similar to: Is there an R program that produces optimal solution/mix of multiple samples' varying volumes and values

Hi I would like to plot bar charts in a particular way. The intervals are not evenly distributed and are in a data frame column called size and the relative frequencies are in a second column called mass. Both size and mass are continuous ratio data to all intents and purposes. The data actually represents sieving of sand where $size is sieve aperture and where $mass is the amount remaining on

Dear experts, I tried to learn about Box-Cox-transformation but found the following thing: When I had to add a constant to make all values of the original variable positive, I found that the lambda estimates (box.cox.powers-function) differed dramatically depending on the specific constant chosen. In addition, the correlation between the transformed variable and the original were not 1 (as I

i am trying to solve a partial differential equation analytically(PDE) in R . i have found some functions that do the stuff numerically. But that will not meet my purpose. is there any function to solve PDE analytically. please help. -- View this message in context: http://r.789695.n4.nabble.com/analytical-solution-of-partial-differential-equation-tp4291618p4291618.html Sent from the R help

Hi there, I would like to draw 10 correlations from a bivariate population - but every draw should be done with a different sample size. I thought I could to this with a loop: r=numeric(10) #Goal vector N = c(1000,100,80,250,125,375,90,211,160,540) #Sample size vector for(i in 1:10) { data <- mvrnorm(n=N,mu=c(0,0),Sigma=matrix(c(1,.3,.3,1),2)) r[i] <- cor(data[,1],data[,2]) }

What's the current best wisdom on how to construct confidence bounds on something like a normal probability plot? I recall having read a suggestion to Monte Carlo something like 201 simulated lines with the same number of points, then sort the order statistics, and plot the 6th and 196th of these. [I use 201 not 200 because quantile(1:201, c(0.025, 0.975)) = 6 and 196 while

Hi All, I am trying to run a loop that will have varying numbers of rows with each output. Previously I have had the same number of rows so I would use (and I appreciate that this will no doubt achieve some gasps as being thoroughly inefficient!): xdfrow<-(0) xdfrow1<-(1:32) xdfrow2<-(33:64) xdfrow3<-(65:96) xdfrow4<-(97:128) xdfrow5<-(129:160) xdfrow6<-(161:192)

Dear All, Thank you for the replies to my first thread here: http://r.789695.n4.nabble.com/global-optimisation-with-inequality-constraints-td3799258.html. So far the best result is achieved via a penalised objective function. This was suggested by someone on this list privately. I am still looking into some of the options mentioned in the original thread, but I have been advised that there may

Hello all, I am working with the dlm package, specifcially doing a dlm multivariate Y linear regression using dlmModReg and dlmFilter and dlmSmooth... I have altereted the inputs into dlmModReg to make them time-varying using JFF, JW etc. How do I track the results of the time varying system matrices? For example what I am really interested in is JW - my system variance matrix for each time

Dear R users, I have repeated measurements on individuals. I want to estimate the time-varying effect of a factor variable X (taking three levels), e.g. a model in the spirit of Hastie and Tibshirani (1993). I am considering using the package "mgvc" which implements generalized additive models, especially the function gamm, which estimates generalized additive mixed models, and thus,

I'd like to estimate time varying coefficients in a linear regression using a Kalman filter. Even if the Kalman Filter seems to be available in some packages I can't figure out how to use it to estimate the coefficients. Is there anyway to do that in R? Any help appreciated Thanks

Hi all, I would like to do a barplot of a dataframe like this one: alfa beta gamma delta qwert 56.5 58.5 56.5 58.5 asdfg 73.0 73.0 43.0 73.0 zxcvb 63.0 63.0 43.0 63.0 yuiop 63.0 63.0 43.0 63.0 with the labels of the rows and columns. I would like to have something that works for dataframes with varying dimensions, and so far I haven't found any way to do it.

Hello, I am hoping someone can help tackle the problem below, for which I require a fast solution. It feels like there should be an elegant approach, but I am drawing blanks. Take a vector 'x' with random values > 0: x = runif(10,1,5) Assume some reasonably small positive value 'delta': delta = 0.75 The task is to find a solution vector 'y' of same length as

Hi - I read through dse package manual a bit. I'm not quite certain how I can use it to estimate a time varying coefficient regression model? I might pick up an inappropriate package. Any suggestion would be greatly appreciated. Thank you. rh

Hi Folks, I'm trying to find a neat solution to an apparently simple problem, but one which turns out to be a bit more intricate and tricky than one might expect. Suppose I have numbers given to a large number of digits. For example 1234567021 where (though I don't know this beforehand) only the last 3 digits will be varying (and all 3 will vary). What I want is, give a vector x of

Hello, I am trying to model an intensity function with time-varying covariates. Before, I have successfully defined a log likelihood function for a Power-Law Process (lambda(t)=alpha*beta*t^(beta-1)) with two paramters and no covariates for a repairable systems with failure times (t). This function was maximized with R optim. No problem! But now I want to include a covariate indicating a

Dear All, I am a researcher doing research in plant growth and I have a statistical problem that seems to not be able to handle. Recently, I conducted an experiment about plant growing in three different nutrient-level sediments. I harvested these every three week (three harvests in all). Some growth traits of these plants were recorded (e.g. total biomass, leaf biomass and stem biomass). In

Dear R useres, I'm interested in getting a series of time-varying correlation, simply between two random variables. Could you please introduce a package to do this task? Thank you so much for any help. Amir --------------------------------- Don't pick lemons. [[alternative HTML version deleted]]

Dear Prof. Brostr?m, Dear R-mailinglist, first of all thanks a lot for your great effort to incorporate time-varying covariates into aftreg. It works like a charm so far and I'll update you with detailled benchmarks as soon as I have them. I have one more questions regarding Accelerated Failure Time models (with aftreg): You mention that left truncation in combination with time-varying

Hello, I have data in the following form age sex Int.Prev.Est.1 Int.Prev.Est.2 Int.Prev.Est.3 Int.Prev.Est.4 Int.Prev.Est.5 93110 93 0 23.75482 57.86592 9.755003 4.343534 4.280714 93610 93 1 53.36475 39.47247 4.381618 1.622119 1.159044 94110 94 0 23.47514 58.23936 10.789339 3.690415 3.805741 94610 94 1

Forgive a possibly naive question. I have a Linux laptop (mandrake 9) which I would like to use as a client on network at work. The work LAN is a mixture of W2K servers and NT4 servers. Some share volumes are ADS. I can read the non-ADS shares just fine (samba 2.2.7a), but (no surprise here) it chokes on ADS volumes using either the smb protocol in Konqueror or w/ Linneighborhood to mount