mem_exact {basket} R Documentation

## Fit the Exact MEM Model

### Description

Fit the MEM model using full Bayesian inference.

### Usage

mem_exact(
responses,
size,
name,
p0 = 0.15,
shape1 = 0.5,
shape2 = 0.5,
prior = diag(length(responses))/2 + matrix(0.5, nrow = length(responses), ncol =
length(responses)),
hpd_alpha = 0.05,
alternative = "greater",
seed = 1000,
cluster_analysis = FALSE,
call = NULL,
cluster_function = cluster_membership
)


### Arguments

 responses the number of responses in each basket. size the size of each basket. name the name of each basket. p0 the null response rate for the poster probability calculation (default 0.15). shape1 the first shape parameter(s) for the prior of each basket (default 0.5). shape2 the second shape parameter(s) for the prior of each basket (default 0.5). prior the matrix giving the prior inclusion probability for each pair of baskets. The default is on on the main diagonal and 0.5 elsewhere. hpd_alpha the highest posterior density trial significance. alternative the alternative case definition (default greater) seed the random number seed. cluster_analysis if the cluster analysis is conducted. call the call of the function (default NULL). cluster_function a function to cluster baskets

### See Also

cluster_membership

### Examples


# 3 baskets, each with enrollement size 5
trial_sizes <- rep(5, 3)

# The response rates for the baskets.
resp_rate <- 0.15

# The trials: a column of the number of responses and a column of the
# the size of each trial.
trials <- data.frame(
responses = rbinom(trial_sizes, trial_sizes, resp_rate),
size = trial_sizes,
name = letters[1:3]
)

summary(mem_exact(trials$responses, trials$size, trials\$name))



[Package basket version 0.10.11 Index]