repVarclus: Variable Clustering Diagrams

Description Usage Arguments Details Examples

Description

Generate variable clustering diagrams for each time period.

Usage

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  repVarclus(data, time, times = sort(unique(time)),
    nmin = 10)

Arguments

data

data.frame. Data used for report.

time

numeric vector. Time for each record.

times

numeric vector. Subset of times to use.

nmin

numeric. Variables must have four responses or at least

Details

Variables are grouped according to how they are correlated with one another, as measured by the square of the Spearman ρ rank correlation coefficient computed on all pairs of variables. Variables connected on lower branches are more highly correlated with one another. Variables missing in more than 75 observations or categorical variables having more than 20 levels are ignored. Categories less than 0.1 prevalent are pooled with other rare categories.

Examples

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## Not run: 
  dat <- data.frame(age=sample(41:80, replace=TRUE, 1000), height=round(rnorm(1000, 68, 4)))
  dat$weight <- sample(140:200, replace=TRUE, 1000)+(dat$height-60)*3
  repVarclus(dat, numeric(1000))

## End(Not run)

harrelfe/rreport documentation built on May 17, 2019, 2:48 p.m.