bracl {brglm2}  R Documentation 
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.
bracl( formula, data, weights, subset, na.action, parallel = FALSE, contrasts = NULL, model = TRUE, x = TRUE, control = list(...), ... )
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 'control' argument if it is not supplied directly. 
The bracl
function fits adjacent category models, which
assume multinomial observations with probabilities with
proportional odds of the form
log(pi[i, j]/pi[i, j+1]) = alpha[j] + sum(beta * x[i, ])
or with nonproportional odds of the form
log(pi[i, j]/pi[i, j+1]) = alpha[j] + sum(beta[j, ] * x[i, ])
where x[i, ] is a vector of covariates and pi[i, j] is the probability that category j is observed at the covariate setting i.
Ioannis Kosmidis ioannis.kosmidis@warwick.ac.uk
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**, 1–10 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
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)