pglm: Panel Estimators for Generalized Linear Models

In pglm: Panel Generalized Linear Models

Description

Estimation by maximum likelihood of glm (binomial and Poisson) and 'glm-like' models (Negbin and ordered) on longitudinal data

Usage

pglm(formula, data, subset, na.action,
     effect = c("individual", "time", "twoways"),
     model = c("random", "pooling", "within", "between"),
     family, other = NULL, index = NULL, start = NULL, R = 20,  ...) 

Arguments

formula	a symbolic description of the model to be estimated,
data	the data: a pdata.frame object or an ordinary data.frame,
subset	an optional vector specifying a subset of observations,
na.action	a function which indicates what should happen when the data contains 'NA's,
effect	the effects introduced in the model, one of "individual", "time" or "twoways",
model	one of "pooling", "within", "between", "random",,
family	the distribution to be used,
other	for developper's use only,
index	the index,
start	a vector of starting values,
R	the number of function evaluation for the gaussian quadrature method used,
...	further arguments.

Value

An object of class "pglm", a list with elements:

coefficients	the named vector of coefficients,
logLik	the value of the log-likelihood,
hessian	the hessian of the log-likelihood at convergence,
gradient	the gradient of the log-likelihood at convergence,
call	the matched call,
est.stat	some information about the estimation (time used, optimisation method),
freq	the frequency of choice,
residuals	the residuals,
fitted.values	the fitted values,
formula	the formula (a mFormula object),
expanded.formula	the formula (a formula object),
model	the model frame used,
index	the index of the choice and of the alternatives.

Author(s)

Yves Croissant

Examples

## an ordered probit example
data('Fairness', package = 'pglm')
Parking <- subset(Fairness, good == 'parking')
op <- pglm(as.numeric(answer) ~ education + rule,
           Parking[1:105, ],
           family = ordinal('probit'), R = 5, print.level = 3,
           method = 'bfgs', index = 'id',  model = "random")

## a binomial (probit) example
data('UnionWage', package = 'pglm')
anb <- pglm(union ~ wage + exper + rural, UnionWage, family = binomial('probit'),
            model = "pooling",  method = "bfgs", print.level = 3, R = 5)

## a gaussian example on unbalanced panel data
data(Hedonic, package = "plm")
ra <- pglm(mv ~ crim + zn + indus + nox + age + rm, Hedonic, family = gaussian,
           model = "random", print.level = 3, method = "nr", index = "townid")

## some count data models
data("PatentsRDUS", package="pglm")
la <- pglm(patents ~ lag(log(rd), 0:5) + scisect + log(capital72) + factor(year), PatentsRDUS,
           family = negbin, model = "within", print.level = 3, method = "nr",
           index = c('cusip', 'year'))
la <- pglm(patents ~ lag(log(rd), 0:5) + scisect + log(capital72) + factor(year), PatentsRDUS,
           family = poisson, model = "pooling", index = c("cusip", "year"),
           print.level = 0, method="nr")

## a tobit example
data("HealthIns", package="pglm")
HealthIns$med2 <- HealthIns$med / 1000
HealthIns2 <- HealthIns[-2209, ]
set.seed(2)
subs <- sample(1:20186, 200, replace = FALSE)
HealthIns2 <- HealthIns2[subs, ]
la <- pglm(med ~ mdu + disease + age, HealthIns2,
           model = 'random', family = 'tobit', print.level = 0,
           method = 'nr', R = 5)

pglm documentation built on July 20, 2021, 1:06 a.m.