BANOVA.Binomial: Estimation of BANOVA with a Binomial dependent variable

In BANOVA: Hierarchical Bayesian ANOVA Models

Estimation of BANOVA with a Binomial dependent variable

Description

BANOVA.Binomial implements a Hierarchical Bayesian ANOVA for a binomial response variable using a logit link and a normal heterogeneity distribution.

Usage

BANOVA.Binomial(l1_formula = "NA", l2_formula = "NA", data,
  id, num_trials, l2_hyper = c(1, 1, 0.0001), burnin = 5000, sample = 2000, 
  thin = 10, adapt = 0, conv_speedup = F, jags = runjags.getOption('jagspath'))
## S3 method for class 'BANOVA.Binomial'
summary(object, ...)
## S3 method for class 'BANOVA.Binomial'
predict(object, newdata = NULL,...)
## S3 method for class 'BANOVA.Binomial'
print(x, ...)

Arguments

l1_formula	formula for level 1 e.g. 'Y~X1+X2'
l2_formula	formula for level 2 e.g. '~Z1+Z2', response variable must not be included
data	a data.frame in long format including all features in level 1 and level 2(covariates and categorical factors) and responses
id	subject ID of each response unit
num_trials	the number of trials of each observation(=1, if it is bernoulli), the type is forced to be 'integer'
l2_hyper	level 2 hyperparameters, c(a, b, γ), default c(1,1,0.0001)
burnin	the number of burn in draws in the MCMC algorithm, default 5000
sample	target samples in the MCMC algorithm after thinning, default 2000
thin	the number of samples in the MCMC algorithm that needs to be thinned, default 10
adapt	the number of adaptive iterations, default 0 (see run.jags)
conv_speedup	whether to speedup convergence, default F
jags	the system call or path for activating 'JAGS'. Default calls findjags() to attempt to locate 'JAGS' on your system
object	object of class BANOVA.Bin (returned by BANOVA.Bin)
newdata	test data, either a matrix, vector or a data frame. It must have the same format with the original data (the same column number)
x	object of class BANOVA.Bin (returned by BANOVA.Bin)
...	additional arguments,currently ignored

Details

Level 1 model:
y_i ~ Binomial(ntrials,p_i), p_i = logit^{-1}(η_i)
where ntrials is the binomial total for each record i, η_i = ∑_{p = 0}^{P}∑_{j=1}^{J_p}X_{i,j}^pβ_{j,s_i}^p, s_i is the subject id of response i. see BANOVA-package

Value

BANOVA.Binomial returns an object of class "BANOVA.Bin". The returned object is a list containing:

anova.table	table of effect sizes BAnova
coef.tables	table of estimated coefficients
pvalue.table	table of p-values table.pvalues
dMatrice	design matrices at level 1 and level 2
samples_l2_param	posterior samples of level 2 parameters
data	original data.frame
mf1	model.frame of level 1
mf2	model.frame of level 2
JAGSmodel	'JAGS' model

Examples

data(colorad)

# mean center Blur for effect coding
colorad$blur <- colorad$blur - mean(colorad$blur)
res <- BANOVA.Binomial(y~typic, ~color*blur, colorad, colorad$id, as.integer(16), 
burnin = 5000, sample = 2000, thin = 10)
summary(res)
# or use BANOVA.run
require(rstan)
res0 <- BANOVA.run(y~typic, ~color*blurfac, data = colorad, model_name = 'Binomial',
id = 'id', num_trials = as.integer(16), iter = 100, thin = 1, chains = 2)
summary(res0)
table.predictions(res0)
# only in-model variables(except numeric variables) will be used
predict(res0, c(1, 0, 8, 2, 1, 0.03400759))

BANOVA documentation built on June 21, 2022, 9:05 a.m.