profileR (version 0.3-2)

mpa: Moderated Profile Analysis

Description

Implements the moderated profile analysis approach developed by Davison & Davenport (unpublished) - EXPERIMENTAL SUPPORT

Usage

mpa(formula, data, moderator, k = 100, na.action = "na.fail",
  stage2 = FALSE)

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.

moderator

Name of the moderator variable.

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.

stage2

Should stage 2 be executed regardless of stage 1 outcome? defaults to FALSE.

Value

• An object of class critpat is returned, listing the following components:
  • r2_full- the proportion of variability attributed to the full model
  • r2_red- the proportion of variability attributed to the reduced model
  • ftable- the associated F-statistic table
  • F-statistic- the F-statistics
  • df- the dfs used in the test
  • pvalue- the p-values for the test

Details

The function returns the criterion-related moderated profile analysis described in Davison & Davenport (unpublished). 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 not yet available but will be in future implementations. 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Davison, M., & Davenport, E. (unpublished). Comparing Criterion-Related Patterns of Predictor Variables across Populations Using Moderated Regression.

See Also

cpa

Examples

data(mod_data)
mod <- mpa(dv ~ pred1 * mod + pred2 * mod, moderator = "mod", data = mod_data)