GroupMean: Calculation of Groupwise Descriptive Statistics for Matrices

In miceadds: Some Additional Multiple Imputation Functions, Especially for 'mice'

Calculation of Groupwise Descriptive Statistics for Matrices

Description

Calculates some groupwise descriptive statistics.

Usage

GroupMean(data, group, weights=NULL, extend=FALSE, elim=FALSE)

GroupSum(data, group, weights=NULL, extend=FALSE)

GroupSD(data, group, weights=NULL, extend=FALSE)

# group mean of a variable
gm(y, cluster, elim=FALSE)

# centering within clusters
cwc(y, cluster)

Arguments

data	A numeric data frame
group	A vector of group identifiers
weights	An optional vector of sample weights
extend	Optional logical indicating whether the group means (or sums) should be extended to the original dimensions of the dataset.
elim	Logical indicating whether a case in a row should be removed from the calculation of the mean in a cluster
y	Variable
cluster	Cluster identifier

Value

A data frame or a vector with groupwise calculated statistics

See Also

mitml::clusterMeans

base::rowsum, stats::aggregate, stats::ave

Examples

## Not run: 

#############################################################################
# EXAMPLE 1: Group means and standard deviations for data.ma02
#############################################################################

data(data.ma02, package="miceadds" )
dat <- data.ma02[[1]] # select first dataset

#--- group means for read and math
GroupMean( dat[, c("read","math") ], group=dat$idschool )
# using rowsum
a1 <- base::rowsum( dat[, c("read","math") ], dat$idschool )
a2 <- base::rowsum( 1+0*dat[, c("read","math") ], dat$idschool )
(a1/a2)[1:10,]
# using aggregate
stats::aggregate(  dat[, c("read","math") ], list(dat$idschool), mean )[1:10,]

#--- extend group means to original dataset
GroupMean( dat[, c("read","math") ], group=dat$idschool, extend=TRUE )
# using ave
stats::ave( dat[, "read" ], dat$idschool  )
stats::ave( dat[, "read" ], dat$idschool, FUN=mean )

#--- group standard deviations
GroupSD( dat[, c("read","math") ], group=dat$idschool)[1:10,]
# using aggregate
stats::aggregate(  dat[, c("read","math") ], list(dat$idschool), sd )[1:10,]

#############################################################################
# EXAMPLE 2: Calculating group means and group mean centering
#############################################################################

data(data.ma07, package="miceadds")
dat <- data.ma07

# compute group means
miceadds::gm( dat$x1, dat$id2 )
# centering within clusters
miceadds::cwc( dat$x1, dat$id2 )

# evaluate formula with model.matrix
X <- model.matrix( ~ I( miceadds::cwc(x1, id2) ) + I( miceadds::gm(x1,id2) ), data=dat )
head(X)

## End(Not run)

miceadds documentation built on May 29, 2024, 11:05 a.m.