pglm (version 0.1-0)

pglm: Panel Estimators for 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.

Examples

## an ordered probit example
data('Fairness', package = 'pglm')
op <- pglm(as.numeric(answer)~recurring+driving+education+rule+
          education:rule, Fairness[Fairness$good == 'parking',],
          family=ordinal('probit'), R=5, print.level=0,
          method='bfgs', index='id', iterlim=2, model="random")

## a binomial (logit) example
data('Unions', package = 'pglm')
anb <- pglm(union~wage+exper+rural, Unions, family=binomial('probit'),
model="pooling",  method = "bfgs", print.level=0, R = 5, iterlim=2)

## 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=0, method="nr",index="townid")

## some count data models
data("PatsRD", package="pglm")
la <- pglm(pat~lag(logr, 0:5)+scisect+logk+factor(year), PatsRD,
family=negbin,model="within", print.level=0, method="nr",
index=c('cusip', 'year'))
la <- pglm(pat~lag(logr, 0:5)+scisect+logk+factor(year), PatsRD,
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, ]
la <- pglm(med~mdu+disease+age,HealthIns2,
model='random',family='tobit', print.level=0, method='nr', R=5)