wrapnlsr: wrapnlsr

In nlsr: Functions for Nonlinear Least Squares Solutions - Updated 2022

wrapnlsr

Description

Provides class nls solution to a nonlinear least squares solution using the Nash Marquardt tools.

Usage

wrapnlsr(formula = NULL,  data = NULL,  start = NULL,  control = NULL,
    trace = FALSE, subset = NULL, lower = -Inf, upper = Inf,  weights = NULL,
    ...)                           

Arguments

formula	The modeling formula. Looks like 'y~b1/(1+b2*exp(-b3*T))'
data	a data frame containing data for variables used in the formula that are NOT the parameters. This data may also be defined in the parent frame i.e., 'global' to this function
start	MUST be a named vector with all elements present e.g., start=c(b1=200, b2=50, b3=0.3)
control	a list of control parameters. See nlsr.control().
trace	TRUE for console output during execution (default FALSE)
subset	an optional vector specifying a subset of observations to be used in the fitting process. NOT used currently by nlxb() or nlfb() and will throw an error if present and not NULL.
lower	a vector of lower bounds on the parameters. If a single number, this will be applied to all parameters Default -Inf.
upper	a vector of upper bounds on the parameters. If a single number, this will be applied to all parameters. Default Inf.
weights	A vector of (usually fixed) weights. The objective function that will be minimized is the sum of squares where each residual is multiplied by the square root of the corresponding weight. Default NULL implies unit weights.
...	additional data needed to evaluate the modeling functions

Value

A solution object of type nls

Examples

library(nlsr)
cat("kvanderpoel.R test of wrapnlsr\n")
x<-c(1,3,5,7)
y<-c(37.98,11.68,3.65,3.93)
pks28<-data.frame(x=x,y=y)
fit0<-try(nls(y~(a+b*exp(1)^(-c*x)), data=pks28, start=c(a=0,b=1,c=1), 
              trace=TRUE))
print(fit0)
fit1<-nlxb(y~(a+b*exp(-c*x)), data=pks28, start=c(a=0,b=1,c=1), trace = TRUE)
print(fit1) 
cat("\n\n or better\n")
fit2<-wrapnlsr(y~(a+b*exp(-c*x)), data=pks28, start=c(a=0,b=1,c=1), 
               lower=-Inf, upper=Inf, trace = TRUE)
fit2

weed <- c(5.308, 7.24, 9.638, 12.866, 17.069, 23.192, 31.443,
          38.558, 50.156, 62.948, 75.995, 91.972)
tt <- 1:12
weeddf <- data.frame(tt, weed)
hobbsu <- weed ~ b1/(1+b2*exp(-b3*tt))
st2 <- c(b1=200, b2=50, b3=0.3)
wts <- 0.5^tt # a straight scaling comes via wts <- rep(0.01, 12)
lo <- c(200, 0, 0)
up <- c(1000, 1000, 1000)
whuw2 <-  try(wrapnlsr(start=st2, formula=hobbsu, data=weeddf, subset=2:11,
                  weights=wts, trace=TRUE, lower=lo, upper=up))
summary(whuw2)
deviance(whuw2)
whuw2a <-  try(nlsr(start=st2, formula=hobbsu, data=weeddf, subset=2:11,
                  weights=wts, trace=TRUE, lower=lo, upper=up))
summary(whuw2a)
deviance(whuw2a)

nlsr documentation built on Sept. 8, 2023, 5:48 p.m.