similar to: Estimating Weibull parameters

Hi R-Community, I have two correlated time series X[t] and Y[t]. X[t] can be modeled as X[t] = a[1]X[t-1] + a[2]X[t-2] + e[t] + b[1]e[t-1] + b[2]e[t-2] + c[0]Y[t] where e[t] is a white noise process. Is there a way to estimate the coefficients a[1], a[2], b[1], b[2] and c[0]? Much thanks in advance, Hagen Schm?ller -- Dipl.-Ing. Hagen K. Schm?ller Institut f?r Elektrische Anlagen und

Hi R-Community, so far I dealt with univariate processes and used the function "arima" to estimate an ARMA(1,1)-model. For multivariate processes there are the functions "estVARXar" and "estVARXls" from package "DSE". But how can I estimate an VARMA(1,1)-model, or even better determine the orders and estimate the parameters? Much thanks in advance, Hagen

Hello, I am trying to get a library for statistical computing. For example, I have to compute the ARIMA parameters of time series and I?d like to do that out of my C/C++ or Fortran source code. The thing I already managed is how to get the standalone library "Rmath", but in this library the ts-modul is not included. Thus, my question is: How can I get a library including the

Hi R-Community, I estimated a VARMA model with bft in dse1 without input: A(L)yt = B(L)et I got the auto-regressive polynomial array A and the moving-average polynomial array B, but how can I access the covariances of the white noise et (disturbance vector), e.g. for simulation? Much thanks in advance, Hagen Schmoeller -- Dipl.-Ing. Hagen K. Schm??ller Leiter Forschungsgruppe Stromerzeugung

GUYS, I NEED HELP WITH ERROR: library(MASS) > dados<-read.table("mediaRGinverno.txt",header=FALSE) > vento50<-fitdistr(dados[[1]],densfun="weibull") Erro em fitdistr(dados[[1]], densfun = "weibull") : Weibull values must be > 0 WHY RETURN THIS ERROR? WHAT CAN I DO? BEST REGARDS [[alternative HTML version deleted]]

Hi, why my script iss always generating the same graph?when I change the parameters and the name of text file? library(MASS) dados<-read.table("inverno.txt",header=FALSE) vento50<-fitdistr(dados[[1]],densfun="weibull") png(filename="invernoRG.png",width=800,height=600) hist(dados[[1]], seq(0, 18, 0.5), prob=TRUE, xlab="Velocidade

Hi all, I'm using the function "fitdistr" (library MASS) to fit a distribution to given data. What I have to do further, is getting the log-Likelihood-Value from this estimation. Is there any simple possibility to realize it? Regards, Carsten

Guys, I'm having an error when I use the command: library(MASS)> dados<-read.table("inverno.txt",header=FALSE)> vento50<-fitdistr(dados[[1]],densfun="weibull")Mensagens de aviso perdidas:1: In dweibull(x, shape, scale, log) : NaNs produzidos2: In dweibull(x, shape, scale, log) : NaNs produzidos3: In dweibull(x, shape, scale, log) : NaNs produzidos4: In

Hello, is there a quick way of estimating Weibull parameters for some data points that are assumed to be Weibull-distributed? I guess I'm just too lazy to set up a Maximum-Likelihood estimation... ...but maybe there is a simpler way? Thanks for any hint (and yes, I've read help(Weibull) ;) Kaspar Pflugshaupt -- Kaspar Pflugshaupt Geobotanical Institute ETH Zurich, Switzerland

Hello everyone, This is Kangmin. I am trying to produce shape and scale of my wind data. My data is based on wind speed frequency with 1km/hr increment. data is described below. Windspeed (km/h) Frequency 1 351 2 147 3 317 4 378 5 527 6 667 7 865 8 970 9 987 10 907 11 905 12 642 13 1000 14 983 15 847 16 842 17 757 18 698 19 632 20 626 21 599 22 529 23 325 24 391

I am attempting to estimate LC50 (analogous to LD50, but uses exposure concentration rather than dose) by fitting a Weibull model; but I can't seem to get it to work. From what I can gather, I should be using survreg() from the survival package. The survreg() function relies on time-to-event data; my data result from 96 h exposures (i.e., dead or alive after a fixed period; 96 h). I've

my problem actually arised with fitting the data to the weibulldistribution, where it is hard to see, if the proposed parameterestimates make sense. data1:2743;4678;21427;6194;10286;1505;12811;2161;6853;2625;14542;694;11491; ?? ?? ?? ?? ?? 14924;28640;17097;2136;5308;3477;91301;11488;3860;64114;14334 how am I supposed to know what starting values i have to take? i get different

Dear Knowledgeable R Community Members, Please excuse my ignorance, I apologize in advance if this is an easy question, but I am a bit stumped and could use a little guidance. I have a finite mixture modeling problem -- for example, a 2-component Weibull mixture -- where the components have a large overlap, and I am trying to adapt the "mclust" package which concern to normal

Dear wine-users, I am experimenting with wine's (wine-1.0.1) ability to interface with native Unix programs, as in $ wineconsole /bin/echo hi hi What I would like to do is something like the following. Create a batch file that calls a Unix-command (in this case `ssh`) and redirects output to a file. Consider, for example, the following lines in a file called 'ssh.bat':

Hello What is the appropriate method to test if a given distribution is a weibull thank you meriema email meriema.belaidouni at int-evry.fr

Dear all, When calling rlm with the following data, I get an error. (R v.1.8.1, WinXP Pro 2002 with service pack 1.) > d <- na.omit(data.frame(CPRATIO, HEIGHTZ, FAMILYID)) > c <- tapply(d$CPRATIO, d$FAMILYID, mean) > h <- tapply(d$HEIGHTZ, d$FAMILYID, mean) > c 1 2 3 6 7 9 10 11 6.000000 2.500000 3.250000

Hi all I try to make a weibull survival analysis on R. I know make this on GLIM, and now I try to make the GLIM exercice GLEX8 on R to learning and compare the test. The variables are: time censor group bodymass In GLIM I make: $calc %s=1 $ to fit weibull rather than exponential $input %pcl weibull $ $macro model group*bodymass $endmac$ $use weibull t w %s $ Then, GLIM estimate an alpha for the

Hi I am using R to fit a survival function to my data (with a weibull distribution). Data: Survival of individuals in relation to 4 treatments ('a','b','c','g') syntax: ---- > survreg(Surv(date2)~males2, dist='weibull') But I have some problems interpreting the outcome and getting the parameters for each curve. --------- Value Std.

I was wondering if someone familiar with survival analysis can help me with the following. I would like to fit a Weibull curve, that may be dependent on a covariate, my dataframe "labdata" that has the fields "cov", "time", and "censor". Do I do the following? wieb<-survreg(Surv(labdata$time, labadata$censor)~labdata$cov,

Dear R-users I would like to fit weibull parameters using "Method of moments" in order to provide the inital values of the parameter to de function 'fitdistr' . I don`t have much experience with maths and I don't know how to do it. Can anyone please put me in the rigth direction? Borja [[alternative HTML version deleted]]