RRPP (version 1.1.2)

anova.lm.rrpp: ANOVA for lm.rrpp model fits

Description

Computes an analysis of variance (ANOVA) table using distributions of random statistics from lm.rrpp. ANOVA can be performed on one model or multiple models. If the latter, the first model is considered a null model for comparison to other models. The ANOVA is functionally similar to a non-parametric likelihood ratio test for all null-full model comparisons Residuals from the null model will be used to generate random pseudo-values via RRPP for evaluation of subsequent models. The permutation schedule from the null model will be used for random permutations. This function does not correct for improper null models. One must assure that the null model is nested within the other models. Illogical results can be generated if this is not the case.

Usage

# S3 method for lm.rrpp
anova(
  object,
  ...,
  effect.type = c("F", "cohenf", "SS", "MS", "Rsq"),
  error = NULL,
  print.progress = TRUE
)

Arguments

object

Object from lm.rrpp

...

Additional lm.rrpp model fits or other arguments passed to anova.

effect.type

One of "F", "cohenf", "SS", "MS", "Rsq" to choose from which distribution of statistics to calculate effect sizes (Z). See lm.rrpp.

error

An optional character string to define MS error term for calculation of F values. See lm.rrpp for examples.

print.progress

A logical argument if multiple models are used and one wishes to view progress for sums of squares (SS) calculations.

Examples

# NOT RUN {
# See examples for lm.rrpp to see how anova.lm.rrpp works in conjunction
# with other functions

data(Pupfish)
names(Pupfish)
Pupfish$logSize <- log(Pupfish$CS) # better to not have functions in formulas

# Single-Model ANOVA

# Note: one should increase RRPP iterations but a smaller number is used 
# here for demonstration efficiency.  Generally, iter = 999 will take less
# than 1s for this example with a modern computer.

fit <- lm.rrpp(coords ~ logSize + Sex*Pop, SS.type = "I", 
data = Pupfish, print.progress = FALSE, iter = 499) 
anova(fit)
anova(fit, effect.type = "MS")
anova(fit, effect.type = "Rsq")
anova(fit, effect.type = "cohenf")

# Multi-Model ANOVA (like a Likelihood Ratio Test)
fit.size <- lm.rrpp(coords ~ logSize, SS.type = "I", data = Pupfish, 
print.progress = FALSE, iter = 499) 
fit.sex <- lm.rrpp(coords ~ logSize + Sex, SS.type = "I", data = Pupfish, 
print.progress = FALSE, iter = 499) 
fit.pop <- lm.rrpp(coords ~ logSize + Pop, SS.type = "I", data = Pupfish, 
print.progress = FALSE, iter = 499) 
anova(fit.size, fit.sex, fit.pop, 
print.progress = FALSE) # compares two models to the first

# see lm.rrpp examples for mixed model ANOVA example and how to vary SS type

# }