search for: cauchy

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

...g 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: curve(dcauchy, from=0, to=200, location=0, scale=25) the probabilities for the half-Cauchy values seem to approach zero almost immediately after 0 whereas in Gelman 2006 the tail appears much fatter giving non-zero probabil...

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,s...

In complex analysis, Cauchy's integral theorem states (loosely speaking) that the path integral of any entire differentiable function, around any closed curve, is zero. I would like to see this numerically, using R (and indeed I would like to use the residue theorem as well). Has anyone coded up path integration?...

Hi I'm using R(2.9.0) and gstat package under Windows to plot sample variograms. When I want to fit them, I don't have the possibility to choose Cauchy functions. Under Mathematica for example, we have it and it seems to be the best one to fit the variograms I have I tested the others functions, they can fit but I would,if possible, use the Cauchy one. If anybody knows something about it, I would know, even with another package To show what I wan...

Hi all, I am using the function COV.WT to estimate the estimators (location and scale) of a bivariate cauchy distribution. My doubt is about the option WT (weight), cause at the R-help shows that the weight is uniform according to the number of observations. But, checking the theory, for example, the mean is given by mean_estimator=mean(u(s)x)/mean(u(s)), where x=my data (bivariate) u(s)=(df+p/df+s), df=d...

...-------------------------------------------------------- and the essential part with trace = 6 : 4 variables are free at GCP on iteration 11 LINE SEARCH 1 times; norm of step = 1.19245 X = -3.34057 6.34639 1.18546 15.2136 G = -1.08654 -1.14056 1.42527 -0.307525 Iteration 11 ---------------- CAUCHY entered------------------- There are 1 breakpoints Piece 1 f1, f2 at start point -4.6074e+000 3.1209e+001 Distance to the next break point = 8.3175e-001 Distance to the stationary point = 1.4763e-001 GCP found in this segment Piece 1 f1, f2 at start point -4.6074e+000 3.1209e+001 Di...

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 provides such functions... just to wave a red flag. > url: www.econ.uiuc.edu/~roger...

...) x<- cbind(mip,div,nom,unity,mood,times,hmajparty,smajparty,pres) cr.PEWMA<- Pewma(y=countlaw,x=as.matrix(x),init.param=c(0.8,init[2:length(init)])) Which results in this output: N = 10, M = 5 machine precision = 2.22045e-16 iterations 34 function evaluations 45 segments explored during Cauchy searches 35 BFGS updates skipped 0 active bounds at final generalized Cauchy point 1 norm of the final projected gradient 0.00806577 final function value 1.78439 final value 1.784390 converged Error in dimnames(x) <- dn : length of 'dimnames' [1] not equal to array extent Any thou...

I found this a few months ago, but for the life of me I can't remember what the function or package was, and I have had no luck finding it this week. I have found, again, the functions for working with distributions like Cauchy, F, normal, &c., and ks.test, but I have not found the functions for estimating the distribution parameters given a vector of values. What I need to do is estimate the distribution parameters for each candidate distribution, and then test to see which gives the best fit to the data. I want to...

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

...er of my data, to explain why there are no parameter estimates. The data I WILL be applying this to comes from the financial markets, and will be reals or floating point numbers that in some cases wil be best modelled by a normal distribution while in most cases, the distribution will be closer to cauchy. (but DistributionFits(fBasics) makes no explicit mention of cauchy, but IIRC cauchy is a special case of a stable distribution one of a family - are these the L-stable distributions Mandelbrot discussed, or something else - correct me if my memory has failed me sooner than anticipated ;-) An UR...

...lue 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' is omitted or only partially specified: `cauchy', `gamma', `logistic', `negative binomial' (R only, parametrized by `mu' and `size'), `t', `uniform', `weibull'. " However I cannot fit an univariate distribution named 'test' I get this: >fitdistr(test,"lognormal") Error in f...

...t;,shape1 = 1, shape2 = 2) beta(shape1 = 1, shape2 = 2) PASSED [1] TRUE > dkwtest("beta",shape1 = 2, shape2 = 2) beta(shape1 = 2, shape2 = 2) PASSED [1] TRUE > dkwtest("beta",shape1 = .2,shape2 = .2) beta(shape1 = 0.2, shape2 = 0.2) PASSED [1] TRUE > > dkwtest("cauchy") cauchy() PASSED [1] TRUE > dkwtest("cauchy",location = 4,scale = 2) cauchy(location = 4, scale = 2) PASSED [1] TRUE > > dkwtest("f",df1 = 1,df2 = 1) f(df1 = 1, df2 = 1) PASSED [1] TRUE > dkwtest("f",df1 = 1,df2 = 10) f(df1 = 1, df2 = 10) PASSED [...

...d=2) > ## extreme points are omitted since dbinom gives 0. > mtext("dbinom(k, log=TRUE)", adj=0) > mtext("extended range", adj=0, line = -1, font=4) > mtext("log(dbinom(k))", col="red", adj=1) > > > > cleanEx(); ..nameEx <- "Cauchy" > > ### * Cauchy > > flush(stderr()); flush(stdout()) > > ### Name: Cauchy > ### Title: The Cauchy Distribution > ### Aliases: Cauchy dcauchy pcauchy qcauchy rcauchy > ### Keywords: distribution > > ### ** Examples > > dcauchy(-1:4) [1] 0.15915494 0...

...uring years. I thought identity-binomial models were unsupported by R. The documentation is correct but misleading too. > The gaussian family accepts the links (as names) identity, log and inverse; the binomial family the > links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and > cloglog (complementary log-log); Without changing the language, this could be fixed by changing the error messages to something more suggestive. Suggestion: Error in binomial(identity) : name identity not available for binomial family; please use a character stri...

Hi, I'd like to know if there's anything in R that could help me do that. Let's suppose I have a density function of a random variable, for example f(x) = (x^3)/4 0 < x < 2 and I would like to simulate it. For the common distributions (exponencial, gamma, cauchy) there are the r-functions (rgamma, rexp, runif, rcauchy, and so on).. But when the variable I want to simulate is not one of those, how should I procede? I read some references on the subject and saw that there are some algorithms that can do that, but I just wonder if there is any implemented in...

Hi Folks, I am interested in extending the repertoire of link functions in glm(Y~X, family=binomial(link=...)) to include a "tan" link: eta = (4/pi)*tan(mu) i.e. this link bears the same relation to the Cauchy distribution as the probit link bears to the Gaussian. I'm interested in sage advice about this from people who know their way aroung glm. >From the surface, it looks as though it might just be a matter of re-writing 'make.link' in the obvious sort of way so as to incorporate "...

Hello, I'm taking samples from certain distributions and drawing a density distribution over the histogram of the samples It works fine for the chi-square and for the normal, but not for the cauchy. Any idea what I'm doing wrong? Thanks x <- rchisq(10000, df = 4) hist(x, freq = FALSE, breaks=100) curve(dchisq(x, df = 4), col = 2, add = TRUE) x <- rnorm(10000) hist(x, freq = FALSE, breaks=100) curve(dnorm(x), col = 2, add = TRUE) x <- rcauchy(10000) hist(x, freq = FALSE, break...

Dear all, A am trying to fit a generalized linear mixed effects model with a binomial link function, my response data is binary, using the lme4 R package, for the glmer model but with the cauchit link function (CDF of Cauchy distribution), under the package this has not yet been coded and was wondering if anyone knew a way in which I could incorporate this link function into the code. Thankyou in advance Liz -- View this message in context: http://www.nabble.com/glmer-for-cauchit-link-function-tp20612464p20612464.ht...