sfaR-deprecated: Deprecated functions of sfaR
In sfaR: Stochastic Frontier Analysis Routines

sfaR-deprecated

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`.