OneSampleNormalGamma: Functions to Support the One Sample Continuous Scenario

In lylyf1987/GNGpkg:

Description

make.ss.ng.ppp: Make One Sample Normal-Gamma Prior/Posterior Plot. Returns a ggplot object.

make.ss.ng.spp: Make One Sample Normal-Gamma Shaded Posterior Plot. Returns a graphic built using grid.arrange.

get.ss.ng.trt.oc.df: Get One Sample Normal-Gamma Treatment Effect OC. Returns a data.frame.

make.ss.ng.trt.oc1: Make One Sample Normal-Gamma Treatment Effect. Returns a graphic built using grid.arrange.

make.ss.ng.trt.oc2: Make One Sample Normal-Gamma Treatment Effect. Returns a graphic built using grid.arrange.

get.ss.ng.ssize.oc.df: Get One Sample Normal-Gamma sample size OC data.frame. Returns a data.frame.

make.ss.ng.ssize.oc: Make One Sample Normal-Gamma Sample size OC plot

Usage

make.ss.ng.ppp(
  mu.0.t = 0,
  n.0.t = 10,
  alpha.0.t = 0.25,
  beta.0.t = 1,
  xbar.t = 1.75,
  s.t = 2,
  n.t = 50
)

make.ss.ng.spp(
  mu.0.t = 0,
  alpha.0.t = 0.25,
  beta.0.t = 1,
  n.0.t = 10,
  xbar.t = 1.97,
  s.t = 2,
  n.t = 20,
  Delta.lrv = 1.25,
  Delta.tv = 1.75,
  tau.tv = 0.1,
  tau.lrv = 0.8,
  tau.ng = 0.65,
  tsize = 4,
  nlines = 25,
  nlines.ria = 20
)

get.ss.ng.df(
  mu.0.t = 0,
  n.0.t = 10,
  alpha.0.t = 0.25,
  beta.0.t = 1,
  xbar.t = seq(-1, 5, 0.1),
  s.t = seq(1, 6, 0.1),
  n.t = 50,
  Delta.tv = 1.75,
  Delta.lrv = 1.5,
  tau.tv = 0.1,
  tau.lrv = 0.8,
  tau.ng = 0.65
)

get.ss.ng.trt.oc.df(
  mu.0.t = 0,
  n.0.t = 10,
  alpha.0.t = 0.25,
  beta.0.t = 1,
  s.t = 2,
  n.t = 40,
  from.here = 0,
  to.here = 4,
  Delta.tv = 1.75,
  Delta.lrv = 1,
  tau.tv = 0.1,
  tau.lrv = 0.8,
  tau.ng = 0.65
)

make.ss.ng.trt.oc1(my.df = get.ss.ng.trt.oc.df(), nlines = 25, tsize = 4)

make.ss.ng.trt.oc2(my.df = get.ss.ng.trt.oc.df(), nlines = 25, tsize = 4)

get.ss.ng.ssize.oc.df(
  mu.0.t = 3,
  n.0.t = 10,
  alpha.0.t = 0.25,
  beta.0.t = 1,
  s.t = 5,
  n.t = 50,
  n_LB_OC = floor(50 * 0.75),
  n_UB_OC = floor(50 * 2),
  npoints = 15,
  Delta.lrv = 2.5,
  Delta.tv = 4,
  Delta.user = 3,
  tau.tv = 0.1,
  tau.lrv = 0.8,
  tau.ng = 0.65
)

make.ss.ng.ssize.oc(for.plot = get.ss.ng.ssize.oc.df(), nlines = 25, tsize = 4)

Arguments

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	observed sample mean
s.t	observed sample standard deviation
n.t	sample size
Delta.lrv	TPP Lower Reference Value aka Min TPP
Delta.tv	TPP Target Value aka Base TPP
tau.tv	threshold associated with Base TPP
tau.lrv	threshold associated with Min TPP
tau.ng	threshold associated with No-Go
tsize	Control for text size
nlines	Control for text spacing
nlines.ria	Control for text spacing
from.here	Lower bound for treatment effect
to.here	Upper bound for treatment effect
my.df	data.frame returned by get.ss.ng.trt.oc.df
n_LB_OC	Lower bound for sample size
n_UB_OC	Upper bound for sample size
npoints	Number of points to use in plot
seed	random seed

Examples

## Not run: 
make.ss.ng.ppp()
make.ss.ng.spp()
get.ss.ng.trt.oc.df()
make.ss.ng.trt.oc1()
make.ss.ng.trt.oc2()
get.ss.ng.ssize.oc.df()
make.ss.ng.ssize.oc()

## End(Not run)

lylyf1987/GNGpkg documentation built on May 19, 2020, 12:07 a.m.