PanelCount (version 1.0.9)

ProbitRE: A Probit Model with Random Effects

Description

Estimate a Probit model with random effects

Usage

ProbitRE(formula, id, data = NULL, delta = 1, max_delta = 3,
  method = "BFGS", lower = NULL, upper = NULL, H = 20, accu = 10,
  reltol = 1e-08, verbose = 0, tol_gtHg = Inf)

Value

A list containing the results of the estimated model

Arguments

formula

Formula of the model

id

A vector that represents the identity of individuals, numeric or character

data

Input data, a data frame

delta

Variance of random effects on the individual level for ProbitRE

max_delta

Largest allowed initial delta

method

Searching algorithm, don't change default unless you know what you are doing

lower

Lower bound for estiamtes

upper

Upper bound for estimates

H

A vector of length 2, specifying the number of points for inner and outer Quadratures

accu

L-BFGS-B only, 1e12 for low accuracy; 1e7 for moderate accuracy; 10.0 for extremely high accuracy. See optim

reltol

Relative convergence tolerance. default typically 1e-8

verbose

Level of output during estimation. Lowest is 0.

tol_gtHg

tolerance on gtHg, not informative for L-BFGS-B

See Also

Other PanelCount: CRE_SS; CRE; PLN_RE; PoissonRE

Examples

# \donttest{
data(rt)
est = ProbitRE(isRetweet~fans+tweets+as.factor(tweet.id),
                    id=rt$user.id, data=rt)
# }