inst/doc/adaptation_level.R

In bayes4psy: User Friendly Bayesian Data Analysis for Psychology

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# knitr options
knitr::opts_chunk$set(fig.width=6, fig.height=4.5)
options(width=800)

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# libs
library(bayes4psy)
library(dplyr)
library(ggplot2)

# load data
data <- adaptation_level

# separate groups and parts
group1_part2 <- data %>% filter(group == 1 & part == 2)
group2_part2 <- data %>% filter(group == 2 & part == 2)

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# fit
fit1 <- b_linear(x=group1_part2$sequence,
                 y=group1_part2$response,
                 s=group1_part2$subject,
                 iter=200, warmup=100, chains=1)

fit2 <- b_linear(x=group2_part2$sequence,
                 y=group2_part2$response,
                 s=group2_part2$subject,
                 iter=200, warmup=100, chains=1)

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# trace plots
plot_trace(fit1)
plot_trace(fit2)

# the commands below are commented out for the sake of brevity
#print(fit1)
#print(fit2)

# visual inspection
plot(fit1)
plot(fit2)

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summary(fit1)
summary(fit2)

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comparison_results <- compare_means(fit1, fit2=fit2)

plot_means_difference(fit1, fit2=fit2, par="intercept")

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plot_distributions(fit1, fit2) +
  labs(title="Part II", x="measurement number", y="") +
  theme(legend.position="none") +
  scale_x_continuous(limits=c(1, 10), breaks=seq(1:10)) +
  ylim(0, 10)