BIFIE.univar | R Documentation |
Computes some univariate descriptive statistics (means and standard deviations).
BIFIE.univar(BIFIEobj, vars, group=NULL, group_values=NULL, se=TRUE) ## S3 method for class 'BIFIE.univar' summary(object,digits=3,...) ## S3 method for class 'BIFIE.univar' coef(object,...) ## S3 method for class 'BIFIE.univar' vcov(object,...)
BIFIEobj |
Object of class |
vars |
Vector of variables for which statistics should be computed |
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. |
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 univariate statistics |
stat_M |
Data frame with means |
stat_SD |
Data frame with standard deviations |
output |
Extensive output with all replicated statistics |
... |
More values |
See BIFIE.univar.test
for a test of equal means and
effect sizes η and d.
Descriptive statistics without statistical inference can be
estimated by the collection of
miceadds::ma.wtd.statNA
functions from the miceadds package.
Further descriptive functions:
survey::svymean
,
intsvy::timss.mean
,
intsvy::timss.mean.pv
,
stats::weighted.mean
,
Hmisc::wtd.mean
,
miceadds::ma.wtd.meanNA
survey::svyvar
,
Hmisc::wtd.var
,
miceadds::ma.wtd.sdNA
,
miceadds::ma.wtd.covNA
############################################################################# # EXAMPLE 1: Imputed TIMSS dataset ############################################################################# data(data.timss1) data(data.timssrep) # create BIFIE.dat object bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT, wgtrep=data.timssrep[, -1 ] ) # compute descriptives for plausible values res1 <- BIFIEsurvey::BIFIE.univar( bdat, vars=c("ASMMAT","ASSSCI","books") ) summary(res1) # split descriptives by number of books res2 <- BIFIEsurvey::BIFIE.univar( bdat, vars=c("ASMMAT","ASSSCI"), group="books", group_values=1:5) summary(res2) ############################################################################# # EXAMPLE 2: TIMSS dataset with missings ############################################################################# data(data.timss2) data(data.timssrep) # use first dataset with missing data from data.timss2 bdat1 <- BIFIEsurvey::BIFIE.data( data.list=data.timss2[[1]], wgt=data.timss2[[1]]$TOTWGT, wgtrep=data.timssrep[, -1 ]) # some descriptive statistics without statistical inference res1a <- BIFIEsurvey::BIFIE.univar( bdat1, vars=c("ASMMAT","ASSSCI","books"), se=FALSE) # descriptive statistics with statistical inference res1b <- BIFIEsurvey::BIFIE.univar( bdat1, vars=c("ASMMAT","ASSSCI","books") ) summary(res1a) summary(res1b) # split descriptives by number of books res2 <- BIFIEsurvey::BIFIE.univar( bdat1, vars=c("ASMMAT","ASSSCI"), group="books") # Note that if group_values is not specified as an argument it will be # automatically determined by the observed frequencies in the dataset summary(res2)
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