In arcuo/bachelors_meta: Bayesian Hierarchical Latent Mixture model meta analysis and plots.

library(BachelorProject)

rm(list = ls() )

dat = EWI

outcomeOptions <- c("Physical", "Psychological")
outcomeOptionsPriors <- matrix(c("dnorm", 0, 1), length(outcomeOptions), 3, byrow = TRUE)

a = metaCategorize(dat, c("StudNo", "OutcomeNo"),
               "Hedges.s.g", "Outcome2",
               outcomeOptions,
               BayesMethod = "jags",
               create.outcomePriors = outcomeOptionsPriors
               )

rm(list = ls() )

dat2 = CBT

dat2$Outcome2 = as.factor(dat2$Outcome)
levels(dat2$Outcome2) = c("outcome1", "outcome2", "outcome3", "outcome4", "outcome5")

outcomeOptions2 <- c("outcome1", "outcome2", "outcome3")
outcomeOptionsPriors2 <- matrix(c("dnorm", 0, 1), length(outcomeOptions2), 3, byrow = TRUE)

a = metaCategorize(dat2, c("StudNo", "OutcomeNo"),
                   "Hedges.s.g", 
                   "Outcome2", outcomeOptions2,
                   BayesMethod = "jags",
                   create.outcomePriors = outcomeOptionsPriors2
)

a = getValuableData(dat2, c("StudNo", "OutcomeNo"),
                    "Hedges.s.g", 
                    "Outcome2", outcomeOptions2)

arcuo/bachelors_meta documentation built on May 5, 2019, 2:41 a.m.