of the variance components are estimated with the same formulas as
the regression coefficients.
Rabe-Hesketh, S., & Skrondal, A. (2006). Multilevel modelling of complex survey data. Journal of the Royal Statistical Society: Series A (Statistics in Society), 169(4), 805--827.
mix and lm.sdf
if (FALSE) {
# save TIMSS 2015 data to ~/TIMSS/2015
downloadTIMSS(root="~/", years=2015)
fin <- readTIMSS(path="~/TIMSS/2015", countries="fin", gradeLvl=4)
# uses all plausible values
mix1 <- mixed.sdf(formula=mmat ~ itsex + (1|idschool), data = fin,
weightVar=c("totwgt","schwgt"), weightTransformation=FALSE)
summary(mix1)
# uses only one plausible value
mix2 <- mixed.sdf(formula=asmmat01 ~ itsex + (1|idschool), data = fin,
weightVar=c("totwgt","schwgt"), weightTransformation=FALSE)
summary(mix2)
}
Run the code above in your browser using DataLab