plot.lm.rrpp: Plot Function for RRPP

Description

Plot Function for RRPP

Usage

# S3 method for 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

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

Run this code

if (FALSE) {
# 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))
 }