forsearch_glm: Create Statistics of Forward Search in a Generalized Linear...

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

Create Statistics of Forward Search in a Generalized Linear Model Database

Description

Prepares summary statistics at each stage of forward search for subsequent plotting. Forward search is conducted in three 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. A preliminary step (Step 0) contains code for pre-processing of the data.

Usage

forsearch_glm(initial.sample=1000, response.cols, indep.cols, family,  
   formula=NULL, binomialrhs=NULL, formula.cont.rhs, data, 
   estimate.phi = TRUE, wiggle=1, skip.step1=NULL, unblinded=TRUE, 
   begin.diagnose=100, verbose=TRUE)

Arguments

initial.sample	Number of random sets of observations in Step 1 of forward search
response.cols	Vector of column numbers (1 or 2) of responses and nonresponses (if binomial)
indep.cols	Column number(s) of independent variables
family	Error distribution and link
formula	Formula relating response to independent variables. Required except for family=binomial
binomialrhs	Quoted character.Right-hand side of formula. Required for family=binomial
formula.cont.rhs	Quoted character.Right-hand side of formula, omitting factor variables. Required for all families
data	Name of database
estimate.phi	TRUE causes phi to be estimated; FALSE causes phi to be set = 1
wiggle	Number multiplier to minimize arbitrary exchange of observations in step 2. Default is 1. Used only if independent variabls are all factors.
skip.step1	NULL, or vector of observation numbers to include at end of Step 1
unblinded	TRUE allows print of formula of analysis function
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

Details

Step 2 is determined by the results of Step 1, which itself is random. So, it is possible to reproduce the entire run by using the skip.step1 argument. Inner subgroups are produced by presence of categorical variables. Current version assumes independent variables are all continuous.

Value

LIST

Rows in stage	Observation numbers of rows included at each stage
Family	Family and link
Number of model parameters	Number of fixed effect parameters
Fixed parameter estimates	Matrix of parameter estimates at each stage
Residual deviance	Vector of deviances
Null deviance	Vector of null deviances
PhiHat	Vector of values of phi parameter
Deviance residuals and augments	Deviance residuals with indication of whether each is included in fit
AIC	Vector of AIC values
Leverage	Matrix of leverage of each observation 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: 
# Train deaths (Atkinson and Riani, 2000) with Rolling Stock as a factor
Observation<-1:67
Month<-c(9,8,3,1,10,6,7,1,8,4,3,3,12,11,10,9,9,4,3,12,12,10,7,2,12,2,12,12,12,
    11,3,10,4,2,12,12,9,11,1,10,8,6,1,10,6,12,8,4,9,6,12,10,7,2,5,12,5,5,4,3,1,
    9,11,9,7,3,2)
Year<-c(97,96,96,95,94,94,91,91,90,89,89,89,88,88,87,86,86,86,86,84,84,84,84,84,
    83,83,82,81,81,80,80,79,79,79,78,78,77,76,76,75,75,75,75,74,74,73,73,73,72,
    72,71,71,71,71,70,69,69,69,69,69,69,68,67,67,67,67,67)
RollingStock<-c(2,2,3,2,1,1,1,1,2,3,1,1,1,2,1,2,1,3,2,2,1,2,2,3,1,2,1,1,2,3,1,
    1,1,1,1,1,1,3,3,2,3,1,2,3,1,1,1,3,3,1,3,3,1,1,1,2,1,1,2,1,1,1,1,1,1,1,1)
RollingStock <- as.factor(RollingStock)    
Traffic<-c(0.436,0.424,0.424,0.426,0.419,0.419,0.439,0.439,0.431,0.436,0.436,
    0.436,0.443,0.443,0.397,0.414,0.414,0.414,0.414,0.389,0.389,0.389,0.389,
    0.389,0.401,0.401,0.372,0.417,0.417,0.43,0.43,0.426,0.426,0.426,0.43,0.43,
    0.425,0.426,0.426,0.436,0.436,0.436,0.436,0.452,0.452,0.433,0.433,0.433,
    0.431,0.431,0.444,0.444,0.444,0.444,0.452,0.447,0.447,0.447,0.447,0.447,
    0.447,0.449,0.459,0.459,0.459,0.459,0.459)
Deaths<-c(7,1,1,1,5,2,4,2,1,1,2,5,35,1,4,1,2,1,1,3,1,3,13,2,1,1,1,4,1,2,1,5,7,
    1,1,3,2,1,2,1,2,6,1,1,1,10,5,1,1,6,3,1,2,1,2,1,1,6,2,2,4,2,49,1,7,5,9)
train2022 <- data.frame(Observation, Year, RollingStock, Traffic, Deaths)
test02<-forsearch_glm(initial.sample = 100, response.cols = 5, 
    indep.cols = 2:4, formula=Deaths~Year + RollingStock + Traffic,
    formula.cont.rhs="Year + Traffic", 
    family = poisson("log"), data = train2022, 
    estimate.phi = TRUE, skip.step1 = NULL, 
    unblinded = TRUE, begin.diagnose=100)

## End(Not run)

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