1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
|
## from rms / rmsb package
# model parameters -------------
#' @export
model_parameters.blrm <- model_parameters.bayesQR
# standard error -------------
#' @export
standard_error.lrm <- function(model, ...) {
se <- sqrt(diag(stats::vcov(model)))
# psm-models returns vcov-matrix w/o dimnames
if (is.null(names(se))) names(se) <- names(stats::coef(model))
.data_frame(
Parameter = .remove_backticks_from_string(names(se)),
SE = as.vector(se)
)
}
#' @export
standard_error.ols <- standard_error.lrm
#' @export
standard_error.rms <- standard_error.lrm
#' @export
standard_error.psm <- standard_error.lrm
# p-values -----------------------
#' @export
p_value.lrm <- function(model, ...) {
stat <- insight::get_statistic(model)
# Issue: 697: typically the degrees of freedom are the same for every
# observation, but the value is repeated. This poses problems in multiple
# imputation models with Hmisc when we get more df values than parameters.
dof <- insight::get_df(model, type = "wald")
dfu <- unique(dof)
if (length(dfu) == 1) {
dof <- dfu
}
p <- 2 * stats::pt(abs(stat$Statistic), df = dof, lower.tail = FALSE)
.data_frame(
Parameter = .remove_backticks_from_string(stat$Parameter),
p = as.vector(p)
)
}
#' @export
p_value.ols <- p_value.lrm
#' @export
p_value.rms <- p_value.lrm
#' @export
p_value.psm <- p_value.lrm
#' @export
p_value.blrm <- p_value.BFBayesFactor
|
