UPG {UPG} | R Documentation |
UPG
estimates Bayesian discrete choice models and returns the full posterior distribution for all parameters that can be used for further analysis and inference.
UPG(y, X, type, Ni = NULL, baseline = NULL, draws = 1000, burnin = 1000, A0 = 1, d0 = 0.5, D0 = 0.5, G0 = 99, verbose = TRUE, BOOST = TRUE, beta.start = NULL)
y |
a binary vector for probit and logit models. A character, factor or numeric vector for multinomial logit models. A vector of the number of successes for the binomial model. |
X |
a matrix of explanatory variables including an intercept in the first column. Rows are individuals, columns are variables. |
type |
indicates the model to be estimated. |
Ni |
a vector containing the number of trials when estimating a binomial logit model. |
baseline |
a string that can be used to change the baseline category in MNL models. Default baseline is the most common category. |
draws |
number of saved Gibbs sampler iterations. Default is 1000 for illustration purposes, you should use more when estimating a model (e.g. 10,000)! |
burnin |
number of burned Gibbs sampler iterations. Default is 1000 for illustration purposes, you should probably use more when estimating a model (e.g. 2,000)! |
A0 |
prior variance for coefficients, 1 is the default. |
d0 |
prior shape for working parameter delta, 0.5 is the default. |
D0 |
prior rate for working parameter delta, 0.5 is the default. |
G0 |
prior variance for the intercept, 99 is the default. |
verbose |
logical variable indicating whether model output and progress should be printed during estimation. |
BOOST |
logical variable indicating whether MCMC boosting should be used. |
beta.start |
provide starting values for beta (e.g. for use within Gibbs sampler) |
Depending on the type of the model, UPG()
returns an UPG.Probit
, UPG.Logit
, UPG.MNL
or UPG.Binomial
object.
Gregor Zens
summary.UPG.Probit
to summarize the estimates of a discrete choice model from an UPG.Probit
object and to create tables.
predict.UPG.Logit
to predict probabilities from a discrete choice model from an UPG.Logit
object.
plot.UPG.MNL
to plot the results of a discrete choice model from an UPG.MNL
object.
# load package library(UPG) # estimate a probit model using example data # warning: use more burn-ins, burnin = 100 is just for demonstration purposes data(lfp) y = lfp[,1] X = lfp[,-1] results.probit = UPG(y = y, X = X, type = "probit", verbose=TRUE, burnin = 100) # estimate a logit model using example data # warning: use more burn-ins, burnin = 100 is just for demonstration purposes data(lfp) y = lfp[,1] X = lfp[,-1] results.logit = UPG(y = y, X = X, type = "logit", verbose=TRUE, burnin = 100) # estimate a MNL model using example data # warning: use more burn-ins, burnin = 100 is just for demonstration purposes data(program) y = program[,1] X = program[,-1] results.mnl = UPG(y = y, X = X, type = "mnl", burnin = 100) # estimate a binomial logit model using example data # warning: use more burn-ins, burnin = 100 is just for demonstration purposes data(titanic) y = titanic[,1] Ni = titanic[,2] X = titanic[,-c(1,2)] results.binomial = UPG(y = y, X = X, Ni = Ni, type = "binomial", burnin = 100)