RRPP (version 2.0.3)

scaleCov: Scaling of a Covariance Matrix

Description

Function performs linear and exponential scaling of a covariance, either including or excluding diagonals or off-diagonal elements.

Usage

scaleCov(
  Cov,
  scale. = 1,
  exponent = 1,
  scale.diagonal = FALSE,
  scale.only.diagonal = FALSE
)

Value

A square matrix.

Arguments

Cov

Square matrix to be scaled.

scale.

The linear scaling parameter. Values are multiplied by this numeric value.

exponent

The exponentiation scaling parameter. Values are raised to this power.

scale.diagonal

Logical to indicate if diagonal should be included.

scale.only.diagonal

Logical to indicate if only the diagonal should be scaled.

Author

Michael Collyer

Details

The function scales covariances as scale * cov ^exponent, where cov is any covariance or variance in the covariance matrix. Arguments allow inclusion or exclusion or either the diagonal or off-diagonal elements to be scaled. It is assumed that a covariance matrix is scaled, but any square matrix will work.

Examples

if (FALSE) {
data(Pupfish)
Pupfish$logSize <- log(Pupfish$CS)
fit1 <- lm.rrpp(coords ~ logSize, data = Pupfish, iter = 0, 
print.progress = FALSE)
fit2 <- lm.rrpp(coords ~ Pop, data = Pupfish, iter = 0, 
print.progress = FALSE)
fit3 <- lm.rrpp(coords ~ Sex, data = Pupfish, iter = 0, 
print.progress = FALSE)
fit4 <- lm.rrpp(coords ~ logSize + Sex, data = Pupfish, iter = 0, 
print.progress = FALSE)
fit5 <- lm.rrpp(coords ~ logSize + Pop, data = Pupfish, iter = 0, 
print.progress = FALSE)
fit6 <- lm.rrpp(coords ~ logSize + Sex * Pop, data = Pupfish, iter = 0, 
print.progress = FALSE)

modComp1 <- model.comparison(fit1, fit2, fit3, fit4, fit5, 
fit6, type = "cov.trace")
modComp2 <- model.comparison(fit1, fit2, fit3, fit4, fit5, 
fit6, type = "logLik", tol = 0.01)

summary(modComp1)
summary(modComp2)

par(mfcol = c(1,2))
plot(modComp1)
plot(modComp2)

# Comparing fits with covariance matrices
# an example for scaling a phylogenetic covariance matrix with
# the scaling parameter, lambda

data("PlethMorph")
Cov <- PlethMorph$PhyCov
lambda <- seq(0, 1, 0.1)

Cov1 <- scaleCov(Cov, scale. = lambda[1])
Cov2 <- scaleCov(Cov, scale. = lambda[2])
Cov3 <- scaleCov(Cov, scale. = lambda[3])
Cov4 <- scaleCov(Cov, scale. = lambda[4])
Cov5 <- scaleCov(Cov, scale. = lambda[5])
Cov6 <- scaleCov(Cov, scale. = lambda[6])
Cov7 <- scaleCov(Cov, scale. = lambda[7])
Cov8 <- scaleCov(Cov, scale. = lambda[8])
Cov9 <- scaleCov(Cov, scale. = lambda[9])
Cov10 <- scaleCov(Cov, scale. = lambda[10])
Cov11 <- scaleCov(Cov, scale. = lambda[11])


fit1 <- lm.rrpp(SVL ~ 1, data = PlethMorph, Cov = Cov1)
fit2 <- lm.rrpp(SVL ~ 1, data = PlethMorph, Cov = Cov2)
fit3 <- lm.rrpp(SVL ~ 1, data = PlethMorph, Cov = Cov3)
fit4 <- lm.rrpp(SVL ~ 1, data = PlethMorph, Cov = Cov4)
fit5 <- lm.rrpp(SVL ~ 1, data = PlethMorph, Cov = Cov5)
fit6 <- lm.rrpp(SVL ~ 1, data = PlethMorph, Cov = Cov6)
fit7 <- lm.rrpp(SVL ~ 1, data = PlethMorph, Cov = Cov7)
fit8 <- lm.rrpp(SVL ~ 1, data = PlethMorph, Cov = Cov8)
fit9 <- lm.rrpp(SVL ~ 1, data = PlethMorph, Cov = Cov9)
fit10 <- lm.rrpp(SVL ~ 1, data = PlethMorph, Cov = Cov10)
fit11 <- lm.rrpp(SVL ~ 1, data = PlethMorph, Cov = Cov11)

par(mfrow = c(1,1))

MC1 <- model.comparison(fit1, fit2, fit3, fit4, fit5, fit6,
fit7, fit8, fit9, fit10, fit11,
type = "logLik")
MC1
plot(MC1)

MC2 <- model.comparison(fit1, fit2, fit3, fit4, fit5, fit6,
fit7, fit8, fit9, fit10, fit11,
type = "logLik", predictor = lambda)
MC2
plot(MC2)


MC3 <- model.comparison(fit1, fit2, fit3, fit4, fit5, fit6,
fit7, fit8, fit9, fit10, fit11,
type = "Z", predictor = lambda)
MC3
plot(MC3)
}