similar to: Standard error of coefficient in linear regression

Playing around with examples from MASS4, I found a font problem in the mosaicplot in R-2.3.0. It doesn't happen in other plots. Running this example from MASS4, page 326... library(MASS) caith1 <- as.matrix(caith) names(dimnames(caith1)) <- c("eyes", "hair") mosaicplot(caith1, color = TRUE) ...I get an image as attached. The column and row labels are unreadable. It

Hi all, I have a question about transforming the data from summary function. Let's say I have a data frame like this: > x = data.frame(a = c(rep("lev1", 5), rep("lev2", 5)), b = c(rnorm(5)+2, rnorm(5))) > x a b 1 lev1 1.5964765 2 lev1 2.2945609 3 lev1 3.5285787 4 lev1 1.4439838 5 lev1 2.2948826 6 lev2 1.7063506 7 lev2 -0.4042742 8 lev2

Hello all R-help list subscribers, I'd like to create a regression tree of a data set with binary response variable. Only 5% of observations are a success, so the regression tree will not find really any variable value combinations that will yield more than 50% of probability of success. I am however interested in areas where the probability of success is noticeably higher than 5%, for

Hello, I'm new to the list, hello everybody! :-) I have a question. I looked carefully through the documentation and googled, and found no answer, so I'm posting it here. Let's say I have a data frame with lots of columns (about 300), which are factors. Many columns-factors have only one level, for example all are "Yes" or all are "No". If I try to do a

Hello, I'd like to create a model with a factor-type response variable. This is an example: > mydata <- data.frame(factor_var = as.factor(c(rep('one', 100), rep('two', 100), rep('three', 100))), real_var = c(rnorm(150), rnorm(150) + 5)) > summary(mydata) factor_var real_var one :100 Min. :-2.742877 three:100 1st Qu.:-0.009493 two

Hello, While using the "plot" function against a model with a long formula, I get a garbled label in the bottom of the plot. It looks like there are two lines printed one over another, here's my example: http://pico.magnum2.pl/maciej/NodalInvolvement/MisclassificationRate?action=AttachFile&do=get&target=TwoFactorResiduals.png This was from using

Hello, I’ve read this Article avout VOIP and HFSC http://automatthias.wordpress.com/2006/06/30/hfsc-and-voip/ I’ve got few questions ? Considering this tc class add dev $DEV parent 1:1 classid 1:2 hfsc \ rt m1 ${UPLINK}kbit d 50ms m2 $[1*$UPLINK/10]kbit \ ls m1 ${UPLINK}kbit d 50ms m2 $[3*$UPLINK/10]kbit \ ul rate ${UPLINK}kbit rt = realtime curve ls = linksharing curve but m1 = ? m2 = ? d =

Hi Experts, I am new to R, and was wondering how to do 3D linear regression in R. In other words, I need to Fit a 3-Dimensional Line to Data Points (input). I googled before posting this, and found that it is possible in Matlab and other commercial packages. For example, see the Matlab link:

Hello, I''ve got somes questions about Bridge and QOS ... I''ve got a serveur with 2 interfaces eth0,eth1 inside br0 bridge ... nothing of special ... If I understand all, normally I should configure TC class and qdisc on each physical or use ebtables to manage packets on output ... right ? I''ve attached my qos_script that hsfc and layer7 module. I use only Iptables

A sample of the data I have is: > head(sensor) logged_on accx accy accz compassx compassy compassz gyrox gyroy gyroz 1 1326561428000 -0.4602 0.8346 0.0936 0.145508 -0.350586 0.259766 59.617390 28.521740 59.617390 2 1326561428050 -0.4212 1.0452 0.1326 0.219727 -0.321289 0.241211 88.695656 27.478260 88.695656 3 1326561428100 -0.2496 1.3416 0.2886 0.214844 -0.326172

Hello, I am running Kruskal-Walis test in R. When I try to save results using write.table it gives me the following error : Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors) : cannot coerce class "htest" into a data.frame The overall code is as follows : >data_file = read.table("~/DATA.dir/data_file.txt", header=T)

Hi, Trying to setup a logistic regression model. (Something new to me. I usually use SVM.) The person explaining the concept explained to me that I can include a "group" variable so that the probabilities predicted by the model will be "per group" Does this make sense to anyone? If so, how would I implement this? Using the glm or lrm function? Thanks! -N

I'm puzzled by the behaviour of factors in rma models, see example and comments below. I'm sure there's a simple explanation but can't see it... Thanks for any input John Hodgson ------------------------------------- code/selected output ----------------- library(metafor) ## Set up data (from Lenters et al A Meta-analysis of Asbestos and Lung Cancer... ##

Hi, I am trying to find m breakpoints of a linear regression model. I used the segmented package. It works fine for small number of predicators and breakpoints.(3 r.v. 3 points). However, my model has 14 variables it even would not work even for just one breakpoints!. The error message is always estimated breakpoints are out of range. Since my problem is time related problem. So I

Hello all! I have a problem to arrange data in another form. My initial data is like this: 'data.frame': 421 obs. of 58 variables: $ 01A: num NA NA NA NA NA NA NA NA NA NA ... $ 01B: num NA NA NA NA NA NA NA NA NA NA ... $ 03A: num NA NA NA NA NA NA NA NA NA NA ... $ 03B: num NA NA NA NA NA NA NA NA NA NA ... $ 05A: num NA NA NA NA NA NA NA NA NA NA ... $ 05B: num NA NA NA NA

Hey, R-listers I am a new user of R and just found the package of PCURVE which can estimate principal curve for arbitrary dimensional data set. Now I have some 2-Dimensional data set X, which is stored as an Nx2 matrix in data.txt file and looks as following: -1.5551 2.4183 1.0051 1.0102 0.90644 0.82163 1.3248 1.7551 -1.3626 1.8568 -1.3554 1.8371 0.039396 0.0015521 -0.99622 0.99245 0.2314

> Hello, > > I have a question about a discrepancy between the > reported F statistics using anova() and add1() from > adding an additional term to form nested models. > > I found and old posting related to anova() and > drop1() regarding a glm with a dispersion parameter. > > The posting is very old (May 2000, R 1.1.0). > The old posting is located here. >

On 23.11.2012, at 15:12, john skaller <skaller at users.sourceforge.net> wrote: > > On 23/11/2012, at 5:46 PM, Sean Silva wrote: > >> Adding LLVMdev, since this is intimately related to the optimization passes. >> >>> I think this is roughly because some function level optimisations are >>> worse than O(N) in the number of instructions. >>

Hello Summary: I have ifplugd managing eth0 and wlan0 (both dhcp). When I plug in the cat5 (which brings up eth0) applications which have already bound to wlan0 stop working, obviously because wlan0 for some reason is unable to get non-local packets out that interface. I figured if poodoze is able to have both interfaces working, then linux must surely be able to do it as well. The

Hi, I have sort of a newbie question. I've seriously put a lot of effort into how to handle simple replicates in a repeated ANOVA design, but haven't had much luck. I really liked reading "Notes on the use of R for psychology experiments and questionnaires", by Jonathan Baron and Yuelin Li ( http://www.psych.upenn.edu/~baron/rpsych/rpsych.html ) but still didn't run across