Description Usage Arguments Value See Also Examples

Trims very high or very low sampling weights to reduce the influence of outlying observations. In a replicate-weight design object, the replicate weights are also trimmed. The total amount trimmed is divided among the observations that were not trimmed, so that the total weight remains the same.

1 2 3 4 5 | ```
trimWeights(design, upper = Inf, lower = -Inf, ...)
## S3 method for class 'survey.design2'
trimWeights(design, upper = Inf, lower = -Inf, strict=FALSE,...)
## S3 method for class 'svyrep.design'
trimWeights(design, upper = Inf, lower = -Inf,compress=FALSE,...)
``` |

`design` |
A survey design object |

`upper` |
Upper bound for weights |

`lower` |
Lower bound for weights |

`strict` |
The reapportionment of the ‘trimmings’ from the weights can push
other weights over the limits. If |

`compress` |
Compress the replicate weights after trimming. |

`...` |
Other arguments for future expansion |

A new survey design object with trimmed weights.

`calibrate`

has a `trim`

option for trimming the
calibration adjustments.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
data(api)
dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)
pop.totals<-c(`(Intercept)`=6194, stypeH=755, stypeM=1018,
api99=3914069)
dclus1g<-calibrate(dclus1, ~stype+api99, pop.totals)
summary(weights(dclus1g))
dclus1t<-trimWeights(dclus1g,lower=20, upper=45)
summary(weights(dclus1t))
dclus1tt<-trimWeights(dclus1g, lower=20, upper=45,strict=TRUE)
summary(weights(dclus1tt))
svymean(~api99+api00+stype, dclus1g)
svymean(~api99+api00+stype, dclus1t)
svymean(~api99+api00+stype, dclus1tt)
``` |

```
Loading required package: grid
Loading required package: Matrix
Loading required package: survival
Attaching package: 'survey'
The following object is masked from 'package:graphics':
dotchart
Min. 1st Qu. Median Mean 3rd Qu. Max.
14.17 25.91 33.58 33.85 40.23 62.05
Min. 1st Qu. Median Mean 3rd Qu. Max.
20.00 26.82 34.48 33.85 41.13 45.74
Min. 1st Qu. Median Mean 3rd Qu. Max.
20.01 26.83 34.49 33.85 41.15 45.00
mean SE
api99 631.91298 0.0000
api00 665.30907 3.4418
stypeE 0.71376 0.0000
stypeH 0.12189 0.0000
stypeM 0.16435 0.0000
mean SE
api99 628.91203 1.1673
api00 663.05195 3.5796
stypeE 0.73651 0.0036
stypeH 0.10121 0.0043
stypeM 0.16228 0.0021
mean SE
api99 628.87690 1.1695
api00 663.02017 3.5842
stypeE 0.73664 0.0036
stypeH 0.10121 0.0043
stypeM 0.16214 0.0021
```

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