cg (version 0.9-2)

cg-package: Comparison of groups

Description

cg is comprehensive data analysis software, and stands for "compare groups." Its genesis and evolution are driven by common needs to compare samples, administrations, conditions, etc. in medicine research & development. The current version provides comparisons of unpaired samples, i.e. a linear model with one factor of at least two levels. Good data graphs, modern statistical methods, and useful displays of results are emphasized.

concept

• compare groups
• comparisons
• point graph
• boxplot
• resistant
• robust
• censored
• diagnostics
• samplesize

Details

Package: cg Type: Package Version: 0.9.0 Date: 2010-12-10 License: GPL (>= 2) LazyLoad: yes LazyData: yes Depends: R (>= 2.11.0), Hmisc, base, utils, methods, stats, graphics, grid, lattice, MASS, survival, multcomp, nlme Imports: VGAM

Examples

data(canine)
canine.data <- prepareCGOneFactorData(canine, format="groupcolumns",
                                      analysisname="Canine",
                                      endptname="Prostate Volume",
                                      endptunits=expression(plain(cm)^3),
                                      digits=1, logscale=TRUE, refgrp="CC")
## Exploratory methods
pointGraph(canine.data)

boxplot(canine.data)

descriptiveTable(canine.data)

## Fits and Comparisons
canine.fit <- fit(canine.data)

canine.comps0 <- comparisonsTable(canine.fit)

errorBarGraph(canine.fit)

canine.comps1 <- comparisonsTable(canine.fit,  mcadjust=TRUE,
                                   type="allgroupstocontrol", refgrp="CC")

comparisonsGraph(canine.comps1)

grpSummaryTable(canine.fit)

## Diagnostics
varianceGraph(canine.fit)

qqGraph(canine.fit)

downweightedTable(canine.fit, cutoff=0.95)

## Sample Size calculations
canine.samplesize <- samplesizeTable(canine.fit, direction="increasing", 
                                     mmdvec=c(10, 25, 50, 75, 100))

samplesizeGraph(canine.samplesize)

## Censored Data Set
data(gmcsfcens)
gmcsfcens.data <- prepareCGOneFactorData(gmcsfcens, format="groupcolumns",
                                         analysisname="cytokine",
                                         endptname="GM-CSF (pg/ml)",
                                         logscale=TRUE)
pointGraph(gmcsfcens.data)
boxplot(gmcsfcens.data)
descriptiveTable(gmcsfcens.data)

gmcsfcens.fit <- fit(gmcsfcens.data, type="aft")