similar to: Looking for a test of standard normality

I have a large dataset similar to this: ID time result A 1 5 A 2 2 A 3 1 A 4 1 A 5 1 A 6 2 A 7 3 A 8 4 B 1 3 B 2 2 B 3 4 B 4 6 B 5 8 I need to extract a number of features for each individual in it (identified by "ID"). These are: * The lowest result (the nadir) * The time of the nadir - but if the nadir level is present at >1 time point, I need the minimum and maximum time of nadir

Hi R experts, I have a data recoding problem I cant get my head around - I am not that great at the subsetting syntax. I have a dataset of longitudinal toxicity data (for multistate modelling) for which I want to also want to do a simple Kaplan-Meier curve of the time to first toxic event. The data for 2 cases presently looks like this (one with an event, the other without), with id representing

Hello I have used recently the kolmogorov smirnov test, which is a test of normality. This test is named ks.test() in ctest library of R. I wonder if the results of ks.test () are true, because the results are strange, time to time. thank you for help meriema -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

I have a large amount of data which I break down into a collection of vectors of 100-125 values each. I would like to test the normality of the vectors and compare them. In the interactive mode I can test any one vector using the Shapiro-Wilk test or the Kolmogorov-Smirnov test. My problem is that when I try to write out the results to a file, the term output is a mixture of alpha characters

Hi all, I want to create a simple plot with 2 type='s' lines on it: plot(a, b, type='s') lines(x, y, type='s') I wish to then fill the area between the curves with a colour to accentuate the differences eg col=gray(0.95). I cant seem to come up with a simple method for this. Any pointers in the right direction much appreciated. Cheers Scott

Hi all, I have two very large samples of data (10000+ data points) and would like to perform normality tests on it. I know that p < .05 means that a data set is considered as not normal with any of the two tests. I am also aware that large samples tend to lead more likely to normal results (Andy Field, 2005). I have a few questions to ensure that I am using them right. 1) The Shapiro-Wilk

Hi, does the shapiro wilk test in R-1.3.0 work correctly? Maybe it does, but can anybody tell me why the following sample doesn't give "W = 1" and "p-value = 1": R> x<-1:9/10;x [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 R> shapiro.test(qnorm(x)) Shapiro-Wilk normality test data: qnorm(x) W = 0.9925, p-value = 0.9986 I can't imagine a sample being

hello! i don't want to test my sample data for normality, but exponential- lognormal- or gammadistribution. as i've learnt the anderson-darling-test in R is only for normality and i am not supposed to use the kolmogorov-smirnov test of R for parameter estimates from sample data, is that true? can you help me, how to do this anyway! thank you very much! nadja

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

Hi all T gurus, I would like to test if my dataset is indeed from N(0, 0.011908969). K.S. test gives following result: > ks.test(data, "pnorm", 0, 0.011908969) One-sample Kolmogorov-Smirnov test data: data D = 0.1092, p-value = 1.318e-05 alternative hypothesis: two-sided How ever "Shapiro-Wilk" test give following : >

Hi all, I am trying to get bootstrap resampled estimates of covariates in a Cox model using cph (Design library). Using the following I get the error: > ddist2.abr <- datadist(data2.abr) > options(datadist='ddist2.abr') > cph1.abr <- cph(Surv(strt3.abr,loc3.abr)~cov.a.abr+cov.b.abr, data=data2.abr, x=T, y=T) > boot.cph1 <- bootcov(cph1.abr, B=100, coef.reps=TRUE,

Hi, I have a problem with Kolmogorov-Smirnov test fit. I try fit distribution to my data. Actualy I create two test: - # First Kolmogorov-Smirnov Tests fit - # Second Kolmogorov-Smirnov Tests fit see below. This two test return difrent result and i don't know which is properly. Which result is properly? The first test return lower D = 0.0234 and lower p-value = 0.00304. The lower 'D'

Hi, I'm doing kolmogorv-smirnov test but I don't know what conclusions to take, I want to know if my data has a normal distribution: > ks.test(dados1,"pnorm") One-sample Kolmogorov-Smirnov test data: dados1 D = 0.972, p-value < 2.2e-16 alternative hypothesis: two-sided Warning message: cannot compute correct p-values with ties in: ks.test(dados1,

Hi everybody, while performing ks.test for a standard exponential distribution on samples of dimension 2500, generated everytime as new, i had this strange behaviour: >data<-rexp(2500,0.4) >ks.test(data,"pexp",0.4) One-sample Kolmogorov-Smirnov test data: data D = 0.0147, p-value = 0.6549 alternative hypothesis: two.sided >data<-rexp(2500,0.4)

I'm using ks.test (mydata, dnorm) on my data. I know some of my different variable samples (mydata1, mydata2, etc) must be normally distributed but the p value is always < 2.0^-16 (the 2.0 can change but not the exponent). I want to test mydata against a normal distribution. What could I be doing wrong? I tried instead using rnorm to create a normal distribution: y = rnorm

Is the kolmogorov-smirnov test valid on both continuous and discrete data? I don't think so, and the example below helped me understand why. A suggestion on testing the discrete data would be appreciated. Thanks, a <- rnorm(1000, 10, 1);a # normal distribution a b <- rnorm(1000, 12, 1.5);b # normal distribution b c <- rnorm(1000, 8, 1);c # normal distribution c d <- rnorm(1000,

I got a distribution function and a empirical distribution function. How do I make to Kolmogorov-Smirnov test in R. Lets call the empirical distribution function >Fn on [0,1] and the distribution function >F on [0,1] ks.test( ) thanks for the help -- View this message in context: http://www.nabble.com/Kolmogorov-Smirnov-test-tp23296096p23296096.html Sent

Hello, I want to do normality test on my data I write this but I don't understand the display of the results ks.test(data,"pnorm") In fact I want to know if my data is a normal distribution. I have to check the p-value or D? Thanks. _____________________________________________________________________________ l [[alternative HTML version deleted]]

I am trying to do something simple with ggplot. I wish to draw a density plot split by group, and fill each group with a different colour (and each with an alpha =0.25). I have tried a number of variations of the following, but cannot find a way to define the colour of the fill, its transparency and the line around it individually - something in the syntax continues to defy me.

Hi, Interpretation problem ! so what i did is by using the: >fit1 <- fitdist(vectNorm,"beta") Warning messages: 1: In dbeta(x, shape1, shape2, log) : NaNs produced 2: In dbeta(x, shape1, shape2, log) : NaNs produced 3: In dbeta(x, shape1, shape2, log) : NaNs produced 4: In dbeta(x, shape1, shape2, log) : NaNs produced 5: In dbeta(x, shape1, shape2, log) : NaNs produced 6: In