View source: R/make.ss.ng.ppp.r
make.ss.ng.ppp | R Documentation |
Make singple sample normal-gamma prior posterior plot
make.ss.ng.ppp( mu.0.t = 0, n.0.t = 1, alpha.0.t = 0.25, beta.0.t = 1, xbar.t = 1.75, s.t = 2, n.t = 50, gamma.IG.sd.limits = c(as.numeric(trimws(unlist(strsplit("5, 25, 25", ","))))) )
mu.0.t |
prior mean |
n.0.t |
prior effective sample size |
alpha.0.t |
prior alpha parameter |
beta.0.t |
prior beta parameter |
xbar.t |
sample mean for treatment group |
s.t |
sample sd for treatment group |
n.t |
sample size for treatment group |
gamma.IG.sd.limits |
limits used for Precision, Variance and standard deviation visualizers |
limits |
upper limits used for visualizations |
A ggplot object is returned
my.ss.ng.ppp <- make.ss.ng.ppp() my.ss.ng.ppp[[1]][[1]] my.ss.ng.ppp[[1]][[2]] gridExtra::grid.arrange(my.ss.ng.ppp[[1]][[1]], my.ss.ng.ppp[[1]][[2]], ncol=2) my.ss.ng.ppp[[2]] my.ss.ng.ppp[[3]] my.ss.ng.ppp[[4]] my.ss.ng.ppp[[5]] my.ss.ng.ppp[[6]] my.ss.ng.ppp[[7]]
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