similar to: Unsuccessful beginner's struggle with lm

Please excuse me for asking such basic questions: Here is my code > y=c(1.21,0.51,0.14,1.62,-0.8,0.72,-1.71,0.84,0.02,-0.12) > ybar=mean(y) > ll=length(y); > ybarv=rep(ybar,ll) > x=1:ll > plot(x,ybarv,pch=1) > segments(x[1],ybar,x[ll],ybar) What I get is a collection of small circles, with a segment "on top" of the circles, which is almost what I want. But I

I am working with Sweave and would like to print out into my latex document the result of the R command version$platform So what I first tried in my .Rnw document was \Sexpr{print(version$platform)}. However, the output from this command is the string "x86_64-apple-darwin10.8.0" (without the quotes). This contains an underscore, which is a special character in tex and so I get an error

Suppose x and y are numeric vectors of the same length. plot(x,y) #scatterplot lmObj1 <- lm(y~x) # best fit line abline(lmObj1) # good lmObj2 <- lm(x~y) #get best fit but with axes interchanged abline(lmObj2) # not what I want. I want the correct line, drawn on the same graph, but with # response and predictor variables interchanged One way to proceed would be to

Here is my interaction with R: > sub(x='>|t|',pattern = '|t',replacement='zz') [1] "zz>|t|" So I say to myself "Clearly the | signs need to be escaped, so let's try this" > sub(x='>|t|',pattern = '\|t',replacement='zz') [1] "zz>|t|" Warning messages: 1: '\|' is an unrecognized escape in a

I have a few hundred files of formatted data. Unfortunately most of them end with a spurious CONTROL-Z. I want to rewrite the files without the spurious character. Here's what I've come up with so far, but my code is unsafe because it assumes without justification that the last row of df contains a control character (and some NAs to fill up the record). options(warn=-1) #turn off

It's probably toadally elementary (and, like, duhhhhh) but I can't figure out why the following doesn't work: curve(function(x){qnorm(x,4,25)},from=0,to=1) I get the error: Error in xy.coords(x, y, xlabel, ylabel, log) : 'x' and 'y' lengths differ But if I do foo <- function(x){qnorm(x,4,25)} curve(foo,from=0,to=1) it goes like a train. Also

Can anyone help me with the following attempt to use an external editor from within R > vi(file="p286.R") Error in edit(name, file, title, editor) : unable to open file to read > edit(file="p286.R") Error in edit(name, file, title, editor) : unable to open file to read I have only recently re-started trying to learn R. (I tried before but failed.) I am working

I have a data frame with two variables that are factors. One is actually a TRUE/FALSE factor, and I have coded it as 1/0, a continuous variable, but I could turn it back into a factor. The second is an ordered factor and consists of five timepoints. There are several continuous variables as well. Now I want to fit a linear model to my data, using lm (or another R procedure if recommended).

How do I append an R-object to a list? I want to start with an empty list, and append R-objects one by one. Does this start with a command like mylist <- NULL ?? I have read a few answers on R-help to questions like this, but they all seem to be well off the point. Sometimes it's assumed that the list is a vector---not my case. One answer I read said that the object appended must be a

Hi Folks, Could anyone point me to a good reference on linear regression models? Specifically, I am trying to gain an intuitive feel for how the standard error values are calculated for the parameter estimates. My understanding is that these are computed using the variance-covariance matrix computed from the input data matrix. Although I think I understand the math, I still don't have a

There is quite a long piece of code defining a certain function in one of the R packages. I think the code has a bug and I want to get the code into a file so that I can take a proper look, and possibly fix it. how does one do this? (I mean getting the code into a file, not fixing the bug.) I suppose I could copy and paste, but that's a bit error prone for various reasons. I want the same

Dear r-help I am trying to run LDA on a training data set, and test it on another data set with the same variables. I found examples using crossvalidation, and using training and testing data sets set up with sample, but not when they are preassigned. Here is what I tried # FIRST SET UP A DATAFRAME WITH ALL THE DATA AND CREATE NEW VARIABLES traintest1 <-

Sweave is very useful, and I'm gradually getting used to it. I've just been battling Sweave over the re-use of code chunks. As I am pretty ignorant in the byways of both Sweave and R, this took a chunk of time to sort out. Here is what I learned: If one re-uses a code chunk, then Sweave (but not Stangle) will insist that <> start in column 1. In particular, white space to its left

I would like to print the label of each code chunk, just before printing the code chunk itself. Is that possible using SweaveHooks, or by some other mechanism? Thanks David -- View this message in context: http://r.789695.n4.nabble.com/Sweave-SweaveHooks-and-printing-the-label-tp3453829p3453829.html Sent from the R help mailing list archive at Nabble.com.

Hello, I don't want to find out how to make packages unless that becomes necessary. Also, I don't want to clog up the computer memory with functions that I'm not using. (It would be great if someone in this forum would explain how memory is used when I type library(MASS) and then use only one function from MASS. Are all the many MASS functions then residing in memory, or only the one I

I use a Mac (10.4.11 Mac Os X). In my .tcshrc I define an environmental variable MY. Is it possible to find out its value from inside R? When one loads R for Mac OS X Cocoa GUI written by: Simon Urbanek Stefano M. Iacus are files like .tcshrc read by R? Can I make the value of this environmental variable available to R? Sys.getenv() produces a lot of output, with the values of many

Dear R-friends, How do you test the goodness of prediction of a model, when you predict on a set of data DIFFERENT from the training set? I explain myself: you train your model M (e.g. glm,gam,regression tree, brt) on a set of data A with a response variable Y. You then predict the value of that same response variable Y on a different set of data B (e.g. predict.glm, predict.gam and so on).

Hallo Samba mailing list I am a VERY new user to Linux and have just installed it (SUSE version 8.1) and can just about make my way around the machine although there a many things that I still don't know how to do (eg. looking at the network neighborhood the Linux machine should be able to see). I have several Windows PCs (running maily NT and Win 98) and the first obvious thing is to get

Hello List, My question is an elementary one. I have run a fuzzy kmeans cluster using FANNY to group freshwater fish assemblages. I then went in the field to validate that classification and have retrieved new assemblage data for a new suite of streams. Therefore I would like to use Predict to determine how well the original clustering fits the new data. However I have not figured out a

dear all, I'm building new dataframes from bigger one's using e.g. columns F76, F83, F90: JJ<-data.frame( c( as.character(rep( gender,3))) , c( F76,6- F83, F90) ) Looking into JJ one has: c.as.character.rep.gender..8... c.6...F73..F78..F79..F82..6...F84..F94..F106..F109 1 w 2 2 w