# Robust location measures and their standard errors (se).

### Description

The following functions for estimating robust location measures and their standard errors are provided: `winmean`

for the Winsorized mean, `winse`

for its se, `trimse`

for the trimmend mean se, `msmedse`

for the median se,
`mest`

for the M-estimator with se in `mestse`

.

### Usage

1 2 3 4 5 6 |

### Arguments

`x` |
a numeric vector containing the values whose measure is to be computed. |

`tr` |
trim lor Winsorizing level. |

`na.rm` |
a logical value indicating whether NA values should be stripped before the computation proceeds. |

`sewarn` |
a logical value indicating whether warnings for ties should be printed. |

`bend` |
bending constant for M-estimator. |

### Details

The standard error for the median is computed according to McKean and Shrader (1984).

### References

Wilcox, R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Elsevier.

McKean, J. W., & Schrader, R. M. (1984). A comparison of methods for studentizing the sample median. Communications in Statistics - Simulation and Computation, 13, 751-773.

Dana, E. (1990). Salience of the self and salience of standards: Attempts to match self to standard. Unpublished PhD thesis, Department of Psychology, University of Southern California.

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 | ```
## Self-awareness data (Dana, 1990): Time persons could keep a portion of an
## apparatus in contact with a specified range.
self <- c(77, 87, 88, 114, 151, 210, 219, 246, 253, 262, 296, 299, 306, 376,
428, 515, 666, 1310, 2611)
mean(self, 0.1) ## .10 trimmed mean
trimse(self, 0.1) ## se trimmed mean
winmean(self, 0.1) ## Winsorized mean (.10 Winsorizing amount)
winse(self, 0.1) ## se Winsorized mean
median(self) ## median
msmedse(self) ## se median
mest(self) ## Huber M-estimator
mestse(self)
``` |