Dev #26
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Kertoo
reviewed
Nov 27, 2023
| @@ -346,19 +357,32 @@ cooks.distance.nonprobsvy <- function(model, | |||
| hatvalues.nonprobsvy <- function(model, | |||
| ...) { # TODO reduce execution time and glm.fit object and customise to variable selection | |||
| if (any(c("nonprobsvy_dr", "nonprobsvy_ipw") %in% class(model))) { | |||
| X <- model$X | |||
| propensity_scores <- model$prob | |||
| W <- Matrix::Diagonal(x = propensity_scores * (1 - propensity_scores)) | |||
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Preety sure you can just leave W as a vector and this will work faster
| } | ||
| #hats <- Matrix::Diagonal(x = W %*% object$X %*% XWX_inv %*% t(object$X)) | ||
| #names(hat_values) <- row.names(model$parameters) | ||
| H <- as.matrix(sqrt(W) %*% X %*% solve(t(X) %*% W %*% X) %*% t(X) %*% sqrt(W)) |
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Also can you not just call hatvalues method on original engine like hatvalues.glm on glm's?
| "pearsonSTD" = r/sqrt( (1 - hatvalues(object)$selection) * object$selection$variance) | ||
| ) # TODO studentized_pearson, studentized_deviance | ||
| "pearsonSTD" = r/sqrt( (1 - hatvalues(object)$selection) * object$selection$variance), | ||
| stop("Invalid type of residual. Choose from 'pearson', 'working', 'deviance', 'response', 'pearsonSTD'.") |
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this switch will result in base R error if type is NULL and will return NULL if type == NA probably doesn't matter here but its always good to remember that stop in switch can break if end user tries to break it :)
| class = "family") | ||
| } | ||
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| gaussian_nonprobsvy <- function(link = "identity") { | ||
| mu <- function(eta) eta | ||
| variance <- function(mu, y = NULL) rep.int(1, length(mu)) #mean((y - mu)^2) rep.int(1, length(mu)) | ||
| variance <- function(mu, y = NULL) mean((y - mu)^2) #rep.int(1, length(mu)) rep.int(1, length(mu)) |
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Just as a reminder you don't have to write all those for every exponential family you want to use you can also just do something simpler like:
gaussian_nonprobsvy <- function(link = "identity") {
x <- stats::gaussian(link = link)
x$mu_der <- function(mu) 1
# Sooner or later you will also probably need a class for those as well
class(x) <- c(class(x), "nonprobsvy_family")
}
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