svynls {survey} | R Documentation |
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)))