scaleCov function

Scaling of a Covariance Matrix

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

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

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.

Returns

A square matrix.

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

## Not run:

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)
## End(Not run)

Author(s)

Michael Collyer