| trimse {WRS2} | R Documentation |
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.
winmean(x, tr = 0.2, na.rm = FALSE) 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)
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. |
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 Unpublished PhD thesis, Department of Psychology, University of Southern California.
## 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)