similar to: cbind

set.seed(1) x <- rnorm(n = 1000, mean = 0, sd = 1) plot(x = x, asp = 2000) Could someone please explain what the 'asp' parameter is doing?

Hello, I'm making a simple plot using xYplot in the Hmisc library and having problems with labeling the values on the x-axis. Using the reproducible example below, how can I have the text (jan, feb,mar, etc.) in place of 1:12. Thanks, AB x <- c(seq(0,0.5,by=0.1),seq(0.5,0,by=-0.1)) ci <- rnorm(12,0,sd=0.1) xupper <- x + ci xlower <- x - ci mo.fac <- c("jan",

Hi everyone, I'm trying to perform a bi exponential Fit with the package NLS. the plinear algorithm seems to be a good choice see: p<-3000 q<-1000 a<--0.03 b<--0.02 t<-seq(0:144);t y<-p*exp(a*t) + q*exp(b*t)+rnorm(t,sd=0.3*(p* exp(a*t) + q*exp(b*t))) fittA <- nls(y~cbind(exp(a*t), exp(b*t)), algorithm="plinear",start=list(a=-.1, b=-0.2), data=list(y=y, t=t),

Hello, I read the help as well as the examples, but I can not figure out why the following code does not produce the *given* row names, "x" and "y": x <- 1:20 y <- 21:40 rbind( x=cbind(N=length(x), M=mean(x), SD=sd(x)), y=cbind(N=length(y), M=mean(y), SD=sd(y)) ) Could you please help? Thank you S?ren

Dear friends, I have a basic question with R. I'm generating a set of random variables and then combining them using the cbind statement. The code for that is given below. for (i in 1:100) { y <- rpois(i,lambda=10) X0 <- seq(1,1,length=i) X1 <- rnorm(i,mean=5,sd=10) X2 <- rnorm(i,mean=17,sd=12) X3 <- rnorm(i,mean=3, sd=24) ind <- rep(1:5,20) }

How can I have both printout and saved results at the same time. The subroutine first return "out" and the printout gets printed, but not saved. I then run the "invisible" line. Results got saved and accessible but no printout. How can I have both printout and also have the results saved? Thank you! > dstat4 <- function(data,digits=3){ +?? Mean??? <-

 Hi R-listers, Savings regression results after a loop is straightforward. But what about when you have nested loops? I am running a regression of the form  lm(y~1+x+M+ D[,i] + D[,j] + D[,k]) where x is the variable of interest. M and D are vectors with other covariates. Vectors "M" and "x" are included in every regression. Then i loop over the columns of D to use all

Hi all, I've got some functioning code that I've literally taken hours to write. My 'R' coding is getting better...it used to take days :) I know I've done a poor job of optimizing the code. In addition, I'm missing an important step and don't know where to put it. So, three questions: 1) I'd like the resulting output to be sorted on distance (ascending) and

Hi, I'm trying to verify the calculation of coefficient of determination (r squared) for linear regression. I've done the calculation manually with a simple test case and using the definition of r squared outlined in summary(lm) help. There seems to be a discrepancy between the what R produced and the manual calculation. Does anyone know why this is so? What does the multiple r squared

Not clear what you mean by "saved". If you call a function and the result is printed, the result is remembered for a wee while in the variable .Last.value, so you can do > function.with.interesting.result(.......) > retained.interesting.result <- .Last.value or even > .Last.value -> retained.interesting.result If you know before you start writing the expression that you

Hi, I am trying to change a SAS macro to R. here is my code. I get an error at the last line. attach(fram) dset1<-cbind(AGE,BMI,DEATH) > BMIGRP<-cut(BMI,breaks=3,right=TRUE) > AGEGRP<-floor(AGE/10)-2 > dset<-cbind(AGEGRP,BMIGRP,DEATH) > maxage<-max(dset[,1]) > minage<-min(dset[,1]) > #maxcls<-dset[,2] > #mincls<-dset[,2] >

Hi, I have a very simple question, but I'm obviously not able to solve the problem on my own. I have a data.frame like sample(c("A","B","C"),size=20,replace = T)->type rnorm(20)->value data.frame(ty=type,val=value)->test There must be some built in functions, that will do some descriptive statistics with tabular output, in the end I like to have

Hi, I' am interested in differences between sample's result when samples consist of full elements and consist of only distinct elements. When sample consist of full elements it take about 120 sec., but when consist of only distinct elements it take about 4.5 or 5 times more sec. I expected that opposite of this result, because unique(sample) has less elements than full sample. Code as

I just like the subroutine to spit out results (Mean, Std.dev, etc.) and also be able to access the results for further processing, i.e., v$Mean v$Std.dev On 3/26/2024 11:24 AM, Richard O'Keefe wrote: > Not clear what you mean by "saved". > If you call a function and the result is printed, the result is > remembered for a wee while in > the variable .Last.value, so

sorry for cross-posting Dear all, I have tow (several) bivariate distributions with a known mean and variance-covariance structure (hence a known density function) that I would like to compare in order to get an intersect that tells me something about "how different" these distributions are (as t-statistics for univariate distributions). In order to visualize what I mean hear a little

Dear list, I have a dataset containing values obtained from two different instruments (x and y). I want to generate 5 samples from normal distribution for each instrument based on their means and standard deviations. The problem is values from both instruments are non-negative, so if using rnorm I would get some negative values. Is there any options to determine the lower bound of normal

Hi, You might need to check library(boot).? I have never used that before.? So, I can't comment much.? It is better to post on R-help list.? I had seen your postings on Nabble in the past.? Unfortunately those postings were not accepted in R-help.? You have to directly post at ? r-help at r-project.org after registering at: https://stat.ethz.ch/mailman/listinfo/r-help ?

Hi everyone, I?m trying to do a spaghetti plot and I know I?m doing all wrong, It must be. What I need: 15 subjects, each with measurements over 5 different times (t1, ..., t5), and the variable that I need to represent in the spaguetti plot is given by: PCR = b0 + b1 * ti + epsilon B0, - baseline of each subject B1 - trajectory of each subject over time (so multiply by t) Epsilon - error

Hi everyones, my function like; e <- rnorm(n=50, mean=0, sd=sqrt(0.5625)) x0 <- c(rep(1,50)) x1 <- rnorm(n=50,mean=2,sd=1) x2 <- rnorm(n=50,mean=2,sd=1) x3 <- rnorm(n=50,mean=2,sd=1) x4 <- rnorm(n=50,mean=2,sd=1) y <- 1+ 2*x1+4*x2+3*x3+2*x4+e x2[1] = 10 #influential observarion y[1] = 10 #influential observarion data.x <- matrix(c(x0,x1,x2,x3,x4),ncol=5) data.y

Hello, I am having trouble creating a (1000,3,201) array in R from a data set created within the same script. My problem is that I want to take a matrix that is 1000x603 (called "landmat") and separate this into 201 separate (1000x3) matrices. My problem is when I try to do particular combinations of the columns. I want to take the first 3 columns of "landmat" and create