mcmcMargEff | R Documentation |
Marginal effects plots for MCMC output using ggplot2
mcmcMargEff( mod, main, int, moderator, pointest = "mean", seq = 100, ci = 0.95, hpdi = FALSE, plot = TRUE, xlab = "Moderator", ylab = "Marginal Effect" )
mod |
Bayesian model object generated by R2jags, rjags, R2WinBUGS, R2OpenBUGS, MCMCpack, rstan, rstanarm, and brms. |
main |
a character with the name of the parameter of interest in the interaction term. |
int |
a character with the name of the moderating parameter in the interaction term. |
moderator |
a vector of values that the moderating parameter takes on in the data. |
pointest |
a character indicating whether to use the mean or median for point estimates in the plot. |
seq |
a numeric giving the number of moderator values used to generate the marginal effects plot. |
ci |
a scalar indicating the confidence level of the uncertainty intervals. |
hpdi |
a logical indicating whether to use highest posterior density intervals or equal tailed credible intervals to capture uncertainty. |
plot |
logical indicating whether to return a |
xlab |
character giving x axis label if |
ylab |
character giving y axis label if |
a ggplot
object or a tidy DataFrame.
Rob Williams, jayrobwilliams@gmail.com
if (interactive()) { ## simulating data set.seed(123456) b0 <- 0.2 # true value for the intercept b1 <- 0.5 # true value for first beta b2 <- 0.7 # true value for second beta n <- 500 # sample size X1 <- runif(n, -1, 1) X2 <- runif(n, -1, 1) Z <- b0 + b1 * X1 + b2 * X2 ## linear model data Y_linear <- rnorm(n, Z, 1) df <- data.frame(cbind(X1, X2, Y = Y_linear)) ## formatting the data for jags datjags <- as.list(df) datjags$N <- length(datjags$Y) ## creating jags model model <- function() { for(i in 1:N){ Y[i] ~ dnorm(mu[i], sigma) ## Bernoulli distribution of y_i mu[i] <- b[1] + b[2] * X1[i] + b[3] * X2[i] + b[4] * X1[i] * X2[i] } for(j in 1:4){ b[j] ~ dnorm(0, 0.001) ## Use a coefficient vector for simplicity } sigma ~ dexp(1) } params <- c("b") inits1 <- list("b" = rep(0, 4)) inits2 <- list("b" = rep(0, 4)) inits <- list(inits1, inits2) ## fitting the model with R2jags set.seed(123) fit <- R2jags::jags(data = datjags, inits = inits, parameters.to.save = params, n.chains = 2, n.iter = 2000, n.burnin = 1000, model.file = model) mcmcMargEff(mod = fit, main = 'b[2]', int = 'b[4]', moderator = sim_data_interactive$X2, plot = TRUE) }
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