Logistic Regression

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

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

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

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

# S3 method for 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)

if (FALSE) {
# 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
}