lmr: Multiple Linear Regression Models

In mlesnoff/rnirs: Dimension reduction, Regression and Discrimination for Chemometrics

Multiple Linear Regression Models

Description

Function lmr fits a multiple linear regression model (MLR) using function lm.

Row observations can eventually be weighted.

Usage

lmr(Xr, Yr, Xu, Yu = NULL, weights = NULL)

Arguments

Xr	A n x p matrix or data frame of reference (= training) observations.
Yr	A n x q matrix or data frame, or a vector of length n, of reference (= training) responses.
Xu	A m x p matrix or data frame of new (= test) observations to predict.
Yu	A m x q matrix or data frame, or a vector of length m, of the true responses for Xu. Default to NULL.
weights	A vector of length n defining a priori weights to apply to the training observations. Internally, weights are "normalized" to sum to 1. Default to NULL (weights are set to 1 / n).

Value

A list of outputs (see examples), such as:

y	Responses for the test data.
fit	Predictions for the test data.
r	Residuals for the test data.
fm	Output of function lm.

Examples

n <- 10
p <- 6
set.seed(1)
X <- matrix(rnorm(n * p, mean = 10), ncol = p, byrow = TRUE)
y1 <- 100 * rnorm(n)
y2 <- 100 * rnorm(n)
Y <- cbind(y1, y2)
set.seed(NULL)

Xr <- X[1:8, ] ; Yr <- Y[1:8, ] 
Xu <- X[9:10, ] ; Yu <- Y[9:10, ] 

fm <- lmr(Xr, Yr, Xu, Yu)
names(fm)
fm$y
fm$fit
fm$r
names(fm$fm)

coef(fm$fm)

mse(fm, nam = "y1")
mse(fm, nam = "y2")
mse(fm)

mlesnoff/rnirs documentation built on April 24, 2023, 4:17 a.m.