lt(formula, data, point = 0, direction = "left", clower = "ml", const = 1, cupper = 2,
beta = "ml", covar = FALSE, na.action, ...)
## S3 method for class 'lt':
print(x, digits = max(3, getOption("digits") - 3), ...)
## S3 method for class 'lt':
summary(object, level=0.95, ...)
## S3 method for class 'summary.lt':
print(x, digits= max(3, getOption("digits") - 3), ...)
## S3 method for class 'lt':
coef(object,...)
## S3 method for class 'lt':
vcov(object,...)
## S3 method for class 'lt':
residuals(object,...)
## S3 method for class 'lt':
fitted(object,...)"lt""left" (the default) or "right""ml" meaning that the residual standard deviation from fitting a maximum likelihood model for truncated regression, using const=0.5 would give a lower threshold value that is half the original size. The default value is 1.cupper=2 (the default value) the upper threshold value is two time"ml", meaning that the estimated regression coefficients from fitting a maximum likelihood model foTRUE the covariance matrix is estimated using bootstrap. The default number of replicates is 2000 but this can be adjusted (see argument ...). HoweveNAs.summary.lt. A number between 0 and 1. The default value is 0.95.lt the number of bootstrap replicates can be adjusted by setting R=the desired number of replicates. Also the control argument of optim can be lt returns an object of class "lt".
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 lt
An object of class "lt", a list with elements:optimcoefficientsoptim. See the documentation for optim for further detailsoptim. 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.optim. A character string giving any additional information returned by the optimizer, or NULL.covar=TRUE, the estimated covariance matrixcovar=TRUE, the number of bootstrap replicatescovar=TRUE, the bootstrap replicatesoptim using the "Nelder--Mead" method, and a maximum number of iterations of 2000. The maximum number of iterations can be adjusted by setting control=list(maxit=...) (for more information see the documentation for optim).
It is recommended to use one of the methods for generating the starting values of the regression coefficients (see argument beta) rather than supplying these manually, unless one is confident that one has a good idea of what these should be. This because the starting values can have a great impact on the result of the minimization.
Note that setting cupper=1 means that the LT estimates will coincide with the estimates from the Quadratic Mode Estimator (see function qme). For more detailed information see Karlsson and Lindmark (2014).lt.fit, the function that does the actual fitting
qme, for estimation of models with truncated response variables using the QME estimator
stls, for estimation of models with truncated response variables using the STLS estimator
truncreg for estimating models with truncated response variables by maximum likelihood, assuming Gaussian errors##Simulate a data.frame (model with asymmetrically distributed errors)
n <- 10000
x1 <- runif(n,0,10)
x2 <- runif(n,0,10)
x3 <- runif(n,-5,5)
eps <- rexp(n,0.2)- 5
y <- 2-2*x1+x2+2*x3+eps
d <- data.frame(y=y,x1=x1,x2=x2,x3=x3)
##Use a truncated subsample
dtrunc <- subset(d, y>0)
##Use lt to consistently estimate the slope parameters
lt(y~x1+x2+x3, dtrunc, point=0, direction="left", clower="ml", const=1,
cupper=2, beta="ml", covar=FALSE)
##Example using data "PM10trunc"
data(PM10trunc)
ltpm10 <- lt(PM10~cars+temp+wind.speed+temp.diff+wind.dir+hour+day,
data=PM10trunc, point=2, control=list(maxit=2500))
summary(ltpm10)Run the code above in your browser using DataCamp Workspace