Estimates ordinal probit switching regression (OPSR) models - a Heckman type selection model with an ordinal selection and continuous outcomes. Different model specifications are allowed for each treatment/regime.
Install from CRAN:
install.packages("OPSR")You can install the development version of OPSR from GitHub with:
# install.packages("devtools")
devtools::install_github("dheimgartner/OPSR")OPSR can be used whenever the ordinal treatment is not
assigned exogenously but self-selected and one is interested in the
treatment effect on a continuous outcome. The motivating example is
telework frequency (conceptually, the treatment) and vehicle miles
driven (the outcome of interest). We assume that two distinct processes
lead people to choose a certain telework frequency and how mobile they
are. Further and most importantly, the possibility of selection on
unobservables exists. I.e., unobserved factors (as part of the errors of
the two processes) might influence both the ordinal and continuous
outcome. This leads to error correlation which needs to be accounted for
in the modeling effort in spirit of Heckman.
library(OPSR)
#>
#> Please cite the 'OPSR' package as:
#> Heimgartner, D. and X. Wang (2024) OPSR: A package for estimating ordinal probit switching regression models in R. tbc.
#>
#> Wang, X. and P. L. Mokhtarian (2024) Examining the treatment effect of teleworking on vehicle-miles driven: Applying an ordered probit selection model and incorporating the role of travel stress, Transportation Research Part A, 186, 104072, doi:10.1016/j.tra.2024.104072.
#>
#> If you have questions, suggestions, or comments regarding the 'OPSR' package, please open an issue on https://github.com/dheimgartner/OPSR
#>
#> To see these entries in BibTeX format, use 'citation('OPSR')'
f <-
## ordinal and continuous outcome
twing_status | vmd_ln ~
## selection model
edu_2 + edu_3 + hhincome_2 + hhincome_3 +
flex_work + work_fulltime + twing_feasibility +
att_proactivemode + att_procarowning +
att_wif + att_proteamwork +
att_tw_effective_teamwork + att_tw_enthusiasm + att_tw_location_flex |
## outcome model NTW
female + age_mean + age_mean_sq +
race_black + race_other +
vehicle + suburban + smalltown + rural +
work_fulltime +
att_prolargehouse + att_procarowning +
region_waa |
## outcome model NUTW
edu_2 + edu_3 + suburban + smalltown + rural +
work_fulltime +
att_prolargehouse + att_proactivemode + att_procarowning |
## outcome model UTW
female + hhincome_2 + hhincome_3 +
child + suburban + smalltown + rural +
att_procarowning +
region_waa
fit <- opsr(f, telework_data, printLevel = 0)
texreg::screenreg(fit, beside = TRUE, include.pseudoR2 = TRUE, include.R2 = TRUE)
#>
#> ===============================================================================================
#> Structural Selection Outcome 1 Outcome 2 Outcome 3
#> -----------------------------------------------------------------------------------------------
#> kappa1 1.15 ***
#> (0.17)
#> kappa2 2.38 ***
#> (0.18)
#> sigma1 1.18 ***
#> (0.05)
#> sigma2 1.24 ***
#> (0.07)
#> sigma3 1.43 ***
#> (0.04)
#> rho1 0.07
#> (0.10)
#> rho2 0.13
#> (0.07)
#> rho3 0.30 ***
#> (0.07)
#> edu_2 0.24 0.22
#> (0.14) (0.34)
#> edu_3 0.40 ** 0.67 *
#> (0.13) (0.33)
#> hhincome_2 0.09 0.47
#> (0.11) (0.26)
#> hhincome_3 0.28 * 0.30
#> (0.11) (0.25)
#> flex_work 0.28 **
#> (0.10)
#> work_fulltime 0.25 * 0.44 *** 0.71 ***
#> (0.10) (0.13) (0.17)
#> twing_feasibility 0.13 ***
#> (0.01)
#> att_proactivemode 0.08 * -0.18 *
#> (0.04) (0.08)
#> att_procarowning -0.08 0.12 0.17 0.25 ***
#> (0.04) (0.07) (0.09) (0.06)
#> att_wif 0.12 **
#> (0.04)
#> att_proteamwork 0.09 *
#> (0.04)
#> att_tw_effective_teamwork 0.32 ***
#> (0.04)
#> att_tw_enthusiasm 0.09 *
#> (0.04)
#> att_tw_location_flex 0.08 *
#> (0.04)
#> (Intercept) 3.74 *** 2.42 *** 2.38 ***
#> (0.28) (0.39) (0.29)
#> female -0.21 * -0.37 ***
#> (0.11) (0.11)
#> age_mean 0.01 **
#> (0.00)
#> age_mean_sq -0.00
#> (0.00)
#> race_black -0.39
#> (0.24)
#> race_other -0.02
#> (0.18)
#> vehicle 0.13 **
#> (0.05)
#> suburban 0.01 0.45 * 0.29 *
#> (0.16) (0.17) (0.14)
#> smalltown 0.41 * 0.22 0.31
#> (0.18) (0.29) (0.28)
#> rural 0.49 * 0.82 * 0.85 **
#> (0.23) (0.32) (0.33)
#> att_prolargehouse 0.19 *** 0.16 *
#> (0.05) (0.08)
#> region_waa -0.24 * -0.27 *
#> (0.11) (0.11)
#> child 0.19 **
#> (0.06)
#> -----------------------------------------------------------------------------------------------
#> AIC 7189.51 7189.51 7189.51 7189.51 7189.51
#> BIC 7490.10 7490.10 7490.10 7490.10 7490.10
#> Log Likelihood -3538.76 -3538.76 -3538.76 -3538.76 -3538.76
#> Pseudo R^2 (EL) 0.49 0.49 0.49 0.49 0.49
#> Pseudo R^2 (MS) 0.46 0.46 0.46 0.46 0.46
#> R^2 0.24 0.24 0.24 0.24 0.24
#> Num. obs. 1584 1584 1584 1584 1584
#> ===============================================================================================
#> *** p < 0.001; ** p < 0.01; * p < 0.05