tests/probit.R

In sampleSelection2: Sample Selection Models

library( "sampleSelection2" )
library( "lmtest" )
options( digits = 3 )

## loading and preparing data
data( Mroz87 )
Mroz87$kids  <- ( Mroz87$kids5 + Mroz87$kids618 > 0 )
Mroz87$age30.39 <- Mroz87$age < 40
Mroz87$age50.60 <- Mroz87$age >= 50

## A simple single MC trial: note probit assumes normal errors
set.seed( 20080225 )
simDat <- data.frame( x = runif( 100 ) )
simDat$e <- rnorm( 100 )
simDat$y <- 2 * simDat$x + simDat$e
probitResult <- probit( (y > 0) ~ x, data = simDat )
print( probitResult )
summary( probitResult )
coef( probitResult )
stdEr( probitResult )
vcov( probitResult )
nobs( probitResult )
nObs( probitResult )
df.residual( probitResult )
logLik( probitResult )
model.frame( probitResult )
model.matrix( probitResult )
fitted( probitResult )
all.equal( fitted( probitResult ), predict( probitResult, type = "response" ) )
all.equal( fitted( probitResult )[ 11:22 ],
   predict( probitResult, newdata = simDat[ 11:22, ], type = "response" ) )
linearPredictors( probitResult )
all.equal( linearPredictors( probitResult ), predict( probitResult ) )
all.equal( linearPredictors( probitResult )[ 11:22 ],
   predict( probitResult, newdata = simDat[ 11:22, ] ) )
residuals( probitResult, type = "response" )
residuals( probitResult, type = "pearson" )
residuals( probitResult, type = "deviance" )
all.equal( residuals( probitResult, type = "deviance" ),
   residuals( probitResult ) )
all.equal( residuals( probitResult, type = "response" ),
   ( simDat$y > 0 ) - fitted( probitResult ) )
lrtest( probitResult )
all.equal( lrtest( probitResult, (y > 0) ~ 1 ), lrtest( probitResult ) )
probitResult0 <- probit( (y > 0) ~ 1, data = simDat )
all.equal( lrtest( probitResult, probitResult0 ), lrtest( probitResult ) )

# estimation with glm()
probitResult2 <- glm( (y > 0) ~ x, family = binomial( link = "probit" ),
   data = simDat )
all.equal( coef( probitResult ), coef( probitResult2 ), tol = 1e-4 )
all.equal( stdEr( probitResult ), stdEr( probitResult2 ), tol = 1e-1 )
all.equal( logLik( probitResult ), logLik( probitResult2 ) )
all.equal( model.frame( probitResult ), model.frame( probitResult2 ) )
all.equal( model.matrix( probitResult ), model.matrix( probitResult2 ) )
all.equal( fitted( probitResult ), fitted( probitResult2 ), tol = 1e-4 )
all.equal( predict( probitResult, type = "response" ),
   predict( probitResult2, type = "response" ), tol = 1e-4 )
all.equal( predict( probitResult, newdata = simDat[ 5:55, ], type = "response" ),
   predict( probitResult2, newdata = simDat[ 5:55, ], type = "response" ),
   tol = 1e-4 )
all.equal( predict( probitResult ), predict( probitResult2 ), tol = 1e-4 )
all.equal( predict( probitResult, newdata = simDat[ 5:55, ] ),
   predict( probitResult2, newdata = simDat[ 5:55, ] ),
   tol = 1e-4 )
all.equal( residuals( probitResult, type = "response" ),
   residuals( probitResult2, type = "response" ), tol = 1e-4 )
all.equal( residuals( probitResult, type = "pearson" ),
   residuals( probitResult2, type = "pearson" ), tol = 1e-4 )
all.equal( residuals( probitResult, type = "deviance" ),
   residuals( probitResult2, type = "deviance" ), tol = 1e-4 )
all.equal( residuals( probitResult ), residuals( probitResult2 ), tol = 1e-4 )
lrtest( probitResult2 )
all.equal( lrtest( probitResult2 ), lrtest( probitResult ) )

# estimation with equal weights
simDat$we <- rep( 0.5, 100 )
probitResultWe <- probit( (y > 0) ~ x, weights = simDat$we, data = simDat )
print( probitResultWe )
summary( probitResultWe )
all.equal( coef( probitResult ), coef( probitResultWe ) )
all.equal( stdEr( probitResult ), stdEr( probitResultWe ) * sqrt(0.5) )
all.equal( vcov( probitResult ), vcov( probitResultWe ) * 0.5 )
all.equal( nobs( probitResult ), nobs( probitResultWe ) )
all.equal( nObs( probitResult ), nObs( probitResultWe ) )
all.equal( df.residual( probitResult ), df.residual( probitResultWe ) )
all.equal( logLik( probitResult ) * 0.5, logLik( probitResultWe ) )
model.frame( probitResultWe )
all.equal( model.matrix( probitResult ), model.matrix( probitResultWe ) )
all.equal( fitted( probitResult ), fitted( probitResultWe ) )
all.equal( predict( probitResult, type = "response" ),
   predict( probitResultWe, type = "response" ), tol = 1e-4 )
all.equal( predict( probitResult, newdata = simDat[ 5:55, ], type = "response" ),
   predict( probitResultWe, newdata = simDat[ 5:55, ], type = "response" ),
   tol = 1e-4 )
all.equal( predict( probitResult ), predict( probitResultWe ), tol = 1e-4 )
all.equal( predict( probitResult, newdata = simDat[ 5:55, ] ),
   predict( probitResultWe, newdata = simDat[ 5:55, ] ),
   tol = 1e-4 )
all.equal( linearPredictors( probitResult ), linearPredictors( probitResultWe ) )
all.equal( residuals( probitResult, type = "response" ),
   residuals( probitResultWe, type = "response" ) )
all.equal( residuals( probitResult, type = "pearson" ),
   residuals( probitResultWe, type = "pearson" ) / sqrt( 0.5 ) )
all.equal( residuals( probitResult, type = "deviance" ),
   residuals( probitResultWe, type = "deviance" ) / sqrt( 0.5 ) )
all.equal( residuals( probitResultWe, type = "response" ),
   ( simDat$y > 0 ) - fitted( probitResultWe ) )
all.equal( coef( probitResult ), coef( probitResultWe ), tol = 1e-4 )
all.equal( logLik( probitResult ) * 0.5, logLik( probitResultWe ) * 1 )
lrtest( probitResultWe )

# estimation with equal weights with glm()
probitResultWe2 <- glm( (y > 0) ~ x, family = binomial( link = "probit" ),
   weights = simDat$we, data = simDat )
all.equal( coef( probitResultWe ), coef( probitResultWe2 ), tol = 1e-4 )
all.equal( stdEr( probitResultWe ), stdEr( probitResultWe2 ), tol = 1e-1 )
logLik( probitResultWe2 )
all.equal( model.frame( probitResultWe ), model.frame( probitResultWe2 ) )
all.equal( model.matrix( probitResultWe ), model.matrix( probitResultWe2 ) )
all.equal( fitted( probitResultWe ), fitted( probitResultWe2 ), tol = 1e-4 )
all.equal( predict( probitResultWe, type = "response" ),
   predict( probitResultWe2, type = "response" ), tol = 1e-4 )
all.equal( predict( probitResultWe, newdata = simDat[ 5:55, ], type = "response" ),
   predict( probitResultWe2, newdata = simDat[ 5:55, ], type = "response" ),
   tol = 1e-4 )
all.equal( predict( probitResultWe ), predict( probitResultWe2 ), tol = 1e-4 )
all.equal( predict( probitResultWe, newdata = simDat[ 5:55, ] ),
   predict( probitResultWe2, newdata = simDat[ 5:55, ] ),
   tol = 1e-4 )
all.equal( residuals( probitResultWe, type = "response" ),
   residuals( probitResultWe2, type = "response" ), tol = 1e-4 )
all.equal( residuals( probitResultWe, type = "pearson" ),
   residuals( probitResultWe2, type = "pearson" ), tol = 1e-4 )
all.equal( residuals( probitResultWe, type = "deviance" ),
   residuals( probitResultWe2, type = "deviance" ), tol = 1e-4 )

# estimation with weights to account for stratified sampling
# proportion in the "population"
yProbPop <- sum( simDat$y > 0 ) / nrow( simDat )
yProbPop
# stratified sample with all observations with y = 0
simDat$sampStrat <- simDat$y <= 0 | rnorm( nrow( simDat ) ) > 0.25
sum( simDat$sampStrat )
# stratified sample of y and x
simDatSamp <- simDat[ simDat$sampStrat, ]
# proportion in the "sample"
yProbSamp <- sum( simDatSamp$y > 0 ) / nrow( simDatSamp )
yProbSamp
# unweighted estimation (ignoring the stratification)
probitResultStrat <- probit( (y > 0) ~ x, data = simDatSamp )
summary( probitResultStrat )
nobs( probitResultStrat )
nObs( probitResultStrat )
df.residual( probitResultStrat )
logLik( probitResultStrat )
lrtest( probitResultStrat )
# weights
simDatSamp$wStrat <- ifelse( simDatSamp$y > 0, yProbPop / yProbSamp,
   ( 1 - yProbPop ) / ( 1 - yProbSamp ) )
probitResultStratW <- probit( (y > 0) ~ x, weights = simDatSamp$wStrat,
   data = simDatSamp )
print( probitResultStratW )
summary( probitResultStratW )
coef( probitResultStratW )
stdEr( probitResultStratW )
vcov( probitResultStratW )
nobs( probitResultStratW )
nObs( probitResultStratW )
df.residual( probitResultStratW )
logLik( probitResultStratW )
model.frame( probitResultStratW )
model.matrix( probitResultStratW )
fitted( probitResultStratW )
all.equal( fitted( probitResultStratW ), predict( probitResultStratW,
   type = "response" ) )
all.equal( fitted( probitResultStratW )[ 11:22 ],
   predict( probitResultStratW, newdata = simDatSamp[ 11:22, ],
      type = "response" ) )
linearPredictors( probitResultStratW )
all.equal( linearPredictors( probitResultStratW ),
   predict( probitResultStratW ) )
all.equal( linearPredictors( probitResultStratW )[ 11:22 ],
   predict( probitResultStratW, newdata = simDatSamp[ 11:22, ] ) )
residuals( probitResultStratW, type = "response" )
residuals( probitResultStratW, type = "pearson" )
residuals( probitResultStratW, type = "deviance" )
all.equal( residuals( probitResultStratW, type = "response" ),
   ( simDatSamp$y > 0 ) - fitted( probitResultStratW ) )
lrtest( probitResultStratW )

# estimation with weights to account for stratified sampling with glm()
probitResultStratW2 <- glm( (y > 0) ~ x, 
   family = binomial( link = "probit" ), weights = simDatSamp$wStrat,
   data = simDatSamp )
all.equal( coef( probitResultStratW ), coef( probitResultStratW2 ), tol = 1e-4 )
all.equal( stdEr( probitResultStratW ), stdEr( probitResultStratW2 ), tol = 1e-1 )
logLik( probitResultStratW2 )
all.equal( model.frame( probitResultStratW ), model.frame( probitResultStratW2 ) )
all.equal( model.matrix( probitResultStratW ), model.matrix( probitResultStratW2 ) )
all.equal( fitted( probitResultStratW ), fitted( probitResultStratW2 ), tol = 1e-4 )
all.equal( predict( probitResultStratW, type = "response" ),
   predict( probitResultStratW2, type = "response" ), tol = 1e-4 )
all.equal(
   predict( probitResultStratW, newdata = simDat[ 5:55, ], type = "response" ),
   predict( probitResultStratW2, newdata = simDat[ 5:55, ], type = "response" ),
   tol = 1e-4 )
all.equal( predict( probitResultStratW ), predict( probitResultStratW2 ),
   tol = 1e-4 )
all.equal( predict( probitResultStratW, newdata = simDatSamp[ 5:44, ] ),
   predict( probitResultStratW2, newdata = simDatSamp[ 5:44, ] ),
   tol = 1e-4 )
all.equal( residuals( probitResultStratW, type = "response" ),
   residuals( probitResultStratW2, type = "response" ), tol = 1e-4 )
all.equal( residuals( probitResultStratW, type = "pearson" ),
   residuals( probitResultStratW2, type = "pearson" ), tol = 1e-4 )
all.equal( residuals( probitResultStratW, type = "deviance" ),
   residuals( probitResultStratW2, type = "deviance" ), tol = 1e-4 )


## female labour force participation probability
lfpResult <- probit( lfp ~ kids + age30.39 + age50.60 + educ + hushrs +
   huseduc + huswage + mtr + motheduc, data = Mroz87 )
print( lfpResult )
summary( lfpResult )
coef( lfpResult )
stdEr( lfpResult )
vcov( lfpResult )
nobs( lfpResult )
nObs( lfpResult )
df.residual( lfpResult )
logLik( lfpResult )
lrtest( lfpResult )
lrtest( lfpResult, lfp ~ age50.60 + educ + hushrs + huswage + mtr )
model.frame( lfpResult )
model.matrix( lfpResult )
fitted( lfpResult )
all.equal( fitted( lfpResult ), predict( lfpResult, type = "response" ) )
all.equal( fitted( lfpResult )[ 11:222 ],
   predict( lfpResult, newdata = Mroz87[ 11:222, ], type = "response" ) )
linearPredictors( lfpResult )
all.equal( linearPredictors( lfpResult ), predict( lfpResult ) )
all.equal( linearPredictors( lfpResult )[ 11:222 ],
   predict( lfpResult, newdata = Mroz87[ 11:222, ] ) )
residuals( lfpResult, type = "response" )
residuals( lfpResult, type = "pearson" )
residuals( lfpResult, type = "deviance" )

# estimation with glm()
lfpResult2 <- glm( lfp ~ kids + age30.39 + age50.60 + educ + hushrs +
      huseduc + huswage + mtr + motheduc, data = Mroz87, 
   family = binomial( link = "probit" ) )
all.equal( coef( lfpResult ), coef( lfpResult2 ), tol = 1e-3 )
all.equal( stdEr( lfpResult ), stdEr( lfpResult2 ), tol = 1e-1 )
all.equal( logLik( lfpResult ), logLik( lfpResult2 ) )
all.equal( lrtest( lfpResult ), lrtest( lfpResult2 ) )
all.equal( lrtest( lfpResult, lfp ~ age50.60 + educ + hushrs + huswage + mtr ),
   lrtest( lfpResult2, lfp ~ age50.60 + educ + hushrs + huswage + mtr ),
   tol = 1e-7 )
all.equal( model.frame( lfpResult ), model.frame( lfpResult2 ) )
all.equal( model.matrix( lfpResult ), model.matrix( lfpResult2 ) )
all.equal( fitted( lfpResult ), fitted( lfpResult2 ), tol = 1e-4 )
all.equal( predict( lfpResult, type = "response" ),
   predict( lfpResult2, type = "response" ), tol = 1e-4 )
all.equal( predict( lfpResult, newdata = Mroz87[ 5:333, ], type = "response" ),
   predict( lfpResult2, newdata = Mroz87[ 5:333, ], type = "response" ),
   tol = 1e-4 )
all.equal( predict( lfpResult ), predict( lfpResult2 ), tol = 1e-4 )
all.equal( predict( lfpResult, newdata = Mroz87[ 2:444, ] ),
   predict( lfpResult2, newdata = Mroz87[ 2:444, ] ), tol = 1e-4 )
all.equal( residuals( lfpResult, type = "response" ),
   residuals( lfpResult2, type = "response" ), tol = 1e-4 )
all.equal( residuals( lfpResult, type = "pearson" ),
   residuals( lfpResult2, type = "pearson" ), tol = 1e-4 )
all.equal( residuals( lfpResult, type = "deviance" ),
   residuals( lfpResult2, type = "deviance" ), tol = 1e-4 )
all.equal( residuals( lfpResult ), residuals( lfpResult2 ), tol = 1e-4 )


## Greene( 2003 ): example 22.8, page 786 (only probit part )
greene <- probit( lfp ~ age + I( age^2 ) + faminc + kids + educ, data = Mroz87 )
print( greene )
summary( greene )
coef( greene )
stdEr( greene )
vcov( greene )
nobs( greene )
nObs( greene )
df.residual( greene )
logLik( greene )
lrtest( greene )
lrtest( greene, lfp ~ age + kids + educ )
model.frame( greene )
model.matrix( greene )
fitted( greene )
all.equal( fitted( greene ), predict( greene, type = "response" ) )
all.equal( fitted( greene )[ 11:222 ],
   predict( greene, newdata = Mroz87[ 11:222, ], type = "response" ) )
linearPredictors( greene )
all.equal( linearPredictors( greene ), predict( greene ) )
all.equal( linearPredictors( greene )[ 11:222 ],
   predict( greene, newdata = Mroz87[ 11:222, ] ) )
residuals( greene, type = "response" )
residuals( greene, type = "pearson" )
residuals( greene, type = "deviance" )

# estimation with glm()
greene2 <- glm( lfp ~ age + I( age^2 ) + faminc + kids + educ, 
   data = Mroz87, family = binomial( link = "probit" ) )
all.equal( coef( greene ), coef( greene2 ), tol = 1e-3 )
all.equal( stdEr( greene ), stdEr( greene2 ), tol = 1e-1 )
all.equal( logLik( greene ), logLik( greene2 ) )
all.equal( lrtest( greene ), lrtest( greene2 ) )
all.equal( lrtest( greene, lfp ~ age + kids + educ ),
   lrtest( greene2, lfp ~ age + kids + educ ) )
all.equal( model.frame( greene ), model.frame( greene2 ) )
all.equal( model.matrix( greene ), model.matrix( greene2 ) )
all.equal( fitted( greene ), fitted( greene2 ), tol = 1e-4 )
all.equal( predict( greene, type = "response" ),
   predict( greene2, type = "response" ), tol = 1e-4 )
all.equal( predict( greene, newdata = Mroz87[ 5:333, ], type = "response" ),
   predict( greene2, newdata = Mroz87[ 5:333, ], type = "response" ),
   tol = 1e-4 )
all.equal( predict( greene ), predict( greene2 ), tol = 1e-4 )
all.equal( predict( greene, newdata = Mroz87[ 2:444, ] ),
   predict( greene2, newdata = Mroz87[ 2:444, ] ), tol = 1e-4 )
all.equal( residuals( greene, type = "response" ),
   residuals( greene2, type = "response" ), tol = 1e-4 )
all.equal( residuals( greene, type = "pearson" ),
   residuals( greene2, type = "pearson" ), tol = 1e-4 )
all.equal( residuals( greene, type = "deviance" ),
   residuals( greene2, type = "deviance" ), tol = 1e-4 )
all.equal( residuals( greene ), residuals( greene2 ), tol = 1e-4 )


## factors as dependent variable (from Achim Zeileis)
probit( lfp ~ exper, data = Mroz87 )
probit( factor( lfp ) ~ exper, data = Mroz87 )
probit( factor( lfp, labels = c( "no", "yes" ) ) ~ exper, data = Mroz87 )

## NA in data/linear predictors/na.exclude (by Gabor Grothendieck)
x <- runif(20)
y <- x + rnorm(length(x)) > 0
y[1] <- y[4] <- NA
m <- probit(y ~ x, na.action = na.exclude)
length(linearPredictors(m))
                           # should be 20
nobs( m )
nObs( m )
df.residual( m )
logLik( m )

# example, where the "cutoff" in the log likelihood function is used
# (inspired by an example from Jon K. Peck)
set.seed( 123 )
nObs <- 1000
dta2 <- data.frame( id = c( 1:nObs ) )
for( i in 1:3 ) {
   dta2[[ paste( "x", i, sep = "" ) ]] <- rnorm( nObs, 5, 3 )
}
dta2$y <- with( dta2, -5.5 + 0.25 * x1 + 0.6 * x2 + 0.85 * x3 + rnorm( nObs ) ) > 0
p2 <- probit( y ~ x1 + x2 + x3, data=dta2 )
nobs( p2 )
nObs( p2 )
df.residual( p2 )
logLik( p2 )

## This test probes the extreme outliers.  We generate a normal
## model, and add a positive and a negative extreme outlier.  The
## likelihood code should be robust and not crash.  The estimates
## (and standard errors) are probably of little value.
x <- runif(20) - 0.5
y <- x + rnorm(20) > 0
x[1] <- 1000
y[1] <- FALSE
x[2] <- -1000
y[2] <- TRUE
m <- probit(y ~ x)
print(summary(m))
nobs( m )
nObs( m )
df.residual( m )
logLik( m )