Extract covariance or correlation components from a fitted model object
summary(<fit object>)$beta_table returns the table of fixed-effect coefficients as it is printed by summary, including standard errors and t-values.
vcov returns the variance-covariance matrix of the fixed-effects coefficients.
Corr returns a correlation matrix of random effects.
VarCorr returns (co)variance parameters of random effects, and optionally the residual variance(s), from a fit object, in a data frame format roughly consistent with the method of objects of class "lme", in particular including columns with consistent names for easier extraction. One may have to consult the summary of the object to check the meaning of the contents of this data frame (e.g., of 'variance' coefficients for non-gaussian random effects). Other extractors to consider are get_ranPars and get_inits_from_fit, the latter providing parameters in a form suitable for initializing a fit.
The covariance matrix of residuals of a fit can be obtained as a block of the hat matrix (get_matrix(., which="hat_matrix")).
This is (as other covariances matrices above) a matrix of expected values, generally assuming that the fitted model is correct and that its parameters are “well” estimated, and should not to be confused with the computation of diagnostic correlations among inferred residuals of a fit.
## S3 method for class 'HLfit' vcov(object, ...) ## S3 method for class 'HLfit' VarCorr(x, sigma = 1, add_residVars=TRUE, ...) Corr(object, ...)
object, x |
A fitted model object, inheriting from class |
add_residVars |
Boolean; whether to include residual variance information in the returned table. |
sigma |
ignored argument, included for consistency with the generic function. |
... |
Other arguments that may be needed by some method. |
vcov returns a matrix. Corr returns a list, for the different random effect terms. For each random-effect term, the returned element is a non-trivial unconditional correlation matrix of the vector “v” of random effects (v as defined in see Details of HLfit) for this term, if there is any such matrix. Otherwise the returned element is a information message.
VarCorr returns either NULL (if no variance to report, as for a poisson GLM) or a data frame with columns for the grouping factor, term, variance of random effect, standard deviation (the root of the variance), and optionally for correlation of random effect in random-coefficient terms. Information about the residual variance is optionally included as the last row(s) of the data frame, when relevant (gaussian- or Gamma-response models with single scalar parameter; beware the meaning of the residual variance parameter for Gamma-response models).
data("wafers")
m1 <- HLfit(y ~ X1+X2+(1|batch), resid.model = ~ 1 ,data=wafers, method="ML")
vcov(m1)
# Example from VarCorr() documentation in 'nlme' package
data("Orthodont",package = "nlme")
sp1 <- fitme(distance ~ age+(age|Subject), data = Orthodont, method="REML")
VarCorr(sp1)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.