| glm.random.a0 {BayesPPD} | R Documentation |
Model fitting for generalized linear models with random a0
Description
Model fitting using normalized power priors for generalized linear models with random a_0
Usage
glm.random.a0(
data.type,
data.link,
y,
x,
n = 1,
borrow.treat = FALSE,
historical,
prior.beta.var = rep(10, 50),
prior.a0.shape1 = rep(1, 10),
prior.a0.shape2 = rep(1, 10),
a0.coefficients,
lower.limits = NULL,
upper.limits = NULL,
slice.widths = rep(0.1, 50),
nMC = 10000,
nBI = 250
)
Arguments
data.type |
Character string specifying the type of response. The options are "Normal", "Bernoulli", "Binomial", "Poisson" and "Exponential". |
data.link |
Character string specifying the link function. The options are "Logistic", "Probit", "Log", "Identity-Positive", "Identity-Probability" and "Complementary Log-Log". Does not apply if |
y |
Vector of responses. |
x |
Matrix of covariates. The first column should be the treatment indicator with 1 indicating treatment group. The number of rows should equal the length of the response vector |
n |
(For binomial data only) vector of integers specifying the number of subjects who have a particular value of the covariate vector. If the data is binary and all covariates are discrete, collapsing Bernoulli data into a binomial structure can make the slice sampler much faster.
The sum of |
borrow.treat |
Logical value indicating whether the historical information is used to inform the treatment effect parameter. The default value is FALSE. If TRUE, the first column of the historical covariate matrix must be the treatment indicator. If FALSE, the historical covariate matrix must NOT have the treatment indicator, since the historical data is assumed to be from the control group only. |
historical |
List of historical dataset(s). East historical dataset is stored in a list which contains two named elements:
For binomial data, an additional element
|
prior.beta.var |
Vector of variances of the independent normal initial priors on |
prior.a0.shape1 |
Vector of the first shape parameters of the independent beta priors for |
prior.a0.shape2 |
Vector of the second shape parameters of the independent beta priors for |
a0.coefficients |
Vector of coefficients for |
lower.limits |
Vector of lower limits for parameters to be used by the slice sampler. If |
upper.limits |
Vector of upper limits for parameters to be used by the slice sampler. If |
slice.widths |
Vector of initial slice widths used by the slice sampler. If |
nMC |
Number of iterations (excluding burn-in samples) for the slice sampler or Gibbs sampler. The default is 10,000. |
nBI |
Number of burn-in samples for the slice sampler or Gibbs sampler. The default is 250. |
Details
The user should use the function normalizing.constant to obtain a0.coefficients (does not apply if data.type is "Normal").
If data.type is "Normal", the response y_i is assumed to follow N(x_i'\beta, \tau^{-1}) where x_i is the vector of covariates for subject i.
Historical datasets are assumed to have the same precision parameter as the current dataset for computational simplicity.
The initial prior for \tau is the Jeffery's prior, \tau^{-1}.
Independent normal priors with mean zero and variance prior.beta.var are used for \beta to ensure the propriety of the normalized power prior. Posterior samples for \beta and \tau are obtained through Gibbs sampling.
Independent beta(prior.a0.shape1, prior.a0.shape1) priors are used for a_0. Posterior samples for a_0 are obtained through slice sampling.
For all other data types, posterior samples are obtained through slice sampling.
The default lower limits are -100 for \beta and 0 for a_0. The default upper limits
for the parameters are 100 for \beta and 1 for a_0. The default slice widths for the parameters are 0.1.
The defaults may not be appropriate for all situations, and the user can specify the appropriate limits
and slice width for each parameter.
Value
The function returns a S3 object with a summary method. If data.type is "Normal", posterior samples of \beta, \tau and a_0 are returned.
For all other data types, posterior samples of \beta and a_0 are returned.
The first column of the matrix of posterior samples of \beta contains posterior samples of the intercept.
The second column contains posterior samples of \beta_1, the parameter for the treatment indicator.
References
Neal, Radford M. Slice sampling. Ann. Statist. 31 (2003), no. 3, 705–767.
See Also
normalizing.constant and power.glm.random.a0
Examples
data.type <- "Bernoulli"
data.link <- "Logistic"
# Simulate current data
set.seed(1)
p <- 3
n_total <- 100
y <- rbinom(n_total,size=1,prob=0.6)
# The first column of x is the treatment indicator.
x <- cbind(rbinom(n_total,size=1,prob=0.5),
matrix(rnorm(p*n_total),ncol=p,nrow=n_total))
# Simulate two historical datasets
# Note that x0 does not have the treatment indicator
historical <- list(list(y0=rbinom(n_total,size=1,prob=0.2),
x0=matrix(rnorm(p*n_total),ncol=p,nrow=n_total)),
list(y0=rbinom(n_total, size=1, prob=0.5),
x0=matrix(rnorm(p*n_total),ncol=p,nrow=n_total)))
# Please see function "normalizing.constant" for how to obtain a0.coefficients
# Here, suppose one-degree polynomial regression is chosen by the "normalizing.constant"
# function. The coefficients are obtained for the intercept, a0_1 and a0_2.
a0.coefficients <- c(1, 0.5, -1)
# Set parameters of the slice sampler
# The dimension is the number of columns of x plus 1 (intercept)
# plus the number of historical datasets
lower.limits <- c(rep(-100, 5), rep(0, 2))
upper.limits <- c(rep(100, 5), rep(1, 2))
slice.widths <- rep(0.1, 7)
nMC <- 500 # nMC should be larger in practice
nBI <- 100
result <- glm.random.a0(data.type=data.type, data.link=data.link, y=y, x=x,
historical=historical, a0.coefficients=a0.coefficients,
lower.limits=lower.limits, upper.limits=upper.limits,
slice.widths=slice.widths, nMC=nMC, nBI=nBI)
summary(result)