search for: unmeasured

I would just like to give a success report, since this list gets mostly bugs and questions.. Due mainly to the help that this list provided, I am now able to successfully deploy Linux/Samba servers in our corporate domain, and allow remote offices to function as part of our vast windows domain. Things are working, and the $$$ it has saved us is almost unmeasurable. Again, thanks for your help,

The new package obsSens is now on the CRAN mirrors. This package has tools for doing senstitivity analysis for observational studies. The common criticism of observational studies is that there is the possibility of an unmeasured variable that is related to both the response and the predictor of interst that could explain the observed relationship. The sensitivity analysis is a "What If?" game where you specify potential relationships between the unmeasured variable and the response and predictor variable and see...

The new package obsSens is now on the CRAN mirrors. This package has tools for doing senstitivity analysis for observational studies. The common criticism of observational studies is that there is the possibility of an unmeasured variable that is related to both the response and the predictor of interst that could explain the observed relationship. The sensitivity analysis is a "What If?" game where you specify potential relationships between the unmeasured variable and the response and predictor variable and see...

More an echo algorithm question than a purely asterisk one... I have the following setup: Handset - PAP2 - Asterisk - SPA3000 - Telco And no matter what I do, I get echo on a call routed out via the PSTN when I talk into the handset, in the order of a hundred ms (my estimate, could be wildly inaccurate!). Echo will occur also when I have a handset plugged into the phone port on the SPA3000

...of the protein looks periodic, but the height of the peaks is highly variable. I'm testing for periodicity using a Monte Carlo simulation envelope approach applied to a cumulative periodogram. Now I want to predict the location of the peaks in time. Of course, the peaks might be occurring on unmeasured days. Sadly, an NDA prohibits me from sharing the real data. The data look something like this: ################################################################## patient <- data.frame( day = c(1, 3, 5, 8, 10, 12, 15, 17, 19, 22, 24, 26), protein = c(5, 3, 10, 7, 2, 8, 25, 22, 7, 10, 12, 5)...

Ah.... LOD's, typically LLOD's ("lower limits of detection"). Disclaimer: I am *NOT* in any sense an expert on such matters. What follows are just some comments based on my personal experience. Please filter accordingly. Also, while I kept it on list as Martin suggested it might be useful to do so, most folks probably can safely ignore the rant that follows as off topic and not

Hola! I started to search for information about multilevel survival models, and found frailty in R. This seems to be something of the same, is it the same? Then: why the name frailty (weekness?) -- Kjetil Halvorsen. Peace is the most effective weapon of mass construction. -- Mahdi Elmandjra

Still OT... but here is my own (I think previously mentioned here) rant on people thrashing about with log transformation and an all-too-common kludge to deal with zeros mixed among small numbers... https://gist.github.com/jdnewmil/99301a88de702ad2fcbaef33326b08b4 OP perhaps posting a link here to your question posed wherever you end up with it will help shorten this thread. On January 22, 2024

...protein = c(5, 3, 10, 7, 2, 8, 25, 12, 7, 20, 10, 5) ) plot(patient$day, patient$protein, type="b") My goal is two-fold: firstly, I need to test for periodicity, and secondly, I need to try to predict the temporal location of future peaks. Of course, the peaks might be occurring on unmeasured days. I have been looking at this model: wave.form <- deriv3( ~ sin(2*pi*((day-offset)/period + 0.25)) * amplitude + mean, c("period", "offset", "amplitude", "mean"), function(day, period, offset, amplitude, mean){}) curve(wave.form...

I''m trying to ensure that I keep the number of queries down a bit. If I have two model (lets call them Foo and Bar). They have a many to many relationship. Now I want to get (and paginate) records from Bar that are related to a certain record in Foo. For example I want all records from Bar whose name starts with ''C'' and are related to record Foo whose name is

...respondent likes the Republican Party in America. By the usual simultaneous equation argument, one should not simply run 2 polr polr (Y1 ~ Y2 + X1 +X2) and polr(Y2 ~ Y1 + X1 + X2) because Y1 and Y2 are endogenous. Where does the problem arise? Thinking back to the theoretical model, there are unmeasured scale variables y1* and y2* that are determined by y1* = b0 + b1 * y2 + b2 * X1 + b3 * X2 + e1 and y2* = c0 + c1 * y1 + c2 * X1 + c3 * X2 + e2 y1* and y2* are not observed, we see only the categorical outputs Y1 and Y2 that correspond to Y1 = 0 if y1* < pi1 Y1 = 1 if pi1 <= y1...

Sorry for replying in this way, I can send to the list ok, but I can't seem to subscribe to the list. majordomo doesn't accept my email address as a valid address (k.j.wierenga@home.nl > This is primarily to allow clean shutdown to proceed normally. The CPU load is > nominal - I doubt you'd be able to measure it. However, if you want to change > this timeout, it's safe

On Wed, 18 Feb 2015, Nathan Kurz wrote: > On Wed, Feb 18, 2015 at 7:19 AM, Radford Neal <radford at cs.toronto.edu> wrote: >>> ... with assignments inside of loops like this: >>> >>> reweight = function(iter, w, Q) { >>> for (i in 1:iter) { >>> wT = w * Q >>> } >>> } >>> ... before the RHS is executed, the

Hi, (Assume VPID is available and enabled.) I''m trying to figure the TLB stuff with VPIDs. I understand from the poorly written chapter in the intel manual that if an HVM vcpu is running then only the TLBs tagged with the vcpu.VPID will be used. If xen or a PV guest is running, then VPID 0 TLBs are what will be used. Now I understand the hvm_asid_flush_vcpu upon new guest cr3, will

> ... with assignments inside of loops like this: > > reweight = function(iter, w, Q) { > for (i in 1:iter) { > wT = w * Q > } > } > ... before the RHS is executed, the LHS allocation would be added > to a small fixed length list of available space which is checked > before future allocations. If the same size is requested before the > next garbage

On Wed, Feb 18, 2015 at 7:19 AM, Radford Neal <radford at cs.toronto.edu> wrote: >> ... with assignments inside of loops like this: >> >> reweight = function(iter, w, Q) { >> for (i in 1:iter) { >> wT = w * Q >> } >> } >> ... before the RHS is executed, the LHS allocation would be added >> to a small fixed length list of available

On Mon, 22 Jan 2024, Martin Maechler wrote: > I think it is a good question, not really only about geo-chemistry, but > about statistics in applied sciences (and engineering for that matter). > John W Tukey (and several other of the grands of the time) had the log > transform among the "First aid transformations": > > If the data for a continuous variable must all be

...d' frailty models are implemented in practice, to group people who are from a cluster to account for homogeneity in outcomes for people from the same cluster. I am more interested in the classic frailty model, 'Early frailty models incorporated subject-specific random effects to account for unmeasured subject characteristics that influenced the hazard of the occurrence of the outcome'. This is because I have data where I would like to estimate the amount of variation between patients risks that has not been accounted for by adjusting for the variables that I have. I initially ran some sim...

I took 5 albums (Classical music) and converted them to Ogg Vorbis at "Full Bitrate" (-q10) and all 5 directories take up about 775 Megs which won't fit on a CD. So I ripped them again in WAV first (And give my friend back his CDs) but now I wanna know what quality setting should I use to fit them on 1 CD (The highest possible with total space used just under 700 Megs)

Hi, This is not an R question, but I've seen opinions given on non R topics, so I wanted to give it a try. :) How would one treat a variable that was measured once, but is known to fluctuate a lot? For example, I want to include a hormone in my regression as an explanatory variable. However, this hormone varies in its levels throughout a day. Nevertheless, its levels differ substantially