similar to: Transform Normal Dist to ArcTan Dist

I need to do a non-linear regression in the form of Y = a0 + a1 * arctan(a2 * x) + error. A data sample (X,Y) is available, but I can't remember how to run this sort of regression through R so that I get a value for a0, a1 and a2. Can someone please give me a hint? Thank you in advance.

Dear users; I am doing GLMs with the Gamma distribution, and I always get errors ("no valid set of coefficients: please supply starting values") or warnings ("NaNs produced in log(x)") when I use the links identity or inverse, but I don´t get them if I use the log link. For example: >

Dear Catarina, I prefer to leave the AIC value as NA for the tweedie GLM family because it takes extra time to compute and is only occasionally wanted. It's easy to compute the AIC yourself using the dtweedie() function of the tweedie package. Best wishes Gordon At 03:05 AM 14/08/2007, Catarina Miranda wrote: >Dear Gordon; > >I have also sent this email to R help mailing list,

Hello! One note/question hier about specification of control-parameters in the lme(...,control=list(...)) function call: i tried to specify tne number of iteration needed via lme(....,control=list(maxIter=..., niterEM=...,msVerbose=TRUE)) but every time i change the defualt values maxIter (e.g. maxIter=1, niterEM=0) on ones specified by me, the call returns all the iterations needed until

Hi, In the course of some work which I describe below, I have found a very significant difference between the BARK function described in the Vorbis specification and its implementation in libvorbis. In the specificationhttp://xiph.org/vorbis/doc/Vorbis_I_spec.pdf bark(x) = 13.1arctan(.00074x) + 2.24arctan(.0000000185x**2 + .0001x) In the libvorbis code

Hi, I am trying to recreate the right graph on page 524 of Gelman's 2006 paper "Prior distributions for variance parameters in hierarchical models" in Bayesian Analysis, 3, 515-533. I am only interested, however, in recreating the portion of the graph for the overlain prior density for the half-Cauchy with scale 25 and not the posterior distribution. However, when I try:

hello all happy new year and hope you r having a good holiday. i would like to calculate the expectation of a particular random variable and would like to approximate it using a number of the functions contained in R. decided to do some experimentation on a trivial example. example ======== suppose x(i)~N(0,s2) where s2 = the variance the prior for s2 = p(s2)~IG(a,b) so the posterior is

Full_Name: Jörg Polzehl Version: 1.5.1 OS: Windows 2000 Submission from: (NULL) (193.175.148.198) When calculating MLE's in a variance component model using constrained optimization, i.e. optim(...,method="L-BFGS-B",...) I observed an inproper behaviour in cases where the likelihood function was evalueted at the constraint. Parameters and value of the function at the constraint

The following has been committed to the Vorbis I spec as of r19445: Errata 20150226: Single entry codebooks A ?single-entry codebook? is a codebook with one active codeword entry. A single-entry codebook may be either a fully populated codebook with only one declared entry, or a sparse codebook with only one entry marked used. The Vorbis I spec provides no means to specify a codeword length of

I have question (curiosity) regarding returned values of R's qcauchy () function, for nonexceedance probability (F). It seems the ideal returned range of cauchy distribution should be [-Inf,Inf]. For F=0 > qcauchy(0) [1] -Inf but for F=1 > qcauchy(1) [1] 8.16562e+15 It seems to me that the proper return value should be Inf??? For default (location=0,scale=1) quantile function of

What is the current state of the R-art for ordered probit models, and more esoterically is there any available R strategy for ordered cauchit models, i.e. ordered multinomial alternatives with a cauchy link function. MCMC is an option, obviously, but for a univariate latent variable model this seems to be overkill... standard mle methods should be preferable. (??) Googling reveals that spss

Dear all, This is a pure statistical question, not necessarly related to R. I could not find it in literature. Suppose I'm intersted in a parameter rho, say, equal to: r=beta1/beta2, where beta1 and beta2 come from a linear model y=beta0+beta1X1+beta2X2+.... Fitting the model I can get the (biased) estimate of r=b1/b2, where b1 and b2 are the estimates in the regression model; I can get the

Hi R lovers! I'd like to know how to use the parameter 'start' in the function fitdistr() obviously I have to provide the initial value of the parameter to optimize except in the case of a certain set of given distribution Indeed according to the help file for fitdistr " For the following named distributions, reasonable starting values will be computed if `start'

I am attempting to run a Poisson EWMA model using Patrick Brandt's source code. I get the following error when I run the code: Error in dimnames(x) <- dn : length of 'dimnames' [1] not equal to array extent Dimnames(x) looks like this: [[1]] NULL [[2]] [1] "mip" "div" "nom" "unity" "mood"

Hello, Once again, I posted a message without a subject line. Sorry.... here is the question again. Is there a simple way to modify the circ.mean function in the CircStats package to include a vector of weights to obtain a weighted average angle? Thanks! Martin -- Martin Biuw Sea Mammal Research Unit Gatty Marine Laboratory, University of St Andrews St Andrews, Fife KY16 8PA Scotland Ph:

Hi the following gave me a shock: > atan(2) [1] 1.107149 > atan(2+0i) [1] -0.4636476+0i > or, perhaps more of a gotcha: > atan(1.0001+0i) [1] -0.7853482+0i > atan(0.9999+0i) [1] 0.7853482+0i > evidently atan()'s branch cuts aren't where I thought they were. Where do I look for documentation on this? -- Robin Hankin Uncertainty Analyst National

l used the following code to generate a sample and then calculated then did a log rank test.can l get the normal version of the logrank eg sqrt of the chisqr(1) will give you the N~(0,1). from my sample can i use the above expression to get the normal dist from the result of the log rank test. thank s=1 while(s!=0){ n=100 m<-matrix(nrow=n,ncol=4)

Dear all, I have the following dataset: each row corresponds to count of forest floor small mammal captured in a plot and vegetation characteristics measured at that plot > sotr plot cnt herbc herbht 1 1A1 0 37.08 53.54 2 1A3 1 36.27 26.67 3 1A5 0 32.50 30.62 4 1A7 0 56.54 45.63 5 1B2 0 41.66 38.13 6 1B4 0 32.08 37.79 7 1B6 0 33.71 30.62

R-Users, Hopefully someone can shed some light on these questions as I had little luck searching the archives (although I probably missed something in my search due to the search phrase). I estimated multinomial probabilities for some count data (number successful offspring) ranging from 0 to 8 (9 possible response categories). I constructed a barplot (using barplot2) and I want to

Hi, I noticed this problem on my home desktop running FC4 and again on my laptop running FC5. Both have previously compiled and passed make check-all on 2.3.1 svn revisions from 10 days ago or so. On both these machines, make check-all is consistently failing (4 out of 4 attempts on the FC 4 desktop and 3 out of 3 on the FC 5 laptop) in the p-r-random-tests tests. This is with both default