bayes_sim_unbalanced {bayesassurance} | R Documentation |
Unbalanced Bayesian Simulation in Conjugate Linear Model Framework
Description
Approximates the Bayesian assurance of attaining
for unbalanced study designs through Monte Carlo sampling.
See Argument descriptions for more detail.
Usage
bayes_sim_unbalanced(
n1,
n2,
repeats = 1,
u,
C,
Xn = NULL,
Vn = NULL,
Vbeta_d,
Vbeta_a_inv,
sigsq,
mu_beta_d,
mu_beta_a,
alt,
alpha,
mc_iter,
surface_plot = TRUE
)
Arguments
n1 |
first sample size (vector or scalar). |
n2 |
second sample size (vector or scalar). |
repeats |
an positive integer specifying number of times to repeat
|
u |
a scalar or vector to evaluate
where |
C |
constant value to be compared to when evaluating |
Xn |
design matrix that characterizes where the data is to be generated from. This is specifically designed under the normal linear regression model
When set to |
Vn |
a correlation matrix for the marginal distribution of the
sample data |
Vbeta_d |
correlation matrix that helps describe the prior
information on |
Vbeta_a_inv |
inverse-correlation matrix that helps describe the prior
information on |
sigsq |
a known and fixed constant preceding all correlation matrices
|
mu_beta_d |
design stage mean |
mu_beta_a |
analysis stage mean |
alt |
specifies alternative test case, where alt = "greater" tests if
|
alpha |
significance level |
mc_iter |
number of MC samples evaluated under the analysis objective |
surface_plot |
when set to |
Value
a list of objects corresponding to the assurance approximations
assurance_table: table of sample size and corresponding assurance values
contourplot: contour map of assurance values
mc_samples: number of Monte Carlo samples that were generated and evaluated
Examples
## Example 1
## Sample size vectors are passed in for n1 and n2 to evaluate
## assurance.
n1 <- seq(20, 75, 5)
n2 <- seq(50, 160, 10)
assur_out <- bayes_sim_unbalanced(n1 = n1, n2 = n2, repeats = 1, u = c(1, -1),
C = 0, Xn = NULL, Vbeta_d = matrix(c(50, 0, 0, 10),nrow = 2, ncol = 2),
Vbeta_a_inv = matrix(rep(0, 4), nrow = 2, ncol = 2),
Vn = NULL, sigsq = 100, mu_beta_d = c(1.17, 1.25),
mu_beta_a = c(0, 0), alt = "two.sided", alpha = 0.05, mc_iter = 1000,
surface_plot = FALSE)
assur_out$assurance_table
## Example 2
## We can produce a contour plot that evaluates unique combinations of n1
## and n2 simply by setting `surfaceplot = TRUE`.
n1 <- seq(20, 75, 5)
n2 <- seq(50, 160, 10)
assur_out <- bayes_sim_unbalanced(n1 = n1, n2 = n2, repeats = 1,
u = c(1, -1), C = 0, Xn = NULL, Vbeta_d = matrix(c(50, 0, 0, 10),
nrow = 2, ncol = 2), Vbeta_a_inv = matrix(rep(0, 4), nrow = 2, ncol = 2),
Vn = NULL, sigsq = 100, mu_beta_d = c(1.17, 1.25),
mu_beta_a = c(0, 0), alt = "two.sided", alpha = 0.05, mc_iter = 1000,
surface_plot = TRUE)
assur_out$assurance_table
assur_out$contourplot