bbl {bbl}  R Documentation 
Boltzmann Bayes Learning Inference
Description
Main driver for bbl inference
Usage
bbl(
formula,
data,
weights,
xlevels = NULL,
verbose = 1,
method = "pseudo",
novarOk = FALSE,
testNull = TRUE,
prior.count = 1,
...
)
Arguments
formula 
Formula for modeling 
data 
Data for fitting 
weights 
Vector of weights for each instance in data. Restricted to
nonnegative integer frequencies, recoding the number of times
each row of data must be repeated. If 
xlevels 
List of factor levels for predictors. If 
verbose 
Output verbosity level. Will be send to downstream function calls with one level lower 
method 
BB inference algorithm; pseudolikelihood inference ( 
novarOk 
If 
testNull 
Repeat the inference for the ‘pooled’ sample; i.e., under the null hypothesis of all rows in data belonging to a single group 
prior.count 
Prior count for computing single predictor and pairwise frequencies 
... 
Other parameters to 
Details
Formula argument and data are used to tabulate xlevels unless explicitly
given as list. Data are expected to be factors or integers. This function
is a driver interepreting formula and calls bbi.fit
. Will stop with
error if any predictor has only one level unless novarOk='TRUE'
.
Use removeConst
to remove the nonvarying predictors before
calling if this happens.
Value
A list of class bbl
with the following elements:
coefficients 
List of inferred coefficients with elements

xlevels 
List of vectors containing predictor levels. 
terms 
The 
groups 
Vector of response groups. 
groupname 
Name of the response variable. 
qJ 
Matrix of logicals whose elements record whether

model 
Model data frame derived from 
lkh 
Log likelihood. 
lz 
Vector log partition function. Used in 
weights 
Vector of integral weights (frequencies). 
call 
Function call. 
df 
Degrees of freedom. 
Author(s)
Jun Woo, junwoo035@gmail.com
References
Examples
titanic < as.data.frame(Titanic)
b < bbl(Survived ~ (Class + Sex + Age)^2, data = titanic, weights = Freq)
b