svynls {survey} R Documentation

## 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, ...)
```

### 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.

### Value

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

### 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)))
```

[Package survey version 4.1-1 Index]