BIFIE.logistreg: Logistic Regression

In BIFIEsurvey: Tools for Survey Statistics in Educational Assessment

Logistic Regression

Description

Computes logistic regression. Explained variance R^2 is computed by the approach of McKelvey and Zavoina.

Usage

BIFIE.logistreg(BIFIEobj, dep=NULL, pre=NULL, formula=NULL,
    group=NULL, group_values=NULL, se=TRUE, eps=1E-8, maxiter=100)

## S3 method for class 'BIFIE.logistreg'
summary(object,digits=4,...)

## S3 method for class 'BIFIE.logistreg'
coef(object,...)

## S3 method for class 'BIFIE.logistreg'
vcov(object,...)

Arguments

BIFIEobj	Object of class BIFIEdata
dep	String for the dependent variable in the regression model
pre	Vector of predictor variables. If the intercept should be included, then use the variable one for specifying it (see Examples).
formula	An R formula object which can be applied instead of providing dep and pre. Note that there is additional computation time needed for model matrix creation.
group	Optional grouping variable(s)
group_values	Optional vector of grouping values. This can be omitted and grouping values will be determined automatically.
se	Optional logical indicating whether statistical inference based on replication should be employed.
eps	Convergence criterion for parameters
maxiter	Maximum number of iterations
object	Object of class BIFIE.logistreg
digits	Number of digits for rounding output
...	Further arguments to be passed

Value

A list with following entries

stat	Data frame with regression coefficients
output	Extensive output with all replicated statistics
...	More values

See Also

survey::svyglm, stats::glm

For linear regressions see BIFIE.linreg.

Examples

#############################################################################
# EXAMPLE 1: TIMSS dataset | Logistic regression
#############################################################################

data(data.timss2)
data(data.timssrep)

# create BIFIE.dat object
bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss2, wgt=data.timss2[[1]]$TOTWGT,
                      wgtrep=data.timssrep[, -1 ] )

#**** Model 1: Logistic regression - prediction of migrational background
res1 <- BIFIEsurvey::BIFIE.logistreg( BIFIEobj=bdat, dep="migrant",
           pre=c("one","books","lang"), group="female", se=FALSE )
summary(res1)

## Not run: 
# same model, but with formula specification and standard errors
res1a <- BIFIEsurvey::BIFIE.logistreg( BIFIEobj=bdat,
              formula=migrant ~ books + lang, group="female"  )
summary(res1a)

#############################################################################
# SIMULATED EXAMPLE 2: Comparison of stats::glm and BIFIEsurvey::BIFIE.logistreg
#############################################################################

#*** (1) simulate data
set.seed(987)
N <- 300
x1 <- stats::rnorm(N)
x2 <- stats::runif(N)
ypred <- -0.75+.2*x1 + 3*x2
y <- 1*( stats::plogis(ypred) > stats::runif(N) )
data <- data.frame( "y"=y, "x1"=x1, "x2"=x2 )

#*** (2) estimation logistic regression using glm
mod1 <- stats::glm( y ~ x1 + x2, family="binomial")

#*** (3) estimation logistic regression using BIFIEdata
# create BIFIEdata object by defining 30 Jackknife zones
bifiedata <- BIFIEsurvey::BIFIE.data.jack( data, jktype="JK_RANDOM", ngr=30 )
summary(bifiedata)
# estimate logistic regression
mod2 <- BIFIEsurvey::BIFIE.logistreg( bifiedata, formula=y ~ x1+x2 )

#*** (4) compare results
summary(mod2)    # BIFIE.logistreg
summary(mod1)   # glm

## End(Not run)

BIFIEsurvey documentation built on May 29, 2024, 2:52 a.m.