View source: R/make.ts.ng.ppp.r
make.ts.ng.ppp | R Documentation |
Make two-sample normal-gamma prior/posterior plot
make.ts.ng.ppp( mu.0.c = 0, alpha.0.c = 2.5, beta.0.c = 10, n.0.c = 10, mu.0.t = 0, alpha.0.t = 0.25, beta.0.t = 1, n.0.t = 1e-04, xbar.c = 0.25, s.c = 1.5, n.c = 55, xbar.t = 2.5, s.t = 1.5, n.t = 55, limits = c(5, 25, 10) )
mu.0.c |
prior mean for control group |
alpha.0.c |
prior alpha parameter for control group |
beta.0.c |
prior beta parameter for control group |
n.0.c |
prior effective sample size parameter for control group |
mu.0.t |
prior mean for treatment group |
alpha.0.t |
prior alpha parameter for treatment group |
beta.0.t |
prior beta parameter for treatment group |
n.0.t |
prior effective sample size parameter for treatment group |
xbar.c |
control mean |
s.c |
control sd |
n.c |
control sample size |
xbar.t |
treatment mean |
s.t |
treatment sd |
n.t |
treatment sample size |
limits |
limits for visualizing precision, variance, standard deviation |
a ggplot object is returned
my.ts.ng.ppp <- make.ts.ng.ppp() my.ts.ng.ppp[[1]][[1]] my.ts.ng.ppp[[1]][[2]] my.ts.ng.ppp[[1]][[3]] my.ts.ng.ppp[[1]][[4]] gridExtra::grid.arrange(my.ts.ng.ppp[[1]][[1]], my.ts.ng.ppp[[1]][[3]], my.ts.ng.ppp[[1]][[2]], my.ts.ng.ppp[[1]][[4]]) my.ts.ng.ppp[[2]] my.ts.ng.ppp[[3]] my.ts.ng.ppp[[4]] my.ts.ng.ppp[[5]] my.ts.ng.ppp[[6]] my.ts.ng.ppp[[7]]
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