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hoshinoyuji

Bridge sampling

Rdata data <- read.csv("~/Williams2017/Williams2017.csv") X1 <- data[data$C=="1",]$X N1 <- length(X1) X2 <- data[data$C=="2",]$X N2 <- length(X2) Stan options(mc.cores = parallel::detectCores()) library(rstan) rstan_options(auto_write =

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    • Posterior probability distributions

      Rdata dat <- read.csv("Williams2017.csv") X1 <- dat[dat$C=="1",]$X N1 <- length(X1) X2 <- dat[dat$C=="2",]$X N2 <- length(X2) dat$C <- factor(dat$C) library(psych) print(describeBy(dat$X, group=dat$C)) Stan options(mc.cores = parallel::

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      • BayesFactor: R package

        dataX1 <- c(3.3, 1, 2, 1.7, 3.7, 5.7, 3.7, 0.7, 4.7, 3.7, 7, 1.7, 6.3, 3.7, 3.3, 2.3, 4.7, 6.7) N1 <- length(X1) X2 <- c(6, 5.7, 6, 3.7, 5, 9, 6, 9, 2.3, 4.7, 5.7, 7.7, 6.7, 4.7, 5.7, 3.3, 6, 5, 6, 5, 5.3) N2 <- length(X1) data <- data.fram

        hoshinoyuji

      Bridge sampling

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