similar to: QQ International -- Almost works, but always says I'm "Away"

As I mentioned in the subject, I have installed the latest version of QQ International (beta 3), under wine in ubuntu lucid. It installs without much trouble, however when I run the application, nothing happens. How do I begin troubleshooting this application to see if I can make it work? Or if anyone happens to know of an x64 linux qq client that actually works that would be great too :D

Hello, I am having two data set original and predicted. I want to dind QQ-plot fot it. I tried in following manner : >qq(original~predicted) and error was : Error in qq.formula(o ~ p) : y must have exactly 2 levels There is an option "qtype" which dosent make any difference. What is the correct way for plotting QQ-plot or am I missing something. -- Amar Kumar Nandan

I have 20 variables: 5,9,6,1,5,9,7,4,5,6,3,2,4,8,9,6,1,8,4,8 How do I calculate the corresponding quantiles from a normal distribution with the same mean and variance as the sample? Also, how do I draw a QQ plot of the data? Thanks for any help! -- View this message in context: http://www.nabble.com/Quantiles-and-QQ-plots-tf4925742.html#a14097909 Sent from the R help mailing list archive at

Hi everyone, I'm a newbie to wine, and am working on QQ or any other messengers in wine. My environment is : ubuntu 10.04, installed wine 1.1.42, QQ2009. And during my debugging, I also check some source code from latest wine 1.2-rc4. Although QQ's level is "garbage", but fortunately it works almost fine under wine, including login, text chat, ..... But the video call can only

Hi, i am working on a data set with EDA. That includes QQ-Plots of residuals vs expected normal distribution. What puzzles me is that the range of ordinate and abscissae is so different: while the theoretical quantiles range from [-2, 2] the sample quantiles on the ordinate do extent from [-20, 50]. Quite obviously some kind of transformation is done. Although i intensively RTFM i could not

Full_Name: Jarno Tuimala Version: 1.5.0 OS: Windows Nt Submission from: (NULL) (193.166.1.21) Running the following analysis gives as a result kukot vs. kisut. aikaero <- -27.5+5*(0:10) frekvenssi.m <- c(0,1,15,37,53,45,23,18,4,1,1) frekvenssi.n <- c(2,8,12,19,33,47,42,15,2,1,0) win.graph() aikaero <- rep(c(aikaero,aikaero), c(frekvenssi.m,frekvenssi.n)) a.ero <-

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, I am trying to figure out how to get the correlation coefficient for a QQ plot (residual plot). So to be more precise, I am creating the plot like this: qq.plot(rstudent(regrname), main = rformula, col=1) But want to also access (or compute) the correlation coefficient for that plot. Thanks, Tom [[alternative HTML version deleted]]

Hello everyone I'm a beginner in Stats and R, I'm using R 2.10.1. I need to create a multivariate qq plot, there is 8 variable group with each has 55 number of input. An example of what I did so far, just to get my point out: > data=read.csv(file.choose(),header=T) > data country village group av_expen P2ary_ed no_fisher 1 Cook Islands Aitutaki D

Hi, is there any similar function in R to S function qq.loglogistic, which produces a Q-Q plot? Thanks a lot Pete

Hi all, I am trying to test wheater the distribution of my samples is normal with QQ plot. I have a values of water content in clays in around few hundred samples. Is the code : qqnorm(w) #w being water content qqline(w) sufficient? How do I know when I get the plots which distribution is normal and which is not? Thanks, m [[alternative HTML version

Yes, that's right! I tried a search, but the (Google) search engine told me that I don't know what I am talking about! I downloaded an application DfuSe, and tried to install it. Wine installer told me that I needed another program and offered to download. I agreed, but this was unsuccessful. However the Install Wizard went ahead and installed. But trying to run was a failure. Today, I

Just got dovecot running inside MEPIS 3.3.2 SOHO (debian). I am impressed. It works really well and I am planning to move my mail away from my Win server and access the mail from dovecot using imap. I have copied some folders from my existing Outlook mail system into the imap mail server without any problems....so here is the dumb question: where are the mail files/folders stored on the MEPIS

Dear R helpers, I generate a qq plot using the following function call. qqmath(~val|ind,data=xx ,distribution=function(p) qt(p,df=19) ,ylab="Sample Quatinles" ,xlab="Theoretical Quantiles" ,aspect=1 ,prepanel = prepanel.qqmathline ,panel=function(x,y) { panel.qqmathline(y, distribution=function(p) qt(p,df=19),col=2)

I would like to change the position of the major tick marks in my qq-plot? Right now the ticks are set at 5.5, 6.0, 6.5 and 7.0. I would like them to be at 5.4, 5.6, 5.8, 6.0, 6.2, 6.4, 6.8 and 7.0. So I would have to remove some of the present ticks. So far I can only add ticks to the plot with: axis(1,at=c(5.4, 5.6, 5.8, 6.0, 6.2, 6.4, 6.8, 7.0)) Is there a solution to my

Dear All, this is my first post to the R-help, so please don't be too strict. My problem concerns a QQ-Plot: I want to show how well empirical samples match with a theoretical distribution. The theoretical distribution has got several parameters, but I made it to fit via ML. Anyway, the theoretical function gives me the density for a given data point x. As far as I'm

Dear R People: In the DASL library, there is a story about hot dogs. Here are the data: Beef 186 495 Beef 181 477 Beef 176 425 Beef 149 322 Beef 184 482 Beef 190 587 Beef 158 370 Beef 139 322 Beef 175 479 Beef 148 375 Beef 152 330 Beef 111 300 Beef 141 386 Beef 153 401 Beef 190 645 Beef 157 440 Beef 131 317 Beef 149 319 Beef 135 298 Beef 132 253 Meat 173 458 Meat 191 506 Meat 182 473 Meat 190

David, Duncan, > Hi > > Following on David's rate argument > > try (with modifications of pch and grid) > > rate <- 1/4 > shape = 8 > rate = c(rep(1/4,100),rep(1/3,100)) I don't think the problem is related to the rate argument, which can well be vectorized, as is the case for a number of arguments in distrib functions in R (note that you are redefining it

Hello! I am trying with this question again: I would like to test few distributional assumptions for some behavioral response data. There are few theories about true distribution of those data, like: normal, lognormal, gamma, ex-Gaussian (exponential-Gaussian), Wald (inverse Gaussian) etc. The best way would be via qq-plot, to show to students differences. First two are trivial: qqnorm(dat$X)

Good Morning: I have attached a text file with one hundred thirty six observations. I would like to create a qq plot with the following features: 1. Observed values on the y-axis. 2. Normal approximation line on the plot. 3. X-axis with vertical reference lines at the following percentiles of the data: 1, 10, 20, 50, 80, 90 and 99. 4. Data appearing on the plot as distinct points. I assume that