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Robin_calibrate <- function(robin.g_object = G_zx$robin.g_object, # robin.g_object includes robin.g_data, car.scheme and car.z
robin.add_x = c("x1"),
robin.add_x_to_include_z = TRUE, # if robin.g_object$car.scheme = SR, this arg will be ignored (force to FALSE)
robin.vcovHC = c("HC0", "HC1", "HC3")){
# Robin_calibrate applies ANHECOVA (robin_linear(robin.adj_method = "ANHECOVA")) to robin.g_object with some additional options/arguments
# customized calibrate on all "g_pred_"s and customized "robin.add_x"
# default double calibration can use all "g_pred_"s and all "x" and "z" used in robin.g_object to generate "g_pred_"
robin.c_data <- robin.g_object$robin.g_data %>%
rename_with(.cols = contains("g_pred_"), .fn = ~ str_replace(.x, "g_pred_", "mu_"))
robin.c_x <- c(robin.add_x, grep("mu_", x = colnames(robin.c_data), value=TRUE)) # !is.null(robin.add_x) --> double calibration
if(robin.g_object$car.scheme == "minimization"){
robin.add_x_to_include_z <- TRUE # give a warning if robin.add_x_to_include_z=F under minimization
}
# We can also use robin_linear with ANHECOVA
robin.c_return <- Robin_g(robin.data = robin.c_data,
car.scheme = robin.g_object$car.scheme,
car.z = robin.g_object$car.z,
robin.x = robin.c_x,
robin.formula = NULL,
robin.x_to_include_z = robin.add_x_to_include_z, # default
robin.adj_method = "heterogeneous",
robin.g_family = gaussian(link = "identity"), # ANHECOVA
robin.g_accuracy = 7,
robin.vcovHC = c("HC0", "HC1", "HC3"))
return(robin.c_return) # robin.c_return can also be robin objective, with similar structure as robin_g or robin_linear
}
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