predict.gbmt: Prediction based on a group-based multivariate trajectory...

In gbmt: Group-Based Multivariate Trajectory Modeling

Prediction based on a group-based multivariate trajectory model

Description

Computation of in-sample and/or out-of-sample prediction of trajectories.

Usage

## S3 method for class 'gbmt'
predict(object, unit=NULL, n.ahead=0, bands=TRUE, conf=0.95, in.sample=FALSE, ...)

Arguments

object	Object of class gbmt.
unit	Character indicating the name of the unit for which prediction should be performed. If NULL (the default), group trajectories are predicted.
n.ahead	Non-negative integer value indicating the number of steps ahead for prediction. If a numerical vector is provided, only the maximum value is considered. If 0 (the default), in-sample prediction is returned.
bands	Logical value indicating whether the prediction bands should be computed.
conf	Numerical value indicating the confidence level for the prediction bands. Default is 0.05. Ignored if bands is FALSE.
in.sample	Logical value indicating whether in-sample prediction should be returned along with out-of-sample one. If FALSE (the default) and n.ahead is greater than 0, out-of-sample prediction is returned. Ignored if n.ahead is 0.
...	Further arguments for the generic predict method.

Value

If unit is NULL, a list with one component for each group, including a list with one object of class data.frame for each indicator. Otherwise, a list with one object of class data.frame for each indicator. Each of these dataframes has one column containing point predictions if bands=FALSE, otherwise three columns containing point predictions and their respective predictive bands.

Note

If unit is not NULL, values are back transformed to the original scales of indicators.

See Also

gbmt.

Examples

data(agrisus2)

# names of indicators (just a subset for illustration)
varNames <- c("TFP_2005", "NetCapital_GVA",
  "Income_rur", "Unempl_rur", "GHG_UAA", "GNB_N_UAA")

# model with 2 polynomial degrees and 3 groups
m3_2 <- gbmt(x.names=varNames, unit="Country", time="Year", d=2, ng=3, data=agrisus2, scaling=4)

# 3 steps ahead prediction of group trajectories
predict(m3_2, n.ahead=3)
predict(m3_2, n.ahead=3, in.sample=TRUE)  ## include in-sample prediction

# 3 steps ahead prediction for unit 'Italy'
predict(m3_2, unit="Italy", n.ahead=3)
predict(m3_2, unit="Italy", n.ahead=3, in.sample=TRUE)  ## include in-sample prediction

gbmt documentation built on March 18, 2022, 6:49 p.m.