forsearch_lm: Create Statistics Of Forward Search in a Linear Model...

In forsearch: Diagnostic Analysis Using Forward Search Procedure for Various Models

Create Statistics Of Forward Search in a Linear Model Database

Description

Prepares summary statistics at each stage of forward search for subsequent plotting. Forward search is conducted in two steps: Step 1 to identify minimal set of observations to estimate unknown parameters, and Step 2 to add one observation at each stage such that observations in the set are best fitting at that stage.

Usage

forsearch_lm(formula, nofactform, data, initial.sample=1000, 
                   skip.step1 = NULL, unblinded = TRUE, begin.diagnose = 100,
                   verbose = TRUE)

Arguments

formula	Fixed effects formula as described in help(lm). The only permitted operators are +, : , and * . Terms must be found in data or as constructed by I(xxx) where xxx is found in data
nofactform	2-sided formula omitting all factors
data	Name of database
initial.sample	Number of observations in Step 1 of forward search
skip.step1	NULL or a vector of integers for observations to be included in Step 1
unblinded	TRUE causes printing of presumed analysis structure
begin.diagnose	Numeric. Indicates where in code to begin printing diagnostics. 0 prints all; 100 prints none
verbose	TRUE causes function identifier to display before and after run

Value

LIST

Rows in stage	Observation numbers of rows included at each stage
Standardized residuals	Matrix of errors at each stage
Number of model parameters	Rank of model
Sigma	Estimate of random error at final stage; used to standardize all residuals
Fixed parameter estimates	Vector of parameter estimates at each stage
s^2	Estimate of random error at each stage
Leverage	Matrix of leverage of each observation at each stage
Modified Cook distance	Estimate of sum of squared changes in parameter estimates at each stage
Call	Call to this function

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000.

Examples

## Not run: 
# Multiple regression
Observation <- 1:16
y <- runif(16)
x1 <- runif(16)
x2 <- runif(16)
x3 <- runif(16)
lmtest1 <- data.frame(Observation,y,x1,x2,x3)
test1 <- forsearch_lm(formula=y~x1+x2+x3, nofactform=y~x1+x2+x3, data=lmtest1, 
     initial.sample=200,begin.diagnose=100)

# Analysis of variance 
Observation <- 1:30
y <- runif(30)
AN1 <- as.factor(c(rep("A1",5),rep("A2",5),rep("A3",5)))
AN1 <- c(AN1,AN1)
AN2 <- as.factor(c(rep("B1",15),rep("B2",15)))
lmtest2 <- data.frame(Observation,y,AN1,AN2)
test2 <- forsearch_lm(formula=y~AN1*AN2, nofactform=y~1, data=lmtest2, 
       initial.sample=200, begin.diagnose=100)

# Analysis of covariance
Observation <- 1:60
y <- runif(60)
AN1 <- as.factor(c(rep("A1",10),rep("A2",10),rep("A3",10)))
AN1 <- c(AN1,AN1)
AN2 <- as.factor(c(rep("B1",30),rep("B2",30)))
COV <- runif(60)
lmtest3 <- data.frame(Observation,y,AN1,AN2,COV)
test3 <- forsearch_lm(formula=y~AN1*AN2+COV, nofactform=y~COV, data=lmtest3, 
      initial.sample=200,begin.diagnose=100)

# Polynomial regression
C1 <- 7*runif(60) + 1
y <- 4 + C1 - 6*C1^2 + 9*C1^3 + rnorm(60)
Observation <- 1:60
dfpoly <- data.frame(Observation,C1,y) 
test4 <- forsearch_lm(formula = y ~ C1 + I(C1^2) + I(C1^3), data = dfpoly,  
     nofactform=y ~ C1 + I(C1^2) + I(C1^3),initial.sample = 200, 
     begin.diagnose=100)

## End(Not run)

forsearch documentation built on April 4, 2025, 5:52 a.m.