similar to: Multifactor boxplots

Gday, This is a repost since I only had one direct reply and I remain mystified- This may be stupidity on my part but it may not be so simple. In brief, my problem is I'm not sure how to extract parameter values/effect sizes from a nonlinear regression model with a significant interaction term. My data sets are dose response curves (force and dose) for muscle that also have two

G'day, My problem is I'm not sure how to extract effect sizes from a nonlinear regression model with a significant interaction term. My data sets are multiple measurements of force response to an agonist with two superimposed treatments each having two levels. This is very similar to the Ludbrook example in Venables and Ripley. The experiment is that a muscle is exposed to an agonist

In an intervention study with subjects randomly allocated to two treatments (treat A and B) and three time points (time) plus an additional baseline measurement (dv_base), I've set up the following model to test for differences in temporal courses of treatments for the outcome (dv), thereby allowing for individual intercepts and slopes: lmer(dv ~ dv_base+treat*time+(1+time|subject)) Fixed

I'm writing a PAM module to do authentication through Signal (as in Open Whisper Systems) [1]. I would like to be able to offer (Public key AND Signal) or (Password AND Signal) for authentication. This suggests setting AuthenticationMethods to publickey,keyboard-interactive:pam password,keyboard-interactive:pam However, when PAM is enabled "password" means "show password

For block effects with small variance, lmer will sometimes estimate the variance as being very close to zero and issue a warning. I don't have a problem with this -- I've explored things a bit with some simulations (see below) and conclude that this is probably inevitable when trying to incorporate random effects with not very much data (the means and medians of estimates are plausibly

In S-Plus, I can obtain polynomial contrasts for an ordered factor with contr.poly(). The function also exists in R, however is limited to factors where the levels are equally spaced. In S-Plus, one can obtain the contrasts for a set of numeric values representing unequally spaced ordered factors. Has anyone implemented this in R? I see that the S-Plus function calls another function (poly.raw())

Dear R-list, I have a data set from 20 pigs, each of which is tested at crossed 9 doses (logdose -4:4) and 3 skin treatment substances when exposed to a standard polluted environment. So there are 27 patches on each pig. The response is irritation=yes/no. I want to determine "equally effective 50% doses" (similar to old LD50), and to test the treatments against each other. I am looking

Hi all, I hope you are doing well? I?m currently using the lm() function from the package stats to fit linear multifactor regressions. Unfortunately, I didn?t yet find a way to fit linear multifactor regressions with constraint coefficients? I would like the slope coefficients to be all inside an interval, let?s say, between 0 and 1. Further, if possible, the slope coefficients should add up to

If the slope coefficients sum to a constant, the regressors are dependent and so a unique solution is impossible (an infinity of solutions would result). So I think you have something going on that you don't understand and should consult a local statistician to help you formulate your problem appropriately. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people

Ok, and if I would just like to force my slope coefficients to be inside an interval, let's say, between 0 and 1? Is there a way in R to formulate such a constraint regression? Thanks in advance and kind regards, Aljosa Aljosa Aleksandrovic, FRM, CAIA Quantitative Analyst - Convertibles aljosa.aleksandrovic at man.com Tel +41 55 417 7603 Man Investments (CH) AG Huobstrasse 3 | 8808

Hi, Is there a way to stack bars in a barchart as well as "beside" bars for the same treatment? eg.... I have one barchart like this: bio<-matrix(c(10,23,9,25),nrow=2,byrow=T) ntreat<-c("n0","n96") colnames(bio)<-ntreat barplot(bio,beside=T) now i want a similar barchart but with stacked bars:

This is a quadratic programming problem that you can solve using either a quadratic programming solver with constraints or a general nonlinear solver with constraints. See https://cran.r-project.org/web/views/Optimization.html for more info on what is available. Here is an example using a nonlinear least squares solver and non-negative bound constraints. The constraint that the coefficients sum

Hi Gabor, Thanks a lot for your help! I tried to implement your nonlinear least squares solver on my data set. I was just wondering about the argument start. If I would like to force all my coefficients to be inside an interval, let?s say, between 0 and 1, what kind of starting values are normally recommended for the start argument (e.g. Using a 4 factor model with b1, b2, b3 and b4, I tried

Have you tried web searching on " R constrained linear regression" or similar. There seemed to be resources related to your issues when I looked. You might also search on rseek.org . There are apparently several packages that do regression with constraints, but I don't know if they fit your situation. Cheers, Bert Bert Gunter "The trouble with having an open mind is that

The nls2 package can be used to get starting values. On Thu, Apr 28, 2016 at 8:42 AM, Aleksandrovic, Aljosa (Pfaeffikon) <Aljosa.Aleksandrovic at man.com> wrote: > Hi Gabor, > > Thanks a lot for your help! > > I tried to implement your nonlinear least squares solver on my data set. I was just wondering about the argument start. If I would like to force all my coefficients to

Dear All I am attempting some power analyses, based on simulated data. My experimental set up is thus: Bleach: main effect, three levels (control, med, high), Fixed. Temp: main effect, two levels (cold, hot), Fixed. Main effect interactions, six levels (fixed) For each main-effect combination I have three replicates. Within each replicate I can take varying numbers of measurements (response

Hello, I want to know if there are some functions or packages to solve differential and integral equation using R. Thanks. Shao chunxuan. [[alternative HTML version deleted]]

We need a two-way non-parametric ANOVA in order to analysis properly some ecological data, do you know any reference in R? or how to do it? Thank you very much All the best diana -- View this message in context: http://www.nabble.com/Two-way-non-parametric-ANOVA--tp15039308p15039308.html Sent from the R help mailing list archive at Nabble.com.

I've recently been working with some California county-level data. The counties can be referred to as either FIPS codes, eg F060102, friendly names such as "Del Norte County", names without 'County' on the end, names with 'CA' on the end ("Del Norte County, CA"). Different data sets use slightly different forms and putting them all together is a pain. So I

hi, i want to plot percentage values using barplot. everything works fine so far. giving a chance level of 50% i want to plot only the area above 50 percent. i can of course cut using ylim=c(50,100) but this does not work properly since i also see some of the area below 50%. thanks for suggestions, jan -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help