forsearch_lme: Create Statistics Of Forward Search For a Linear Mixed...

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

Create Statistics Of Forward Search For a Linear Mixed Effects Database

Description

Prepares summary statistics at each stage of forward search for subsequent plotting. Forward search is conducted in four steps: Step 0 to set up accounting for group structure, Step 1 to identify minimal set of observations to estimate unknown fixed parameters, Step 2 to identify the order of the remaining observations, and a final stage to extract the intermediate statistics based on increasing sample size.

Usage

forsearch_lme(fixedform, nofactform, alldata, randomform, groupname, randfactnames=NULL,
   initial.sample=1000, skip.step1=NULL, unblinded=TRUE, begin.diagnose = 100, 
   incCont=FALSE, verbose = TRUE)

Arguments

fixedform	2-sided formula for fixed effects
nofactform	2-sided formula for fixed effects, omitting factors
alldata	data frame, first column of which must be "Observation"
randomform	1-sided formula for random effects
groupname	Quoted name of group variable in randomform. Nested grouping not permitted in this version
randfactnames	Vector of quoted names of random factor variables
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 indicator of place in coding to begin printing diagnostic information. 0 prints all information, 100 prints none.
incCont	Logical. Currently ignored
verbose	TRUE causes function identifier to display before and after run

Details

data will be grouped within the function, regardless of initial layout. 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. Variables in the randomform formula must be character variables, but *not* factors

Value

LIST

Number of observations in Step 1	Number of observations included in Step 1
Step 1 observation numbers	Observation numbers useful in skipping step 1
Rows by outer subgroup	List of row numbers, by outer subgroup
Rows by outer-inner subgroups	List of row numbers, by outer-inner subgroup
Rows in stage	Observation numbers of rows included at each stage
Sigma	Estimate of random error at final stage; used to standardize all residuals
Standardized residuals	Matrix of errors at each stage
Fixed parameter estimates	Matrix of parameter estimates at each stage
Random parameter estimates	Matrix of parameter estimates 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
Dims	Dims from fit of lme function
t statistics	t statistics for each fixed parameter
Fit statistics	AIC, BIC, and log likelihood
Call	Call to this function

Author(s)

William R. Fairweather

References

Atkinson, A and M Riani. Robust Diagnostic Regression Analysis, Springer, New York, 2000. Pinheiro, JC and DM Bates. Mixed-Effects Models in S and S-Plus, Springer, New York, 2000. https://CRAN.R-project.org/package=nlme

Examples

## Not run: 
# Multiple regression in grouped data
Observation <- 1:160
y <- runif(160)
x1 <- runif(160)
x2 <- runif(160)
x3 <- runif(160)
group <- rep(c("G1","G2"),each=80)
lmetest1 <- data.frame(Observation,y,x1,x2,x3,group)
forsearch_lme(fixedform=y~x1+x2+x3, nofactform=y~x1+x2+x3, alldata=lmetest1, 
   randomform= ~1|group, groupname="group", initial.sample=200)

# Analysis of variance in grouped data
Observation <- 1:60
y <- runif(60)
AN1 <- as.factor(c(rep("A1",5),rep("A2",5),rep("A3",5)))
AN1 <- c(AN1,AN1,AN1,AN1)
AN2 <- as.factor(c(rep("B1",15),rep("B2",15)))
AN2 <- c(AN2,AN2)
group <- rep(c("G1","G2"),each=30)
lmetest2 <- data.frame(Observation,y,AN1,AN2,group)
forsearch_lme(fixedform=y~AN1*AN2, nofactform=y~1, alldata=lmetest2, 
   randomform= ~1|group, groupname="group",initial.sample=500)

# Analysis of covariance in grouped data

Observation <- 1:120
y <- runif(120)
AN1 <- as.factor(c(rep("A1",10),rep("A2",10),rep("A3",10),rep("A4",10)))
AN1 <- c(AN1,AN1,AN1)
AN2 <- as.factor(c(rep("B1",5),rep("B2",5)))
AN2 <- c(AN2,AN2,AN2,AN2,AN2,AN2)
AN2 <- c(AN2,AN2)
COV <- runif(120)
group <- rep(c("G1","G2"),each=30)
group <- c(group,group)
lmetest3 <- data.frame(Observation,y,AN1,AN2,COV,group)
test3<-forsearch_lme(fixedform=y~AN1*AN2+COV,nofactform=y~COV,alldata=lmetest3,
        randomform= ~ 1 | group,groupname="group",initial.sample=500,
        begin.diagnose=100)

## End(Not run)

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