> mdata  <-read.table("wage.csv", header=T, sep=",")
> work   <-mdata[,1]
> hour   <-mdata[,2]
> child  <-mdata[,3]
> age    <-mdata[,4]
> edu    <-mdata[,5]
> wage   <-mdata[,6]
> hinc   <-mdata[,7]
> year   <-mdata[,8]
> mydata <-data.frame(work,hour,child,age,edu,wage,hinc,year)
> library(sampleSelection)
> mysampsel <-selection(work~edu+year+age+child+hinc, wage~edu+year)
> summary(mysampsel)
--------------------------------------------
Tobit 2 model (sample selection model)
Maximum Likelihood estimation
Newton-Raphson maximisation, 3 iterations
Return code 2: successive function values within tolerance limit
Log-Likelihood: -156.4665 
100 observations (50 censored and 50 observed)
11 free parameters (df = 89)
Probit selection equation:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.84499    1.18624   1.555 0.123417    
edu          0.15487    0.06955   2.227 0.028482 *  
year         0.07992    0.01977   4.043 0.000112 ***
age         -0.09418    0.02197  -4.286 4.59e-05 ***
child       -1.00061    0.39124  -2.558 0.012233 *  
hinc        -0.06654    0.03649  -1.823 0.071628 .  
Outcome equation:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept) -0.56580    2.39752  -0.236   0.8140  
edu          0.39974    0.16773   2.383   0.0193 *
year         0.01349    0.03710   0.364   0.7170  
   Error terms:
      Estimate Std. Error t value Pr(>|t|)    
sigma   2.2457     0.2956   7.597  2.9e-11 ***
rho    -0.6005     0.2400  -2.502   0.0142 *  
---
Signif. codes:  0 �e***�f 0.001 �e**�f 0.01 �e*�f 0.05 �e.�f 0.1 �e �f 1
--------------------------------------------