similar to: How to resample one per group

Hello, I think I am not to far from a solution. I want to do lm regressions with several variables which I define before in a list. What I've done so far is like: y <- c(1,5,6,2,5,10) # response x1 <- c(2,12,8,1,16,17) # predictor x2 <- c(2,14,5,1,17,17) df <- data.frame(y,x1,x2) predictorlist <- list("x1","x2") for (i in predictor.list){ model <-

Hello, I know that it'll be quite easy to do what I want but somehow I am lost as I am new to R. I want to get summary results arranged by groups. In detail I'd like get the number (levels) of Species per Family like for this dataset: SPEC <-

Hello, assume we have following dataframe: group <-c(rep("A",5),rep("B",6),rep("C",4)) x <- c(runif(5,1,5),runif(6,1,10),runif(4,2,15)) df <- data.frame(group,x) Now I want to select all cases (rows) for those groups which have more or equal 5 cases (so I want to select all cases of group A and B). How can I use the indexing for such questions? df[??]...

Hi, I just started with writing functions in R and so some questions popped up. I provide some values as argument to my function such as: function(a,b,c){} Now i want that the function first checks if the arguments are valid for the function. E.g argument "a" has to be a number in the range 0-1. How can that easily done? So far I have: a <- as.numeric(a) if(0 <= a &&

Hello, I want to use a for loop for repeadely calculating a maxent model (package dismo, function maxent()) which creates an object of the class maxent (S4). I want to collect all the resulting object in a list. I tried to simplify my for loop to explain what I want. There are two problems/questions: 1) How can I create the new variables in the loop (using paste) and assign the objects 2) How

Just to inform: I posted that before in R-sig-ecology but as it might be interesting also for other useRs, I post it also to the general r-user list: Hello Alexandre, thank you very much. I also found another way to extract summarizing information from lm results over e.g. 1000 repeated model runs: results2 <- t(as.data.frame(results)) summary(results2) Although some questions popped up in

Dear all, I am trying to generate bootstrap replicate matrixes (rows=samples, column=species, sampling with replacement) from a matrix dataset, but I do not know how to do it in R. I have tried boot() and bootstrap(), but they require an statistic, which in my case is cluster analysis (generating bootstrap values for a cluster analysis is a topic that has been mentioned previously in

Hi, I have a dataframe and want to remove columns from it that are populated with a similar value (for the total column) (the variation of that column is 0). Is there an easier way than to calculate the statistics and then remove them by hand? A <- runif(100) B <- rep(1,100) C <- rep(2.42,100) D <- runif(100) df <- data.frame(A,B,C,D) # if want to conditionally remove column B and

Hi, I have a 1-column dataframe with rownames and I want to sort it based on the single column. The typical procedure that is recommended in diverse posts is to use order in the index. But that "destroys" my dataframe structure. Probabaly it is a very simple solution. Here is a short reproducable example: x <- c(1,3,51,2,34,44,12,33,2,8) df <- data.frame(x) rownames(df) <-

Hello, just to follow up a question from last week. Here what I've done so far (here an example): library(MCMCpack) Y=c(15,14,23,18,19,9,19,13) X1=c(0.2,0.6,0.45,0.27,0.6,0.14,0.1,0.52) X2a=c(17,22,21,18,19,25,8,19) X2b=c(22,22,29,34,19,26,17,22) X2 <- function()runif(length(X2a), X2a, X2b) model1 <- MCMCregress(Y~X1+X2()) summary(model1) but I am not sure if my X2-function is

Hi, I'd like to know if it is possible to use wildcards * for indexing... E.g. I have a vector of strings. Now I'd like to select all elements which start with A_*? I'd also need to combine that with logical operators: "Select all elements of a vector that start with A (A*) OR that start with B (B*)" Probably that is quite easy. I looked into grep() which I think might

Hi List, Now I will send to you more specific what I am trying to do. I have one Asterisk Channel where receives Midia Frames in the codecs format: Speex UltraWideBand and Speex NarrowBand. When I use Speex NarrowBand the Asterisk is able to convert this frame to G711. But when I use Speex UltraWideBand the Asterisk don't convert it. Then I need in my Asterisk Channel Source include the Speex

I understand that the capture/playback signals need to be sync'd for an AEC to adapt. I'm a little bit confused on the requirements of synchronous sampling between the near end (mic/speaker) and the far end (phone line). I have an embedded DSP system with mic and speaker getting 1msec packets containing 8 samples. We can watch the DSP and ISDN clock frames drift and every few minutes we

All - I have an uneven set of replicates and would like to sample from this set X number of times to generate a mean for each grouping variable. I was thinking the boot package would be the thing to use, but now I'm not so sure ... given the discussion here: http://finzi.psych.upenn.edu/Rhelp10/2010-June/243828.html Given these data (a small subset): > dput(x) structure(list(numSpp

hello, i need to calculate ci's for each of 4 groups within a dataset, to be able to infere about differences in the variable "similarity". the problem is that data within groups is dependent, as assigned by the blocking-factor "site". my guess was to use a block bootstrap but samples within in these blocks / sites are not of same length. i was not able to find a method to

Hi, I would like to plot some extra text in my plot. This should be a two line text including a special character (sigma). I tried so far a to use expression in combination with paste and "\n"... but I can't get the line break... Here what I've done so far: plot(1,type="n", xaxt='n', yaxt='n', ann=FALSE) text(1,1,labels=expression(paste(sigma,"\n

Hello, I am doing a simple regression using lm(Y~X). As my response and my predictor seemed to be skewed and I can't meet the model assumptions. Therefore I need to transform my variables. I wanted to ask what is the preferred way to find out if predictor and/or response needs to be transformed and if yes how (log-transform?). I found a procedure in "A modern approach to Regressoin in

From: Jyri Sarha <jsarha at ti.com> This bug happens quite often when resampling from a low to a high sample-rate with big enough factor. Also the resampling call has to be limited by the output buffer size and some unused samples needs be left in the input buffer. Sometimes when up-sampling with a big factor the resampling function wants to peek one more sample from the input buffer to

Hi; I want to use bootstrap .632 with predab.resample, but the description is not clear for me. Can any body give me an example please. Thanks; Azad. [[alternative HTML version deleted]]

Hi, I'm trying to take a function from a workspace download provided in a stats textbook book, so I have it in my workspace to use all the time. I opened the workspace and typed the names of the two functions to get the code that makes them up: ------------------------------------------------------------------------------------- > resample function(d, size,