# Function for fitting LT Function to find LT estimates of the regression coefficients for regression models with truncated response variables. Uses `optim`. Intended to be called through `lt`, not on its own, since `lt` also transforms data into the correct form etc. ```r lt.fit(formula, mf, point, direction, bet, cl, cu, ...) ``` ## Arguments - `formula`: a symbolic description of the model to be estimated - `mf`: the `model.frame` containing the variables to be used when fitting the model. `lt` transforms the model frame to the correct form before calling `lt.fit`. If `lt.fit` is called on its own the model frame needs to be transformed manually. - `point`: point of truncation - `direction`: direction of truncation - `bet`: starting values to be used by `optim`. Column matrix with p rows. - `cl`: lower threshold value to be used, number or numeric vector of length 1. (See `lt`, argument `clower`, for more information). - `cu`: upper threshold value to be used, number or numeric vector of length 1. (See `lt`, argument `cupper`, for more information). - ``...``: additional arguments to be passed to `optim` (see the documentation for `lt` for further details). ## Returns a list with components: - **startcoef**: the starting values of the regression coefficients used by `optim` - **coefficients**: the named vector of 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 - **df.residual**: the residual degrees of freedom - **fitted.values**: the fitted values ## Author(s) Anita Lindmark and Maria Karlsson ## See Also `lt` ## Examples ```r require(utils) ##Model frame n <- 10000 x <- rnorm(n,0,2) y <- 2+x+4*rnorm(n) d <- data.frame(y=y, x=x) dl0 <- subset(d, y>0) mf <- model.frame(y~x, data=dl0) ##Starting values and threshold values lmmod <- lm(data=mf) bet <- lmmod$coef bet <- matrix(bet) cl <- sqrt(deviance(lmmod)/df.residual(lmmod)) cu <- 2*cl str(lt. <- lt.fit(y~x,mf,point=0,direction="left",bet,cl,cu)) ```

lt.fit function