similar to: densities from a list with data.frames

Hi there, I get a strange behaviour of a boxplot with the following code. There seems to be a problem with the xlim-parameter. Did I do anything wrong? What else can I do to force the boxplot to have a defined x-range? x <- rnorm(100) boxplot(x, notch=TRUE, xlab=parameter, xlim <- c(-4,4), horizontal = TRUE) Antje

Hello, I hope this question is not too stupid. I would like to know how to update levels after subsetting data from a data.frame. df <- data.frame(factor(c("a","a","c","b","b")), c(4,5,6,7,8), c(9,1,2,3,4)) names(df) <- c("X1","X2","X3") my.sub <- subset(df, X1 == "a" | X1 == "b")

Hello, I was wondering if I can plot two curves I get from "density(data)" into one plot. I want to compare both. With the following commad, I just get one curve plotted: plot( density(mydata) ) Sorry for this stupid question but I could not find a solution until now... Antje

Hi all, I'm looking to add a "density" smoother on top of a hist when Freq=T. In order to do this I can use the relation between count and density, but I would like to know if there is a way for me to predict it upfront. Here is an example: set.seed(242) z = rnorm(30) hist_z <- hist(z) hist_z$counts / hist_z$density # the relation is 15 # why is this 15 ?? # So I can now do:

Hi there, I have two series of data. plotting the density function of both gives me an idea about the difference of the data. But I would like to quantify the difference I see. a <- rnorm(100) b <- rnorm(100) da <- density(a) db <- density(b) The problem is that da$x and db$x are different and so I have difficulties to compare them... Is there any way to force the density

Hello everybody, I don't understand the warning I get when executing the following code: x <- rnorm(100) h <- hist(x, plot= FALSE, freq = FALSE) If I don't use the plot-argument, I don't get a warning... Any explanations for me? Antje

Hello everybody. I am new to R. Yesterday I passed the afternoon reading the introduction and language reference, but I could'nt find a way to do a 3d plot of the density of a data table of size 2. I am trying with: plot(density(t(t2))) but it mixes the two columns and calculate the density like it is a 1-dimensional casual variable. I have looked at the documentation of density, but I was

Hello, I have some problems with sapply. I wanted to do the following: s <- sapply(filelist, function(x) { if(file.exists(x)) { ... return( list(density(file$V1)$x, density(file$V1)$y)) } else { print(paste("plotDensity ERROR - File does not exist: ",x,sep="")) return( NULL) } }) if(is.null( - ??? - )) { print("no plot") } That

Hi there, I hope this question is not as stupid as the one before ... I tried to shorten my histogram (because the distribution is quite skewed and I simply don't want to see the long tail but still use the histogram plot). How can I do something like this? (The example does not work but I don't know why...) data <- rnorm(100) # as example, of course this is not skewed... h <-

Hi folks, can anybody give me a hint how to solve the following problem? I have several plots in one window like this: layout(matrix(c(1,2,3,4), nrow = 2, byrow = TRUE)) plot(rnorm(100)) plot(rnorm(200)) plot(rnorm(300)) plot(rnorm(400)) Now, I'd like to create a legend below each plot and generate a common title. How can I do that? Antje

Hello, I tried to fit a poisson distribution but looking at the function fitdistr() it does not optimize lambda but simply estimates the mean of the data and returns it as lambda. I'm a bit confused because I was expecting an optimization of this parameter to gain a good fit... If I would use mle() of stats4 package or mle2() of bbmle package, I would have to write the function by myself

I've just found some behaviour which strikes me as odd, but I'm not sure whether it's a bug or a feature. If you don't mind, I'd like to explain via a couple of examples. Let x = 1:10. Then intuitively, to me at least, the command x[-integer(0)] should leave x untouched. However the actual output under R2.4.0 is integer(0). A slightly more involved example demonstrates

Hi there, I though there has been a possibility to force the output on the console with one element per line. Instead of this: > 1:10 [1] 1 2 3 4 5 6 7 8 9 10 something like this > 1:10 [1] 1 [2] 2 [3] 3 [4] 4 [5] 5 [6] 6 [7] 7 [8] 8 [9] 9 [10] 10 Can anybody help? Antje

Hi there, it might be a very simple question and I'd be glad to even get a link to some useful documentation... I have several data sets, I'd like to fit to a gaussian distribution. I've tried to give an estimate of the mean and the sd of this distribution but still, I run into problems if these estimates are not close enough. For example, nls() breaks with this message: singular

Dear Sir I am new user of R. I am interested in modeling hydrological extreme events. I found MSClaio2008 very interesting function. In this function four criterions for choosing distributions. Can we call these criterions as model selection techniques or goodness of fit techniques or both? Because goodness of fit techniques are usually performed after modle selection. Can I found

Hi all, I have the following x1<-paste("A", 1:6, sep = "") x2<- round(rgamma(6,2,1)) x3<-paste("B", 1:6, sep = "") x4<- round(rgamma(6,2,1)) data1 <- data.frame(x1,x2,x3,x4) I would like to get data2 <- c(A1=4, A2=1, A3=0,...) Is there any standard for such a case? Thank you very much in advance, Diego

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 everyone, I get some data in the following format and I would like to combine them to form a dataframe. The data is like: cbcname1 = 0.1, cbcname2= 0.2, cbcname3=0.3,... name1, name2, name2 are just some random names. I would like to achieve sth like: (cbcname1=0.1, cbcname2=0.2, cbcname3=0.3,......) I am using the following codes do.call(cbind,

I want to run deconvolution of a time series by an impulse or point-spread function through Wiener filter, regularized filter, Lucy-Richardson method, or any other approaches. I searched the CRAN website and the mailing list archive, but could not find any package for such a deconvolution analysis. Does anybody know an existing R function for deconvolution? TIA, Gang

hi i am new to R language. I want to use aaMI package which calculates the amino acid mutual interaction for a given protein sequence. I had installed the package but when i run the program it gives me the error could not find function "aaMI". can anyone tell me what might be the problem.. -- View this message in context: http://www.nabble.com/aaMI-tp14915744p14915744.html Sent from