# Scaling of a Covariance Matrix Function performs linear and exponential scaling of a covariance, either including or excluding diagonals or off-diagonal elements. ```r 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 ```r ## 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

scaleCov function