bracl {brglm2}  R Documentation 
Bias reduction for adjacent category logit models for ordinal responses using the Poisson trick.
Description
bracl()
is a wrapper of brglmFit()
that fits adjacent category
logit models with or without proportional odds using implicit and
explicit bias reduction methods. See Kosmidis & Firth (2011) for
details.
Usage
bracl(
formula,
data,
weights,
subset,
na.action,
parallel = FALSE,
contrasts = NULL,
model = TRUE,
x = TRUE,
control = list(...),
...
)
Arguments
formula 
a formula expression as for regression models, of the form

data 
an optional data frame, list or environment in which to interpret
the variables occurring in 
weights 
optional case weights in fitting. Default to 1. 
subset 
expression saying which subset of the rows of the data should be used in the fit. All observations are included by default. 
na.action 
a function to filter missing data. 
parallel 
if 
contrasts 
a list of contrasts to be used for some or all of the factors appearing as variables in the model formula. 
model 
logical for whether the model matrix should be returned. 
x 
should the model matrix be included with in the result
(default is 
control 
a list of parameters for controlling the fitting
process. See 
... 
arguments to be used to form the default 
Details
The bracl()
function fits adjacent category models, which assume
multinomial observations with probabilities with proportional odds
of the form
\log\frac{\pi_{ij}}{\pi_{ij + 1}} = \alpha_j + \beta^T x_i
or with nonproportional odds of the form
\log\frac{\pi_{ij}}{\pi_{ij + 1}} = \alpha_j + \beta_j^T x_i
where x_i
is a vector of covariates and \pi_{ij}
is the
probability that category j
is observed at the covariate setting i
.
Author(s)
Ioannis Kosmidis [aut, cre]
ioannis.kosmidis@warwick.ac.uk
References
Kosmidis I, Kenne Pagui E C, Sartori N (2020). Mean and median bias reduction in generalized linear models. Statistics and Computing, 30, 4359. doi:10.1007/s11222019098606.
Agresti, A (2010). Analysis of Ordinal Categorical Data (2nd edition). Wiley Series in Probability and Statistics. Wiley.
Albert A, Anderson J A (1984). On the Existence of Maximum Likelihood Estimates in Logistic Regression Models. Biometrika, 71, 110. doi:10.2307/2336390.
Kosmidis I, Firth D (2011). Multinomial logit bias reduction via the Poisson loglinear model. Biometrika, 98, 755759. doi:10.1093/biomet/asr026.
Palmgren J (1981). The Fisher Information Matrix for Log Linear Models Arguing Conditionally on Observed Explanatory Variables. Biometrika, 68, 563566. doi:10.1093/biomet/68.2.563.
See Also
nnet::multinom()
, brmultinom()
Examples
data("stemcell", package = "brglm2")
# Adjacent category logit (nonproportional odds)
fit_bracl < bracl(research ~ as.numeric(religion) + gender, weights = frequency,
data = stemcell, type = "ML")
# Adjacent category logit (proportional odds)
fit_bracl_p < bracl(research ~ as.numeric(religion) + gender, weights = frequency,
data = stemcell, type = "ML", parallel = TRUE)