survey (version 4.1-1)

svynls: Probability-weighted nonlinear least squares

Description

Fits a nonlinear model by probability-weighted least squares. Uses nls to do the fitting, but estimates design-based standard errors with either linearisation or replicate weights. See nls for documentation of model specification and fitting.

Usage

svynls(formula, design, ...)

Value

Object of class svynls. The fitted nls object is included as the fit element.

Arguments

formula

Nonlinear model specified as a formula; see nls

design

Survey design object

...

Other arguments to nls (especially, start). Also supports return.replicates for replicate-weight designs and influence for other designs.

See Also

svymle for maximum likelihood with linear predictors on one or more parameters

Examples

set.seed(2020-4-3)
x<-rep(seq(0,50,1),10)
y<-((runif(1,10,20)*x)/(runif(1,0,10)+x))+rnorm(510,0,1)

pop_model<-nls(y~a*x/(b+x), start=c(a=15,b=5))

df<-data.frame(x=x,y=y)
df$p<-ifelse((y-fitted(pop_model))*(x-mean(x))>0, .4,.1)

df$strata<-ifelse(df$p==.4,"a","b")

in_sample<-stratsample(df$strata, round(table(df$strat)*c(0.4,0.1)))

sdf<-df[in_sample,]
des<-svydesign(id=~1, strata=~strata, prob=~p, data=sdf)
pop_model
(biased_sample<-nls(y~a*x/(b+x),data=sdf, start=c(a=15,b=5)))
(corrected <- svynls(y~a*x/(b+x), design=des, start=c(a=15,b=5)))