stls: Estimation of truncated regression models using the...

In truncSP: Semi-parametric estimators of truncated regression models

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, ...)
## S4 method for signature 'stls'
print(x, digits = max(3, getOption("digits") - 3), ...)
## S4 method for signature 'stls'
summary(object, level=0.95, ...)
## S4 method for signature 'summary.stls'
print(x, digits= max(3, getOption("digits") - 3), ...)
## S4 method for signature 'stls'
coef(object,...)
## S4 method for signature 'stls'
vcov(object,...)
## S4 method for signature 'stls'
residuals(object,...)
## S4 method for signature '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 for truncated regression, assuming Gaussian errors, are used. The maximum likelihood model is fitted using truncreg. Method "ols" means that the estimated regression coefficients from fitting a linear model with lm. The third option is to manually provide starting values as either a vector, column matrix or row matrix.
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 ...). However, since the bootstrap procedure is time-consuming the default is covar=FALSE.
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 be set by control=list() (for more information, see Details).

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).

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

Author(s)

Anita Lindmark and Maria Karlsson

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)

truncSP documentation built on May 1, 2019, 8:46 p.m.