cpa() R function from [profileR]

Criterion-Related Profile Analysis

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


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.

Returns

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 a nd 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.

Examples


## Not run:

data(IPMMc)
mod <- cpa(R ~ A + H + S + B, data = IPMMc)
print(mod)
summary(mod)
plot(mod)
anova(mod)
## End(Not run)

References

Davison, M., & Davenport, E. (2002). Identifying criterion-related patterns of predictor scores using multiple regression. Psychological Methods, 7(4), 468-484. DOI: 10.1037/1082-989X.7.4.468.

cpa function