sfaR-deprecated {sfaR}R Documentation

Deprecated functions of sfaR

Description

These functions are provided for compatibility with older versions of ‘sfaR’ only, and could be defunct at a future release.

Usage

lcmcross(
  formula,
  uhet,
  vhet,
  thet,
  logDepVar = TRUE,
  data,
  subset,
  weights,
  wscale = TRUE,
  S = 1L,
  udist = "hnormal",
  start = NULL,
  whichStart = 2L,
  initAlg = "nm",
  initIter = 100,
  lcmClasses = 2,
  method = "bfgs",
  hessianType = 1,
  itermax = 2000L,
  printInfo = FALSE,
  tol = 1e-12,
  gradtol = 1e-06,
  stepmax = 0.1,
  qac = "marquardt"
)

## S3 method for class 'lcmcross'
print(x, ...)

## S3 method for class 'lcmcross'
bread(x, ...)

## S3 method for class 'lcmcross'
estfun(x, ...)

## S3 method for class 'lcmcross'
coef(object, extraPar = FALSE, ...)

## S3 method for class 'summary.lcmcross'
coef(object, ...)

## S3 method for class 'lcmcross'
fitted(object, ...)

## S3 method for class 'lcmcross'
ic(object, IC = "AIC", ...)

## S3 method for class 'lcmcross'
logLik(object, individual = FALSE, ...)

## S3 method for class 'lcmcross'
marginal(object, newData = NULL, ...)

## S3 method for class 'lcmcross'
nobs(object, ...)

## S3 method for class 'lcmcross'
residuals(object, ...)

## S3 method for class 'lcmcross'
summary(object, grad = FALSE, ci = FALSE, ...)

## S3 method for class 'summary.lcmcross'
print(x, digits = max(3, getOption("digits") - 2), ...)

## S3 method for class 'lcmcross'
efficiencies(object, level = 0.95, newData = NULL, ...)

## S3 method for class 'lcmcross'
vcov(object, ...)

Arguments

formula

A symbolic description of the model to be estimated based on the generic function formula (see section ‘Details’).

uhet

A one-part formula to account for heteroscedasticity in the one-sided error variance (see section ‘Details’).

vhet

A one-part formula to account for heteroscedasticity in the two-sided error variance (see section ‘Details’).

thet

A one-part formula to account for technological heterogeneity in the construction of the classes.

logDepVar

Logical. Informs whether the dependent variable is logged (TRUE) or not (FALSE). Default = TRUE.

data

The data frame containing the data.

subset

An optional vector specifying a subset of observations to be used in the optimization process.

weights

An optional vector of weights to be used for weighted log-likelihood. Should be NULL or numeric vector with positive values. When NULL, a numeric vector of 1 is used.

wscale

Logical. When weights is not NULL, a scaling transformation is used such that the weights sums to the sample size. Default TRUE. When FALSE no scaling is used.

S

If S = 1 (default), a production (profit) frontier is estimated: \epsilon_i = v_i-u_i. If S = -1, a cost frontier is estimated: \epsilon_i = v_i+u_i.

udist

Character string. Distribution specification for the one-sided error term. Only the half normal distribution 'hnormal' (Aigner et al., 1977, Meeusen and Vandenbroeck, 1977) is currently implemented.

start

Numeric vector. Optional starting values for the maximum likelihood (ML) estimation.

whichStart

Integer. If 'whichStart = 1', the starting values are obtained from the method of moments. When 'whichStart = 2' (Default), the model is initialized by solving the homoscedastic pooled cross section SFA model. 'whichStart = 1' can be fast.

initAlg

Character string specifying the algorithm used for initialization and obtain the starting values (when 'whichStart = 2'). Only maxLik package algorithms are available:

  • 'bfgs', for Broyden-Fletcher-Goldfarb-Shanno (see maxBFGS)

  • 'bhhh', for Berndt-Hall-Hall-Hausman (see maxBHHH)

  • 'nr', for Newton-Raphson (see maxNR)

  • 'nm', for Nelder-Mead - Default - (see maxNM)

  • 'cg', for Conjugate Gradient (see maxCG)

  • 'sann', for Simulated Annealing (see maxSANN)

initIter

Maximum number of iterations for initialization algorithm. Default 100.

lcmClasses

Number of classes to be estimated (default = 2). A maximum of five classes can be estimated.

method

Optimization algorithm used for the estimation. Default = 'bfgs'. 11 algorithms are available:

  • 'bfgs', for Broyden-Fletcher-Goldfarb-Shanno (see maxBFGS)

  • 'bhhh', for Berndt-Hall-Hall-Hausman (see maxBHHH)

  • 'nr', for Newton-Raphson (see maxNR)

  • 'nm', for Nelder-Mead (see maxNM)

  • 'cg', for Conjugate Gradient (see maxCG)

  • 'sann', for Simulated Annealing (see maxSANN)

  • 'ucminf', for a quasi-Newton type optimization with BFGS updating of the inverse Hessian and soft line search with a trust region type monitoring of the input to the line search algorithm (see ucminf)

  • 'mla', for general-purpose optimization based on Marquardt-Levenberg algorithm (see mla)

  • 'sr1', for Symmetric Rank 1 (see trust.optim)

  • 'sparse', for trust regions and sparse Hessian (see trust.optim)

  • 'nlminb', for optimization using PORT routines (see nlminb)

hessianType

Integer. If 1 (default), analytic Hessian is returned. If 2, bhhh Hessian is estimated (g'g).

itermax

Maximum number of iterations allowed for optimization. Default = 2000.

printInfo

Logical. Print information during optimization. Default = FALSE.

tol

Numeric. Convergence tolerance. Default = 1e-12.

gradtol

Numeric. Convergence tolerance for gradient. Default = 1e-06.

stepmax

Numeric. Step max for ucminf algorithm. Default = 0.1.

qac

Character. Quadratic Approximation Correction for 'bhhh' and 'nr' algorithms. If 'qac = stephalving', the step length is decreased but the direction is kept. If 'qac = marquardt' (default), the step length is decreased while also moving closer to the pure gradient direction. See maxBHHH and maxNR.

x

an object of class lcmcross (returned by the function lcmcross).

...

additional arguments of frontier are passed to lcmcross; additional arguments of the print, bread, estfun, nobs methods are currently ignored.

object

an object of class lcmcross (returned by the function lcmcross).

extraPar

Logical (default = FALSE). If TRUE, additional parameters are returned (see coef or vcov).

IC

Character string. Information criterion measure. Three criteria are available:

  • 'AIC' for Akaike information criterion (default)

  • 'BIC' for Bayesian information criterion

  • 'HQIC' for Hannan-Quinn information criterion

.

individual

Logical. If FALSE (default), the sum of all observations' log-likelihood values is returned. If TRUE, a vector of each observation's log-likelihood value is returned.

newData

Optional data frame that is used to calculate the efficiency estimates. If NULL (the default), the efficiency estimates are calculated for the observations that were used in the estimation.

grad

Logical. Default = FALSE. If TRUE, the gradient for the maximum likelihood (ML) estimates of the different parameters is returned.

ci

Logical. Default = FALSE. If TRUE, the 95% confidence interval for the different parameters (OLS or/and ML estimates) is returned.

digits

Numeric. Number of digits displayed in values.

level

A number between between 0 and 0.9999 used for the computation of (in-)efficiency confidence intervals (defaut = 0.95). Not used in the case of lcmcross.

Details

The following functions are deprecated and could be removed from sfaR in a near future. Use the replacement indicated below:

[Package sfaR version 1.0.0 Index]