PrintShow: Print and Show methods for forecast data

In EBMAforecast: Estimate Ensemble Bayesian Model Averaging Forecasts using Gibbs Sampling or EM-Algorithms

Print and Show methods for forecast data

Description

Functions to print and show the contents of a data object of the class 'ForecastData' or 'SummaryForecastData'.

Usage

## S4 method for signature 'ForecastData'
print(x, digits = 3, ...)

## S4 method for signature 'ForecastData'
show(object)

## S4 method for signature 'SummaryForecastData'
print(x, digits = 3, ...)

## S4 method for signature 'SummaryForecastData'
show(object)

## S4 method for signature 'ForecastData'
print(x, digits = 3, ...)

## S4 method for signature 'ForecastData'
show(object)

Arguments

x	An object of the class 'ForecastData' or 'SummaryForecastData'.
digits	An integer specifying the number of significant digits to print. The default is 3.
...	Not implemented
object	An object of the class 'ForecastData' or 'SummaryForecastData'.

Author(s)

Michael D. Ward <[e-mail]> and Jacob M. Montgomery <[e-mail]> and Florian M. Hollenbach <[e-mail]>

References

Montgomery, Jacob M., Florian M. Hollenbach and Michael D. Ward. (2015). Calibrating ensemble forecasting models with sparse data in the social sciences. International Journal of Forecasting. 31: 930-942.

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"))

this.ensemble <- calibrateEnsemble(this.ForecastData, model="logit", tol=0.001,exp=3)

summary.object <- summary(this.ensemble, period="calibration") 
print(summary.object)
show(summary.object)

## End(Not run)

EBMAforecast documentation built on Nov. 10, 2023, 5:06 p.m.