profileR (version 0.3-2)

cpa: Criterion-Related Profile Analysis

Description

Implements the criterion-related profile analysis described in Davison & Davenport (2002).

Usage

cpa(formula, data, k = 100, na.action = "na.fail", family = "gaussian",
  weights = NULL)

Arguments

formula

An object of class formula of the form response ~ terms.

data

An optional data frame, list or environment containing the variables in the model.

k

Corresponds to the scalar constant and must be greater than 0. Defaults to 100.

na.action

How should missing data be handled? Function defaults to failing if missing data are present.

family

A description of the error distribution and link function to be used in the model. See family.

weights

An option vector of weights to be used in the fitting process.

Value

• An object of class critpat is returned, listing the following components:
  • lvl.comp- the level component
  • pat.comp- the pattern component
  • b- the unstandardized regression weights
  • bstar- the mean centered regression weights
  • xc- the scalar constant times bstar
  • k- the scale constant
  • Covpc- the pattern effect
  • Ypred- the predicted values
  • r2- the proportion of variability attributed to the different components
  • F.table- the associated F-statistic table
  • F.statistic- the F-statistics
  • df- the df used in the test
  • pvalue- the p-values for the test

Details

The cpa function requires two arguments: criterion and predictors. The function returns the criterion-related profile analysis described in Davison & Davenport (2002). Missing data are presently handled by specifying na.action = "na.omit", which performs listwise deletion and na.action = "na.fail", the default, which causes the function to fail. The following S3 generic functions are available: summary(),anova(), print(), and plot(). These functions provide a summary of the analysis (namely, R2 and the level and pattern components); perform ANOVA of the R2 for the pattern, the level, and the overall model; provide output similar to lm(), and plots the pattern effect.

References

Davison, M., & Davenport, E. (2002). Identifying criterion-related patterns of predictor scores using multiple regression. Psychological Methods, 7(4), 468-484.

See Also

pcv

Examples

data(IPMMc)
mod <- cpa(R ~ A + H + S + B, data = IPMMc)
print(mod)
summary(mod)
plot(mod)
anova(mod)