BANOVA.Poisson: Estimation of BANOVA with Poisson dependent variables

In BANOVA: Hierarchical Bayesian ANOVA Models

Estimation of BANOVA with Poisson dependent variables

Description

BANOVA.Poisson implements a Hierarchical Bayesian ANOVA for models with a count-data response variable and normal heterogeneity distribution.

Usage

BANOVA.Poisson(l1_formula = "NA", l2_formula = "NA",
  data, id, 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.Poisson'
summary(object, ...)
## S3 method for class 'BANOVA.Poisson'
predict(object, newdata = NULL,...)
## S3 method for class 'BANOVA.Poisson'
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, if missing, the single level model will be generated
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
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.Poisson (returned by BANOVA.Poisson)
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.Poisson (returned by BANOVA.Poisson)
...	additional arguments,currently ignored

Details

Level 1 model:
y_i ~ Poisson(λ_i), λ_i = exp(η_i + ε_i)
where 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. ε_i is a dispersion term.

Value

BANOVA.Poisson returns an object of class "BANOVA.Poisson". 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
samples_l2_sigma_param	posterior samples of level 2 standard deviations
data	original data.frame
mf1	model.frame of level 1
mf2	model.frame of level 2
JAGSmodel	'JAGS' model

Examples

# use the bpndata dataset
data(bpndata)
# within-subjects model using the dependent variable : PIC_FIX 
res1 <- BANOVA.Poisson(PIC_FIX ~ AD_ID + PIC_SIZE+ PAGE_NUM 
+ PAGE_POS, ~1, bpndata, bpndata$RESPONDENT_ID, burnin = 500, 
sample = 200, thin = 5)
summary(res1)

# use the goalstudy dataset
data(goalstudy)
goalstudy$bid <- as.integer(goalstudy$bid + 0.5)
res2<-BANOVA.Poisson(bid~1, ~progress*prodvar,  goalstudy, goalstudy$id, 
burnin = 5000, sample = 2000, thin = 10)
summary(res2)

# or use the BANOVA.run based on 'Stan'
require(rstan)
res3 <- BANOVA.run(bid~progress*prodvar, data = goalstudy, 
model_name = 'Poisson', id = 'id', iter = 100, thin = 1, chains = 2)

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