Calculation of Groupwise Descriptive Statistics for Matrices
Calculates some groupwise descriptive statistics.
GroupMean(data, group, weights=NULL, extend=FALSE) GroupSum(data, group, weights=NULL, extend=FALSE) GroupSD(data, group, weights=NULL, extend=FALSE) # group mean of a variable gm(y, cluster) # centering within clusters cwc(y, cluster)
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. |
y |
Variable |
cluster |
Cluster identifier |
A data frame or a vector with groupwise calculated statistics
############################################################################# # EXAMPLE 1: Group means and SDs 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$idschool, FUN=mean ) ## Not run: #--- group standard deviations GroupSD( dat[, c("read","math") ], group=dat$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)
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