XTMEM
Meta-analysis of experiments
Molly Lewis
2017-01-25
Reproduce SPSS Fig. 3
(plotting proportion basic level)
Read in all previous data and bind to current dataset
files = dir("../data/")
all.d = data.frame()
for (i in 1:length(files)[1]) {
s <- read.csv(paste0("../data/", files[i]))
all.d = bind_rows(all.d, data.frame(s))
}
exp_key = read.csv("experiment_key.csv")Get means and plot
all.d$condition = plyr::mapvalues(all.d$condition,
from = c("one", "3bas", "3sub", "3sup"),
to = c("one", "three_basic",
"three_subordinate",
"three_superordinate"))
all.d$condition = as.factor(all.d$condition)
all.ms = all.d %>%
gather(variable, value, c(prop_sub, prop_bas, prop_sup)) %>%
group_by(condition,variable, exp, subids) %>%
mutate(value = as.numeric(value)) %>%
summarize(value = mean(value)) %>%
group_by(condition,variable, exp) %>%
multi_boot_standard(column = "value") %>%
ungroup() %>%
mutate(variable = as.factor(variable)) %>%
filter(condition == "one" | condition == "three_subordinate") %>%
filter(variable == "prop_bas") %>%
mutate(exp = as.factor(exp)) %>%
left_join(exp_key)
ggplot(all.ms, aes(x = exp, y = mean, group = condition, fill = condition)) +
geom_bar(position = "dodge", stat = "identity") +
geom_linerange(aes(ymin = ci_lower,
ymax = ci_upper),
position=position_dodge(width = .9)) +
facet_grid(order * one_3sub_label ~ timing) +
ylim(0,1)+
ylab("Proportion basic-level choices ") +
xlab("Experiment") +
theme_bw() +
theme(legend.title = element_blank())
same vs. different refers to whether the one and 3 subordinate trials recieved the same lable. 3–1 and 1–3 refers to the relative order of the one and 3 subordinate trials.
Plot effect size
Get effect sizes of our experiments.
all.ms.subj = all.d %>%
gather(variable, value, c(prop_sub, prop_bas, prop_sup)) %>%
group_by(condition,variable, exp, subids) %>%
mutate(value = as.numeric(value)) %>%
summarize(value = mean(value)) %>%
filter(condition == "one" | condition == "three_subordinate", variable == 'prop_bas') %>%
spread(condition, value) %>%
ungroup () %>%
select(-variable)
effect.sizes.npaired = all.ms.subj %>%
group_by(exp) %>%
summarize(m.one = mean(one),
sd.one = sd(one),
m.3sub = mean(three_subordinate),
sd.3sub = sd(three_subordinate),
n= n()) %>%
do(data.frame(d = mes(.$m.one, .$m.3sub, .$sd.one,
.$sd.3sub, .$n, .$n, verbose = F)$d,
var.d = mes(.$m.one, .$m.3sub, .$sd.one,
.$sd.3sub, .$n, .$n, verbose = F)$var.d))%>%
mutate(high = d + (1.96*var.d),
low = d - (1.96*var.d),
es.type = "nonpaired",
exp = as.numeric(c(1:8))) # fix this
effect.sizes = effect.sizes.npaired %>%
mutate(n = 50,
exp = as.factor(exp)) Calculate effect sizes from previous experiments. Here are the means and sd based on SPSS table 1.
original.effect.sizes = data.frame(exp = c("XT_adults_e1", "XT_children_e2",
"SPSS_e1",
"SPSS_e2",
"SPSS_e3",
"SPSS_eS1",
"SPSS_eS2"),
one_means = c(76, 40, 48.24, 30.83, 39.91, 24.56, 15.79),
three_means = c(9, 6, 10.53, 53.33, 51.75, 16.67, 11.40),
one_sd = c(40.40, 40.40, 40.40,37.18,35.20,32.09,24.51),
three_sd = c(24.97, 24.97, 24.97,36.11,42.63,25.45,24.25),
n = c(22, 36, 19, 20, 19,19,19))
kable(original.effect.sizes)| exp | one_means | three_means | one_sd | three_sd | n |
|---|---|---|---|---|---|
| XT_adults_e1 | 76.00 | 9.00 | 40.40 | 24.97 | 22 |
| XT_children_e2 | 40.00 | 6.00 | 40.40 | 24.97 | 36 |
| SPSS_e1 | 48.24 | 10.53 | 40.40 | 24.97 | 19 |
| SPSS_e2 | 30.83 | 53.33 | 37.18 | 36.11 | 20 |
| SPSS_e3 | 39.91 | 51.75 | 35.20 | 42.63 | 19 |
| SPSS_eS1 | 24.56 | 16.67 | 32.09 | 25.45 | 19 |
| SPSS_eS2 | 15.79 | 11.40 | 24.51 | 24.25 | 19 |
Calculate previous effect sizes
original.effect.sizes$d = mes(original.effect.sizes$one_means/100,
original.effect.sizes$three_means/100,
original.effect.sizes$one_sd/100,
original.effect.sizes$three_sd/100,
original.effect.sizes$n,
original.effect.sizes$n, verbose = F)$d
original.effect.sizes$d_var = mes(original.effect.sizes$one_means/100,
original.effect.sizes$three_means/100,
original.effect.sizes$one_sd/100,
original.effect.sizes$three_sd/100,
original.effect.sizes$n,
original.effect.sizes$n,
verbose = F)$var.d
original.effect.sizes = original.effect.sizes %>%
mutate(high = d + (1.96*d_var),
low = d - (1.96*d_var)) %>%
select(-one_means, -three_means, -one_sd, -three_sd) %>%
mutate(es.type = "nonpaired")Bind previous and current effect sizes together and plot
all.es = original.effect.sizes %>%
bind_rows(effect.sizes) %>%
left_join(exp_key)
ggplot(all.es, aes(x = exp, y = d)) +
geom_point(color = "red", size = 2)+
geom_linerange(aes(ymax =high, ymin=low),
position = position_dodge(width = 0), color = "red") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
facet_grid(order * one_3sub_label ~ timing, drop = TRUE, scales = "free_y")+
coord_flip() +
geom_hline(yintercept = 0, linetype = 2) +
ggtitle("Effect size") +
xlab("Experiment")
same vs. different refers to whether the one and 3 subordinate trials recieved the same lable. 3–1 and 1–3 refers to the relative order of the one and 3 subordinate trials.