predict.ProbitSpatial: Spatial probit model predictions.

In ProbitSpatial: Probit with Spatial Dependence, SAR, SEM and SARAR Models

Description

Predicts of a ProbitSpatial model on a set X of covariates. Works on both in-sample and out-of-sample using BLUP formula from Goulard et al. (2017)

Usage

## S3 method for class 'ProbitSpatial'
predict(
  object,
  X,
  type = c("link", "response", "binary"),
  cut = 0.5,
  oos = FALSE,
  WSO = NULL,
  ...
)

Arguments

object	an object of class ProbitSpatial.
X	a matrix of explanatory variables. If oos=TRUE, it may contain more observations than the dataset on which the model has been trained
type	the type of output: "link" the value of the latent variable. Default "response" probability. "binary" binary 0/1 output.
cut	the threshold probability for the "binary" type. Default is 0.5.
oos	logical. If TRUE, out-of-sample predictions are returned.
WSO	W matrix containing weights of in-sample and out-of-sample data. Observations must be ordered in such a way that the first elements belong to the in-sample data and the remaining ones to the out-of-sample data.
...	ignored

Details

If oos=FALSE, the function computes the predicted values for #' the estimated model (same as fitted). Otherwise, it applies the BLUP #' formula of Goulard et al. (2017):

\hat{y} = (\hat(y_S),\hat(y_O)),

where the sub-indexes S and O refer, respectively, to the in-sample and out-of-sample data. \hat{y_S} corresponds to fitted values, while \hat{y_O} is computed as follows:

\hat{y_O} = (I-ρ W)^{-1}(Xβ)-Q_{OO}^{-1}Q_{OS}(y_S-\hat{y_S}),

where Q is the precision matrix of Σ=σ^2((I-ρ W)'(I-ρ W))^{-1}. and the sub-indexes OO and OS refer to the corresponding block matrices.

Value

Returns a vector of predicted values for the set X of covariates if oos=FALSE or the best linear unbiased predictors of the #' set XOS if oos=TRUE.

References

Goulard et al. (2017)

M. Goulard, T. Laurent and C. Thomas-Agnan. About predictions in spatial autoregressive models: optimal and almost optimal strategies. Spatial Economic Analysis 12, 304-325, 2017.

ProbitSpatial documentation built on June 30, 2021, 9:06 a.m.