Description Usage Arguments Details References Examples

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`

. The functions `onestep`

and `mom`

compute the one-step and
modified one-step (MOM) M-estimator. The Winsorized variance is implemented in `winvar`

.

1 2 3 4 5 6 7 8 9 | ```
winmean(x, tr = 0.2, na.rm = FALSE)
winvar(x, tr = 0.2, na.rm = FALSE, STAND = NULL)
winse(x, tr = 0.2)
trimse(x, tr = 0.2, na.rm = FALSE)
msmedse(x, sewarn = TRUE)
mest(x, bend = 1.28, na.rm = FALSE)
mestse(x, bend = 1.28)
onestep(x, bend = 1.28, na.rm = FALSE, MED = TRUE)
mom(x, bend = 2.24, na.rm = TRUE)
``` |

`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. |

`MED` |
if |

`STAND` |
no functionality, kept for WRS compatibility purposes. |

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

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 Unpublished PhD thesis, Department of Psychology, University of Southern California.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
## 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
winvar(self, 0.1) ## Winsorized variance
median(self) ## median
msmedse(self) ## se median
mest(self) ## Huber M-estimator
mestse(self)
onestep(self) ## one-step M-estimator
mom(self) ## modified one-step M-estimator
``` |

```
[1] 342.7059
[1] 103.2686
[1] 380.1579
[1] 92.9417
[1] 129679
[1] 262
[1] 77.83901
[1] 285.1576
[1] 52.59286
[1] 285.1576
[1] 245.4375
```

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