BANOVA.run: Estimation of BANOVA models

In BANOVA: Hierarchical Bayesian ANOVA Models

Estimation of BANOVA models

Description

BANOVA.run implements Hierarchical Bayesian ANOVA models using 'Stan'

Usage

BANOVA.run(l1_formula = "NA", l2_formula = "NA", fit = NULL, model_name = 'NA', 
dataX = NULL, dataZ = NULL, data = NULL, y_value = NULL, id, iter = 2000, 
num_trials = 1, contrast = NULL, y_lowerBound = -Inf, y_upperBound = Inf, ...)
## S3 method for class 'BANOVA'
summary(object, ...)
## S3 method for class 'BANOVA'
predict(object, newdata = NULL, Xsamples = NULL, Zsamples = 
                        NULL, ...)
## S3 method for class 'BANOVA'
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 NULL, the single-level model is used
fit	a fitted BANOVA models, an object of class "BANOVA.build", default NULL which needs compilation
model_name	a character string in c('Normal', 'T', 'Bernoulli', 'Binomial', 'Poisson', 'ordMultinomial', 'Multinomial', 'multiNormal', 'truncNormal')
dataX	a list of data frames (each corresponds to the choice set of each observation) that includes all covariates and factors, for the Multinomial model only, default NULL
dataZ	a data frame (long format) that includes all level 2 covariates and factors, for the Multinomial model only, default NULL
data	a data.frame in a long format including all features in level 1 and level 2 (covariates and categorical factors) and responses, default NULL. For the Multivariate Normal model the data must be specially prepared: first, combine the set of dependent variables in a single matrix; next, add this matrix to an original data frame used in the analysis. For an example of the specification of the data, please, see below.
id	subject ID (string) of each response unit
y_value	choice responses, 1,2,3..., for the Multinomial model only, default NULL
iter	target samples in the 'Stan' algorithm after thinning, default 2000
num_trials	the number of trials of each observation(=1, if it is Bernoulli), the type is forced to be 'integer', for the Binomial model only, default 0
contrast	a list of contrasts for planned comparisons, default: effect coding (NULL value)
y_lowerBound	lower bound of the dependent variable, for the Truncated Normal model only, default -Inf.
y_upperBound	upper bound of the dependent variable, for the Truncated Normal model only, default Inf.
object	an object of class BANOVA (returned by BANOVA.run)
x	an object of class BANOVA (returned by BANOVA.run)
newdata	test data, either a matrix, vector or a data frame. It must have the same format as the original data (the same column number)
Xsamples	a list of sample data frames(each corresponds to the choice set of each observation) that includes all covariates and factors, for the Multinomial model only, default NULL
Zsamples	a data frame(long format) that includes all level 2 covariates and factors, for the Multinomial model only, default NULL
...	additional arguments, for BANOVA.run, it can include standard 'Stan' arguments, e.g. warmup, thin, chains, etc., see sampling for more details, for other functions, ignored currently

Value

BANOVA.run returns an object of class "BANOVA". 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_l1_param	posterior samples of level 1 parameters
samples_l2_param	posterior samples of level 2 parameters
samples_l2_sigma_param	posterior samples of level 2 standard deviations
samples_cutp_param	posterior samples of cutpoints
data	original data.frame
mf1	model.frame of level 1
mf2	model.frame of level 2
model_code	'Stan' code
single_level	if this is a single level model
stan_fit	fitted samples
model_name	the name of the model
contrast	contrasts for planned comparisons
new_id	id values coded in 1,2,3,...
old_id	original id values

Examples

# Analysis of a single-level Normal dependent variable
# Use the ipadstudy data set
data(ipadstudy)
library(rstan)
# build the BANOVA model first so that it can be reused
model <- BANOVA.model('Normal', single_level = TRUE)
banova_model <- BANOVA.build(model)
res_1 <- BANOVA.run(attitude~owner + age + gender + selfbrand*conspic, 
fit = banova_model, data = ipadstudy, id = 'id', iter = 2000, chains = 2)
summary(res_1)
# or call the function directly without specifying the fit argument
# but it needs compilation
res_1 <- BANOVA.run(attitude~owner + age + gender + selfbrand*conspic,
model_name = 'Normal', data = ipadstudy, id = 'id', iter = 2000, chains = 2)

# Hierarchical analysis of multiple dependent variables (Multivariate Normal distribution)
# Use the colorad data set
data(colorad)
# Prepare dependent variables to be analyzed
colorad$blur_squared <- (colorad$blur)^2
dv <- cbind(colorad$blur, colorad$blur_squared)
colnames(dv) <- c("blur", "blur_squared")
colorad$dv <- dv
# Build and analyze the model for the dependent variables
model <- BANOVA.model('multiNormal')
banova_multi_norm_model <- BANOVA.build(model)
res_2 <- BANOVA.run(dv ~ typic, ~ color, fit = banova_multi_norm_model,
                    data = colorad, id = 'id', iter = 2000, thin = 1, chains = 2)

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