daewr (version 1.1-8)

step.forward: RSM forward regression keeping model hierarchy

Description

This function performs a hierarchical forward stepwise regression. If an interaction or quadratic term is entered in the model, the parent main effects are also entered into the model.

Usage

step.forward(y,x,step)

Arguments

y

input - this is a vector containing a single numeric column of response data.

x

input - this is a data frame containing the numeric columns of the candidate independent variables.

step

input - this is a single numeric value containing the n umber of steps requested.

Value

returned data frame the first column is a factor variable containing the formula for the model fit at each step, the second numeric column is the R-square statistic for the model fit with each formula.

Examples

# NOT RUN {
#Definitive Screening Design
library(daewr)
set.seed(1234)
x <- DefScreen(m=5,c=0)
colnames(x) <- paste("x",1:5,sep="")
x$y <- 3*x$x1 + 2*x$x2 + 2*x$x4*x$x5 + x$x3^2 + 2*x$x1^2 + rnorm(nrow(x),0,1)
(z <- step.forward(x$y,x[,-6], 4 ))
# Example p. 240 Design and Analysis of Experiments with R PB Design
library(BsMD)
data(PB12Des,package="BsMD")
colnames(PB12Des)<-c("c11","c10","c9","c8","G","F","E","D","C","B","A")
#Reorder the columns to match Table 6.11
castf<-PB12Des[c(11,10,9,8,7,6,5,4,3,2,1)]
y<-c(4.733,4.625,5.899,7.0,5.752,5.682,6.607,5.818,5.917,5.863,6.058,4.809)
castf<-cbind(castf,y)
castfr <- castf[ , c(1:7, 12)]
library(daewr)
des<-castfr[ ,c(1:7)]
y<-castfr[ , 8]
step.forward(y,des,2)
# }