R vs SAS Tobit Regression

Tobit Regression Comparison

The following table shows the tobit regression analysis, the capabilities of each language, and whether or not the results from each language match.

Analysis Supported in R Supported in SAS Results Match Notes
Tobit Regression (normal distributed data assumption) Yes Yes Yes The results from censReg::censReg and survival::survreg match the SAS PROC LIFEREG results

Comparison Results

Normally distributed data assumption

Here is a table of comparison values between the R functions censReg::censReg, survival::survreg, VGAM::vglm, and SAS PROC LIFEREG for the dataset used. The statistics around the treatment effect (difference between group A and B, B-A) are provided. Further we also present the estimate of \(\sigma\). All numbers are rounded to 4 digits

Statistic censReg() survreg() vglm() LIFEREG Match Notes
Treatment effect 1.8225 1.8225 1.8226 1.8225 Yes see below
Standard error 0.8061 0.8061 0.7942 0.8061 Yes see below
p-value 0.0238 0.0238 0.0217 0.0238 Yes see below
95% CI (Wald based) 0.2427 ; 3.4024 0.2427 ; 3.4024 0.2661 ; 3.3791 0.2427 ; 3.4024 Yes see below
\(\sigma\) 1.7316 1.7316 1.7317 1.7316 Yes see below

Note: The results of VGAM::vglm() are slightly different since an iteratively reweighted least squares (IRLS) algorithm is used for estimation.

Summary and Recommendation

Comparison between SAS PROC LIFEREG and R functions censReg::censReg and survival::survreg show identical results for the dataset tried.

Historically and typically the Tobit model is based on the assumption of normal distributed data. Within SAS PROC LIFEREG and R survival::survreg multiple other different distributional assumption are possible. These include weibull, exponential, gaussian, logistic, lognormal and loglogistic for survival::survreg. These include EXPONENTIAL, GAMMA, LLOGISTIC, LOGISTIC, LOGNORMAL, NORMAL, WEIBULL for PROC LIFEREG.

References

Breen, R. (1996). Regression models. SAGE Publications, Inc., https://doi.org/10.4135/9781412985611

Tobin, James (1958). “Estimation of Relationships for Limited Dependent Variables”. Econometrica. 26 (1): 24-36. doi:10.2307/1907382