Probability-weighted nonlinear least squares
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.
svynls(formula, design, ...)
formula |
Nonlinear model specified as a formula; see |
design |
Survey design object |
... |
Other arguments to |
Object of class svynls. The fitted nls object is
included as the fit element.
svymle for maximum likelihood with linear predictors on
one or more parameters
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)))
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