elo (version 2.1.1)

predict.elo: Make Predictions on an elo Object

Description

Make Predictions on an elo Object

Usage

# S3 method for elo.run
predict(object, newdata, ...)
# S3 method for elo.run.regressed
predict(object, newdata, regressed = FALSE, ...)
# S3 method for elo.glm
predict(object, newdata, type = "response", ...)
# S3 method for elo.running
predict(object, newdata, running = TRUE, ...)
# S3 method for elo.markovchain
predict(object, newdata, ...)
# S3 method for elo.colley
predict(object, newdata, ...)
# S3 method for elo.winpct
predict(object, newdata, ...)

Arguments

object

An model from which to get predictions.

newdata

A new dataset containing the same variables as the call that made object. If missing, the predicted win probabilities from object will be returned.

...

Other arguments.

regressed

See the note on final.elos.

type

See predict.glm

running

logical, denoting whether to use the running predicted values. Only makes sense if newdata is missing.

Value

A vector of win probabilities.

Details

Note that the "elo.glm.running" objects will use a model fit on all the data to predict.

Examples

# NOT RUN {
data(tournament)
t1 <- head(tournament, -3)
t2 <- tail(tournament, 3)
results <- elo.run(score(points.Home, points.Visitor) ~ team.Home + team.Visitor,
                   data = t1, k = 20)
predict(results)
predict(results, newdata = t2)

results <- elo.glm(score(points.Home, points.Visitor) ~ team.Home + team.Visitor, data = t1,
  subset = points.Home != points.Visitor)
predict(results)
predict(results, newdata = t2)

results <- elo.markovchain(score(points.Home, points.Visitor) ~ team.Home + team.Visitor, data = t1,
  subset = points.Home != points.Visitor, k = 0.7)
predict(results)
predict(results, newdata = t2)

results <- elo.colley(score(points.Home, points.Visitor) ~ team.Home + team.Visitor, data = t1,
  subset = points.Home != points.Visitor)
predict(results)
predict(results, newdata = t2)

results <- elo.winpct(score(points.Home, points.Visitor) ~ team.Home + team.Visitor, data = t1,
  subset = points.Home != points.Visitor, k = 0.7)
predict(results)
predict(results, newdata = t2)
# }