PanelCount (version 1.0.1)

PLN_RE: A Poisson Lognormal Model with Random Effects

Description

Estimate a Poisson Lognormal model with random effects in panel counting data. This model accounts for heterogeneity on the individual level, and heterogeneity on the level.

Usage

PLN_RE(formula, id, data = NULL, par = NULL, gamma = 1, sigma = NULL,
  method = "BFGS", lower = NULL, upper = NULL, H = 20, psnH = 20,
  accu = 1e+10, verbose = 0)

Arguments

formula

Formula of the model

id

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

data

Input data, a data frame

par

Starting values for estimates

gamma

Variance of random effects on the level for PLN_RE

sigma

Variance of random effects on the individual level for PLN_RE

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

psnH

Number of Quadrature points for Poisson RE model

accu

1e12 for low accuracy; 1e7 for moderate accuracy; 10.0 for extremely high accuracy. See optim

verbose

Level of output during estimation. Lowest is 0.

Value

• A list containing the results of the estimated model

See Also

Other PanelCount: CRE_SS; CRE; PoissonRE; ProbitRE

Examples

data(rt)
est = PLN_RE(num.words~fans+tweets+as.factor(tweet.id),
              id=rt$user.id[rt$isRetweet==1],
              data=rt[rt$isRetweet==1,])