similar to: getting the std errors in the lm function

Hello , just a simple question from a beginner: I write the function: plotsinx <- function() { x<-seq(0,2*pi,0.01) sinx<-sin(x) plot(sinx) } I recall it: plotsinx() and the plot works properly. but then in the workspace if I want to look at the values of sinx the following error is displayed: Error: Object "sinx" not found. How to save the variables created by the function on

Hello, I created a simple histogram with: myHist<-hist(myData) the object myHist now has two arrays (among the others): myHist$mids myHist$counts Since myHist$counts contains some "0", and I want to calculate the linear fit among myHist$mids and log(myHist$counts), I want remove the elements of both arrays where these "0" occurs. which are the possible solutions to

Dear all, I have 2 variables, x<-c(1,2,3,4,5,6,7,8,9,10) y<-c(1.3,2.5,4.6,5.3,5.9,6.7,7.4,8.5,9.4,10.4) each point of the y variable has an error: erry<-(0.2,0.3,0.2,0.1,0.4,0.2,0.3,0.4,0.3,0.2) how to plot(x,y) with the errors segments? Thank you, Copex --------------------------------- [[alternative HTML version deleted]]

Hello everyone, I'd like to have suggestions about a common basic statistical approach, hope to be useful also for other R beginners. When you first get some data (i.e length of river) you may want to look at its distribution. Then you probably want to find which law follows this distribution, and to test the goodness of the fit. For doing this simple analysis I am writing some code

Hello everyone, I know that to calculate the linear regression between x and y I have to use the function "lm". But how to do if x and y have a standard deviation like in the following example? How to compute the chi square test in this case? Thank you, Fulvio. example: x +- dx 5 2 13 4 17 4 23 6 y +- dy 6.3 0.6 9.2 0.9 14 1 21 4

Hi folks, I am running a very simple regression using mylm <- lm(mass ~ tarsus, na.action=na.exclude) I would like the use the residuals from this analysis for more regression but I'm running into a snag when I try cbind(mylm$residuals, mydata) # where my data is the original data set The error tells me that it cannot use cbind because the length of mylm$residuals is

I'd trying to subclass the "lm" class to produce a "mylm" class whose instances behave like lm objects (are accepted by methods like summary.lm) but have additional data or slots of my own design. For starters: setClass("mylm", "lm") produces the somewhat cryptic: Warning message: Old-style (``S3'') class "mylm" supplied as a

Hello list, I have a linear regression: mylm = lm(y~x-1) I've been reading old mail postings as well as the plotmath demo and I came up with a way to print an equation resulting from a linear regression: model = substitute(list("y"==slope%*%"x", R^2==rsq), list(slope=round(mylm$coefficients[[1]],2),rsq=round(summary(mylm)$adj.r.squared, 2))) I have four models and I

Hello, I have just learnt some python, and would like to call R scripts from python, without using batch mode. I learnt how to do it from perl, in fact reading in the mail archive I found the following good example: *************************************** #!/usr/local/bin/perl open (FILE, ">test.txt"); print FILE "a,b,c,d,e\n1,2,3,4,5"; close FILE; ####### Start R

Dear all - We perform some measurements with a machine that needs to be recalibrated. The best calibration we get with polynomial regression. The data might look like follows: > true_y <- c(1:50)*.8 > # the real values > m_y <- c((1:21)*1.1, 21.1, 22.2, 23.3 ,c(25:50)*.9)/0.3-5.2 > # the measured data > x <- c(1:50) > # and the x-axes > > # Now I do the following:

Hello, I am randomly generating values and then using an ANOVA table to find the mean square value. I would like to form a loop that extracts the mean square value from ANOVA in each iteration. Below is an example of what I am doing. a<-rnorm(10) b<-factor(c(1,1,2,2,3,3,4,4,5,5)) c<-factor(c(1,2,1,2,1,2,1,2,1,2)) mylm<-lm(a~b+c) anova(mylm) Since I would like to use a loop to

Hi All, I had posted a question on a similar topic, but I think it was not focused. I am posting a modification that I think better accomplishes this. I hope this is ok, and I apologize if it is not. :) I am looping through variables and running several regressions. I have reason to believe that the data is being duplicated because I have been monitoring the memory use on unix. How can I avoid

Hi, I wonder if someone has already figured out a way of making summary(mylm) # where mylm is an object of the class lm() to print the "(Intercept)" at the last line, rather than the first line of the output. I don't know about, say, biostatistics, but in economics the intercept is usually the least interesting of the parameters of a regression model. That's why, say, Stata

DeaR list I would like to predict the values of each column of a matrix A by regressing it on all other columns of the same matrix A. I do this with a for loop: A <- B <- matrix(round(runif(10*3,1,10),0),10) A for (i in 1:length(A[1,])) B[,i] <- as.matrix(predict(lm( A[,i] ~ A[,-i] ))) B It works fine, but I need it to be faster. I've looked at *apply but just can't

Dear R-help, I have a four-part question about regression, matrices, and sandwich package. 1) In the sandwich package, I would like to better understand the meat() function. >From the bread() documentation, for a simple OLS regression, bread() returns (1/n * X'X)^(-1) That is, for a simple regression (per the documentation on bread()): MyLM <- lm(y ~ x) bread(MyLM)

Dear R-guRus: I am stuck with the following problem in R: Let's say i have a data.frame with columns X,Y & Z. X is a column of numbers, Y is a column of N factors "A", "B", "C", "D", etc and Z is a column of n factors "a", "b", "c", etc if i do MyLm<-lm(X~Y*Z - 1) i get all N coefficients for Y-factors, but only

> Use an Intel pro/100 or pro/1000 driver..they are known to > work with VLANs. > > I guess it's also possible that it's a problem in the > bridging code, so try the latest kernel > as well... I compiled the latest 2.6.19 kernel and tested it with two 3COM 3c905c fast ethernet interfaces which work fine with vlan 802.1q tagging, but when I have added the eth0 and eth1

Inspired from a similar patch posted on grub-devel for Grub2 I immersed myself into the code of Grub Legacy and came up with this absurdedly simple patch to allow syslinux or other bootloaders residing in logical partition to be chainloaded from GRUB Legacy (0.9x). Hoping it to be of help, regards Fulvio Scapin -------------- next part -------------- A non-text attachment was scrubbed... Name:

Hi, I'm a biologist, so please forgive me if my question sounds absurd! I have 3 parameters x1, x2, x3 and a response variable y.The sample size is 75. I tried to do the following: mylm<-lm(y~ x1 + x2 + x3, data="mydata") but i can only get stats from anova for the first 2 variables. The third comes up as NA. The degrees of freedom for the third variable are 0. Is there

Hi, I have just configured a Linux box with kernel 2.6.16.7 and configured two ethernet interfaces (with MTU 1500) in bridge mode. CONFIG_BRIDGE_NETFILTER is enabled. The problem is that ping -s 1500 192.168.0.2 doesn't work from 192.168.0.1 if the systems are separated by the bridge. Normal ping with smaller packet size works ok. What is wrong? Best Regards Fulvio Ricciardi