similar to: how to find the index of points selected from a scatter plot?

AdvisoRs: Is the following a bug, feature, hinky error message, or dumb Bert? > mtest <- matrix(1:12,nr=4) > dftest <- data.frame(mtest) > ix <- cbind(1:2,2:3) > mtest[ix] <- NA > mtest [,1] [,2] [,3] [1,] 1 NA 9 [2,] 2 6 NA [3,] 3 7 11 [4,] 4 8 12 ## But ... > dftest[ix] <- NA Error in `[<-.data.frame`(`*tmp*`, ix, value

Hi all, I am looking for a command for doing 2D filtering (rectangular or Gaussian) in R... I have looked at ksmooth, filter and convolve but they seem to be 1D... Any thoughts? Thanks a lot! [[alternative HTML version deleted]]

Is there a data/variable explorer in R? Hi all, I am inspecting a complex variable which has lists inside lists... Is there a data-explorer that can help me view the structure and content of such variables? Thank you! [[alternative HTML version deleted]]

head, tail and fix commands don't really work well if I have large matrix/array for which I would like to be able to scroll up and dow, left and right ... Could anybody please help me? Thanks [[alternative HTML version deleted]]

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Hi all, I have a newbie question: If I have a function with the following documentation: ca.jo(x, type = c("eigen", "trace"), ecdet = c("none", "const", "trend"), K = 2, spec=c("longrun", "transitory"), season = NULL, dumvar = NULL) Let's take "type" as an example... if I omit this parameter when calling the

Hi all, I found that the PCA gave chaotic results when there are big changes in a few data points. Are there "improved" versions of PCA in R that can help with this problem? Please give me some pointers... Thank you! [[alternative HTML version deleted]]

Hello I am novice to R and i was learning how to do a scatter plot with R using an example from a website. My setup is iMac with Mac OS X 10.8.3, with R 3.0.1, default install, without additional packages loaded I created a .csv file in vim with the following content userID,user,posts 1,user1,581 2,user2,281 3,user3,196 4,user4,150 5,user5,282 6,user6,184 7,user7,90 8,user8,74 9,user9,45

Hi all, I am reading the MASS book but it doesn't give examples about the diagnosis and model checking for rlm... My data is highly non-Gaussian so I am using rlm instead of lm. My questions are: 0. Are goodness-of-fit and model-checking using rlm completely the same as usual regression? 1. Please give me some pointers about how to do goodness-of-fit and residual diagnosis for

I have some square matrices with na values in corresponding rows and columns. M<-matrix(1:2,10,10) M[6,1:2]<-NA M[10,9]<-NA M<-as.matrix(as.dist(M)) print (M) 1 2 3 4 5 6 7 8 9 10 1 0 2 1 2 1 NA 1 2 1 2 2 2 0 1 2 1 NA 1 2 1 2 3 1 1 0 2 1 2 1 2 1 2 4 2 2 2 0 1 2 1 2 1 2 5 1 1 1 1 0 2 1 2 1 2 6 NA NA 2 2 2 0 1 2 1 2 7 1 1 1 1 1 1 0 2 1 2 8

I need to generate 6 x-y scatter plots (lattice xyplot) for each of 11 data frames. I'm sure the tedium of doing these one at a time can be avoided by writing a script that uses nested for loops or the apply() function. My Web search has not provided me with a tutorial on R programming that will introduce me to the language and allow me to write such a script. Please provide pointers to

Dear Group, I am working with a large dataset where I need to select for each unique id the the unique lastpk row. Here is a sample subject: id wtdt wt lastpk 64050256 2010-09-18 275 2010-09-16 64050256 2010-09-19 277 2010-09-18 64050256 2010-09-20 272 2010-09-18 64050256 2010-09-21 277 2010-09-18 I want the result as:

Hi everyone, I am Evelina and i have just joined the forum and hope to be able to support soembody as well as finding some help for my stats problem. I am trying to plot Frequencies against Densities of an ecological community using a Olmstead-Tukey diagram (O-T diagram). it is based on the Corner test for Association. I seem to undestand that the test can be run in R using the cor.test()

Dear R Mailinglist, I want to understand how predictors are associated with a dependent variable in a regression. I have 3 measurement points. I'm not interested in understanding the associations of regressors and the predictor at each measurement separately, instead I would like to use the whole sample in one regression, "pooling" the measurement points. I cannot simply throw them

Dear All, would you please provide your thoughts on the following: let us say I have: a <-c(1,5,8,15,32,69) b <-c(8.5,33) and I would like to extract from "a" the two values that are closest to the values in "b", where the length of this vectors may change but b will allways be shorter than "a". So at the end based on this example I should have the result

Dear All, I was wondering whether someone has created a kernel density evaluator that estimates the density at given specified points. The regular density() function evaluates the kernel at equidistant points, but I am interested in doing such evaluation along a list of values existing in a pre-specified vector. (Similar to the option at() in the kdensity command in Stata). This question has

Hello, I am brand new in this site as well as in R. Please accept my early apology if it's not the right place to ask such a question. I have the following equation: b=[2(1-a)(1+2a)^0.5]/(1+3a) How can I solve the above equation for 'a' using R? Thanks in advance. KD [[alternative HTML version deleted]]

Sarah et. al.: As a matter of aesthetics (i.e. my personal ocd-ness) I prefer using the public API of an object, i.e. *not* to makes use of the representation of a factor as essentially an integer vector with labels, but rather to use its documented behavior. (Feel free to ignore this remark!) Anyway, >cumsum(!duplicated(dat1$B)) [1] 1 1 1 2 2 3 3 3 3 3 4 4 will do it. This is very

Hi, You could try: A<- matrix(unlist(read.table(text=" 1 2 3 4 5 6 7 8 9 9 8 7 6 5 4 3 2 1 ",sep="",header=FALSE)),ncol=3,byrow=FALSE,dimnames=NULL) library(matrixStats) ?res1<-t(sapply(split(as.data.frame(A),as.numeric(gl(nrow(A),2,6))),colProds)) ?res1 #? [,1] [,2] [,3] #1??? 4?? 10?? 18 #2?? 63?? 64?? 63 #3?? 18?? 10??? 4

suppose I have two sets of vectors: x1,x2,...,xN and y1,y2,...,yN. I want N correlations: cor(x1,y1), cor(x2,y2), ..., cor(xN,yN). my sets of vectors are arranged as data frames x & y (vector=column): x <- data.frame(a=rnorm(10),b=rnorm(10),c=rnorm(10)) y <- data.frame(d=rnorm(10),e=rnorm(10),f=rnorm(10)) cor(x,y) returns a _matrix_ of all pairwise correlations: cor(x,y)