similar to: I need guidance on better data management in preparation for time series analysis

Hello R-world, I am facing a peculiar problem and hope someone out there can comment on it. In goodness-of-fit tests for evaluation of distributions, there are three well-known methods: 1. Chi-square 2. Anderson-Darling 3. Kolmogorov-Sminrov I am trying to use the second test. Many researchers have reported results using this test. I wrote programs in C and now in R to do this. I run into

I am trying something I haven't attempted before and the available documentation doesn't quite answer my questions (at least in a way I can understand). My usual course of action would be to extract my data from my DB, do whatever manipulation is necessary, either manually or using a C++ program, and then import the data into R. Now I need to try to do it all within R+RMySQL+MySQL. I

R-help readers, I''m having a problem with an R script (see below), which regularly generates the error message, Error in start:(start + (sample.length - 1)) : NA/NaN argument , for which I am unsure of the cause. In essence, the script (below) generates the start and end points for random subsamples from along a vector (in reality a transect (of a given length,

I just thought of a useful metaphore for the problem I face. I am dealing with a problem in business finance, with two kinds of related events. However, imagine you have a known amount of carbon (so many kilograms), but you do not know what fraction is C14 (and thus radioactive). Only the C14 will give decay events (and once that event has occurred, the atom that decayed will never decay

Hello R-users, I am trying to obtain Type III SS for an ANOVA with subsampling. My design is slightly unbalanced with either 3 or 4 subsamples per replicate. The basic aov model would be: fit <- aov(y~x+Error(subsample)) But this gives Type I SS and not Type III. But, using the drop() option: drop1(fit, test="F") I get an error message: "Error in

Dear All, I have two questions regarding distribution fitting. I have several datasets, all left-truncated at x=1, that I am attempting to fit distributions to (lognormal, weibull and exponential). I had been using fitdistr in the MASS package as follows: fitdistr<-(x,"weibull") However, this does not take into consideration the truncation at x=1. I read another posting in this

Hello, I'm most grateful for your time to read this. I have a uber size 30GB file of 6 million records and 3000 (mostly categorical data) columns in csv format. I want to bootstrap subsamples for multinomial regression, but it's proving difficult even with my 64GB RAM in my machine and twice that swap file , the process becomes super slow and halts. I'm thinking about generating

I have a simple query as follows: "SELECT m_id,sale_date,YEAR(sale_date),WEEK(sale_date),return_type,DATEDIFF(return_date,sale_date) AS elapsed_time FROM risk_input" I can get, and view, all the data that that query returns. The question is, sale_date is a timestamp, and I need to call split to group this data by m_id and the week in which the sale occurred. Obviously, I would

In the example in the documentation, I see: rs <- dbSendQuery(con, "select Agent, ip_addr, DATA from pseudo_data order by Agent") out <- dbApply(rs, INDEX = "Agent", FUN = function(x, grp) quantile(x$DATA, names=FALSE)) Maybe I am a bit thick, but it took me a while, and a kind hint from Phil, to figure much of this out. It is clear that the SQL

In the docs I see: Usage fitdistr(x, densfun, start, ...) Arguments x A numeric vector. densfun Either a character string or a function returning a density evaluated at its first argument. Distributions "beta", "cauchy", "chi-squared", "exponential", "f", "gamma", "geometric", "log-normal", "lognormal",

Dear R-List,   I would like to have a large number of stratified random subsamples drawn from my dataframe and automatically test for correlation differences in every subsample.   Let this be my dataframe   df<-data.frame(group=c(rep(1,5),rep(2,5),rep(3,5)),a=c(3,4,5,6,3,4,5,4,5,4,1,2,1,2,1),b=c(1,2,3,4,5,3,4,3,4,5,6,5,6,2,3),c=c(2,2,3,3,5,1,1,6,6,5,6,1,1,2,1))   Then I would like to have n

Dear all, I have a set of data which seem to be distributed almost exponentially but only on [0;1]. I guess that the probability distribution in this case would look like \frac{lambda}{1-e^{-\lambda}) e^{-\lambda x} I would like to use fitdistr to estimate the value of \lambda. 1) Would it be correct first to find lambda for the exponential distribution and then to substitute it in the formula

Hello R-Users, I'm new to R so I apologize in advance for any big mistake I might be doing. I'm trying to fit a set of samples with some probabilistic curve, and I have an important question to ask; in particular I have some data, from which I calculate manually the CDF, and then I import them into R and try to fit: I have the x values (my original samples) and the y values

hi, I would like to subsample the array c(1:200) at random into ten subsamples v1,v2,...,v10. I tried with to go progressively like this: > x<-c(1:200) > v1<-sample(x,20) > y<-x[-v1] > v2<-sample(y,20) and then I want to do: >x<-y[-v2] Error: subscript out of bounds.

Why the warning messages (2:4)? > x <- rexp(1000,0.2) > fitdistr(x,"exponential",list(rate=1)) rate 0.219824219 (0.006951308) Warning messages: 1: one-diml optimization by Nelder-Mead is unreliable: use optimize in: optim(start, mylogfn, x = x, hessian = TRUE, ...) 2: NaNs produced in: dexp(x, 1/rate, log) 3: NaNs produced in: dexp(x, 1/rate, log) 4: NaNs

Hello I apologise for the length of this entry but please bear with me. In short: I need a way of subsampling communities from all possible communities of n taxa taken 1:n at a time without having to calculate all possible combinations (because this gives me a memory error - using combn() or expand.grid() at least). Does anyone know of a function? Or can you help me edit the combn or

Dear R users: 1. Is there a function which fits to the data the t-student distribution with parameters mu, sigma, nu. Is the function fitdistr of MASS with the syntax fitdistr(x, "t") appropriate for this? 2. Is there a function which can fit the exponential power distribution? Thanks. --------------------------------- ÁðïêôÞóôå ôçí äùñåÜí óáò@yahoo.gr [[alternative

Hello, I am using fitdistr function for parameter estimation. When I use fd<-fitdistr(V2,"gamma") I get following error: Error in optim(x = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, : initial value in 'vmmin' is not finite fd<-fitdistr(V2,"weibull") Error in optim(x = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, :

Dear all, I have wrote some sample code that would allow me easier fit fast many distributions and check which of the fits performs better. My sample code (that you can of course execute it looks like that) distrList<-list(   "exponential",  "geometric", "log-normal",  "normal", "Poisson") fitfunction<-function(Type,x){     return

Dear all, My question concerns using repetitions and simulations (loops?) in R. I am very new R user, so any help that can be offered would be greatly appreciated! I am using fitdistr() to determine the distribution of empirical univariate datasets, and ks.test to assess the goodness of fit. Because the null distribution of the KS statistic is not known when the distribution parameters are