View source: R/BIFIE.logistreg.R
BIFIE.logistreg | R Documentation |
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 class 'BIFIE.logistreg' summary(object,digits=4,...) ## S3 method for class 'BIFIE.logistreg' coef(object,...) ## S3 method for class 'BIFIE.logistreg' vcov(object,...)
BIFIEobj |
Object of class |
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 |
formula |
An R formula object which can be applied instead of
providing |
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 |
digits |
Number of digits for rounding output |
... |
Further arguments to be passed |
A list with following entries
stat |
Data frame with regression coefficients |
output |
Extensive output with all replicated statistics |
... |
More values |
survey::svyglm
,
stats::glm
For linear regressions see BIFIE.linreg
.
############################################################################# # 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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.