similar to: ANOVA in R

Hello, I am solving a set of ODEs using deSolve and have run into a problem I would appreciate some advice on. One of the parameters (m) in the ODEs changes between two states when one of the variables (D) crosses a threshold (D_T) for the first time in either direction. Additionally, when the variable crosses the threshold (either by increasing or decreasing), there is a time delay (delay)

Hi all, Is there any grid search function to compute the maximum likelihood for a random variable? thanks, Samor --------------------------------- [[alternative HTML version deleted]]

Hello, Is there any test  for bimodality in R that x <- c(rnorm(1000,0,1),rnorm(1000,3,1)) hist(x,nclass=100) Thank you in advance for any help. Regards, Samor [[alternative HTML version deleted]]

> On Apr 18, 2018, at 1:04 AM, Hannah Meredith <hrmeredith12 at gmail.com> wrote: > > Hello, > > I am solving a set of ODEs using deSolve and have run into a problem I > would appreciate some advice on. One of the parameters (m) in the ODEs > changes between two states when one of the variables (D) crosses a > threshold (D_T) for the first time in either direction.

Hello everybody, I am trying to install the library stepplr under windows (http://www.maths.bris.ac.uk/R/web/packages/stepPlr/index.html), in order to use the function plr, but I still have problem to find the right link for this purpose! I am very thankful for your help! Samor

Dear all, I am very thankful, if you could tell wheather it is possible to write paste("Mus., 10 ", expression(mu)," g", sep="") Thank you in advance, Samor --------------------------------- [[alternative HTML version deleted]]

Sorry for starting another topic, this is actually a reply to Segher's post on Sun Oct 22 on the 'mdct question' topic. I wasn't subscribed properly and so I didn't get email confirmation and thus can't add to that thread. So Segher, if the equation is indeed what you say it is, then replacing mdct_backward with this version should work, but it doesn't. Am I applying

Hello, I was wondering if someone knows the formula used by the function lm to compute the t-values. I am trying to implement a linear regression myself. Assuming that I have K variables, and N observations, the formula I am using is: For the k-th variable, t-value= b_k/sigma_k With b_k is the coefficient for the k-th variable, and sigma_k =(t(x) x )^(-1) _kk is its standard deviation.

Hello, Is it possible to have two meta-plots in one graph (not par(mfrow=c(2,1))? But somthing like  library(metafor)  library("igraph")    if (interactive()) {     forest(dat.Treat$RR, ci.lb=dat.Treat$lower, ci.ub=dat.Treat$upper, xlab="Relative Risk",slab=dat.Treat$ID,refline=1)     forest(dat.Control$RR, ci.lb=dat.Control$lower, ci.ub=dat.Control$upper, xlab="Relative

Hi, I am trying to figure how to use dlm with constant terms (possibly time-dependent) added to both equations y_t = c_t + F_t\theta_t + v_t \theta_t = d_t + G_t\theta_{t-1} + w_t, in the way that S-PLUS Finmetrics does? Is there any straightforward way to transform the above to the default setup? Thanks, Tsvetan -------------------------------------------------------- NOTICE: If received in

Hello, I am calling the following code with the loop! It makes 3 out graphs but empty! COuld you help me plase on that? Thank you in advance z1 <- dbConnect(MyData, "something", "A1", "A2") for (tt in c("xyz", "abc", "m1")) { message(paste("Here", tt, "!!!")) mydata <- dbReadTable(z1, tt)

Hi, Can someone tell me which MDCT and invMDCT equation uses? I implemented the invMDCT one given in eusipco.corrected.ps file (handed out by Monty way back) and it produces different time domain samples. I tried both the FFT method and the slow way directly from the equation and couldn't reproduce the results from the original code. This leads me to believe that the forward MDCT used in

Is there a weighted median function out there similar to weighted.mean() but for medians? If not, I'll try implement or port it myself. The need for a weighted median came from the following optimization problem: x* = arg_x min (a|x| + sum_{k=1}^n |x - b_k|) where a : is a *positive* real scalar x : is a real scalar n : is an integer b_k: are negative and positive scalars

Dear all, I've only just joined this list, but I can't find any mention of this idea anywhere else, so I thought I'd just post here before getting too deep into programming and possibly reinventing the wheel. Here at Aber, we have around 30 unix and linux servers doing core services. Each one is maintaining its own logfiles and, for various reasons, we want to keep these on the

dear list, I am wondering if one can find examples and/or more detailed descriptions of modifications needed when going beyond standard corStruct classes (i.e. those already provided for use in lme/nlme)? When I looked at pages 238-239 of Pinheiro/Bates (2000): Mixed-effects models in S and S-plus, I found that I would need a bit more explicit guidance what to do for implementing a new

Dear readers, Is it possible to specify a model y=X %*% beta + Z %*% b ; b=(b_1,..,b_k) and b_i~N(0,v^2) for i=1,..,k that is, a model where the random slopes for different covariates are i.i.d., in lmer() and how? In lme() one needs a constant grouping factor (e.g.: all=rep(1,n)) and would then specify: lme(fixed= y~X, random= list(all=pdIdent(~Z-1)) ) , that?s how it's done in the

Hello, Say one starts with creating an archive rsync work -> archive and periodically (below, i = 1 to N) does rsync --backup-dir=a_<i> work -> archive and rsync --compare-dest=archive work -> b_<i> Then suppose one wants to recover the work directory as it was at time k. Using the b_<i> directories, one would merely merge

Dear Friends, I am seeking for any help on an error message in lme functions. I use mixed model to analyze a data with compound symmetric correlation structure. But I get an error message: "Error in corMatrix.corCompSymm(object) : NA/NaN/Inf in foreign function call (arg 1)". If I change the correlation structure to corAR1, then no error. I have no clue how to solve this problem.

Hello dear r-gurus! I have a question about the logit-model. I think I have misunderstood something and I'm trying to find a bug from my code or even better from my head. Any help is appreciated. The question is shortly: why I'm not having same coefficients from the logit-regression when using a link-function and an explicite transformation of the dependent. Below some details. I'm

I'm trying to learn how to do a repeated measures ANOVA in R using lme(). A data set that comes from the book Design and Analysis has the following structure: Measurements (DV) were taken on 8 subjects (SUB) with two experimental levels (GROUP) at four times (TRIAL). In SAS, I use the code: PROC MIXED DATA=[data set below]; CLASS sub group trial; MODEL dv = group trial group*trial;