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Edits round 1 #12

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da7b11a
rm .DS_Store
tgerke May 28, 2023
a4986fd
light edits and linting
tgerke May 28, 2023
8176a5f
wrangle duplicate cases in a single call
tgerke May 28, 2023
92dea8a
lint, minor edits
tgerke May 28, 2023
720df64
big refactor; move most switch and if/else to explicit if blocks
tgerke May 29, 2023
02656d3
fixes #17
tgerke Jun 2, 2023
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76 changes: 48 additions & 28 deletions R/caseduplicate.R
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,8 @@
estimate_duplicate <- function(formula, data, ...) {
yvar <- as.character(all.vars(formula)[1])

#TODO the following assumes that outc is coded as 0/1. We should throw an
# error when this is not true
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100%! How about here:

risks/R/estimate_risk.R

Lines 216 to 217 in f48f0a0

...) {
implausible <- 0.99999

data <- data |>
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see earlier comment about base R pipe

dplyr::mutate(.clusterid = dplyr::row_number()) |>
dplyr::rename(outc = dplyr::one_of(!!yvar)) |>
Expand All @@ -17,12 +19,21 @@ estimate_duplicate <- function(formula, data, ...) {
dplyr::ungroup() |>
dplyr::rename(!!yvar := "outc")

fit <- eval(substitute(stats::glm(formula = formula,
family = binomial(link = "logit"),
data = data)))
fit <- eval(substitute(
stats::glm(
formula = formula,
family = binomial(link = "logit"),
data = data
)
))
class(fit) <- c("duplicate", class(fit))
fit <- estimate_maxprob(fit = fit, formula = formula, data = data,
link = "logit")
fit <- estimate_maxprob(
fit = fit,
formula = formula,
data = data,
link = "logit"
)

return(fit)
}

Expand All @@ -48,19 +59,24 @@ confint.duplicate <- function(object, parm = NULL, level = 0.95, ...) {
# parm <- pnames[parm]
a <- (1 - level)/2
a <- c(a, 1 - a)
pct <- paste(format(100 * a, trim = TRUE, scientific = FALSE, digits = 3),
"%")
pct <- paste(
format(100 * a, trim = TRUE, scientific = FALSE, digits = 3),
"%"
)
fac <- qnorm(a)
ci <- array(NA, dim = c(length(parm), 2L), dimnames = list(parm, pct))
# Robust covariance accounting for clustering
obj_sandwich <- object
class(obj_sandwich) <- "glm"
ses <- sqrt(diag(sandwich::sandwich(
x = obj_sandwich,
bread. = sandwich::bread(obj_sandwich),
meat. = sandwich::meatCL(obj_sandwich,
type = "HC0",
cluster = object$data$.clusterid))))
ses <- sqrt(diag(
sandwich::sandwich(
x = obj_sandwich,
bread. = sandwich::bread(obj_sandwich),
meat. = sandwich::meatCL(
obj_sandwich, type = "HC0", cluster = object$data$.clusterid
)
)
))
ci[] <- cf[parm] + ses %o% fac
return(ci)
}
Expand All @@ -79,13 +95,14 @@ confint.duplicate <- function(object, parm = NULL, level = 0.95, ...) {
#' @param symbolic.cor Not used
#' @param ... Other arguments, not used
#' @export
summary.duplicate <- function(object, dispersion = NULL, correlation = FALSE,
symbolic.cor = FALSE, ...) {
summary.duplicate <- function(
object, dispersion = NULL, correlation = FALSE, symbolic.cor = FALSE, ...
) {
# a modification of summary.glm()
est.disp <- FALSE
df.r <- object$df.residual
if (is.null(dispersion))
dispersion <- if (object$family$family %in% c("poisson", "binomial"))
if(is.null(dispersion))
dispersion <- if(object$family$family %in% c("poisson", "binomial"))
1
else if (df.r > 0) {
est.disp <- TRUE
Expand Down Expand Up @@ -113,9 +130,9 @@ summary.duplicate <- function(object, dispersion = NULL, correlation = FALSE,
covmat.unscaled <- sandwich::sandwich(
x = obj_sandwich,
bread. = sandwich::bread(x = obj_sandwich),
meat. = sandwich::meatCL(x = obj_sandwich,
type = "HC0",
cluster = object$data$.clusterid))
meat. = sandwich::meatCL(
x = obj_sandwich, type = "HC0", cluster = object$data$.clusterid)
)
###### end changes ########
covmat <- dispersion * covmat.unscaled
var.cf <- diag(covmat)
Expand All @@ -125,26 +142,29 @@ summary.duplicate <- function(object, dispersion = NULL, correlation = FALSE,
if (!est.disp) {
pvalue <- 2 * pnorm(-abs(tvalue))
coef.table <- cbind(coef.p, s.err, tvalue, pvalue)
dimnames(coef.table) <- list(names(coef.p), c(dn,
"z value", "Pr(>|z|)"))
dimnames(coef.table) <- list(
names(coef.p), c(dn, "z value", "Pr(>|z|)")
)
}
else if (df.r > 0) {
pvalue <- 2 * pt(-abs(tvalue), df.r)
coef.table <- cbind(coef.p, s.err, tvalue, pvalue)
dimnames(coef.table) <- list(names(coef.p), c(dn,
"t value", "Pr(>|t|)"))
dimnames(coef.table) <- list(
names(coef.p), c(dn, "t value", "Pr(>|t|)")
)
}
else {
coef.table <- cbind(coef.p, NaN, NaN, NaN)
dimnames(coef.table) <- list(names(coef.p), c(dn,
"t value", "Pr(>|t|)"))
dimnames(coef.table) <- list(
names(coef.p), c(dn, "t value", "Pr(>|t|)")
)
}
df.f <- NCOL(Qr$qr)
}
else {
coef.table <- matrix(, 0L, 4L)
coef.table <- matrix(data = NA, 0L, 4L)
dimnames(coef.table) <- list(NULL, c("Estimate", "Std. Error", "t value", "Pr(>|t|)"))
covmat.unscaled <- covmat <- matrix(, 0L, 0L)
covmat.unscaled <- covmat <- matrix(data = NA, 0L, 0L)
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Are you certain that data = NA is exactly the same as leaving this space blank? This is verbatim code from stats::glm(), which we know works. I see the aesthetics point, but why change a winning horse?

df.f <- length(aliased)
}
keep <- match(c("call", "terms", "family", "deviance", "aic",
Expand Down