similar to: Barplots and error indicators: Some R-Code

Dear all When using Sweave, I'm always hitting the same bump: I want to group repetitive calls in a function, but I want both the results and the function calls in the printed output. Let me explain myself. Consider the following computation in an Sweave document: summary(iris[,1:2]) cor(iris[,1:2]) When using these two calls directly, I obtain the following output: > summary(iris[,1:2])

Hi all, I'm running a monte carlo test of a neural network tool I've developed, and it looks like it's going to take a very long time if I run it in R so I'm interested in translating my code (included below) into something faster like Fortran (which I'll have to learn from scratch). However, as you'll see my code loads the nnet library and uses it quite a bit, and I

I am producing 2X2 correlation matrices by a class variable. I need to extract a vector of correlation coefficients only. I am doing that in a loop (see below) but I am sure there would be a simpler way. Please help! > by(d1[,c(2,3)],d1[,1],cor) d1[, 1]: 1 c e c 1.0000000 0.1972309 e 0.1972309 1.0000000

I'm new to R (and to S) and am wondering about code from pages 72 and 83 of MASS (Venables+Ripley, 3rd edition), to draw an envelope on a QQ plot. Copying from the book, I've got: #... code whose gist is "a.fit <- nls(..." num.points <- length(resid(a.fit)) qqnorm(residuals(a.fit)) # illustrate data-model residuals qqline(residuals(a.fit)) samp <-

Hi, (1.) How is it possible to get automatic a "lower triangle of correlation matrix" ? h.cor <- cor(dat,use="pairwise.complete.obs") zz <- lower.tri(h.cor,diag=T) ### that's not what i wish and "wrong" ? results <- matrix(unlist(h.cor[upper.tri(h.cor,diag=T)])) results <- matrix(unlist(h.cor[upper.tri(h.cor,diag=T)]),5) Must i take the lowest

Hi, I have a correlation matrix of about 3000 items, i.e., a 3000*3000 matrix. For each of the 3000 items, I want to get the top 50 items that have the highest correlation with it (excluding itself) and generate a data frame with 3 columns like ("ID", "ID2", "cor"), where ID is those 3000 items each repeat 50 times, and ID2 is the top 50 correlated items with ID,

Dear all, Sorry to post my query once again in the list, since I did not get attention from anyone in my previous mail to this list. Now I make it simple here that please give me a code for find out the columns of a dataframe whose correlation coefficient is below a pre-determined threshold. (For detailed query please see my previous message to this list, pasted hereunder) Thanks and regards,

Hi all: I get the following error message that I am not able to resolve. Error in if (const(t, min(1e-08, mean(t)/1e+06))) { : missing value where TRUE/FALSE needed It appears right before the last data.frame statement. Below is the program that simulates data from one way random effects model and then computes normality and bootstrap confidence interval for

Hello. I would like to estimate the correlation coefficient from two samples with Bootstrapping using the R-function sample(). The problem is, that I have to sample pairwise. For example if I have got two time series and I draw from the first series the value from 1912 I need the value from 1912 from the second sample, too. Example: Imagine that a and b are two time series with returns for

Hello, I am trying to plot time-series data with certain weeks highlighted using symbols. require(ggplot2) #plotting time series data timescale <- seq(as.Date("01/01/09","%m/%d/%y"), length.out=12, by=7) data.all <- data.frame( id = c(rep('111',12),rep('222',12),rep('333',12)), week=c(timescale,timescale,timescale),

Dear All, I have two matrices A (40 x 732) and B (40 x 1230) and would like to calculate correlation between them.  I can use: cor(A,B, method="pearson") to calculate correlation between all possible pairs. But the issue is that there is one-many specific mappings between A and B and I just need to calculate correlations for those pairs (not all). Some variables in A (proteins, say p1)

Hi Has anyone used R to conduct confirmatory factor analysis? This email pertains to use of SEM. For context consider an example: the basic idea is that there are a bunch of observables variables (say study habbits, amount of time reading in the bus, doing homework, helping other do homework, doing follow-up on errors etc.) and one believes that all these variables maybe measured by two or

Hi, Although I set check.names to FALSE in read.table, the duplicate names get modified. What should be done in this case? the text file to be read by read.table AM2      AM2     AM2     FAL 2               3             4         5 1               -1           -3        -2 t = read.table ("my_file", check.names = FALSE, header = T) > t AM2      AM2.1     AM2.2     FAL

Dear list, I have a problem with the "corrgram" function. It does not seem to "color" large negative correlations, while the same correlation, if positive, provides no problems. Is this a bug? require(corrgram) a = seq(1,100) b = -jitter(seq(1,100), 80) cor(a,b) # r about -.96 c=as.data.frame(cbind(a,b)) corrgram(c, order=NULL, lower.panel=panel.pie,upper.panel=NULL,

Hi, i have 656 attributes ind INTERVALL_VAR & 119 in GROUP and this morning i'm little confused why the inner loop hang if it arrive 656th column. My Task is a t-test and correlation with all columns in INTERVALL_VAR for all attributes in GROUP. many thanks & regards, christian for( k in 1:length(GROUP)){

Dear R users, Simple question. Can anyone help with the code that would allow me to view only the variables who's correlation output is >0.8? This is the code I'm using to date >cor(data, method="spearman") Kind regards Krys -------------------------------------------- _________________________________________________________________ Save time by

Dear R users, suppose I have a matrix of observations for which I calculate all pair-wise correlations: m=matrix(sample(1:100,replace=T),10,10) w=cor(m,use="pairwise.complete.obs") How do I extract only those correlations that are >0.6? w[w>0.6] #obviously doesn?t work, and I can?t find a way around it. I would very much appreciate any help! Best wishes Christoph (using R

Dear All, I would be very grateful for your help concerning the following question: Below mentioned programme is available on net to generate longitudinal data. Usually we get almost same parameter estimates as used to generate the data. The problem here is I am not able to get it for data used here, despite increasing sample size and number of simulations. Is it normal to expect this type of

Hello, I'm attempting to create a data frame with correlations between every pair of variables in a data frame, so that I can then sort by the value of the correlation coefficient and see which pairs of variables are most strongly correlated. The sm2vec function in the corpcor library works very nicely as shown here: library(Hmisc) library(corpcor) # Create example data x1 = runif(50) x2 =

Hello. I have a large dataset with sales pr month for 56 products with 10 months and i have tried to see how the sales are correlated using cor() This has given me a 56X56 matrix with the R value for each product pair. Most of these correlations are insignificant, and i want only to retain the instances were the R value is significant (for 10 observations it should be above 0.64) Can someone