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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

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, I have a vector and would like to create a data frame, which contains all unique combination of two elements, regardless of order. myVec <- c(1,2,3) what expand.grid does: 1,1 1,2 1,3 2,1 2,2 2,3 3,1 3,2 3,3 what I would like to have 1,1 1,2 1,3 2,2 2,3 3,3 Can anybody help?

A few day ago, I was looking for an answer to my question but didn't get one. Anybody who can help now? Hello, I tried to use mle to fit a distribution(zero-inflated negbin for count data). My call is very simple: mle(ll) ll() takes the three parameters, I'd like to be estimated (size, mu and prob). But within the ll() function I have to judge if the current parameter-set gives a nice

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

Hello, I tried to use mle to fit a distribution(zero-inflated negbin for count data). My call is very simple: mle(ll) ll() takes the three parameters, I'd like to be estimated (size, mu and prob). But within the ll() function I have to judge if the current parameter-set gives a nice fit or not. So I have to apply them to observation data. But how does the method know about my observed

Hi there, probably there is a very simple solution, but I cannot think of one... I have a vector with values: data <- c(1,6,3,4,8,4,2,9) and I have a vector with bin breaks: bins <- c(1,3,5,7,9,11) Now, I'd like to get for each data point the index of the bin-vector where the value falls in (or equals the lower bin break). In the example case, I'd like to get:

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Hi there, I have a question concerning the behaviour of the colouring with levelplot. (I hope, I manage to explain) If I give the parameters "at" and "col.regions" like this: at <- c(1,2,3,4,5,6) col.regions <- c("blue","blue","blue","yellow","yellow","yellow") Which color would have the value 3.5? I would

Hello, unfortunately, I don't know a better subject. I would like to be very flexible in how to process my data. Assume the following dataset: par1 <- seq(0,1,length.out = 100) par2 <- seq(1,100) fac1 <- factor(rep(c("group1", "group2"), each = 50)) fac2 <- factor(rep(c("group3", "group4", "group5", "group6"), each =

Hi folks, I've just discovered that the following code leads to boxplot (surprisingly to me). Can anybody explain to me why? Is this documented somewhere? I've never consider this option before. x <- rnorm(300) l <- c(rep("label1",100), rep("label2",50), rep("label3",150)) df <- data.frame(as.factor(l), x) plot(df) Thank you! Antje

Hello, I'm looking for a solution for the following problem: I have two vectors V1 <- c("apple","honey","milk","bread","butter") V2 <- c("bread","milk") now, I would like to know for each element in V1 if it's equal to one of the elements in V2 I could do: which(V1 == V2[1] | V1 == V2[2]) but what if I

Hi there, I'm pretty new to the field of fitting (anything). I try to fit a distribution with mle, because my real data seems to follow a zero-inflated poisson distribution. So far, I tried a simple example to see whether I understand how to do it or not: # example count data x <- 0:10 y <- dpois(x, lambda = 1.4) # zero-inflated poisson zip <- function(x, lambda, prop) { (1 -

Hi there, I have a list of dataframes (generated by reading multiple files) and all dataframes are comparable in dimension and column names. They also have a common column, which, I'd like to use for merging. To give a simple example of what I have: df1 <- data.frame(c(LETTERS[1:5]), c(2,6,3,1,9)) names(df1) <- c("pos", "data") df3 <- df2 <- df1 df2$data

Hello, I have a question how to reshape a given matrix to a data frame. # ---------------------------------- > a <- matrix(1:25, nrow=5) > a [,1] [,2] [,3] [,4] [,5] [1,] 1 6 11 16 21 [2,] 2 7 12 17 22 [3,] 3 8 13 18 23 [4,] 4 9 14 19 24 [5,] 5 10 15 20 25 > colnames(a) <- LETTERS[1:5] > rownames(a) <-

Hi there, I have a barplot and the labels at the x-axis are strings, which are rotated by 90?. But now the sub-title of the barplot is in between these labels, which does not look very nice... Could anybody help me finding the parameter-setting to prevent this? par(mai=c(1.5,1,1,0.6)) barplot(data, las=2, ylim = c(0,plotMax+1), ylab = "Percentage", main = filename, sub = subtitle)

Hi all, I have written a R-script under Windows using choose.dir. Now, I have seen that this function is missing at MacOS. Does anybody know an alternative? Antje

Hello, I have a quite simple question, I guess. I have a data frame and I would like to process the data in several ways. The processing is dependent on grouping (factors) and the parameter itself. Meaning for parameter1 in the df, I would like to calculate the mean and sd when grouping by factor1. But parameter2 might be different. I thought of a structure like this: group1 param1

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