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Applying hedges G. #1

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32 changes: 20 additions & 12 deletions scripts/Task_Force_SpecCurve.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -105,29 +105,37 @@ res_ttest_d <- data %>% filter(stimulus %in% c('cspe', 'csm')) %>%
group_by(approach) %>%
rstatix::cohens_d(scr_mean ~ stimulus, data = .,paired = T,ci = T)

# effect size (using Hedges G)
res_ttest_g <- data %>% filter(stimulus %in% c('cspe', 'csm')) %>%
group_by(approach) %>%
rstatix::cohens_d(scr_mean ~ stimulus, data = .,paired = T,ci = T,hedges.correction = TRUE)

### For positive Cohen's d: convert ESs and CIs to absolute values
res_ttest_d$effsize <- abs(res_ttest_d$effsize)
res_ttest_d$conf.low <- abs(res_ttest_d$conf.low)
res_ttest_d$conf.high <- abs(res_ttest_d$conf.high)
res_ttest_g$effsize <- abs(res_ttest_g$effsize)
res_ttest_g$conf.low <- abs(res_ttest_g$conf.low)
res_ttest_g$conf.high <- abs(res_ttest_g$conf.high)

# add index to order by effect size
# we have n + 1 unique approaches
res_ttest_d$approach_num <- 1:nrow(res_ttest_d)
res_ttest_d[order(res_ttest_d$effsize),'approach_eff_order'] <- 1:nrow(res_ttest_d)
res_ttest_g$approach_num <- 1:nrow(res_ttest_g)
res_ttest_g[order(res_ttest_g$effsize),'approach_eff_order'] <- 1:nrow(res_ttest_g)

### Add columns for transformation (log, sqrt, box cox, z-transformation) and range correction type (none, CS corrected, US corrected)
res_ttest_d$transf_type <- str_split_fixed(res_ttest_d$approach, "_", 3)[,2]
res_ttest_d$rc_type <- str_split_fixed(res_ttest_d$approach, "_", 3)[,3]
res_ttest_d$rc_type[res_ttest_d$rc_type == ""] <- "none"
res_ttest_g$transf_type <- str_split_fixed(res_ttest_g$approach, "_", 3)[,2]
res_ttest_g$rc_type <- str_split_fixed(res_ttest_g$approach, "_", 3)[,3]
res_ttest_g$rc_type[res_ttest_g$rc_type == ""] <- "none"

```

```{r ttest-Bayesian}

appr_list <- names(data_long)[grep("^scr_", names(data_long))]
appr_list <- appr_list[appr_list != "scr_max_cs" & appr_list != "scr_max_us"]

n_appr = length(appr_list)

#*****
n_its <- 1000 # number of iterations, should this be 1000000? Then I run into problems, too big.
n_its <- 10000 # number of iterations, should this be 1000000? Then I run into problems, too big.
res_tBF <- list(approach = NA,
ttest_BF = NA,
ttest_BF_post = NA)
Expand Down Expand Up @@ -215,11 +223,11 @@ fig_res <- 300

# labels
x_lab <- 'Approach number ordered by effect size'
y_lab <- 'Effect size (Cohen\'s D)'
y_lab <- 'Effect size (Hedge\'s G)'

# order by effect size
# colored by approach
p1 <- res_ttest_d %>%
p1 <- res_ttest_g %>%
ggplot(aes(x = approach_eff_order, y = effsize, color = transf_type)) +
geom_point() +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high)) +
Expand All @@ -235,7 +243,7 @@ p1 <- res_ttest_d %>%


# plot specifications of parameters
s1 <- ggplot(res_ttest_d, aes(x = approach_eff_order, y = rc_type)) +
s1 <- ggplot(res_ttest_g, aes(x = approach_eff_order, y = rc_type)) +
geom_point(shape = '|', size = size_point) +
#scale_color_manual(values = rep('black', length(levels(res_ttest_d$approach)) )) +
scale_y_discrete(name = 'Range correction', labels = c("none", "CS corrected", "US corrected")) +
Expand Down