pairwise.model.Z: Pairwise comparisons of model effects

In RRPP: Linear Model Evaluation with Randomized Residuals in a Permutation Procedure

Pairwise comparisons of model effects

Description

Function generates pairwise statistics for comparing model fits and returns important statistics for hypothesis tests.

Usage

pairwise.model.Z(
  ...,
  nsamp = NULL,
  two.tailed = TRUE,
  predictor = NULL,
  tol = NULL,
  pc.no = NULL,
  gls.null = FALSE
)

Arguments

...	Either an object of class model.comparison, or several objects of class lm.rrpp. If the former, arguments of type = 'Z' and verbose = TRUE are required. If the latter, a model comparsion analysis will first be performed with these arguments.
nsamp	An optional vector containing the sample sizes for each model fit
two.tailed	A logical value to indicate whether a two-tailed test (typical and default) should be performed.
predictor	An optional vector to be passed to model.comparison, if used.
tol	An optional value to be passed to model.comparison, if used.
pc.no	An optional value to be passed to model.comparison, if used.
gls.null	An optional logical value to be passed to model.comparison, if used.

Details

The function statistically compares the effect sizes of two or more models fit and evaluated using RRPP. Input for the function is a list of fitted models of the class 'model.comparison', whose options included type = 'Z' and verbose = TRUE when the models were compared with that function.

A two-sample test is performed on each pair of models, comparing the strength of model fits to one another (Collyer and Adams 2025). This might be used to compare the strength of fit of the data to differing statistical models (as in model selection) or for comparing the fit across differing datasets for the same model to determine whether the strength of fit in one dataset is greater than that found in another (see Collyer and Adams 2025). In the latter case, one is advised to include a vector containing the sample sizes of each dataset, so that two-sample tests may account for differences in sample size.

Value

A list containing the following

sample.z	A vector of model effect sizes.
pairwise.z	A matrix of pairwise test statistics comparing model effect sizes.
pairwise.P	A matrix of pairwise significance levels.
tails	Number of tails used for P-value calculation.

Author(s)

Dean Adams and Michael Collyer

References

Collyer, M.L., and D.C. Adams. 2025. Permutational Biometry. Volume 1: Univariate Data. Iowa State University Digital Press. (Forthcoming).

Examples

## Not run: 
 data(Pupfish)
 Pupfish$logSize <- log(Pupfish$CS)
 fit1 <- lm.rrpp(coords ~ logSize, data = Pupfish,
 print.progress = FALSE)
 fit2 <- lm.rrpp(coords ~ Pop, data = Pupfish,
 print.progress = FALSE)
 fit3 <- lm.rrpp(coords ~ Sex, data = Pupfish, 
 print.progress = FALSE)
 fit6 <- lm.rrpp(coords ~ logSize + Sex * Pop, data = Pupfish, 
 print.progress = FALSE)
 Mod.C <- model.comparison(fit1, fit2, fit3, fit6,
 pc.no = 4, type = "Z", verbose = TRUE)
 res <- pairwise.model.Z(Mod.C)
 summary(res, stats.table = TRUE)
 summary(res, stats.table = FALSE)
 
## End(Not run)

RRPP documentation built on April 4, 2025, 3:45 a.m.