-
Notifications
You must be signed in to change notification settings - Fork 303
/
survival-coxph-tidiers.R
194 lines (185 loc) · 4.73 KB
/
survival-coxph-tidiers.R
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
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
#' @templateVar class coxph
#' @template title_desc_tidy
#'
#' @param x A `coxph` object returned from [survival::coxph()].
#' @template param_confint
#' @template param_exponentiate
#' @template param_unused_dots
#'
#' @evalRd return_tidy(
#' "estimate",
#' "std.error",
#' "statistic",
#' "p.value"
#' )
#'
#' @examples
#'
#' library(survival)
#'
#' cfit <- coxph(Surv(time, status) ~ age + sex, lung)
#'
#' tidy(cfit)
#' tidy(cfit, exponentiate = TRUE)
#'
#' lp <- augment(cfit, lung)
#' risks <- augment(cfit, lung, type.predict = "risk")
#' expected <- augment(cfit, lung, type.predict = "expected")
#'
#' glance(cfit)
#'
#' # also works on clogit models
#' resp <- levels(logan$occupation)
#' n <- nrow(logan)
#' indx <- rep(1:n, length(resp))
#' logan2 <- data.frame(
#' logan[indx,],
#' id = indx,
#' tocc = factor(rep(resp, each=n))
#' )
#'
#' logan2$case <- (logan2$occupation == logan2$tocc)
#'
#' cl <- clogit(case ~ tocc + tocc:education + strata(id), logan2)
#' tidy(cl)
#' glance(cl)
#'
#' library(ggplot2)
#'
#' ggplot(lp, aes(age, .fitted, color = sex)) +
#' geom_point()
#'
#' ggplot(risks, aes(age, .fitted, color = sex)) +
#' geom_point()
#'
#' ggplot(expected, aes(time, .fitted, color = sex)) +
#' geom_point()
#'
#'
#' @aliases coxph_tidiers
#' @export
#' @seealso [tidy()], [survival::coxph()]
#' @family coxph tidiers
#' @family survival tidiers
tidy.coxph <- function(x, exponentiate = FALSE, conf.int = FALSE,
conf.level = .95, ...) {
# backward compatibility (in previous version, conf.int was used instead of conf.level)
if (is.numeric(conf.int)) {
conf.level <- conf.int
conf.int <- TRUE
}
if (conf.int) {
s <- summary(x, conf.int = conf.level)
} else {
s <- summary(x, conf.int = FALSE)
}
co <- stats::coef(s)
if (! is.null(x$frail)){
nn <- c("estimate", "std.error", "statistic", "p.value")
}else if (s$used.robust) {
nn <- c("estimate", "std.error", "robust.se", "statistic", "p.value")
} else {
nn <- c("estimate", "std.error", "statistic", "p.value")
}
if (is.null(x$frail)){
ret <- fix_data_frame(co[, -2, drop = FALSE], nn)
} else{
ret <- fix_data_frame(co[, -c(3, 5), drop = FALSE], nn)
}
if (exponentiate) {
ret$estimate <- exp(ret$estimate)
}
if (!is.null(s$conf.int)) {
CI <- as.matrix(unrowname(s$conf.int[, 3:4, drop = FALSE]))
colnames(CI) <- c("conf.low", "conf.high")
if (!exponentiate) {
CI <- log(CI)
}
ret <- cbind(ret, CI)
}
as_tibble(ret)
}
#' @templateVar class coxph
#' @template title_desc_augment
#'
#' @inherit tidy.coxph params examples
#' @template param_data
#' @template param_newdata
#' @template param_type_residuals
#' @template param_type_predict
#' @template param_unused_dots
#'
#' @template augment_NAs
#'
#' @evalRd return_augment(".se.fit")
#'
#' @export
#' @seealso [augment()], [survival::coxph()]
#' @family coxph tidiers
#' @family survival tidiers
augment.coxph <- function(x, data = NULL, newdata = NULL,
type.predict = "lp", type.residuals = "martingale",
...) {
if (is.null(data) && is.null(newdata)) {
stop("Must specify either `data` or `newdata` argument.", call. = FALSE)
}
augment_columns(x, data, newdata,
type.predict = type.predict,
type.residuals = type.residuals
)
}
#' @templateVar class coxph
#' @template title_desc_glance
#'
#' @inherit tidy.coxph params examples
#'
#' @evalRd return_glance(
#' "nevent",
#' "statistic.log",
#' "p.value.log",
#' "statistic.sc",
#' "p.value.sc",
#' "statistic.wald",
#' "p.value.wald",
#' "statistic.robust",
#' "p.value.robust",
#' "r.squared",
#' "r.squared.max",
#' "concordance",
#' "std.error.concordance",
#' "logLik",
#' "AIC",
#' "BIC",
#' "nobs"
#' )
#'
#' @export
#' @seealso [glance()], [survival::coxph()]
#' @family coxph tidiers
#' @family survival tidiers
glance.coxph <- function(x, ...) {
s <- summary(x)
# including all the test statistics and p-values as separate
# columns. Admittedly not perfect but does capture most use cases.
ret <- list(
nevent = s$nevent,
statistic.log = s$logtest[1],
p.value.log = s$logtest[3],
statistic.sc = s$sctest[1],
p.value.sc = s$sctest[3],
statistic.wald = s$waldtest[1],
p.value.wald = s$waldtest[3],
statistic.robust = s$robscore[1],
p.value.robust = s$robscore[3],
r.squared = s$rsq[1],
r.squared.max = s$rsq[2],
concordance = s$concordance[1],
std.error.concordance = s$concordance[2],
logLik = as.numeric(stats::logLik(x)),
AIC = stats::AIC(x),
BIC = stats::BIC(x),
nobs = stats::nobs(x)
)
ret <- as_tibble(purrr::compact(ret))
ret
}