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

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

Usage

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

Arguments

obj

Fitted model for which to calculate the variance-covariance matrix

cluster

Optional expression or vector indicating which observations belong to the same cluster. If not specified, will be set to the studynum used in fitting the robu object.

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 not specified, the target is taken to be the inverse of the estimated weights used in fitting the robu object.

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("robumeta", quietly = TRUE)) withAutoprint({
library(robumeta)
data(hierdat)

robu_fit <- robu(effectsize ~ binge + followup + sreport + age, 
                 data = hierdat, studynum = studyid, 
                 var.eff.size = var, modelweights = "HIER")
robu_fit

robu_CR2 <- vcovCR(robu_fit, type = "CR2")
robu_CR2
coef_test(robu_fit, vcov = robu_CR2, test = c("Satterthwaite", "saddlepoint"))

Wald_test(robu_fit, constraints = constrain_zero(c(2,4)), vcov = robu_CR2)
Wald_test(robu_fit, constraints = constrain_zero(2:5), vcov = robu_CR2)

})
#> > library(robumeta)
#> > data(hierdat)
#> > robu_fit <- robu(effectsize ~ binge + followup + sreport + age, 
#> +                  data = hierdat, studynum = studyid, 
#> +                  var.eff.size = var, modelweights = "HIER")
#> > robu_fit
#> RVE: Hierarchical Effects Model with Small-Sample Corrections 
#> 
#> Model: effectsize ~ binge + followup + sreport + age 
#> 
#> Number of clusters = 15 
#> Number of outcomes = 68 (min = 1 , mean = 4.53 , median = 2 , max = 29 )
#> Omega.sq = 0.1086551 
#> Tau.sq = 0.02362071 
#> 
#>                Estimate   StdErr t-value  dfs P(|t|>) 95% CI.L 95% CI.U Sig
#> 1 X.Intercept.  0.39695 0.658006   0.603 3.06  0.5882 -1.67534  2.46924    
#> 2        binge  0.45158 0.101641   4.443 3.59  0.0144  0.15628  0.74689  **
#> 3     followup  0.00133 0.000723   1.842 2.03  0.2048 -0.00173  0.00439    
#> 4      sreport  0.53876 0.143398   3.757 4.36  0.0170  0.15324  0.92428  **
#> 5          age -0.04371 0.037874  -1.154 2.69  0.3404 -0.17235  0.08492    
#> ---
#> Signif. codes: < .01 *** < .05 ** < .10 *
#> ---
#> Note: If df < 4, do not trust the results> robu_CR2 <- vcovCR(robu_fit, type = "CR2")
#> > robu_CR2
#>               X.Intercept.         binge      followup       sreport
#> X.Intercept.  0.4329720570 -8.055322e-03 -2.957783e-04 -7.180868e-04
#> binge        -0.0080553221  1.033098e-02 -1.580973e-05  3.890863e-03
#> followup     -0.0002957783 -1.580973e-05  5.221116e-07 -1.951302e-05
#> sreport      -0.0007180868  3.890863e-03 -1.951302e-05  2.056284e-02
#> age          -0.0245235730  3.757319e-04  1.683702e-05 -8.892270e-04
#>                        age
#> X.Intercept. -2.452357e-02
#> binge         3.757319e-04
#> followup      1.683702e-05
#> sreport      -8.892270e-04
#> age           1.434475e-03
#> > coef_test(robu_fit, vcov = robu_CR2, test = c("Satterthwaite", "saddlepoint"))
#>         Coef. Estimate       SE t-stat d.f. (Satt) p-val (Satt) Sig.    s.p.
#>  X.Intercept.  0.39695 0.658006  0.603        3.06       0.5882      -0.6440
#>         binge  0.45158 0.101641  4.443        3.59       0.0144    *  0.4170
#>      followup  0.00133 0.000723  1.842        2.03       0.2048       0.2537
#>       sreport  0.53876 0.143398  3.757        4.36       0.0170    *  0.4112
#>           age -0.04371 0.037874 -1.154        2.69       0.3404       0.0919
#>  p-val (Saddle) Sig.
#>         0.59343     
#>         0.00424   **
#>         0.18900     
#>         0.00767   **
#>         0.34521     
#> > Wald_test(robu_fit, constraints = constrain_zero(c(2,4)), vcov = robu_CR2)
#>  test Fstat df_num df_denom  p_val sig
#>   HTZ  10.4      2     3.48 0.0339   *
#> > Wald_test(robu_fit, constraints = constrain_zero(2:5), vcov = robu_CR2)
#>  test Fstat df_num df_denom p_val sig
#>   HTZ 0.176      4   0.0388 0.962