# Heteroscedasticity-consistent covariance matrix estimation for singleRStaticCountData class S3 method for `vcovHC` to handle `singleRStaticCountData` class objects. Works exactly like `vcovHC.default` the only difference being that this method handles vector generalised linear models. Updating the covariance matrix in variance/standard error estimation for population size estimator can be done via `redoPopEstimation()` ```r ## S3 method for class 'singleRStaticCountData' estfun(x, ...) ## S3 method for class 'singleRStaticCountData' bread(x, ...) ## S3 method for class 'singleRStaticCountData' vcovHC( x, type = c("HC3", "const", "HC", "HC0", "HC1", "HC2", "HC4", "HC4m", "HC5"), omega = NULL, sandwich = TRUE, ... ) ``` ## Arguments - `x`: a fitted `singleRStaticCountData` class object. - `...`: for `vcovHC` additional optional arguments passed to the following functions: * `estfun` -- for empirical estimating functions. * `hatvalues` -- for diagonal elements of projection matrix. * `sandwich` -- only if `sandwich` argument in function call was set to `TRUE`. * `vcov` -- when calling `bread` internally. - `type`: a character string specifying the estimation type, same as in `sandwich::vcovHC.default`. HC3 is the default value. - `omega`: a vector or a function depending on the arguments residuals (i.e. the derivative of log-likelihood with respect to each linear predictor), diaghat (the diagonal of the corresponding hat matrix) and df (the residual degrees of freedom), same as in `sandwich::vcovHC.default`. - `sandwich`: logical. Should the sandwich estimator be computed? If set to FALSE only the meat matrix is returned. Same as in `sandwich::vcovHC()` ## Returns Variance-covariance matrix estimation corrected for heteroscedasticity of regression errors ## Examples ```r set.seed(1) N <- 10000 gender <- rbinom(N, 1, 0.2) eta <- -1 + 0.5*gender counts <- rpois(N, lambda = exp(eta)) df <- data.frame(gender, eta, counts) df2 <- subset(df, counts > 0) mod1 <- estimatePopsize( formula = counts ~ 1 + gender, data = df2, model = "ztpoisson", method = "optim", popVar = "analytic" ) require(sandwich) HC <- sandwich::vcovHC(mod1, type = "HC4") Fisher <- vcov(mod1, "Fisher") # variance covariance matrix obtained from #Fisher (expected) information matrix HC Fisher # usual results summary(mod1) # updated results summary(mod1, cov = HC, popSizeEst = redoPopEstimation(mod1, cov = HC)) # estimating equations mod1_sims <- sandwich::estfun(mod1) head(mod1_sims) # bread method all(vcov(mod1, "Fisher") * nrow(df2) == sandwich::bread(mod1, type = "Fisher")) ``` ## See Also `sandwich::vcovHC()` `redoPopEstimation()` ## Author(s) Piotr Chlebicki, Maciej Beręsewicz

vcovHC.singleRStaticCountData function