# computeFrac: Numerical weighting functions In surveysd: Survey Standard Error Estimation for Cumulated Estimates and their Differences in Complex Panel Designs

 computeLinear R Documentation

## Numerical weighting functions

### Description

Customize weight-updating within factor levels in case of numerical calibration. The functions described here serve as inputs for ipf.

### Usage

```computeLinear(curValue, target, x, w, boundLinear = 10)

computeLinearG1(curValue, target, x, w, boundLinear = 10)

computeFrac(curValue, target, x, w)
```

### Arguments

 `curValue` Current summed up value. Same as `sum(x*w)` `target` Target value. An element of `conP` in ipf `x` Vector of numeric values to be calibrated against `w` Vector of weights `boundLinear` The output `f` will satisfy `1/boundLinear <= f <= boundLinear`. See `bound` in ipf

### Details

`computeFrac` provides the "standard" IPU updating scheme given as

f = target/curValue

which means that each weight inside the level will be multtiplied by the same factor when doing the actual update step (`w := f*w`). `computeLinear` on the other hand calculates `f` as

fi = a · xi + b

where `a` and `b` are chosen, so f satisfies the following two equations.

∑ fi wi xi = target
∑ fi wi = ∑ wi

`computeLinearG1` calculates `f` in the same way as `computeLinear`, but if `f_i*w_i<1` `f_i` will be set to `1/w_i`.

### Value

A weight multiplier `f`

surveysd documentation built on Dec. 28, 2022, 2:15 a.m.