-
Notifications
You must be signed in to change notification settings - Fork 1
/
est-param-cauchy.R
96 lines (87 loc) · 2.42 KB
/
est-param-cauchy.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
#' Estimate Cauchy Parameters
#'
#' @family Parameter Estimation
#' @family Cauchy
#'
#' @author Steven P. Sanderson II, MPH
#'
#' @details This function will attempt to estimate the cauchy location and scale
#' parameters given some vector of values.
#'
#' @description The function will return a list output by default, and if the parameter
#' `.auto_gen_empirical` is set to `TRUE` then the empirical data given to the
#' parameter `.x` will be run through the `tidy_empirical()` function and combined
#' with the estimated cauchy data.
#'
#' @param .x The vector of data to be passed to the function.
#' @param .auto_gen_empirical This is a boolean value of TRUE/FALSE with default
#' set to TRUE. This will automatically create the `tidy_empirical()` output
#' for the `.x` parameter and use the `tidy_combine_distributions()`. The user
#' can then plot out the data using `$combined_data_tbl` from the function output.
#'
#' @examples
#' library(dplyr)
#' library(ggplot2)
#'
#' x <- tidy_cauchy(.location = 0, .scale = 1)$y
#' output <- util_cauchy_param_estimate(x)
#'
#' output$parameter_tbl
#'
#' output$combined_data_tbl |>
#' tidy_combined_autoplot()
#'
#' @return
#' A tibble/list
#'
#' @export
#'
util_cauchy_param_estimate <- function(.x, .auto_gen_empirical = TRUE) {
# Tidyeval ----
x_term <- as.numeric(.x)
minx <- min(x_term)
maxx <- max(x_term)
n <- length(x_term)
unique_terms <- length(unique(x_term))
# Checks ----
if (!inherits(x_term, "numeric")) {
rlang::abort(
message = "The '.x' parameter must be numeric.",
use_cli_format = TRUE
)
}
location <- stats::median(x_term)
scale <- stats::IQR(x_term)
# Return Tibble ----
if (.auto_gen_empirical) {
te <- tidy_empirical(.x = x_term)
td <- tidy_cauchy(.n = n, .location = round(location, 3), .scale = round(scale, 3))
combined_tbl <- tidy_combine_distributions(te, td)
}
ret <- dplyr::tibble(
dist_type = "Cauchy",
samp_size = n,
min = minx,
max = maxx,
method = "MASS",
location = location,
scale = scale,
ratio = (location / scale)
)
# Return ----
attr(ret, "tibble_type") <- "parameter_estimation"
attr(ret, "family") <- "cauchy"
attr(ret, "x_term") <- .x
attr(ret, "n") <- n
if (.auto_gen_empirical) {
output <- list(
combined_data_tbl = combined_tbl,
parameter_tbl = ret
)
} else {
output <- list(
parameter_tbl = ret
)
}
return(output)
}