similar to: KS Test Warning Message

Hello, While doing test of normality under R and SAS, in order to prove the efficiency of R to my company, I notice that Anderson Darling, Cramer Van Mises and Shapiro-Wilk tests results are quite the same under the two environnements, but the Kolmogorov-smirnov p-value really is different. Here is what I do: > ks.test(w,pnorm,mean(w),sd(w)) One-sample Kolmogorov-Smirnov test data: w D

I'm using ks.test() to compare two different measurement methods. I don't really know how to interpret the output in the absence of critical value table of the D statistic. I guess I could use the p-value when available. But I also get the message "cannot compute correct p-values with ties ..." does it mean I can't use ks.test() for these data or I can still use the D

On the cover of Zivot and Wang's Modeling Financial Time Series with S Plus, there is a correlation plot that seems to indicate the strength of correlation with color-coded squares, so that more highly correlated stocks appear darker red. If anybody out there is familiar with the book or understands what I am talking about, I am curious as to whether or not there is a similar function in R

This seems like a question that I should be able to answer on my own but after looking at the documentation I cannot seem to find the correct method. How do I add lines to a bar plot that extend from the vertical axis? For example, my vertical axis is numbered in increments of 10 and I would like these to go across the whole graph. Also, is there a way to have R label the value of each bar so

Dear All, I am working with a generalized linear mixed-effects model with poisson error using the lme4 package in R. I created a model with the lmer function including some main effects, three two way interactions and two random effects. The model works well, but I have troubles when removing on of the two-way interactions. The Warning message: "In mer_finalize(ans) : false convergence

When I run a linear regression and include a variable in the regression with as.factor i.e. lm(y ~x +as.factor(x1) and i read the output as as.factor(x1)1.... as.factor(x1)2... etc. how do i interpret the estimate for each level? Is this simply to be regarded as a shift in the equation predicted by the intercept and independent variable x? jdr -- Justin Rapp 409 S. 22nd St. Apt. 1

Consider I have the following data: AgeRange AgeOfPerson PersonNo FriendsAtYear0 FriendsAtYear1 FriendsAtYear2 FriendsAtYear3 FriendsAtYear4 FriendsAtYear5 10 - 12 11 1 0 1 2 2 3 3 10 - 12 12 2 0 1 2 2 3 3 15 - 18 13 3 1 2 3 4 6 7 15 - 18 14 4 1 3 4 5 7 7 30 - 40 33 5 3 5 5 6 8 9 30 - 40 36 6 4 4 4 4 4 4 I want to plot the number of friends against number of years, as to show how friendships

Hi I just started using RGui.exe under widnows. I have a text file containing date arranged in columns and rows, each column has the same format, each row with different formats. 3 of the columns are something like this 1/12/2006 3:59:45 PM I need to calculate the different in seconds between 2 selected periods using their row’s index My solution: Read the file in a data frame and

Hello :) I have two data frames, one has properties taken on a piece by piece basis and the other has performance on a lot by lot basis. I wish to combine these two data frames but the problem is that each lot has multiple pieces and hence i need to take a mean of the properties of multiple pieces and match it to the row having data about the lot. I was wondering if there is a simple commmand,

Dear R-core There is a different output for the apply function due to the change of unlist as mentioned in the R news. Newly, applying as.factor() (or factor()) in str(dat <- data.frame(x = 1:10, f1 = gl(2,5,labels = c("A", "B")))) (d1 <- apply(dat,2,as.factor)) newly returns a character matrix while in R-2.3.1 the same command resulted in an integer matrix that was

Hello everbody, i'm currently using the anova()-test for a small data.frame of 40 rows and 2 columns. It works well, but is there any preconditions for a valid variance analysis, that i should consider? Thank you for your answer, Daniel

Dear R-list I have a question concerning the argument "adjustSigma" in the function "lme" of the package "nlme". The help page says: "the residual standard error is multiplied by sqrt(nobs/(nobs - npar)), converting it to a REML-like estimate." Having a look into the code I found: stdFixed <- sqrt(diag(as.matrix(object$varFix))) if (object$method

hi list, i have to ask you again, having tried and searched for several days... i want to do a TukeyHSD after an Anova, and want to get the adjusted p-values after the Tukey Correction. i found the p.adjust function, but it can only correct for "holm", "hochberg", bonferroni", but not "Tukey". Is it not possbile to get adjusted p-values after

I have some doubts about the validity of my procedure to estimeate linear contrasts ina a factorial design. For sake of semplicity, let's imagine a one way ANOVA with three levels. I am interested to test the significance of the difference between the first and third level (called here contrast C1) and between the first and the seconda level (called here contrast C2). I used the following

hi all, I wish to draw on the same graphic device 3 functions. But i don't want them to be on different graph, i want to compare them on the same I don't need mfrow or mfcol, I need something else... 1 graph on 1 device inside this graph 3 ploted function. I saw something unsing data.frame, but i think it's overkill, and something less complicated must exist, if not why? why not

Hello Could you please suggest a way to find out the r square values for each independent variable while using "lm" for developing a multiple linear regression model. Thank you avneet "I have no data yet. It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories instead of theories to suit facts." ~ Sir Arthur Conan Doyle

Hi In mixed-model with lme() How can I obtain Type II SS or Type III SS for fixed effect? Thanks Julien

Hi, I'm studying math, and i have to make an analysys using Randomised Block Design. I have two factors, i know how to do this in Statistica, but how to do this in R, i read some manuals but the only thing that i have found was 2 factor ANOVA. Please could someone help me, or give some usefull links ?? Krzytsztof Suwada

Hi All. I need a bit of help extracting the residual error variance from the VarCorr structure from lmer. #Here's a 2-way random effects model lmer.1 <- lmer(rating ~ (1|person)+(1|rater), data = dat) #Get the structure vc.fit <- VarCorr(lmer.1) #results in..... $person 1 x 1 Matrix of class "dpoMatrix" (Intercept) (Intercept) 0.7755392 $rater 1 x 1 Matrix

useRs, A no doubt simple question, but I am baffled. Indeed, I think I once knew the answer, but can't recover it. The default contrasts for aov (and lm, and...) are contr.treatment and contr.poly for unordered and ordered factors, respectively. But, how does one invoke the latter? That is, in a data.frame, how does one indicate that a factor is an *ordered* factor such that