cdf: Cumulative distribution function (CDF) of IDR or raw...
In isodistrreg: Isotonic Distributional Regression (IDR)

Description Usage Arguments Details Value See Also Examples

Evaluate the the cumulative distribution function (CDF) of IDR predictions or of unprocessed forecasts in a data.frame.

cdf(predictions, thresholds)

## S3 method for class 'idr'
cdf(predictions, thresholds)

## S3 method for class 'data.frame'
cdf(predictions, thresholds)

`predictions`	either an object of class `idr` (output of `predict.idrfit`), or a `data.frame` of numeric variables. In the latter case, the CDF is computed using the empirical distribution of the variables in `predictions`.
`thresholds`	numeric vector of thresholds at which the CDF will be evaluated.

The CDFs are considered as piecewise constant stepfunctions: If x are the points where the IDR fitted CDF (or the empirical distribution of the forecasts) has jumps and p the corresponding CDF values, then for x[i] <= x < x[i + 1], the CDF at x is p[i].

A matrix of probabilities giving the evaluated CDFs at the given thresholds, one column for each threshold.

predict.idrfit qpred, bscore

data("rain")

## Postprocess HRES forecast using data of 3 years

X <- rain[1:(3 * 365), "HRES", drop = FALSE]
y <- rain[1:(3 * 365), "obs"]

fit <- idr(y = y, X = X)

## Compute probability of precipitation given that the HRES forecast is
## 0 mm, 0.5 mm or 1 mm

predictions <- predict(fit, data = data.frame(HRES = c(0, 0.5, 1)))
1 - cdf(predictions, thresholds = 0)