calibrationSample {EBMAforecast}

Sample data Insurgency Predictions

Description

This includes the data for the predictions of insurgencies in 29 countries for 2010.

Usage

calibrationSample

testSample

Format

An object of class matrix (inherits from array) with 696 rows and 4 columns.

An object of class matrix (inherits from array) with 348 rows and 4 columns.

Details

The predictions included in the dataset are:

• LMER Predictions from a generalized linear mixed effects model using a logistic link function and including a randomeffects term for lagged GDP per capita and the lagged number of conflictual events involving the United States in the country of interest.

• SAE Predictions from a one model developed as part of the ICEWS project and was designed by Strategic Analysis Enterprises.

• GLM Predictions from a crude logistic model that includes only population size, GDP growth (both lagged 3 months), the number of minority groups at risk in the country, and a measure of anocracy supplied in the Polity IV data set.

More detail about each model can be found in Mongomery et al. (2012)

References

Montgomery, Jacob M., Florian M. Hollenbach and Michael D. Ward. (2012). Improving Predictions Using Ensemble Bayesian Model Averaging. Political Analysis. 20: 271-291.

Examples

## Not run: 
data(calibrationSample)
data(testSample)

this.ForecastData <- makeForecastData(.predCalibration=calibrationSample[,c("LMER", "SAE", "GLM")],
.outcomeCalibration=calibrationSample[,"Insurgency"],.predTest=testSample[,c("LMER", "SAE", "GLM")],
.outcomeTest=testSample[,"Insurgency"], .modelNames=c("LMER", "SAE", "GLM"))
initW <- rep(1/3,3)

this.ensemble.em <- calibrateEnsemble(this.ForecastData, model="logit", tol=0.001)

this.ensemble.gibbs <- calibrateEnsemble(this.ForecastData, model="logit", method = "gibbs")

## End(Not run)