# Variance Covariance matrix of for g(n)ls and (n)lme models Extracts the variance covariance matrix (residuals, random or all) ```r var_cov( object, type = c("residual", "random", "all", "conditional", "marginal"), aug = FALSE, sparse = FALSE, data = NULL ) ``` ## Arguments - `object`: object which inherits class `lm`, `gls` or `lme` - `type`: residual for the variance-covariance for the residuals, random for the variance-covariance of the random effects or all for the sum of both. - `aug`: whether to augment the matrix of the random effects to the dimensions of the data - `sparse`: whether to return a sparse matrix (default is FALSE) - `data`: optional passing of data , probably needed when using this function inside other functions. ## Returns It returns a `matrix` or a sparse matrix `Matrix`. ## Details Variance Covariance matrix for (non)linear mixed models ## Note See Chapter 5 of Pinheiro and Bates. This returns potentially a very large matrix of N x N, where N is the number of rows in the data.frame. The function `getVarCov` only works well for `lme` objects. The equivalence is more or less: getVarCov type = random.effects equivalent to var_cov type = random . getVarCov type = conditional equivalent to var_cov type = residual . getVarCov type = marginal equivalent to var_cov type = all . The difference is that getVarCov has an argument that specifies the individual for which the matrix is being retrieved and var_cov returns the full matrix only. ## Examples ```r require(graphics) require(nlme) data(ChickWeight) ## First a linear model flm <- lm(weight ~ Time, data = ChickWeight) vlm <- var_cov(flm) ## First model with no modeling of the Variance-Covariance fit0 <- gls(weight ~ Time, data = ChickWeight) v0 <- var_cov(fit0) ## Only modeling the diagonal (weights) fit1 <- gls(weight ~ Time, data = ChickWeight, weights = varPower()) v1 <- var_cov(fit1) ## Only the correlation structure is defined and there are no groups fit2 <- gls(weight ~ Time, data = ChickWeight, correlation = corAR1()) v2 <- var_cov(fit2) ## The correlation structure is defined and there are groups present fit3 <- gls(weight ~ Time, data = ChickWeight, correlation = corCAR1(form = ~ Time | Chick)) v3 <- var_cov(fit3) ## There are both weights and correlations fit4 <- gls(weight ~ Time, data = ChickWeight, weights = varPower(), correlation = corCAR1(form = ~ Time | Chick)) v4 <- var_cov(fit4) ## Tip: you can visualize these matrices using image(log(v4[,ncol(v4):1])) ``` ## See Also `getVarCov`

var_cov function