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1-data_analyze.R
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1-data_analyze.R
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library(dplyr)
library(magrittr)
library(tidyr)
library(gdata)
library(stringr)
library(brms)
library(MASS)
library(bayesplot)
library(tidybayes)
######################################### CORRECT RESPONSES ANALYSIS #########################################
df_ncr <- readRDS("df_ncr.rds")
### contrast coding for the predictors ###
contrasts(df_ncr$category)
contrasts(df_ncr$category) <- contr.sdif(2)
contrasts(df_ncr$category)
contrasts(df_ncr$cat2)
contrasts(df_ncr$cat2) <- contr.sum(3)/2
contrasts(df_ncr$cat2)
contrasts(df_ncr$difficulty)
contrasts(df_ncr$difficulty) <- contr.sdif(3)
contrasts(df_ncr$difficulty)
# reorder factor for contrast setting
df_ncr$group %<>% reorder.factor(new.order = c("Late","Native"))
contrasts(df_ncr$group)
contrasts(df_ncr$group) <- contr.sdif(2)
contrasts(df_ncr$group)
# model responses
ncr_model <- brm(ncr ~ cat2*difficulty*group,
family = poisson(link="log"), data = df_ncr,
chains = 4, cores = 4, iter= 3000, warmup = 2000, file = "ncr_model")
# model df for plotting
ncr_model_df <- ncr_model %>%
mcmc_intervals_data(pars = vars(starts_with("b_")), prob_outer = 0.95) %>%
separate(col = "parameter", into = c("x","parameter"), sep = "\\_") %>%
subset(parameter !="Intercept") %>% dplyr::select(-x)
# recode levels to make readable
ncr_model_df$parameter %<>% dplyr::recode(`cat21` = "HS",`cat22` = "LOC", `group2M1` = "Native",
`difficulty2M1` = "Medium-Easy", `difficulty3M2` = "Hard-Medium",
`cat21:difficulty2M1` = "HS*Med-Easy",
`cat21:difficulty3M2` = "HS*Hard-Med",
`cat22:difficulty2M1` = "LOC*Med-Easy",
`cat22:difficulty3M2` = "LOC*Hard-Med",
`cat21:group2M1`= "HS*Native",
`cat22:group2M1`= "LOC*Native",
`difficulty2M1:group2M1` = "Med-Easy*Native",
`difficulty3M2:group2M1` = "Hard-Med*Native",
`cat21:difficulty2M1:group2M1` = "HS*M-E*Native",
`cat21:difficulty3M2:group2M1` = "HS*H-M*Native",
`cat22:difficulty2M1:group2M1` = "LOC*M-E*Native",
`cat22:difficulty3M2:group2M1` = "LOC*H-M*Native") %>%
reorder.factor(new.order = c("HS", "LOC", "Native", "Medium-Easy",
"Hard-Medium", "HS*Med-Easy", "HS*Hard-Med", "LOC*Med-Easy", "LOC*Hard-Med",
"HS*Native", "LOC*Native", "Med-Easy*Native", "Hard-Med*Native",
"HS*M-E*Native", "HS*H-M*Native", "LOC*M-E*Native", "LOC*H-M*Native"))
saveRDS(ncr_model_df, "ncr_model_df.rds")
write.csv(ncr_model_df,"ncr_model_results.csv")
############################################ TIME COURSE ANALYSIS ############################################
df_time <- readRDS("df_time.rds")
### contrast coding for the predictors ###
contrasts(df_time$category)
contrasts(df_time$category) <- contr.sdif(2)
contrasts(df_time$category)
contrasts(df_time$cat2)
contrasts(df_time$cat2) <- contr.sum(3)/2
contrasts(df_time$cat2)
contrasts(df_time$difficulty)
contrasts(df_time$difficulty) <- contr.sdif(3)
contrasts(df_time$difficulty)
df_time$group %<>% reorder.factor(new.order = c("Late","Native"))
contrasts(df_time$group)
contrasts(df_time$group) <- contr.sdif(2)
contrasts(df_time$group)
df_time$time %<>% as.factor()
contrasts(df_time$time)
contrasts(df_time$time) <- contr.sdif(6)
contrasts(df_time$time)
# regression model
time_model <- brm(time_cum ~ cat2*group*time*difficulty,
family = poisson(link="log"), data = df_time,
chains = 4, cores = 4, iter= 3000, warmup = 2000, file = "time_model")
# time model df
time_model_df <- time_model %>%
mcmc_intervals_data(pars = vars(starts_with("b_")), prob_outer = 0.95) %>%
separate(col = "parameter", into = c("x","parameter"), sep = "\\_") %>%
subset(parameter !="Intercept") %>% dplyr::select(-x)
# recode parameters for easy reading
time_model_df %<>% subset(parameter %in% c("group2M1","time2M1","time3M2",
"time4M3","time5M4","time6M5",
"group2M1:time2M1","group2M1:time3M2",
"group2M1:time4M3","group2M1:time5M4",
"group2M1:time6M5"))
time_model_df$parameter %<>% dplyr::recode(`group2M1` = "Native", `time2M1` = "20s-10s",`time3M2` = "30s-20s",
`time4M3` = "40s-30s", `time5M4` = "50s-40s", `time6M5` = "60s-50s",
`group2M1:time2M1` = "Native*20s-10s", `group2M1:time3M2` = "Native*30s-20s",
`group2M1:time4M3` = "Native*40s-30s", `group2M1:time5M4` = "Native*50s-40s",
`group2M1:time6M5` = "Native*60s-50s") %>%
reorder.factor(new.order = c("Native","20s-10s","30s-20s","40s-30s","50s-40s","60s-50s",
"Native*20s-10s","Native*30s-20s","Native*40s-30s","Native*50s-40s","Native*60s-50s"))
saveRDS(time_model_df, "time_model_df.rds")
write.csv(time_model_df,"time_model_results.csv")