trim.ranef.test {asbio} | R Documentation |

## Robust test for random factors using trimmed means.

### Description

Provides a robust hypothesis test for the null: *Var*(*X*) = 0, for a population of random factor levels.

### Usage

```
trim.ranef.test(Y, X, tr = 0.2)
```

### Arguments

`Y` |
Vector of response data. A quantitative vector. |

`X` |
Vector of factor levels |

`tr` |
Amount of trimming. A number from 0-0.5. |

### Details

Robust analyses for random effect designs are particularly important since standard random effects models provide poor control over type I error when assumptions of normality and homoscedasticity are violated. Specifically, Wilcox (1994) showed that even with equal sample sizes, and moderately large samples, actual probability of type I error can exceed 0.3 if normality and homoscedasticity are violated.

### Value

Returns a list with three components dataframe describing numerator and denominator degrees of freedom, the *F* test statistic and the *p*-value.

### Note

code based on Wilcox (2005)

### Author(s)

Ken Aho

### References

Wilcox, R. R. (2005) *Introduction to Robust Estimation and Hypothesis Testing, Second
Edition*. Elsevier, Burlington, MA.

### Examples

```
rye<-c(50,49.8,52.3,44.5,62.3,74.8,72.5,80.2,47.6,39.5,47.7,50.7)
nutrient<-factor(c(rep(1,4),rep(2,4),rep(3,4)))
trim.ranef.test(rye,nutrient,tr=.2)
```

*asbio*version 1.9-7 Index]