Cluster-robust variance-covariance matrix for a glm object.

vcovCR returns a sandwich estimate of the variance-covariance matrix of a set of regression coefficient estimates from an glm object.

Usage

# S3 method for class 'glm'
vcovCR(
  obj,
  cluster,
  type,
  target = NULL,
  inverse_var = NULL,
  form = "sandwich",
  ...
)

Arguments

obj

Fitted model for which to calculate the variance-covariance matrix

cluster

Expression or vector indicating which observations belong to the same cluster. Required for glm objects.

type

Character string specifying which small-sample adjustment should be used, with available options "CR0", "CR1", "CR1p", "CR1S", "CR2", or "CR3". See "Details" section of vcovCR for further information.

target

Optional matrix or vector describing the working variance-covariance model used to calculate the CR2 and CR4 adjustment matrices. If a vector, the target matrix is assumed to be diagonal. If not specified, the target is taken to be the estimated variance function.

inverse_var

Optional logical indicating whether the weights used in fitting the model are inverse-variance. If not specified, vcovCR will attempt to infer a value.

form

Controls the form of the returned matrix. The default "sandwich" will return the sandwich variance-covariance matrix. Alternately, setting form = "meat" will return only the meat of the sandwich and setting form = B, where B is a matrix of appropriate dimension, will return the sandwich variance-covariance matrix calculated using B as the bread. form = "estfun" will return the (appropriately scaled) estimating function, the transposed crossproduct of which is equal to the sandwich variance-covariance matrix.

...

Additional arguments available for some classes of objects.

Value

An object of class c("vcovCR","clubSandwich"), which consists of a matrix of the estimated variance of and covariances between the regression coefficient estimates.

See also

vcovCR

Examples

if (requireNamespace("geepack", quietly = TRUE)) {

  data(dietox, package = "geepack")
  dietox$Cu <- as.factor(dietox$Cu)
  weight_fit <- glm(Weight ~ Cu * poly(Time, 3), data=dietox, family = "quasipoisson")
  V_CR <- vcovCR(weight_fit, cluster = dietox$Pig, type = "CR2")
  coef_test(weight_fit, vcov = V_CR, test = "Satterthwaite")
  
}
#>                   Coef. Estimate     SE  t-stat d.f. (Satt) p-val (Satt) Sig.
#>             (Intercept)   4.0124 0.0190 211.193        22.0       <0.001  ***
#>                 CuCu035  -0.0134 0.0286  -0.469        45.7        0.641     
#>                 CuCu175   0.0330 0.0333   0.993        44.8        0.326     
#>          poly(Time, 3)1  12.7115 0.2414  52.655        22.0       <0.001  ***
#>          poly(Time, 3)2  -1.6810 0.1456 -11.545        22.0       <0.001  ***
#>          poly(Time, 3)3   0.0292 0.0566   0.517        21.9        0.611     
#>  CuCu035:poly(Time, 3)1  -0.0823 0.3120  -0.264        45.6        0.793     
#>  CuCu175:poly(Time, 3)1  -0.3242 0.3433  -0.944        44.8        0.350     
#>  CuCu035:poly(Time, 3)2   0.0927 0.2113   0.439        45.6        0.663     
#>  CuCu175:poly(Time, 3)2  -0.1777 0.1656  -1.073        44.8        0.289     
#>  CuCu035:poly(Time, 3)3  -0.1010 0.1013  -0.997        45.5        0.324     
#>  CuCu175:poly(Time, 3)3   0.1146 0.0998   1.149        44.7        0.257