forsearch_nls: Create Statistics Of Forward Search in a Nonlinear Model...

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

Create Statistics Of Forward Search in a Nonlinear 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_nls(nlsform, data, start, algorithm="default", 
   nls.control=FALSE, initial.sample=1000, skip.step1=NULL, begin.diagnose=100, 
   verbose=TRUE)

Arguments

nlsform	Formula for nls function
data	Name of database. First 2 variables are Observation and Section (both mandatory)
start	LIST of starting values for nls
algorithm	algorithm for nls function
nls.control	Logical. TRUE makes nls controls more liberal
initial.sample	Number of observation sets in Step 1 of forward search
skip.step1	NULL or a vector of integers for observations to be included in Step 1
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	Same as number of levels of start variable
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
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/web/packages/nlstools/vignettes/vignetteJSS.pdf

Examples

## Not run: 

Observation <- 1:70
Section <- rep(c(1,1,1,1,1,2,2,2,2,2), times=7)
Tree <- rep(1:7, each=10)
age <- rep(c(1,3,5,7,9,11,13,15,17,19), times = 7) * 100
circum <- forsearch::logist3(age, a=170, b=7, c=500) + rnorm(70)*0.1
test02 <- data.frame(Observation, Section, Tree, age, circum) 
startPru <- list(Asym=170, xmid=7, scal=500)
formulaPru  <- circum ~ I(logist3(x=age, a=Asym, b=xmid, c=scal))
forsearch_nls(nlsform = formulaPru,  
           data=test02, start=startPru, nls.control=TRUE,  
           initial.sample = 179, skip.step1=NULL, begin.diagnose=100, verbose=TRUE)
           

t <- (0:35)/3
VO2<- c(377.1111, 333.3333, 352.1429, 328.7500, 369.8750 ,394.4000, 352.6667, 
       337.3333, 366.4286, 364.0000, 293.8889, 387.0000, 364.8889, 342.2222, 
       400.3000, 375.1111 ,320.5556, 385.1667)
VO2<- c(VO2,527.0714,688.6364,890.8182,1145.1538, 1254.9091, 1327.5000,1463.9000,
       1487.8333 ,1586.6667, 1619.1000, 1494.4167 ,1640.4545, 1643.3750,
       1583.6364, 1610.8000 ,1568.5000, 1464.5833, 1652.8000) 
test01 <- data.frame(t,VO2)       
Observation <- 1:36
Section <- c(rep(1,20),rep(2,8),rep(3,8))
test01 <- cbind(Observation, Section, test01)
pstart <- list(VO2rest=400, VO2peak = 1600, mu = 1)
pformula <- as.formula(VO2~(t<=5.883)*(VO2rest)+          
            (t>5.883)*(VO2rest+(VO2peak-VO2rest)*
            (1-exp(-(t-5.883)*I(1/mu)))))
test2 <- forsearch_nls(nlsform=pformula, data = test01, 
           start=pstart, nls.control=FALSE, initial.sample = 300, skip.step1=NULL, 
           begin.diagnose=100, verbose=TRUE)


## End(Not run)

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