Predict method for fitted sample selection models
Calculate predicted values for sample selection models
fitted with function selection.
## S3 method for class 'selection'
predict( object, newdata = NULL,
part = ifelse( type %in% c( "unconditional", "conditional" ),
"outcome", "selection" ),
type = "unconditional", ... )object |
a fitted object of class |
newdata |
optionally, a data frame in which to look for variables with which to predict. If omitted, the fitted linear predictors or the fitted response values are returned. |
part |
character string indicating for which equation
the predicted variables should be calculated:
|
type |
if argument |
... |
further arguments (currently ignored). |
In most cases, a numeric vector of the predicted values is returned. However, there are three exceptions: (i) when predicting the unconditional expectations of a Tobit-5 model, a matrix with two columns is returned, where the two columns correspond to the two outcome equations (E[yo1] and E[yo2]); (ii) when predicting the conditional expectations of a Tobit-2 model, a matrix with two columns is returned, where the first column returns the expectations conditional on the observation being not selected (E[yo|ys=0]), while the second column returns the expectations conditional on the observation being selected (E[yo|ys=1]); (iii) when predicting the conditional expectations of a Tobit-5 model, a matrix with four columns is returned, where the first two columns return the conditional expectations of the first outcome equation (E[yo1|ys=0] and E[yo1|ys=1]) and the last two columns return the conditional expectations of the second outcome equation (E[yo2|ys=0] and E[yo2|ys=1]).
Arne Henningsen and ‘fg nu’ (the code is partly based on the code posted by ‘fg nu’ at https://stackoverflow.com/questions/14005788/predict-function-for-heckman-model)
## Greene( 2003 ): example 22.8, page 786 data( Mroz87 ) Mroz87$kids <- ( Mroz87$kids5 + Mroz87$kids618 > 0 ) # ML estimation m <- selection( lfp ~ age + I( age^2 ) + faminc + kids + educ, wage ~ exper + I( exper^2 ) + educ + city, Mroz87 ) predict( m ) predict( m, type = "conditional" ) predict( m, type = "link" ) predict( m, type = "response" ) predict( m, newdata = Mroz87[ 3:9, ] )
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