| most probable value of a data set [message #86225] |
Sat, 19 October 2013 05:35  |
gunvicsin11
Messages: 93
Registered: November 2012
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Hello everyone,
Instead of finding the mean value of a data set, I want to find the most probable value of a data set. How can I find that.
Please do help me in this regard
thanking you in advance
sid
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| Re: most probable value of a data set [message #86251 is a reply to message #86225] |
Mon, 21 October 2013 16:22  |
Gordon Farquharson
Messages: 48
Registered: December 2010
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On Saturday, October 19, 2013 5:35:11 AM UTC-7, sid wrote:
> Instead of finding the mean value of a data set, I want to find the most probable value of a data set. How can I find that.
Here is an IDL implementation for the half sample mode function found in the modeest package for R [1]. It is a fairly crude direct copy, but it works well for me. (Not sure what Google is going to do to the formatting of the code - you may have to fix some lines.)
BTW, if you interested in mode estimation techniques, look at the paper by Bickel and Frühwirth.
Gordon
---
;; Author: Wolfgang Huber and Ligia Pedroso Bras (coauthors of package 'genefilter')
;; Modifications: P. Poncet
FUNCTION _deal_ties, ny, i, tie_action, tie_limit
;; ny, : length of the data
;; i, : index
;; tie_action, : action to be taken
;; tie_limit) : limit
compile_opt IDL2, LOGICAL_PREDICATE, STRICTARRSUBS, HIDDEN
;; Deal with ties
maxi = max(i)
mini = min(i)
IF (maxi - mini GT tie_limit * ny) THEN BEGIN
message, "encountered a tie, and the difference between minimal and maximal value is > length('x') * 'tie.limit', so the distribution could be multimodal", /INFO
ENDIF
;; Take the action specified in "tie.action"
CASE tie_action OF
'mean' : return, mean(i)
'median' : return, median(i)
'max' : return, maxi
'min' : return, mini
ELSE : message, "invalid value" + tie_action + " for argument 'tie.action'"
ENDCASE
END
;;##################################################
;;# Robertson and Cryer's / FSM / HSM mode estimator
;;# FSM = fraction-of-sample mode
;;# HSM = half-sample mode
;;##################################################
;; Author: D.R. Bickel
;; Modications: P. Poncet
FUNCTION hsm, x, bw, k, TIEACTION=tie_action, TIELIMIT=tie_limit
;; x : sample (the data)
;; bw : bandwidth (fraction of the observations to consider)
;; k : length of the intervals
compile_opt IDL2, LOGICAL_PREDICATE, STRICTARRSUBS
IF ~keyword_set(tie_action) THEN tie_action = "mean"
IF ~keyword_set(tie_limit) THEN tie_limit = 0.05
if (n_elements(k) NE 0 AND n_elements(bw) EQ 0) THEN BEGIN
bw = (k+1) / n_elements(x)
ENDIF ELSE IF (n_elements(k) EQ 0 AND n_elements(bw) EQ 0) THEN BEGIN
bw = 0.5
ENDIF
IF (bw LE 0 OR bw GT 1) THEN BEGIN
message, "argument 'bw' must belong to (0, 1]", /INFO
return, !values.f_nan
ENDIF
y = x[sort(x)]
WHILE n_elements(y) GE 4 DO BEGIN
ny = n_elements(y)
k = ceil(bw*ny) - 1
inf = y[0:(ny-k)-1]
sup = y[k:ny-1]
diffs = sup - inf
i = where(diffs EQ min(diffs))
;; Ties?
IF (n_elements(i) gt 1) THEN BEGIN
i = _deal_ties(ny, i, tie_action, tie_limit)
ENDIF
if (diffs[i] EQ 0) THEN BEGIN
y = y[i]
ENDIF ELSE BEGIN
y = y[i:(i+k)]
ENDELSE
ENDWHILE
IF n_elements(y) EQ 3 THEN BEGIN
z = 2*y[1] - y[0] - y[2]
IF z LT 0 THEN BEGIN
M = mean(y[0:1])
ENDIF ELSE IF z GT 0 THEN BEGIN
M = mean(y[1:2])
ENDIF ELSE IF z EQ 0 THEN BEGIN
M = y[1]
ENDIF
ENDIF ELSE BEGIN
M = mean(y)
ENDELSE
return, M
END
---
[1] http://cran.r-project.org/web/packages/modeest/index.html
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