predict_wbaconlm: Predicted Values Based on the Weighted BACON Linear...

In wbacon: Weighted BACON Algorithms

Predicted Values Based on the Weighted BACON Linear Regression

Description

This function does exactly what predict does for the linear model lm; see predict.lm for more details.

Usage

## S3 method for class 'wbaconlm'
predict(object, newdata, se.fit = FALSE, scale = NULL,
    df = Inf, interval = c("none", "confidence", "prediction"), level = 0.95,
    type = c("response", "terms"), terms = NULL, na.action = na.pass, ...)

Arguments

object	Object of class inheriting from "lm"
newdata	An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.
se.fit	A switch [logical] indicating if standard errors are required.
scale	Scale parameter for std.err. calculation, [numeric].
df	Degrees of freedom for scale, [integer].
interval	Type of interval calculation, [character]. Can be abbreviated.
level	Tolerance/confidence level, [numeric].
type	Type of prediction (response or model term), [character]. Can be abbreviated.
terms	If type = "terms", which terms (default is all terms), a [character] vector.
na.action	function determining what should be done with missing values in newdata. The default is to predict NA.
...	further arguments passed to predict.lm

Value

predict.wbaconlm produces a vector of predictions or a matrix of predictions and bounds with column names fit, lwr, and upr if interval is set. For type = "terms" this is a matrix with a column per term and may have an attribute "constant".

If se.fit is TRUE, a list with the following components is returned:

fit	vector or matrix as above
se.fit	standard error of predicted means
residual.scale	residual standard deviations
df	degrees of freedom for residual

See Also

wBACON_reg

Examples

data(iris)
m <- wBACON_reg(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width,
    data = iris)
predict(m, newdata = data.frame(Sepal.Width = 1, Petal.Length = 1,
    Petal.Width = 1))

wbacon documentation built on June 8, 2025, 10:07 a.m.