-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathsim_MIvsSI.R
327 lines (262 loc) · 11.5 KB
/
sim_MIvsSI.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
316
317
318
319
320
321
322
323
324
325
326
327
#load packages
library(mice)
library(dplyr)
#set seeds and params
set.seed(4242)
repetitions = 2 #(number minus 1)
iter = 25 #iterations for MI
# generate complete data frame
data <- data.frame(X1=rnorm(1000), X2=rnorm(1000))
data$Y <- with(data, X1 + 4*X2 + rnorm(1000, sd=0.1))
write.csv(data, "data.csv", row.names=FALSE)
#extract summaries
a <- summary(lm(Y~X1+X2, data=data))$coefficients
colnames(a) = c("estimate", "std.error", "statistic", "p.value")
confo <- confint(lm(Y~X1+X2, data=data))
a <- cbind (a, confo)
#generate missingness with mar, mcar
data_mar10 = ampute(data, prop = 0.10, mech = "MAR")
data_mar25 = ampute(data, prop = 0.25, mech = "MAR")
data_mar40 = ampute(data, prop = 0.40, mech = "MAR")
data_mar55 = ampute(data, prop = 0.55, mech = "MAR")
data_mcar10 = ampute(data, prop = 0.10, mech = "MCAR")
data_mcar25 = ampute(data, prop = 0.25, mech = "MCAR")
data_mcar40 = ampute(data, prop = 0.40, mech = "MCAR")
data_mcar55 = ampute(data, prop = 0.55, mech = "MCAR")
#create empty dfs for summaries
Pattern_list <- list(mget(ls(pattern = "data_")))[[1]]
mod_summaries <- data.frame()
mod_summaries_sd <- list()
#complete case analyses
for (i in Pattern_list) {
sumX <- as.data.frame(summary(lm(Y~X1+X2, data=i$amp))$coefficients) #get summary
colnames(sumX) = c("estimate", "std.error", "statistic", "p.value")
conf <- confint(lm(Y~X1+X2, data=i$amp))
sumX <- cbind(sumX, conf)
name1 = paste0(i$mech, i$prop, "cc")
sumX <- cbind(sumX, name1)
mod_summaries <- bind_rows(list(mod_summaries, sumX)) #combine summary + sds for output
}
#repat from here based on the iter number (minus one)
#impute
#select imp method
method = "pmm"
#impute N times
x <- 1
repeat {
print(x)
mimp_mar10 <- mice(data_mar10$amp, maxit = iter, m = 10, meth = method)
mimp_mar25<- mice(data_mar25$amp, maxit = iter, m = 25, meth = method)
mimp_mar40 <- mice(data_mar40$amp, maxit = iter, m = 40, meth = method)
mimp_mar55 <- mice(data_mar55$amp, maxit = iter, m = 55, meth = method)
mimp_mcar10 <- mice(data_mcar10$amp, maxit = iter, m = 10, meth = method)
mimp_mcar25 <- mice(data_mcar25$amp, maxit = iter, m = 25, meth = method)
mimp_mcar40 <- mice(data_mcar40$amp, maxit = iter, m = 40, meth = method)
mimp_mcar55 <- mice(data_mcar55$amp, maxit = iter, m = 55, meth = method)
#get the list of imputed sets, analayse and extract summaries
Pattern1_list <- list(mget(ls(pattern = "mimp")))[[1]]
for (i in Pattern1_list) {
fit <- with(data=i,exp=lm(Y~X1+X2)) #linear model
sumX <- summary(pool(fit), conf.int = T) #pool results
name = as.data.frame(as.character(i$call$data)[2])
name1 = paste0(name[,1],"mi")
sumX <- bind_cols(sumX, as.data.frame(name1))#intialise name
mod_summaries <- bind_rows(list(mod_summaries, sumX))
}
x = x+1
if (x == repetitions){
break
}
}
#single impute and analyse
x <- 1
repeat {
print(x)
simp_mar10 <- mice(data_mar10$amp, maxit = iter, m = 1, meth = method)
simp_mar25 <- mice(data_mar25$amp, maxit = iter, m = 1, meth = method)
simp_mar40 <- mice(data_mar40$amp, maxit = iter, m = 1, meth = method)
simp_mar55 <- mice(data_mar55$amp, maxit = iter, m = 1, meth = method)
simp_mcar10 <- mice(data_mcar10$amp, maxit = iter, m = 1, meth = method)
simp_mcar25 <- mice(data_mcar25$amp, maxit = iter, m = 1, meth = method)
simp_mcar40 <- mice(data_mcar40$amp, maxit = iter, m = 1, meth = method)
simp_mcar55 <- mice(data_mcar55$amp, maxit = iter, m = 1, meth = method)
Pattern2_list <- list(mget(ls(pattern = "simp")))[[1]]
for (i in Pattern2_list) {
sumX <- as.data.frame(summary(lm(Y~X1+X2, data=complete(i)))$coefficients) #get summary
colnames(sumX) = c("estimate", "std.error", "statistic", "p.value")
confs <- confint(lm(Y~X1+X2, data=complete(i)))
sumX <- cbind(sumX, confs)
name = as.data.frame(as.character(i$call$data))
name1 = paste0(name[2,],"si")
sumX <- cbind(sumX, name1)
sumX$term = rownames(sumX)
mod_summaries <- bind_rows(list(mod_summaries, sumX)) #combine summary + sds for output
}
x = x+1
if (x == repetitions){
break
}
}
#combine the cummaries and add the full set summary at the bottom
mod_summaries <- bind_rows(list(mod_summaries, as.data.frame(a)))
mod_summaries$name1[nrow(mod_summaries)] = c("original")
mod_summaries$name1[nrow(mod_summaries)-1] = c("original")
mod_summaries$name1[nrow(mod_summaries)-2] = c("original")
summaries <- mod_summaries #keep a backup in case
#calculate the bias
#check this after changing numer of predictors!!!!
#remove the intercep
toDelete <- seq(1, nrow(mod_summaries), 3)
mod_summaries = mod_summaries[ -toDelete,]
#create a sequence (even nrs X1, odd nrs X2)
biasX1 <- seq(1, nrow(mod_summaries), 2)
biasX2 <- seq(0, nrow(mod_summaries), 2)
biasX2 = biasX2[-1] #remove the 0
#create dfs to store X1 and X2 results separately
result1 = data.frame()
result2 = data.frame()
#get se from the full model to calculate bias
result1se = mod_summaries[biasX1,2]
result2se = mod_summaries[biasX2,2]
#get the X1 and X2 estimate to subtract from the estimate from imputation
X1basetoremove = mod_summaries[c(nrow(mod_summaries)-1),c(1,3:6)]
X2basetoremove = mod_summaries[nrow(mod_summaries),c(1,3:6)]
#biasX1
for(i in biasX1){
resultX1 = mod_summaries[i,c(1,3:6)] - X1basetoremove
result1 = rbind(result1, resultX1)
}
#biasX2
for(i in biasX2){
resultX2 = mod_summaries[i,c(1,3:6)] - X2basetoremove
result2 = rbind(result2, resultX2)
}
#add the names
result1$names = mod_summaries$name1[biasX1]
result2$names = mod_summaries$name1[biasX2]
#bind in one df
result = rbind(result1, result2)
#calculate bias for SE (Pythagorean distance)
seX1 = mod_summaries[c(nrow(mod_summaries)-1),2]
seX2 = mod_summaries[nrow(mod_summaries),2]
result1se = mod_summaries[biasX1,2]
result2se = mod_summaries[biasX2,2]
for(i in 1: length(result1se)){
resultSE[i] = (sqrt(result1se[i]^2 + result2se[i]^2))/sqrt(seX1^2 + seX2^2)
}
#add the std error twice (for X1 and X2) - it's the same
result$std.error = rep(resultSE, 2)
# PLOTTING
require(cowplot)
require(ggplot2)
#order cats
result$names <- factor(result$names, levels = c("original", "MAR0.1cc", "data_mar10mi", "data_mar10si", "MAR0.25cc", "data_mar25mi", "data_mar25si", "MAR0.4cc", "data_mar40mi", "data_mar40si", "MAR0.55cc", "data_mar55mi", "data_mar55si", "MCAR0.1cc", "data_mcar10mi", "data_mcar10si", "MCAR0.25cc", "data_mcar25mi", "data_mcar25si", "MCAR0.4cc", "data_mcar40mi", "data_mcar40si", "MCAR0.55cc", "data_mcar55mi", "data_mcar55si"))
#set theme for ggplot (will be overriden later)
theme_set(
theme_classic() +
theme(legend.position = "top")
)
#aggregate the data dropping the full dataset level and setting it as 0 or 1 for SE
res <- aggregate(cbind(`2.5 %`, `97.5 %`, estimate, std.error)~names,data=result,FUN=mean)
res = res[-1,] #remove the full datatset results
#add mechanism, number of iterations and proper names as separate columns
res$mech = rep(c("MAR", "MCAR"), times = c(12, 12))
res$num = rep(c("10", "25", "40", "55"), times = c(3, 3, 3, 3))
res$names = c("MAR CC", "MAR MI", "MAR SI", "MAR CC", "MAR MI", "MAR SI", "MAR CC", "MAR MI", "MAR SI", "MAR CC", "MAR MI", "MAR SI", "MCAR CC", "MCAR MI", "MCAR SI", "MCAR CC", "MCAR MI", "MCAR SI", "MCAR CC", "MCAR MI", "MCAR SI", "MCAR CC", "MCAR MI", "MCAR SI")
res$Type = rep(c("Complete case", "Multiple imputation", "Single imputation"), times = 8)
#set colours for plotting
cols = rep(c("black", "red2", "dodgerblue3"), times = 8)
#plot estimate
asd <- ggplot(res, aes(y = names, x = estimate)) +
geom_errorbar(aes(xmin = `2.5 %`, xmax = `97.5 %`),
color = cols, alpha = 0.7) +
geom_vline(aes(xintercept = 0), color = "red",
linewidth = 0.5, alpha = 0.8, linetype = "dashed") +
geom_point(color = cols, size = 1.5) +
facet_grid(num ~ ., scales = "free", space = "free") +
theme_light() +
theme(strip.background = element_blank(),
strip.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_text(colour = cols)) +
labs(x = "Estimate", y = "Missingness pattern")
#plot SE
asd1 <- ggplot(res, aes(y = std.error, x = names)) +
geom_segment( aes(x = names, xend=names, y=0, yend=std.error), color=cols) +
geom_hline(aes(yintercept = 1), color = "red",
linewidth = 0.5, alpha = 0.8, linetype = "dashed") +
geom_point( color=cols, size=2, alpha=0.6) +
theme_light() +
coord_flip() +
theme(
panel.grid.major.y = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y=element_blank(),
axis.title.y=element_blank()) +
facet_grid(num ~ ., scales = "free", space = "free") +
labs(y = "Std. error")
#combined plot
plot_grid(asd, asd1, labels = "AUTO", ncol = 2, rel_widths = c(4, 1.5), align="hv", hjust = 0, vjust = 1.5, scale = 1)
#after generating N plots combine them in a pdf of png or whatever
pdf("a4_output.pdf", paper="a4")
plot_grid(a1, a2, a3, labels = NULL, nrow = 3, align="hv")
dev.off()
#export summary tables
require(gt)
gt_tbl = res[,-1] #remove the abbreviated names
gt_tbl = gt_tbl[,c(7,6,5,3,4, 1, 2)] #reorder columns
colnames(gt_tbl)[colnames(gt_tbl) == 'num'] <- 'Missingness (percent)' #fix names
colnames(gt_tbl)[colnames(gt_tbl) == 'estimate'] <- 'Estimate'
colnames(gt_tbl)[colnames(gt_tbl) == 'std.error'] <- 'Std. error'
colnames(gt_tbl)[colnames(gt_tbl) == 'mech'] <- 'Missingness mechanism'
rownames(gt_tbl) = NULL #remove rownames
gt_tbl <- gt(gt_tbl)
gt_tbl
gt_tab = gt_tbl |> tab_header(
title = md("**Simulation results**"),
subtitle = md("Bias of the estimates, std.error and CIs of different imputation results compared to the original full dataset")) |>
cols_align(
align = "center")
#output file name
gtsave(gt_tab, "table3.docx")
#OR
#
# asd <- ggplot(res, aes(y = names, x = estimate)) +
# geom_errorbar(aes(xmin = `2.5 %`, xmax = `97.5 %`),
# color = cols, alpha = 0.7)+
# geom_vline(aes(xintercept = 0), color = "red",
# linewidth = 0.5, alpha = 0.8, linetype = "dashed") +
# geom_point(color = cols, size = 2) +
# coord_flip() +
# theme_light() +
# theme(panel.grid.major.y = element_blank(),
# #panel.border = element_blank(),
# panel.grid.minor.y = element_blank(),
# axis.title.x=element_blank()) +
# guides(x = "none") +
# facet_grid(.~ num, scales = "free", space = "free") +
# labs(y = "Estimate")
#
#
# asd1 <- ggplot(res, aes(y = std.error, x = names)) +
# geom_segment( aes(x = names, xend=names, y=0, yend=std.error), color=cols) +
# geom_point( color=cols, size=2, alpha=0.6) +
# theme_light() +
# theme(
# panel.grid.major.y = element_blank(),
# #panel.border = element_blank(),
# strip.background = element_blank(),
# strip.text.x = element_blank(),
# axis.text.x = element_text(colour = cols)) +
# facet_grid(.~ num, scales = "free", space = "free") +
# labs(y = "Std. error", x = "Missingness pattern")
#
#
# plot_grid(asd, asd1, labels = "AUTO", nrow = 2, rel_heights = c(3, 1), align="hv", hjust = -2, vjust = 1.5)
#Alternative
#
# bp <- ggplot(result[-c(25,50),], aes(x=names, y=estimate))+ geom_boxplot(fill="gray") + coord_cartesian(ylim = c(-0.025, 0.055)) + geom_hline(yintercept=0, color = "red") #geom_violin() + geom_jitter(height = 0, width = 0.1)
# bps <- ggplot(result[-c(25,50),], aes(x=names, y=std.error))+ geom_boxplot(fill="gray") + coord_cartesian(ylim = c(0, 0.025)) + geom_hline(yintercept=0, color = "red")#geom_violin() + geom_jitter(height = 0, width = 0.1)
# plot_grid(bp, bps, labels = "AUTO", nrow = 2)
#
# final <- ggplot(res[-1,], aes(x=names, y=estimate)) + geom_point() + geom_errorbar(aes(ymin = `2.5 %`, ymax = `97.5 %`)) + geom_hline(yintercept=0, color = "red")