# Tobit Regression Fits a Tobit regression model. ```r tobit(Lower = 0, Upper = Inf, lmu = "identitylink", lsd = "loglink", imu = NULL, isd = NULL, type.fitted = c("uncensored", "censored", "mean.obs"), byrow.arg = FALSE, imethod = 1, zero = "sd") ``` ## Arguments - `Lower`: Numeric. It is the value $L$ described below. Any value of the linear model $x_i^T beta$ that is less than this lowerbound is assigned this value. Hence this should be the smallest possible value in the response variable. May be a vector (see below for more information). - `Upper`: Numeric. It is the value $U$ described below. Any value of the linear model $x_i^T beta$ that is greater than this upperbound is assigned this value. Hence this should be the largest possible value in the response variable. May be a vector (see below for more information). - `lmu, lsd`: Parameter link functions for the mean and standard deviation parameters. See `Links` for more choices. The standard deviation is a positive quantity, therefore a log link is its default. - `imu, isd, byrow.arg`: See `CommonVGAMffArguments` for information. - `type.fitted`: Type of fitted value returned. The first choice is default and is the ordinary uncensored or unbounded linear model. If `"censored"` then the fitted values in the interval $[L, U]$. If `"mean.obs"` then the mean of the observations is returned; this is a doubly truncated normal distribution augmented by point masses at the truncation points (see `dtobit`). See `CommonVGAMffArguments` for more information. - `imethod`: Initialization method. Either 1 or 2 or 3, this specifies some methods for obtaining initial values for the parameters. See `CommonVGAMffArguments` for information. - `zero`: A vector, e.g., containing the value 1 or 2. If so, the mean or standard deviation respectively are modelled as an intercept-only. Setting `zero = NULL` means both linear/additive predictors are modelled as functions of the explanatory variables. See `CommonVGAMffArguments` for more information. ## Details The Tobit model can be written $$ y_i^* = x_i^T \beta + \varepsilon_i%y_i^* = x_i^T beta + e_i $$ where the $e_i ~ N(0,sigma^2)$ independently and $i=1,...,n$. However, we measure $y_i = y_i^*$ only if $y_i^* > L$ and $y_i^* < U$ for some cutpoints $L$ and $U$. Otherwise we let $y_i=L$ or $y_i=U$, whatever is closer. The Tobit model is thus a multiple linear regression but with censored responses if it is below or above certain cutpoints. The defaults for `Lower` and `Upper` and `lmu` correspond to the **standard** Tobit model. Fisher scoring is used for the standard and nonstandard models. By default, the mean $x_i^T beta$ is the first linear/additive predictor, and the log of the standard deviation is the second linear/additive predictor. The Fisher information matrix for uncensored data is diagonal. The fitted values are the estimates of $x_i^T beta$. ## Warning If values of the response and `Lower` and/or `Upper` are not integers then there is the danger that the value is wrongly interpreted as uncensored. For example, if the first 10 values of the response were `runif(10)` and `Lower` was assigned these value then testing `y[1:10] == Lower[1:10]` is numerically fraught. Currently, if any `y < Lower` or `y > Upper` then a warning is issued. The function `round2` may be useful. ## Returns An object of class `"vglmff"` (see `vglmff-class`). The object is used by modelling functions such as `vglm`, and `vgam`. ## References Tobin, J. (1958). Estimation of relationships for limited dependent variables. **Econometrica** 26 , 24--36. ## Author(s) Thomas W. Yee ## Note The response can be a matrix. If so, then `Lower` and `Upper` are recycled into a matrix with the number of columns equal to the number of responses, and the recycling is done row-wise **if** `byrow.arg = TRUE`. The default order is as `matrix`, which is `byrow.arg = FALSE`. For example, these are returned in `fit4@misc$Lower` and `fit4@misc$Upper` below. If there is no censoring then `uninormal` is recommended instead. Any value of the response less than `Lower` or greater than `Upper` will be assigned the value `Lower` and `Upper` respectively, and a warning will be issued. The fitted object has components `censoredL` and `censoredU` in the `extra` slot which specifies whether observations are censored in that direction. The function `cens.normal` is an alternative to `tobit()`. When obtaining initial values, if the algorithm would otherwise want to fit an underdetermined system of equations, then it uses the entire data set instead. This might result in rather poor quality initial values, and consequently, monitoring convergence is advised. ## See Also `rtobit`, `cens.normal`, `uninormal`, `double.cens.normal`, `posnormal`, `CommonVGAMffArguments`, `round2`, `mills.ratio`, `margeff`, `rnorm`. ## Examples ```r # Here, fit1 is a standard Tobit model and fit2 is nonstandard tdata <- data.frame(x2 = seq(-1, 1, length = (nn <- 100))) set.seed(1) Lower <- 1; Upper <- 4 # For the nonstandard Tobit model tdata <- transform(tdata, Lower.vec = rnorm(nn, Lower, 0.5), Upper.vec = rnorm(nn, Upper, 0.5)) meanfun1 <- function(x) 0 + 2*x meanfun2 <- function(x) 2 + 2*x meanfun3 <- function(x) 3 + 2*x tdata <- transform(tdata, y1 = rtobit(nn, mean = meanfun1(x2)), # Standard Tobit model y2 = rtobit(nn, mean = meanfun2(x2), Lower = Lower, Upper = Upper), y3 = rtobit(nn, mean = meanfun3(x2), Lower = Lower.vec, Upper = Upper.vec), y4 = rtobit(nn, mean = meanfun3(x2), Lower = Lower.vec, Upper = Upper.vec)) with(tdata, table(y1 == 0)) # How many censored values? with(tdata, table(y2 == Lower | y2 == Upper)) # Ditto with(tdata, table(attr(y2, "cenL"))) with(tdata, table(attr(y2, "cenU"))) fit1 <- vglm(y1 ~ x2, tobit, data = tdata, trace = TRUE) coef(fit1, matrix = TRUE) summary(fit1) fit2 <- vglm(y2 ~ x2, tobit(Lower = Lower, Upper = Upper, type.f = "cens"), data = tdata, trace = TRUE) table(fit2@extra$censoredL) table(fit2@extra$censoredU) coef(fit2, matrix = TRUE) fit3 <- vglm(y3 ~ x2, tobit(Lower = with(tdata, Lower.vec), Upper = with(tdata, Upper.vec), type.f = "cens"), data = tdata, trace = TRUE) table(fit3@extra$censoredL) table(fit3@extra$censoredU) coef(fit3, matrix = TRUE) # fit4 is fit3 but with type.fitted = "uncen". fit4 <- vglm(cbind(y3, y4) ~ x2, tobit(Lower = rep(with(tdata, Lower.vec), each = 2), Upper = rep(with(tdata, Upper.vec), each = 2), byrow.arg = TRUE), data = tdata, crit = "coeff", trace = TRUE) head(fit4@extra$censoredL) # A matrix head(fit4@extra$censoredU) # A matrix head(fit4@misc$Lower) # A matrix head(fit4@misc$Upper) # A matrix coef(fit4, matrix = TRUE) ## Not run: # Plot fit1--fit4 par(mfrow = c(2, 2)) plot(y1 ~ x2, tdata, las = 1, main = "Standard Tobit model", col = as.numeric(attr(y1, "cenL")) + 3, pch = as.numeric(attr(y1, "cenL")) + 1) legend(x = "topleft", leg = c("censored", "uncensored"), pch = c(2, 1), col = c("blue", "green")) legend(-1.0, 2.5, c("Truth", "Estimate", "Naive"), lwd = 2, col = c("purple", "orange", "black"), lty = c(1, 2, 2)) lines(meanfun1(x2) ~ x2, tdata, col = "purple", lwd = 2) lines(fitted(fit1) ~ x2, tdata, col = "orange", lwd = 2, lty = 2) lines(fitted(lm(y1 ~ x2, tdata)) ~ x2, tdata, col = "black", lty = 2, lwd = 2) # This is simplest but wrong! plot(y2 ~ x2, data = tdata, las = 1, main = "Tobit model", col = as.numeric(attr(y2, "cenL")) + 3 + as.numeric(attr(y2, "cenU")), pch = as.numeric(attr(y2, "cenL")) + 1 + as.numeric(attr(y2, "cenU"))) legend(x = "topleft", leg = c("censored", "uncensored"), pch = c(2, 1), col = c("blue", "green")) legend(-1.0, 3.5, c("Truth", "Estimate", "Naive"), lwd = 2, col = c("purple", "orange", "black"), lty = c(1, 2, 2)) lines(meanfun2(x2) ~ x2, tdata, col = "purple", lwd = 2) lines(fitted(fit2) ~ x2, tdata, col = "orange", lwd = 2, lty = 2) lines(fitted(lm(y2 ~ x2, tdata)) ~ x2, tdata, col = "black", lty = 2, lwd = 2) # This is simplest but wrong! plot(y3 ~ x2, data = tdata, las = 1, main = "Tobit model with nonconstant censor levels", col = as.numeric(attr(y3, "cenL")) + 2 + as.numeric(attr(y3, "cenU") * 2), pch = as.numeric(attr(y3, "cenL")) + 1 + as.numeric(attr(y3, "cenU") * 2)) legend(x = "topleft", pch = c(2, 3, 1), col = c(3, 4, 2), leg = c("censoredL", "censoredU", "uncensored")) legend(-1.0, 3.5, c("Truth", "Estimate", "Naive"), lwd = 2, col = c("purple", "orange", "black"), lty = c(1, 2, 2)) lines(meanfun3(x2) ~ x2, tdata, col = "purple", lwd = 2) lines(fitted(fit3) ~ x2, tdata, col = "orange", lwd = 2, lty = 2) lines(fitted(lm(y3 ~ x2, tdata)) ~ x2, tdata, col = "black", lty = 2, lwd = 2) # This is simplest but wrong! plot(y3 ~ x2, data = tdata, las = 1, main = "Tobit model with nonconstant censor levels", col = as.numeric(attr(y3, "cenL")) + 2 + as.numeric(attr(y3, "cenU") * 2), pch = as.numeric(attr(y3, "cenL")) + 1 + as.numeric(attr(y3, "cenU") * 2)) legend(x = "topleft", pch = c(2, 3, 1), col = c(3, 4, 2), leg = c("censoredL", "censoredU", "uncensored")) legend(-1.0, 3.5, c("Truth", "Estimate", "Naive"), lwd = 2, col = c("purple", "orange", "black"), lty = c(1, 2, 2)) lines(meanfun3(x2) ~ x2, data = tdata, col = "purple", lwd = 2) lines(fitted(fit4)[, 1] ~ x2, tdata, col="orange", lwd = 2, lty = 2) lines(fitted(lm(y3 ~ x2, tdata)) ~ x2, data = tdata, col = "black", lty = 2, lwd = 2) # This is simplest but wrong! ## End(Not run) ```

tobit function