similar to: Plotting positions in qqnorm?

Dear List, invoking qqnorm-plots in Version 63.3 produces funny things: using the option `type="s"´ on qqnorm should give a nice *line* of observed quantiles. Now, the line is walking along in order to the points index instead from lowest to highest, wich makes funny slopes. try x <- table(rnorm(1000) # or similar and qqnorm(x,type="s") # in 0.63.2 and 63.3 Well, the

Hello, I have a doubt about the plotting position of the theoretical quantile for the qqnorm command in R. Let F be the theoretical distribution of Y, we observed a sample of size n, y1,y2, ..., yn. We then sort it and comspare these empirical quantiles to the expected ones from F. For the plotting poition, there are several options: 1. i/(n+1) 2. (i-.375)/(n+.25) 3. (i- .3175)/ (n + .365) etc.

I think qqline does not do exactly what it is advertised to do ("`qqline' adds a line to a normal quantile-quantile plot which passes through the first and third quartiles."). Consider the graph: tmp <- qnorm(ppoints(10)) qqnorm(tmp) qqline(tmp) The line (which I expected go through all the points), has a slightly shallower slope than does the points plotted by qqnorm. I think

When I look at ppoints I see: ppoints<-function (x) { n <- length(x) if (n == 1) n <- x (1:n - 0.5)/n } However Venables & Ripley (2nd ed, p 165) say ppoints() should return (i-1/2)/n for n>=11; (i-3/8)/(n+1/4) for n<=10. The version below should work as described: ppoints<-function (x) { n <- length(x) if (n <= 10) (1:n - 0.375)/(n + 0.25) else (1:n - 0.5)/n

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)

Stock returns and other financial data have often found to be heavy-tailed. Even Cauchy distributions (without even a first absolute moment) have been entertained as models. Your qq function subtracts numbers on the scale of a normal (0,1) distribution from the input data. When the input data are scaled so that they are insignificant compared to 1, say, then you get essentially the

Dear list, could someone point me to the normal score transform and it's use in R? I would like to transform my data with normal score transform, do some geostatsitical predictions and would like to transform the estimated values back including the estimation variance. Any suggestions? Ulrich -- __________________________________________________ Ulrich Leopold MSc. Department of

Dear Community, I want to identify outliers in my data. I don't know how to use identify command in the plots obtained. I've gone through help files and use mahalanobis example for my purpose: NormalMultivarianteComparefunc <- function(x) { Sx <- cov(x) D2 <- mahalanobis(x, colMeans(x), Sx) plot(density(D2, bw=.5), main="Squared Mahalanobis distances, n=nrow(x),

Hello, Our professor asked us to do probability plotting using weibull paper, exponential paper, normal, log-normal paper, etc. I know I can create Q-Q plot for normal dist. and see if all te points are on one line. How do I go about other distributions? I tried generating different samples and use the general qq function. However, I could not do it since I don't know the population

Dear r-bugs, Whilst playing with ppoints I discovered that when one uses it directly, occasional NA's in a vector also become data fractions: ppoints(c(1,2,NA,4)) Would it be a good idea to add a warning message as in: ppoints <- function (n, a = ifelse(n <= 10, 3/8, 1/2)) { if(any(is.na(n))) warning("'n' contains NA's") if(length(n) > 1) n <-

I want to qqnorm every column in a table. When I try the first column using qqnorm(table$column1), it worked. But when I use qqnorm(table[1]), it tells me "Error in stripchart(x1, ...) : invalid plotting method". What happen? How can I make a function that qqnorms every column? thanks a lot. -jiong The email message (and any attachments) is for the sole use of the intended

Hi all, I have just started using R to produce qqnorm plots. I am trying to switch the x and y axes so that the theoretical values are plotted on the y axis and my data on the x axis. Can anyone help me with this? Thanks -- View this message in context: http://www.nabble.com/Flipping-axes-of-qqnorm-tp23248007p23248007.html Sent from the R help mailing list archive at Nabble.com.

I know this topic has had plenty of discussion in the last couple of days, but.... I've been trying to compare the effects of different fitted methods for systems of equations (OLS, SUR, 2SLS, 3SLS ) and would like to compare the residual plots (easy) and the qqnorm/qqplot of the fits for the different fitted methdos. For example, qqnorm( residuals( lm( q ~ p + f + a ) ) ) par( new = TRUE )

# I can not draw a 45 degree line on a qqnorm() plot, jj <- sample(c(1:100), 10) qqnorm(jj) abline() don't work. Thank you.

Referring to "Using R for Data Analysis and Graphics" by J H Maindonald, and available from the R site, I found the example on p.30 non-working: > stem(qqnorm(possum$hdlngth)) Error in stem(qqnorm(possum$hdlngth)) : 'x' must be numeric Since qqnorm(possum$hdlngth) plots, and > class(possum$hdlngth) [1] "numeric" , the problem must be here: >

Hello! I want to plot a P-P plot. So I've implemented this function: ppplot <- function(x,dist,...) { pdf <- get(paste("p",dist,sep=""),mode="function"); x <- sort(x); plot( pdf(x,...), ecdf(x)(x)); } I have two questions: 1. Is it right to draw as reference line the following: xx <- pdf(x,...); yy <- ecdf(x)(x); l <- lm(

Hello, I am trying to do some plotting to check random effect assumptions for a model I fit using lme. I want to use qqnorm and pairs (similarly to examples given in Pinheiro & Bates p. 188), but it's not working. Here's some relevant code and the error message: library(nlme) data(Machines) m1 <- lme(fixed=score~Machine,random=~1|Worker/Machine, data=Machines) qqnorm(m1,

Hello R friends, I'm wondering why I get funny qqnorm() results. It seems that they should all be reflected in the normal qqline(). For instance: if I qqnorm() bimodal or uniform data I get a sigmoidal in which the qqnorm() points lie above the qqline() at -ve theoretical quantiles, and the qqnorm() points lie below the qqline() at +ve theoretical quantiles. Yet I expect such platykurtic

Hi all, I am an R newbie, and I have a question about scripting. I have the following lines which I want to put int\ o a script which I can call from the shell of a Mac/Linux machine : myrns <- read.csv(file="/Users/vihan/test.csv",sep="",header=FALSE) qqnorm(myrns) qqline(myrns) This works fine on an interactive R session. However, as I understand the basic framework of

Dear all, I have two variables called c2 and c3 and want to plot these variables in the same qqnorm-plot with two different symbols or colors to distinguish them so I can easily compare the variables aginst each other. How can I do in R? I only manage to do two separated qqnorn-plots. Thanks for your help, All the best, Jenny c2= -0.1545775144 -0.0601161235