/
plot_MADE.power.R
320 lines (284 loc) · 8.7 KB
/
plot_MADE.power.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
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
#' @title Plot function for a 'power' object
#'
#' @template plot_MADE-arg
#' @param power_min Either an integer specify a horizontal line or a length-2
#' vector to specify an interval, indicating a benchmark level of power
#' (default is \code{NULL}).
#' @param expected_studies Optional vector of length 2 specifying a range for
#' the number of studies one expects to include in the meta-analysis. If
#' specified, this interval will be shaded across facet_grip plots (default is
#' \code{NULL}).
#' @param model_comparison Logical indicating whether power estimates should be
#' plotted across different working models for dependent effect size estimates
#' (default is \code{FALSE}) instead of across values for the sampling
#' correlation.
#'
#'
#' @description Creates a faceted plot or plots for power analyses conducted
#' with \code{power_MADE}.
#'
#' @details In general, it can be rather difficult to guess/approximate the true
#' model parameters and sample characteristics a priori. Calculating power
#' under only a single set of assumptions can easily be misleading even if the
#' true model and data structure only slightly diverge from the yielded data
#' and model assumptions. To maximize the informativeness of the power
#' approximations, Vembye, Pustejovsky, & Pigott (In preparation) suggest
#' accommodating the uncertainty of the power approximations by reporting or
#' plotting power estimates across a range of possible scenarios, which can be
#' done using \code{plot_MADE.power}.
#'
#' @references Vembye, M. H., Pustejovsky, J. E., & Pigott, T. D. (In
#' preparation). Conducting power analysis for meta-analysis of dependent
#' effect sizes: Common guidelines and an introduction to the POMADE R
#' package.
#'
#' @return A \code{ggplot} plot showing power across the expected number of
#' studies, faceted by the between-study and within-study SDs, with different
#' colors, lines, and shapes corresponding to different values of the assumed
#' sample correlation. If \code{model_comparison = TRUE}, it returns a
#' \code{ggplot} plot showing power across the expected number of studies,
#' faceted by the between-study and within-study SDs, with different colors,
#' lines, and shapes corresponding to different working models for dependent
#' effect size estimates
#'
#' @seealso \code{\link{plot_MADE}}
#'
#' @examples
#' power_dat <-
#' power_MADE(
#' J = c(50, 56),
#' mu = 0.15,
#' tau = 0.1,
#' omega = 0.05,
#' rho = 0,
#' sigma2_dist = 4 / 200,
#' n_ES_dist = 6
#' )
#'
#' power_example <-
#' plot_MADE(
#' data = power_dat,
#' power_min = 0.8,
#' expected_studies = c(52, 54),
#' warning = FALSE,
#' caption = TRUE,
#' color = TRUE,
#' model_comparison = FALSE,
#' numbers = FALSE
#' )
#'
#' power_example
#'
#'
#' @export
plot_MADE.power <-
function(
data,
v_lines = NULL,
legend_position = "bottom",
color = TRUE,
numbers = TRUE,
number_size = 2.5,
numbers_ynudge = 0,
caption = TRUE,
x_lab = NULL,
x_breaks = NULL,
x_limits = NULL,
y_breaks = seq(0, 1, .2),
y_limits = c(0, 1),
y_expand = NULL,
warning = TRUE,
traffic_light_assumptions = NULL,
power_min = NULL,
expected_studies = NULL,
model_comparison = FALSE,
...
){
if (is.null(x_lab)) x_lab <- "Number of Studies (J)"
if (!model_comparison) {
if (warning) {
if(n_distinct(data$model) > 1){
warning("We recommend to create the plot for one model only", call. = FALSE)
}
}
plot_dat <-
data |>
mutate(
tau_name = factor(paste("Study Level SD =", tau)),
omega_name = factor(paste("ES Level SD =", omega))
) |>
rename(cor = rho) |>
group_nest(mu, d, alpha, model) |>
mutate(
cap = paste0("Note: Effect size of practical concern = ", mu, ", ", "contrast value = ", d,
", and ", "alpha = ", alpha, "."),
y_lab = paste0("Power", " (", model, ")")
) |>
rowwise() |>
select(data, cap, y_lab)
if (!caption) {
plot_dat$cap <- NULL
}
if (color) {
plot <- suppressWarnings(dplyr::group_map(
plot_dat,
~ plot_MADE_engine(
data = .x$data[[1]],
x = J,
y = power,
x_grid = tau_name,
y_grid = omega_name,
color = cor,
shape = cor,
linetype = cor,
h_lines = power_min,
v_lines = v_lines,
v_shade = expected_studies,
x_breaks = x_breaks,
x_limits = x_limits,
y_breaks = y_breaks,
y_limits = y_limits,
y_expand = y_expand,
x_lab = x_lab,
y_lab = .x$y_lab,
color_lab = "Cor",
shape_lab = "Cor",
line_lab = "Cor",
caption = .x$cap,
legend_position = legend_position,
grid_labs = numbers,
labs_ynudge = numbers_ynudge,
labs_size = number_size,
assumptions = traffic_light_assumptions
))
)
} else {
plot <- suppressWarnings(dplyr::group_map(
plot_dat,
~ plot_MADE_engine(
data = .x$data[[1]],
x = J,
y = power,
x_grid = tau_name,
y_grid = omega_name,
color = NULL,
shape = cor,
linetype = cor,
h_lines = power_min,
v_lines = v_lines,
v_shade = expected_studies,
x_breaks = x_breaks,
x_limits = x_limits,
y_breaks = y_breaks,
y_limits = y_limits,
y_expand = y_expand,
x_lab = x_lab,
y_lab = .x$y_lab,
color_lab = NULL,
shape_lab = "Cor",
line_lab = "Cor",
caption = .x$cap,
legend_position = legend_position,
grid_labs = numbers,
labs_ynudge = numbers_ynudge,
labs_size = number_size,
assumptions = traffic_light_assumptions
))
)
}
}
if (model_comparison) {
if (n_distinct(data$model) == 1) {
stop("Power approximations for more than one model are needed")
}
plot_dat <-
data |>
mutate(
tau_name = factor(paste("Study Level SD =", tau)),
omega_name = factor(paste("ES Level SD =", omega))
) |>
rename(cor = rho) |>
group_nest(mu, d, alpha, cor) |>
mutate(
cap = paste0("Note: Effect size of practical concern = ", mu, ", ", "contrast value = ", d,
", ", "alpha = ", alpha, ", and ", "sample correlation = ", cor, "."),
y_lab = "Power"
) |>
rowwise() |>
select(data, cap, y_lab)
if (!caption) {
plot_dat$cap <- NULL
}
if (color) {
plot <- suppressWarnings(dplyr::group_map(
plot_dat,
~ plot_MADE_engine(
data = .x$data[[1]],
x = J,
y = power,
x_grid = tau_name,
y_grid = omega_name,
color = model,
shape = model,
linetype = model,
h_lines = power_min,
v_lines = v_lines,
v_shade = expected_studies,
x_breaks = x_breaks,
x_limits = x_limits,
y_breaks = y_breaks,
y_limits = y_limits,
y_expand = y_expand,
x_lab = x_lab,
y_lab = .x$y_lab,
color_lab = "Model",
shape_lab = "Model",
line_lab = "Model",
shape_scale = "model",
caption = .x$cap,
legend_position = legend_position,
grid_labs = numbers,
labs_ynudge = numbers_ynudge,
labs_size = number_size,
assumptions = traffic_light_assumptions
))
)
} else {
plot <- suppressWarnings(dplyr::group_map(
plot_dat,
~ plot_MADE_engine(
data = .x$data[[1]],
x = J,
y = power,
x_grid = tau_name,
y_grid = omega_name,
color = NULL,
shape = model,
linetype = model,
h_lines = power_min,
v_lines = v_lines,
v_shade = expected_studies,
x_breaks = x_breaks,
x_limits = x_limits,
y_breaks = y_breaks,
y_limits = y_limits,
y_expand = y_expand,
x_lab = x_lab,
y_lab = .x$y_lab,
color_lab = NULL,
shape_lab = "Model",
line_lab = "Model",
shape_scale = "model",
caption = .x$cap,
legend_position = legend_position,
grid_labs = numbers,
labs_ynudge = numbers_ynudge,
labs_size = number_size,
assumptions = traffic_light_assumptions
))
)
}
}
if (length(plot) == 1) plot <- plot[[1]]
plot
}