plot.lm.rrpp: Plot Function for RRPP

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

Plot Function for RRPP

Description

Plot Function for RRPP

Usage

## S3 method for class 'lm.rrpp'
plot(
  x,
  type = c("diagnostics", "regression", "PC"),
  resid.type = c("p", "n"),
  fitted.type = c("o", "t"),
  predictor = NULL,
  reg.type = c("PredLine", "RegScore"),
  ...
)

Arguments

x	plot object (from lm.rrpp)
type	Indicates which type of plot, choosing among diagnostics, regression, or principal component plots. Diagnostic plots are similar to lm diagnostic plots, but for multivariate data. Regression plots plot multivariate dispersion in some fashion against predictor values. PC plots project data onto the eigenvectors of the covariance matrix for fitted values.
resid.type	If type = "diagnostics", an optional argument for whether Pearson ("p") or normalized ("n") residuals should be used. These residuals are the same for ordinary least-squares (OLS) estimation but differ for generalized least-squares (GLS) estimation. For the latter, normalizing residuals requires multiplying them by the transformation matrix obtained for GLS estimation.
fitted.type	As with resid.type, whether fitted values use observed ("o") or transformed ("t") values.
predictor	An optional vector if "regression" plot type is chosen, and is a variable likely used in lm.rrpp. This vector is a vector of covariate values equal to the number of observations.
reg.type	If "regression" is chosen for plot type, this argument indicates whether prediction line (PredLine) or regression score (RegScore) plotting is performed. For explanation of prediction line, see Adams and Nistri (2010). For explanation of regression score, see Drake and Klingenberg (2008).
...	other arguments passed to plot (helpful to employ different colors or symbols for different groups). See plot.default and par

Author(s)

Michael Collyer

References

Drake, A. G., and C. P. Klingenberg. 2008. The pace of morphological change: Historical transformation of skull shape in St Bernard dogs. Proc. R. Soc. B. 275:71-76.

Adams, D. C., and A. Nistri. 2010. Ontogenetic convergence and evolution of foot morphology in European cave salamanders (Family: Plethodontidae). BMC Evol. Biol. 10:1-10.

Examples

## Not run: 
# Univariate example
data(PlethMorph)
fitGLS <- lm.rrpp(TailLength ~ SVL, data = PlethMorph, Cov = PlethMorph$PhyCov, 
 print.progress = FALSE, iter = 0)
 
 par(mfrow = c(2, 2))
 plot(fitGLS)
 plot(fitGLS, resid.type = "n") # use normalized (transformed) residuals
 plot(fitGLS, resid.type = "n", fitted.type = "t") # use also transformed fitted values
 
 # Multivariate example
 
Y <- as.matrix(cbind(PlethMorph$TailLength,
PlethMorph$HeadLength,
PlethMorph$Snout.eye,
PlethMorph$BodyWidth,
PlethMorph$Forelimb,
PlethMorph$Hindlimb))
PlethMorph$Y <- Y
fitGLSm <- lm.rrpp(Y ~ SVL, data = PlethMorph, 
Cov = PlethMorph$PhyCov,
print.progress = FALSE, iter = 0)

par(mfrow = c(2, 2))
 plot(fitGLSm)
 plot(fitGLSm, resid.type = "n") # use normalized (transformed) residuals
 plot(fitGLSm, resid.type = "n", fitted.type = "t") # use also transformed fitted values
 par(mfrow = c(1, 1))
 
## End(Not run)

RRPP documentation built on June 22, 2024, 6:45 p.m.