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library(brms) # for the analysis
library(haven) # to load the SPSS .sav file
library(tidyverse) # needed for data manipulation.
library(RColorBrewer) # needed for some extra colours in one of the graphs
library(ggmcmc)
library(ggthemes)
library(ggridges)\[Y = N(\mu, \sigma_{\epsilon}^{2})\] \[\mu = \alpha + \beta_{Tm}Tm + \beta_{TD}TD + \beta_{Sm}Sm+\beta_{Sd}Sd + \epsilon\] Where Tm, Td, Sm, and Sd are covariates.
Tm: Trait mean Td: Triat difference (uncon - con) Sm: State mean Sd: State difference (uncon - con)
\[N(0,100): \alpha\] \[N(0,100): \beta_{Tm}\] \[N(0,100): \beta_{Td}\] \[N(0,100): \beta_{Sm}\] \[N(0,100): \beta_{Sd}\] \[Cauchy(0,100): \sigma_{\epsilon}\]
setwd("C:/Users/Chirag/Box/Box/UMD/Project_UMD/eCON/RBA/uncon_v_con_rBNST_with_covariates")
df <- read.table('uncon_v_con_rBNST_with_covariates.txt',header=TRUE)
prior1 <- c(prior(normal(0,100),class=Intercept),
prior(normal(0,100),class=b, coef="TRAITmean"),
prior(normal(0,100),class=b, coef="TRAITdiff"),
prior(normal(0,100),class=b, coef="STATEmean"),
prior(normal(0,100),class=b, coef="STATEdiff"),
prior(cauchy(0,100),class=sigma)
)
bmod1 <- brm(Y ~ TRAITmean + TRAITdiff + STATEmean + STATEdiff,
data = df,
family = gaussian(),
prior = prior1,
warmup = 2000, iter = 5000,
chains = 4,
cores = 2)Compiling the C++ model
recompiling to avoid crashing R session
Start samplingsummary(bmod1) Family: gaussian
Links: mu = identity; sigma = identity
Formula: Y ~ TRAITmean + TRAITdiff + STATEmean + STATEdiff
Data: df (Number of observations: 61)
Samples: 4 chains, each with iter = 5000; warmup = 2000; thin = 1;
total post-warmup samples = 12000
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 0.11 0.06 -0.01 0.23 1.00 15556 8385
TRAITmean -0.05 0.08 -0.21 0.11 1.00 11028 9079
TRAITdiff 0.07 0.06 -0.06 0.19 1.00 12712 8581
STATEmean 0.21 0.08 0.05 0.36 1.00 10850 9512
STATEdiff 0.05 0.06 -0.07 0.18 1.00 13098 8541
Family Specific Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma 0.48 0.05 0.40 0.58 1.00 13886 9365
Samples were drawn using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).