svynls: Probability-weighted nonlinear least squares

In bschneidr/fastsurvey: Analysis of Complex Survey Samples

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, start, weights=NULL, ...)

Arguments

formula	Nonlinear model specified as a formula; see nls
design	Survey design object
start	starting values, passed to nls
weights	Non-sampling weights, eg precision weights to give more efficient estimation in the presence of heteroscedasticity.
...	Other arguments to nls (especially, start). Also supports return.replicates for replicate-weight designs and influence for other designs.

Value

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

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)))

bschneidr/fastsurvey documentation built on March 13, 2024, 11:12 a.m.