-
Notifications
You must be signed in to change notification settings - Fork 0
/
4C12_Meta-analysis.R
405 lines (256 loc) · 12.3 KB
/
4C12_Meta-analysis.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
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
############ NT Bio4C12 SSD and SShD Meta-analysis #######################
# Libraries
library(dplyr)
library(ggplot2)
library(tidyverse)
library(brms)
library(tidybayes)
library(tidyr)
library(HardyWeinberg)
library(emmeans)
library(bayesplot)
library(reshape2)
library(RColorBrewer)
# Setting working directory
setwd("~/OneDrive - McMaster University/SSD and SShD Meta-analysis/Data")
# Reading in data-set
metadata <- read.csv("metadata_4c12.csv")
# Changing the phyla for Bonduriansky
metadata$Phyla <- with(metadata, ifelse(Study == "Bonduriansky", "Arthropoda", Phyla))
# Strata Type for Houle et al.
metadata$Strata_Type <- with(metadata, ifelse(is.na(Strata_Type), "None", Strata_Type))
# Make male-biased positive
metadata$SSD <-metadata$SSD*(-1)
# Add new column with info about female or male bias
metadata$Biased <- with(metadata, ifelse(SSD*(-1) < 0, "Male", "Female"))
# Convert to factor
metadata[,c(3, 4, 5, 6, 7, 8, 9, 13, 12, 25)] <- lapply(metadata[,c(3, 4, 5, 6, 7, 8, 9, 13, 12, 25)], factor)
# Taking the abolute value of SSD
metadata$SSD <- abs(metadata$SSD)
# Sample sizes
with(metadata, table(Phyla, Biased))
# Remove one Chordata female-based species for now (till more data-sets are added)
metadata <- metadata %>%
filter((Phyla == "Chordata" & Biased == "Male")| Phyla == "Arthropoda")
# Create interaction term for phyla and biased
metadata$phyla_biased <- with(metadata, interaction(Phyla, Biased))
##################### Analyzing the bootstrap samples ######################
# Calculate sample sizes (back from Fisher's standard errors)
metadata$Sample.Sizes <- with(metadata, 1/(Fisher_SE)^2 - 3)
# Bootstrap standard errors for SSD
png(filename = "SSDBoot.png", width = 20, height = 15, units = "cm", res = 300)
ggplot(metadata, aes(x = Sample.Sizes, y = SSD.SE)) + geom_point() + labs(x = "Sample Sizes", y = "SSD Bootstrapped Standard Error") + theme_bw() + xlim(c(0, 250))
dev.off()
png(filename = "SShDBoot.png", width = 20, height = 15, units = "cm", res = 300)
ggplot(metadata, aes(x = Sample.Sizes, y = SShD.SE)) + geom_point() + labs(x = "Sample Sizes", y = "SShD Bootstrapped Standard Error") + theme_bw() + xlim(c(0, 250))
dev.off()
ggplot(metadata, aes(x = Sample.Sizes, y = Mag_Diff_SE)) + geom_point() + labs(x = "Sample Sizes", y = "SShD Bootstrapped Standard Error") + theme_bw() + xlim(c(0, 1000))
#############################################################################
# Some exploratory plots
# SSD vs Phyla
ggplot(metadata, aes(x = Biased, y = SSD)) + geom_boxplot(aes(colour= Phyla)) + labs(x = "Biased", y = "SSD") + theme_bw()
# SSD in male and female biased organisms (Sexually Selected as Strata)
ggplot(metadata, aes(x = Biased, y = SSD)) + geom_boxplot(aes( colour = Sexually_Selected, fill = Phyla)) + labs(x = "Bias", y = "SSD") + theme_bw()
# SSD in different phyla by bias
ggplot(metadata, aes(x = Phyla, y = SSD)) + geom_boxplot(aes(colour= Biased)) + labs(x = "Phyla", y = "SSD") + theme_bw()
ggplot(metadata, aes(x = Sexually_Selected, y = SSD)) + geom_boxplot(aes(colour= interaction(Phyla, Biased))) + labs(x = "Strata Type", y = "SSD") + theme_bw()
# SSD vs SShD
ggplot(metadata, aes(x = Biased, y = SShD)) + geom_boxplot(aes(colour = Phyla)) + labs(x = "Bias", y = "SShD") + theme_bw()
ggplot(metadata, aes(x = Biased, y = SShD)) + geom_point(aes(colour= Sexually_selected)) + labs(x = "Biased", y = "SShD") + theme_bw()
# SShD vs Strata-Type
ggplot(metadata, aes(x = Sexually_Selected, y = SShD)) + geom_boxplot(aes(colour= interaction(Phyla, Biased))) + labs(x = "Strata Type", y = "SShD") + theme_bw()
ggplot(metadata, aes(x = SSD, y = SShD)) + geom_boxplot(aes(colour= interaction(Phyla, Biased))) + labs(x = "Strata Type", y = "SShD") + theme_bw()
# SShD sexually selected
ggplot(metadata, aes(x = Sexually_Selected, y = SShD)) + geom_boxplot(aes(colour= Phyla)) + labs(x = "Sexually Selected", y = "SShD") + theme_bw()
# Vector correlations by strata
# Phyla
png(filename = "CorrOB.png", width = 20, height = 15, units = "cm", res = 300)
ggplot(metadata, aes(x = Corr)) + geom_density(aes(fill = Phyla), alpha = 0.5) +
labs(title = "Observed Distribution of Vector Correlations")
dev.off()
png(filename = "CorrOB.png", width = 20, height = 15, units = "cm", res = 300)
ggplot(metadata, aes(x = Corr)) + geom_histogram(aes(fill = Phyla), alpha = 0.5,
binwidth = 0.1)
dev.off()
ggplot(metadata, aes(x = SShD)) + geom_density()
# Phyla biased
ggplot(metadata, aes(x = Corr)) + geom_density(aes(fill = phyla_biased)) + labs(x = "Strata Type", y = "Corr")
ggplot(metadata, aes(x = Corr)) + geom_density()
ggplot(metadata, aes(x = FisherZ)) + geom_density(aes(fill = Strata_Type)) + labs(x = "Strata Type", y = "Corr")
ggplot(metadata, aes(x = FisherZ)) + geom_density()
ggplot(metadata, aes(x = Sexually_Selected, y = Mag_Diff)) + geom_boxplot(aes(colour= Phyla)) + labs(x = "Strata Type", y = "Corr") + theme_bw()
ggplot(metadata, aes(x = Biased, y = Corr)) + geom_boxplot(aes(colour= Biased)) + labs(x = "Biased", y = "Corr") + theme_bw()
ggplot(metadata, aes(x = Corr)) + geom_density()
# Log SSD vs bootstrapped standard error
with(metadata, plot(SSD.SE, SSD))
# Extracting the approx sample sizes from Fisher SE
metadata$Sample.Sizes <- (1/(metadata$Fisher_SE)^2) + 3
# Sample sizes vs SSD.SE
with(metadata, plot(SSD.SE, Sample.Sizes))
# Sample sizes vs SShD.SE
with(metadata, plot(SShD.SE, Sample.Sizes))
ggplot(metadata, aes(x = Corr)) + geom_density()
metadata$SSD <- abs(metadata$SSD)
# Some basic models
##################### Model for SSD #################################
# Setting priors
priors <- c(prior(normal(0, 2), class = Intercept),
prior(normal(0, 5), class = "b"))
SSD_fit <- brm(data = metadata, family = student(),
formula = SSD|trunc(lb = 0)| se(SSD.SE, sigma = TRUE) ~ Phyla + Sexually_Selected + (1|Study), seed = 801, iter = 20000, warmup = 1000, inits = "random", chains = 1,
prior = priors)
# Posterior predictive check
png(filename = "pp_checkSSD.png", width = 20, height = 15, units = "cm", res = 300)
pp_check(SSD_fit, nsamples = 100) + xlim(c(-0.3, 0.3))
dev.off()
#creating conditional effects object
c_eff <- conditional_effects(SSD_fit)
# Plot of phyla and Sexually_Selected effects
dat_1 <- conditional_effects(SSD_fit)[[1]]
png(filename = "SShDss.png", width = 20, height = 15, units = "cm", res = 300)
p1 <-
conditional_effects(SSD_fit,
effects = "Phyla", points = TRUE)
plot(p1,
plot = F)[[1]] +
theme_bw() +
theme(panel.grid.minor = element_blank()) +
labs(y = "SShD", colour = "Sexually Selected", x = "Phyla and Direction of Bias") + theme_bw()
dev.off()
conditions <- data.frame(Biased = c("Female", "Male"))
# Grouped posterior distributions
y <- metadata$SSD
yrep1 <- posterior_predict(SSD_fit, nsamples = 1000)
png(filename = "SSD_Biased.png", width = 20, height = 15, units = "cm", res = 300)
color_scheme_set("purple")
ppc_stat_grouped(metadata$SSD, yrep1, stat = "median", group = metadata$Biased)
dev.off()
conditional_effects(SSD_fit, effects = "Phyla:Sexually_Selected")
################### Model for SShD #################################
# Setting priors
priors <- c(prior(normal(0, 10), class = Intercept),
prior(normal(0,5), class = "b"))
# Student's t
# Recovers kurtosis beautifully!!
SShD_fit2 <- brm(data = metadata, family = student(),
formula = SShD|trunc(lb = 0) ~ SSD + (1|Study),seed = 800, iter = 15000, warmup = 1000, inits = "random", chains = 1)
summary(SShD_fit2)
# Posterior predictive check
png(filename = "pp_checkSShD.png", width = 20, height = 15, units = "cm", res = 300)
pp_check(SShD_fit2, nsamples = 10) + xlim(c(0,0.1))
dev.off()
# Grouped posterior predictive plots
y <- metadata$SShD
yrep1 <- posterior_predict(SShD_fit2, nsamples = 1000)
png(filename = "SShD_Phyla1.png", width = 20, height = 15, units = "cm", res = 300)
color_scheme_set("blue")
ppc_stat_grouped(metadata$SShD, yrep1, stat = 'median', group = metadata$phyla_biased) +
legend_none()
dev.off()
ggsave(filename = "plots/ppc_skew1.png", width = 4.5, height = 3.75)
# Pareto k values
SShD_loo <- loo(SShD_fit1, save_psis = TRUE, cores= 2)
# No bad pareto k values
plot(SShD_loo)
conditions <- data.frame(phyla_biased = c("Arthropoda.Female","Arthropoda.Male","Chordata.Male"))
conditions <- data.frame(Phyla= c("Chordata", "Arthropoda"))
# Figures
png(filename = "SShDss.png", width = 20, height = 15, units = "cm", res = 300)
p1 <-
conditional_effects(SShD_fit2,
effects = "phyla_biased:Sexually_Selected")
plot(p1,
plot = F)[[1]] +
theme_bw() +
theme(panel.grid.minor = element_blank()) +
labs(y = "Shape Dimorphism", x = "Size Dimorphism")
dev.off()
png(filename = "SShDfit.png", width = 20, height = 15, units = "cm", res = 300)
p1 <-
conditional_effects(SShD_fit2,
effects = "SSD:phyla_biased", spaghetti = TRUE, nsamples = 200)
plot(p1, points = TRUE, point_args = c(alpha = 1/2, size = 1),
plot = F)[[1]] +
theme_bw() +
theme(panel.grid.minor = element_blank()) +
labs(y = "SShD", colour = "Phyla and Direction of Bias")
dev.off()
nb.cols <- 18
mycolors <- colorRampPalette(brewer.pal(8, "Dark2"))(nb.cols)
png(filename = "SSDvsSShDStudy.png", width = 20, height = 15, units = "cm", res = 300)
metadata %>%
group_by(phyla_biased) %>%
add_fitted_draws(SShD_fit2, n = 100) %>%
ggplot(aes(x = SSD, y = SShD, color = ordered(phyla_biased))) +
geom_line(aes(y = .value, group = paste(phyla_biased, .draw)), alpha = .1) +
geom_point(data = metadata, aes(alpha = 0.0000000001, shape = ".")) +
scale_colour_manual(values = mycolors) + theme_bw()
dev.off()
################## Model for correlation ############################
corr_fit <- brm(data = metadata, family = skew_normal(),
bf(FisherZ|se(Fisher_SE, sigma = TRUE) ~ phyla_biased + Sexually_Selected + (1|Study)), seed =800, iter = 20000, warmup = 1000, inits = "random", chains = 1)
corr_fit <- brm(data = metadata, family = Beta(),
Corr ~ phyla_biased + Sexually_Selected + (1|Study), seed =800, iter = 20000, warmup = 1000, inits = "random", chains = 1)
plot(corr_fit)
summary(corr_fit)
png(filen)
y <- metadata$FisherZ
yrep <- posterior_predict(corr_fit, draws = 1000)
yrep <- ifisherz(yrep)
# Model fit checks
png(filename = "pp_checkCorrfit.png", width = 20, height = 15, units = "cm", res = 300)
pp_check(corr_fit, nsamples = 100)
dev.off()
png(filename = "DistCorrfit3.png", width = 20, height = 15, units = "cm", res = 300)
color_scheme_set("viridis")
ppc_stat(metadata$Corr, yrep, stat = "median")
dev.off()
pp <- posterior_predict(SShD_fit2)
pp <- transpose(data.frame(pp)
hist(pp$X3)
ppc_intervals(metadata$Corr, yrep)
# Pareto k values
corr_loo <- loo(corr_fit, save_psis = TRUE, cores= 2)
# No bad pareto k values
plot(corr_loo)
data <- ppc_data(y, yrep)
# Some conditional effects plots
posterior <- as.matrix(corr_fit)
mcmc_areas(posterior,
pars = c("b_phyla_biasedArthropoda.Male", "b_phyla_biasedChordata.Male"),
prob = 0.8)
plot(conditional_effects(corr_fit, effects = "Sexually_Selected:Strata_Type"))
draws <- metadata %>%
group_b(Study) %>%
add_fitted_draws(SShD_fit1, dpar = TRUE) %>%
ggplot(aes(y = condition)) +
stat_dist_slab(aes(dist = "norm", arg1 = mu, arg2 = sigma),
slab_color = "gray65", alpha = 1/10, fill = NA
) +
geom_point(aes(x = response), data = ABC, shape = 21, fill = "#9ECAE1", size = 2)
save.image("thesis.RData")
# Contrasts
tt <- corr_fit %>%
emmeans( ~ Strata_Type) %>%
contrast(method = "pairwise") %>%
gather_emmeans_draws() %>%
median_qi()
png(filename = "ContSShDfit1.png", width = 20, height = 15, units = "cm", res = 300)
SShD_fit2 %>%
emmeans( ~ Sexually_Selected) %>%
contrast(method = "pairwise") %>%
gather_emmeans_draws() %>%
ggplot(aes(x = .value, y = contrast)) +
stat_halfeye(colour = "black") + theme_bw() +
labs(x = "Values", y = "Posterior Distribution of Contrasts")
dev.off()
# Attempt to plot the posterior distributions
pp <- melt(yrep)
pp$index <- rep(seq(1, 546, 1), 19000)
pp$Phyla <- rep(metadata$Phyla, 19000)
# Stratified samples
ss <- pp %>%
group_by(Phyla) %>%
sample_n(1000)
ss <- data.frame(ss)
ggplot(ss, aes(x = value)) + geom_histogram(aes(colour = Phyla, alpha = 0.1))