truncSP (version 1.2.2)

stls: Estimation of truncated regression models using the Symmetrically Trimmed Least Squares (STLS) estimator

Description

Function for estimation of linear regression models with truncated response variables (fixed truncation point), using the STLS estimator (Powell 1986)

Usage

stls(formula, data, point = 0, direction = "left", beta = "ml", 
    covar = FALSE, na.action, ...)
## S3 method for class 'stls':
print(x, digits = max(3, getOption("digits") - 3), ...)
## S3 method for class 'stls':
summary(object, level=0.95, ...)
## S3 method for class 'summary.stls':
print(x, digits= max(3, getOption("digits") - 3), ...)
## S3 method for class 'stls':
coef(object,...)
## S3 method for class 'stls':
vcov(object,...)
## S3 method for class 'stls':
residuals(object,...)
## S3 method for class 'stls':
fitted(object,...)

Arguments

x, object

an object of class "stls"

formula

a symbolic description of the model to be estimated

data

an optional data frame

point

the value of truncation (the default is 0)

direction

the direction of truncation, either "left" (the default) or "right"

beta

the method of determining the starting values of the regression coefficients (See Details for more information):

• The default method is"ml", meaning that the estimated regression coefficients from fitting a maximum likelihood model fo

covar

logical. Indicates whether or not the covariance matrix should be estimated. If TRUE the covariance matrix is estimated using bootstrap. The default number of replicates is 2000 but this can be adjusted (see argument ...). Howeve

na.action

a function which indicates what should happen when the data contain NAs.

digits

the number of digits to be printed

level

the desired level of confidence, for confidence intervals provided by summary.stls. A number between 0 and 1. The default value is 0.95.

...

additional arguments. For stls the number of bootstrap replicates can be adjusted by setting R=the desired number of replicates. Also the control argument of optim can b

Value

• stls returns an object of class "stls". The function summary prints a summary of the results, including two types of confidence intervals (normal approximation and percentile method). The generic accessor functions coef, fitted, residuals and vcov extract various useful features of the value returned by stls An object of class "stls", a list with elements:
• coefficients
  the named vector of coefficients
• startcoef
  the starting values of the regression coefficients used by optim
• value
  the value of the objective function corresponding to coefficients
• counts
  number of iterations used by optim. See the documentation for optim for further details
• convergence
  from optim. An integer code. 0 indicates successful completion. Possible error codes are 1 indicating that the iteration limit maxit had been reached. 10 indicating degeneracy of the Nelder--Mead simplex.
• message
  from optim. A character string giving any additional information returned by the optimizer, or NULL.
• residuals
  the residuals of the model
• fitted.values
  the fitted values
• df.residual
  the residual degrees of freedom
• call
  the matched call
• covariance
  if covar=TRUE, the estimated covariance matrix
• R
  if covar=TRUE, the number of bootstrap replicates
• bootrepl
  if covar=TRUE, the bootstrap replicates

Details

Uses optim ("Nelder--Mead" method) to minimize the objective function described in Powell (1986) wrt the vector of regression coefficients in order to find the STLS estimates (see Karlsson and Lindmark 2014 for more detailed information and background). The maximum number of iterations is set at 2000, but this can be adjusted by setting control=list(maxit=...) (for more information see the documentation for optim). As the starting values of the regression coefficients can have a great impact on the result of the minimization it is recommended to use one of the methods for generating these rather than supplying the values manually (unless one is confident that one has a good idea of what the starting values should be).

References

Karlsson, M., Lindmark, A. (2014) truncSP: An R Package for Estimation of Semi-Parametric Truncated Linear Regression Models, Journal of Statistical Software, 57(14), pp 1--19, http://www.jstatsoft.org/v57/i14/ Powell, J. (1986) Symmetrically Trimmed Least Squares Estimation for Tobit Models, Econometrika, 54(6), pp 1435--1460

See Also

stls.fit, the function that does the actual fitting qme, for estimation of models with truncated response variables using the QME estimator lt, for estimation of models with truncated response variables using the LT estimator truncreg for estimating models with truncated response variables by maximum likelihood, assuming Gaussian errors

Examples

##Simulate a data.frame
n <- 10000
x1 <- runif(n,0,10)
x2 <- runif(n,0,10)
x3 <- runif(n,-5,5)
y <- 1-2*x1+x2+2*x3+rnorm(n,0,2)
d <- data.frame(y=y,x1=x1,x2=x2,x3=x3)

##Use a truncated subsample
dtrunc <- subset(d, y>0)
  
##Use stls to estimate the model
stls(y~x1+x2+x3, dtrunc, point=0, direction="left", beta="ml", covar=FALSE)


##Example using data "PM10trunc"
data(PM10trunc)

stlspm10 <- 
stls(PM10~cars+temp+wind.speed+temp.diff+wind.dir+hour+day, data=PM10trunc, point=2)

summary(stlspm10)

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