Fits a logit or probit that
uses weights and variance estimates
appropriate for the edsurvey.data.frame,
the light.edsurvey.data.frame, or the edsurvey.data.frame.list.
glm.sdf(formula, family = binomial(link = "logit"), data,
weightVar = NULL, relevels = list(),
varMethod=c("jackknife", "Taylor"), jrrIMax = 1,
dropOmittedLevels = TRUE, defaultConditions = TRUE, recode = NULL,
returnNumberOfPSU=FALSE, returnVarEstInputs = FALSE,
omittedLevels = deprecated())logit.sdf(
formula,
data,
weightVar = NULL,
relevels = list(),
varMethod = c("jackknife", "Taylor"),
jrrIMax = 1,
dropOmittedLevels = TRUE,
defaultConditions = TRUE,
recode = NULL,
returnNumberOfPSU = FALSE,
returnVarEstInputs = FALSE,
omittedLevels = deprecated()
)
probit.sdf(
formula,
data,
weightVar = NULL,
relevels = list(),
varMethod = c("jackknife", "Taylor"),
jrrIMax = 1,
dropOmittedLevels = TRUE,
defaultConditions = TRUE,
recode = NULL,
returnNumberOfPSU = FALSE,
returnVarEstInputs = FALSE,
omittedLevels = deprecated()
)
An edsurveyGlm with the following elements:
the function call
the formula used to fit the model
the estimates of the coefficients
the standard error estimates of the coefficients
the estimated variance caused by uncertainty in the scores (plausible value variables)
the estimated variance from sampling
the number of plausible values
the number of PSUs used in the calculation
the variance estimates under the various plausible values
the values of the coefficients under the various plausible values
the coefficient matrix (typically produced by the summary of a model)
the name of the weight variable
the number of plausible values
the number of the jackknife replicates used
always jackknife
when returnVarEstInputs is TRUE,
this element is returned. These are
used for calculating covariances with
varEstToCov.
a formula for the
linear model. See glm.
For logit and probit, we recommend using the I() function
to define the level used for success. (See Examples.)
the glm.sdf function currently fits only the binomial
outcome models, such as logit and probit, although other link
functions are available for binomial models. See the link
argument in the help for
family.
an edsurvey.data.frame
character indicating the weight variable to use (see Details).
The weightVar must be one of the weights for the
edsurvey.data.frame. If NULL, uses the default
for the edsurvey.data.frame.
a list; used to change the contrasts from the default treatment contrasts to the treatment contrasts with a chosen omitted group. The name of each element should be the variable name, and the value should be the group to be omitted.
a character set to “jackknife” or “Taylor” that indicates the variance estimation method to be used. See Details.
the Vjrr sampling variance term (see
Statistical Methods Used in EdSurvey)
can be estimated with
any positive number of plausible values and is estimated on
the lower
of the number of available plausible values and jrrIMax. When
jrrIMax is set to Inf, all plausible values will be used.
Higher values of jrrIMax lead to longer computing times and more
accurate variance estimates.
a logical value. When set to the default value of TRUE, drops
those levels of all factor variables that are specified
in edsurvey.data.frame. Use print on an
edsurvey.data.frame to see the omitted levels.
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.
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")).
a logical value set to TRUE to return the number of
primary sampling units (PSUs)
a logical value set to TRUE to return the
inputs to the jackknife and imputation variance
estimates, which allow for
the computation
of covariances between estimates.
this argument is deprecated. Use dropOmittedLevels
Of the common hypothesis tests for joint parameter testing, only the Wald
test is widely used with plausible values and sample weights. As such, it
replaces, if imperfectly, the Akaike Information Criteria (AIC), the
likelihood ratio test, chi-squared, and analysis of variance (ANOVA, including F-tests).
See waldTest or
the vignette titled
Methods and Overview of Using EdSurvey for Running Wald Tests.
Paul Bailey
This function implements an estimator that correctly handles left-hand side
variables that are logical, allows for survey sampling weights, and estimates
variances using the jackknife replication or Taylor series.
The vignette titled
Statistical Methods Used in EdSurvey
describes estimation of the reported statistics and how it depends on varMethod.
The coefficients are estimated using the sample weights according to the section “Estimation of Weighted Means When Plausible Values Are Not Present” or the section “Estimation of Weighted Means When Plausible Values Are Present,” depending on if there are assessment variables or variables with plausible values in them.
How the standard errors of the coefficients are estimated depends on the presence of plausible values (assessment variables), But once it is obtained, the t statistic is given by $$t=\frac{\hat{\beta}}{\sqrt{\mathrm{var}(\hat{\beta})}}$$ where \( \hat{\beta} \) is the estimated coefficient and \(\mathrm{var}(\hat{\beta})\) is its variance of that estimate.
logit.sdf and probit.sdf are included for convenience only;
they give the same results as a call to glm.sdf with the binomial family
and the link function named in the function call (logit or probit).
By default, glm fits a logistic regression when family is not set,
so the two are expected to give the same results in that case.
Other types of generalized linear models are not supported.
All variance estimation methods are shown in the vignette titled
Statistical Methods Used in EdSurvey.
When the predicted
value does not have plausible values and varMethod is set to
jackknife, the variance of the coefficients
is estimated according to the section
“Estimation of Standard Errors of Weighted Means When
Plausible Values Are Not Present, Using the Jackknife Method.”
When plausible values are present and varMethod is set to
jackknife, the
variance of the coefficients is estimated according to the section
“Estimation of Standard Errors of Weighted Means When
Plausible Values Are Present, Using the Jackknife Method.”
When the predicted
value does not have plausible values and varMethod is set to
Taylor, the variance of the coefficients
is estimated according to the section
“Estimation of Standard Errors of Weighted Means When
Plausible Values Are Not Present, Using the Taylor Series Method.”
When plausible values are present and varMethod is set to
Taylor, the
variance of the coefficients is estimated according to the section
“Estimation of Standard Errors of Weighted Means When
Plausible Values Are Present, Using the Taylor Series Method.”
if (FALSE) {
# read in the example data (generated, not real student data)
sdf <- readNAEP(path=system.file("extdata/data", "M36NT2PM.dat", package = "NAEPprimer"))
# by default uses the jackknife variance method using replicate weights
table(sdf$b013801)
logit1 <- logit.sdf(formula=I(b013801 %in% c("26-100", ">100")) ~ dsex + b017451, data=sdf)
# use summary to get detailed results
summary(logit1)
# Taylor series variance estimation
logit1t <- logit.sdf(formula=I(b013801 %in% c("26-100", ">100")) ~ dsex + b017451, data=sdf,
varMethod="Taylor")
summary(logit1t)
logit2 <- logit.sdf(formula=I(composite >= 300) ~ dsex + b013801, data=sdf)
summary(logit2)
logit3 <- glm.sdf(formula=I(composite >= 300) ~ dsex + b013801, data=sdf,
family=quasibinomial(link="logit"))
# Wald test for joint hypothesis that all coefficients in b013801 are zero
waldTest(model=logit3, coefficients="b013801")
summary(logit3)
}
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