TwoSampleNormalGamma: Functions to Support the Two Sample Continuous Scenario

In lylyf1987/GNGpkg:

Description

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

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

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

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

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

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

make.ts.ng.ssize.oc: Make Two Sample Normal-Gamma Sample size OC plot

Usage

make.ts.ng.ppp(
  mu.0.c = 0,
  alpha.c = 0.25,
  beta.c = 1,
  n.0.c = 1,
  mu.0.t = 0,
  alpha.t = 0.25,
  beta.t = 1,
  n.0.t = 1,
  xbar.c = 1.5,
  s.c = 4,
  n.c = 40,
  xbar.t = 3,
  s.t = 4,
  n.t = 40
)

make.ts.ng.spp(
  mu.0.c = 0,
  alpha.c = 0.25,
  beta.c = 1,
  n.0.c = 1,
  mu.0.t = 0,
  alpha.t = 0.25,
  beta.t = 1,
  n.0.t = 1,
  xbar.c = 1.5,
  s.c = 4,
  n.c = 40,
  xbar.t = 26,
  s.t = 4,
  n.t = 40,
  Delta.lrv = 1,
  Delta.tv = 1.5,
  tau.ng = 0.65,
  tau.lrv = 0.8,
  tau.tv = 0.1,
  seed = 1234,
  n.MC = 1000,
  nlines = 25,
  nlines.ria = 20,
  tsize = 4
)

get.ts.ng.mc.df(
  mu.0.c = 0,
  n.0.c = 10,
  alpha.0.c = 0.25 * 4,
  beta.0.c = 1 * 4,
  xbar.c = seq(-3, 3, length.out = 20),
  s.c = 3,
  n.c = 25,
  group.c = "Control",
  mu.0.t = 0,
  n.0.t = 10,
  alpha.0.t = 0.25 * 4,
  beta.0.t = 1 * 4,
  xbar.t = seq(0, 6, length.out = 20),
  s.t = 2,
  n.t = 25,
  group.t = "Treatment",
  Delta.tv = 1.75,
  Delta.lrv = 1.5,
  tau.tv = 1,
  tau.lrv = 1,
  tau.ng = 0.65,
  n.MC = 1000,
  seed = 1234
)

get.ts.ng.mc(
  mu.0.c = 1.5,
  n.0.c = 1,
  alpha.0.c = 0.25,
  beta.0.c = 1,
  xbar.c = 1.5,
  s.c = 1,
  n.c = 25,
  group.c = "Control",
  mu.0.t = 1.75,
  n.0.t = 1,
  alpha.0.t = 0.25,
  beta.0.t = 1,
  xbar.t = 0,
  s.t = 1,
  n.t = 25,
  group.t = "Treatment",
  Delta.tv = 1.5,
  Delta.lrv = 1,
  tau.tv = 1,
  tau.lrv = 1,
  tau.ng = 0.35,
  seed = 1234,
  n.MC = 5000
)

get.ts.ng.decision(
  mu.0.c = 1.5,
  n.0.c = 10,
  alpha.0.c = 0.25 * 4,
  beta.0.c = 1 * 4,
  xbar.c = 1.5,
  s.c = 1,
  n.c = 25,
  group.c = "Control",
  mu.0.t = 1.75,
  n.0.t = 10,
  alpha.0.t = 0.25 * 4,
  beta.0.t = 1 * 4,
  xbar.t = 1.85,
  s.t = 1,
  n.t = 25,
  group.t = "Treatment",
  Delta.tv = 0.5,
  Delta.lrv = 0.25,
  tau.tv = 1,
  tau.lrv = 1,
  tau.ng = 0.35,
  seed = 1234,
  n.MC = 1000
)

get.ts.ng.trt.oc.df(
  mu.0.c = 0,
  n.0.c = 1,
  alpha.0.c = 0.25,
  beta.0.c = 1,
  xbar.c = 1.5,
  s.c = 2,
  group.c = "Control",
  mu.0.t = 3.75,
  n.0.t = 1,
  alpha.0.t = 0.25,
  beta.0.t = 1,
  xbar.t = 1.85,
  s.t = 2,
  group.t = "Treatment",
  Delta.LB = 0,
  Delta.UB = 5,
  ARatio = 1,
  N = 50,
  Delta.tv = 2.5,
  Delta.lrv = 1.5,
  Delta.user = 4,
  tau.tv = 1,
  tau.lrv = 1,
  tau.ng = 0.65,
  npoints = 5,
  n.MC = 500,
  seed = 1234
)

make.ts.ng.trt.oc1(for.plot = get.ts.ng.trt.oc.df(), nlines = 20, tsize = 4)

make.ts.ng.trt.oc2(for.plot = get.ts.ng.trt.oc.df(), nlines = 25, tsize = 4)

get.ts.ng.ssize.oc.df(
  mu.0.c = 0,
  n.0.c = 1,
  alpha.0.c = 0.25,
  beta.0.c = 1,
  s.c = 2,
  group.c = "Control",
  mu.0.t = 3.75,
  n.0.t = 1,
  alpha.0.t = 0.25,
  beta.0.t = 1,
  s.t = 2,
  group.t = "Treatment",
  ARatio = 2,
  N = 50,
  n_LB_OC = floor(50 * 0.75),
  n_UB_OC = floor(50 * 2),
  Delta.tv = 2.5,
  Delta.lrv = 1.5,
  Delta.user = 4,
  tau.tv = 0.1,
  tau.lrv = 0.2,
  tau.ng = 0.35,
  npoints = 10,
  n.MC = 500,
  seed = 1234
)

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

Arguments

mu.0.c	prior mean for control group
n.0.c	prior effective sample size for control group
mu.0.t	prior mean for treatment group
n.0.t	prior effective sample size for treatment group
xbar.c	observed sample mean for control group
s.c	observed sample standard deviation for control group
n.c	sample size for control group
xbar.t	observed sample mean for treatment group
s.t	observed sample standard deviation for treatment group
n.t	sample size for treatment group
Delta.lrv	TPP Lower Reference Value aka Min TPP
Delta.tv	TPP Target Value aka Base TPP
tau.ng	threshold associated with No-Go
tau.lrv	threshold associated with Min TPP
tau.tv	threshold associated with Base TPP
seed	random seed
n.MC	Number of MC samples
nlines	Control for text spacing
nlines.ria	Control for text spacing
tsize	Control for text size
alpha.0.c	prior alpha parameter for control group
beta.0.c	prior beta parameter for control group
alpha.0.t	prior alpha parameter for treatment group
beta.0.t	prior beta parameter for treatment group
Delta.LB	Lower bound for treatment effect
Delta.UB	Upper bound for treatment effect
ARatio	Allocation ratio
npoints	number of points
n_LB_OC	Lower bound for sample size
n_UB_OC	Upper bound for sample size

Examples

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

## End(Not run)

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