Topic:
The same four proportional weighting factors work for each 00/01/10/11 when 0.25 is subtracted from each !!!
Replies:
506
Last Post:
Nov 20, 2012 9:21 PM
On May 21, 6:22 pm, djh <halitsk...@att.net> wrote: > You asked: > >> So all the following are treated as fixed constants for each length >> interval: >> a. The coefficients (slope & intercept) of both driver regressions; >> b. The medians of the absolute adjusted residuals; >> c. All the (x1,x2) pairs; >> d. w = {c,-c,-c,c}, where c = the (x1,x2) covariance ? > > Yes. Computation of the two driver regressions create all of (a-d), > none of which change after creatiom, regardless of how segments are > paired off to check the alignability of the substructures arising > from them. > > You also asked: > >> So if a segment is omitted then only the (n0,n1) pair for that >> segment's (x1,x2) cell changes? > > Again yes. > > Thanks again for taking the time to understand all this.
Several posts ago, I said
| Jackknifing requires combining the results of n+1 analyses, | where n is the number of segments. Here's how it's done. | | Let b denote the vector of estimated coefficients (including the | intercept) you get from the usual analysis. For i = 1,...,n, let | b'i denote the vector of coefficients you get when you omit the | comparisons involving segment i, and let b"i = n*b - (n-1)*b'i. | For a 7-predictor model you would end up with an n x 8 matrix B". | The trick is to treat B" as if its rows were independent estimates | of the coefficients. The column means are reduced-bias estimates | of the coefficients, and 1/n times the (inferential) covariance | matrix is an empirical estimate of the covariance matrix of the | estimated coefficients.
And then you said
: If we were dealing with just one of these 32 sets of input : segments, I would understand exactly what you mean about running : nvk times, omitting in each run the comparisons for segment sivk : from ivk=1 to ivk=nvk. : : But since we run the 7-predictor model on all 32 sets of segments : simultaneously, I don't at all understand what you mean. In : particular, it doesn't make any sense to me that you want me to : run the model nvk times on just one cell Sk of the 32. How can : the model generate coefficients for just one cell? : : When you described this originally, I thought that what was : necessary was simply to get nvk (n0,n1) pairs for each cell : 1...k...32, where each nvk pair results from omitting results : involving a different sivk in s1vk,...,sivk,...,snvk. : : But now it seems that you meant something entirely different, : and I'm hoping I've conveyed the source of my confusion well : enough for you to help me through it.
When you do a logistic regression with an m-predictor model, you give the program 32 rows of input. Each row contains m+2 numbers: m predictor variables, followed by a pair (n0,n1) that contain dependent information.
When you omit a segment, you give the program the same 32 rows of m+2 numbers, except the (n0,n1) pair in one row will be different: n0 and/or n1 will be smaller; n0+n1 will be smaller by the number of comparisons that the omitted segment participated in.
The estimated coefficients (including the intercept) from each such run with the data from one segment omitted comprise what I referred to above as a b' vector.
