Joris Meys

2,550 reputation

725

bio	website	biomath.ugent.be/biomath/…
	location	Ghent, Belgium
	age	36
visits	member for	2 years, 8 months
	seen	Apr 15 at 14:13
stats	profile views	298

Statistician and R programmer at the faculty of Bio-Engineering, university of Ghent

Co-author of 'R for Dummies' (out in july 2012 )

contact : Joris - dot - Meys - at - Ugent - dot - be

	2,550 reputation	bio	website	biomath.ugent.be/biomath/…	visits	member for	2 years, 8 months
725 badges		location	Ghent, Belgium		seen	Apr 15 at 14:13

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Mar 19	comment	Is normality testing 'essentially useless'? @maximus with the function `qqnorm`in R
Feb 22	comment	Use coefficients of thin plate regression splines in a clustering method A very belated thank you!
Dec 12	comment	Comparison of before and after ordinal data across two groups I don't agree on the test on the median. We're talking an ordinal scale here, and all tests on "median" are actually tests on location shift, which require distributions with the same shape. In an ordinal context, this is a rather dangerous assumption.
Oct 8	comment	Non negative lasso implementation in R Sorry for closing your question, but it is better asked and answered at www.crossvalidated.com I flagged the question for migration, so the mods will take care of it shortly. This said, please make your question clear and explain exactly what you want. The lasso expert in our research group couldn't possibly figure out what you were aiming at...
Jul 29	comment	Meta analysis on studies with 0-frequency cells Common solutions are not always correct solutions.
Jul 11	comment	Expected value of small sample @Néstor reformulate as 'the probability that the 95% confidence interval contains the true value', which is, all assumptions taken into account and defining probability as the 'relative frequency of occurrence' or 'propensity', 0.95. Don't forget as well that probability is defined differently in frequentist and bayesian theories. BTW, I've read the whole discussion, but if I remember correctly it was you who brought the arguments given there to this discussion...
Jul 10	comment	Expected value of small sample @Néstor I've had this semantic discussion a hundred times over, mostly with bayesians. As long as you don't know the true value, you have a probability. Once you know it, the CI renders itself useless. As long as the winning numbers of the lottery aren't known, you can talk about your chance to win the lottery. Once you know the winning numbers, you either won or you didn't. So as long as you can't tell me for sure whether or not the true value is contained in my calculated CI, I can only talk about the probability that it's in that calculated CI. YMMV.
Jul 10	comment	Expected value of small sample @Néstor As in: Do this 10,000 times and 95% of the confidence intervals you construct, will contain the true expected value. And since you don't know which of these CI you have, you have a probability of 95% that your confidence interval contains the true value. Delete the expected, that's a typo.
Jun 14	comment	How to determine the effect size of a Wilcoxon rank-sum test in R? Welcome to SO. I flagged your question for migration to www.crossvalidated.com , as your question is more statistical than anything else. In short : Wilcoxon RANK test works with ranks, so I'm not sure about which effect size you're talking. Obviously it doesn't give you a z value, as that one is linked to parametric testing, not to non-parametric tests like Wilcoxon. Wilcoxon has to be interpreted in terms of location shift.
Jun 13	comment	How do we create a confidence interval for the parameter of a permutation test? @Kevin : Code was darn right. Read the code again: the `x[6:11]` refers to the argument `x` of the anonymous function within the apply. Confusing maybe, but your edit gave very wrong results. Please comment about what you think it should be before editing the code. Saves me a rollback. To avoid further confusion, I changed that `x` to `i`
Jun 2	comment	Differences between PROC Mixed and lme / lmer in R - degrees of freedom @mbq : Thank you for the effort, but as far as I'm concerned this isn't necessary.
Jun 1	comment	Differences between PROC Mixed and lme / lmer in R - degrees of freedom @StéphaneLaurent Sorry I couldn't save your answer. Thanks for the link again.
Jun 1	comment	How do you explain the difference between relative risk and absolute risk? @MichaelChernick I was about to say that proportion differences are conditional, and odds ratio is not. But that's not the case, both give exactly the same result after transposing the table (in the case of a 2X2 table). I've been running some simulations, but I can't force the p-values of `prop.test` (or `chisq.test` as it is equivalent in the 2x2 case) and `fisher.test` to be more than 0.005 apart. So I wonder which tests she used...
Jun 1	comment	Differences between PROC Mixed and lme / lmer in R - degrees of freedom @mbq : That would be nice, although I simulated some data (which I use here) and edited the answer of Aaron accordingly. For the other answer, that's going to be a bit more complicated, but I can try as well.
Jun 1	comment	Differences between PROC Mixed and lme / lmer in R - degrees of freedom @Aaron : Please find your answer included in this post. If you could copy and paste that as an answer, I give you the rep for it. It has been very helpful, so I really want to keep it here on crossvalidated. After you've done that, I delete your answer from the question.
May 21	comment	Goodness of fit test for a mixture in R Question flagged for migration, so you don't have to add a new question to that site.
Apr 27	comment	Variance on the sum of predicted values from a mixed effect model on a timeseries @probabilityislogic That's basically what the r program is doing. Thx for the math
Apr 25	comment	Extrapolating a lowess model @BenoitB. I'm not closing it, I'm merely moving it to a place where people will be able to help you more. And I'm very sorry, you cannot extrapolate a local regression model (see the comment of DWin). It says so explicitly in every text book on the topic. If you want to predict values WITHIN the X domain, use `loess` instead of `lowess`. `loess` has a predict function. If you use that to predict values outside the domain of X, get decent statistical advice. Which is why I direct you kindly to crossvalidated.com.
Apr 25	comment	Extrapolating a lowess model Flagged the question for moving it to crossvalidated.com. Some moderator will take care of it shortly, no need to repost the question there.
Apr 25	comment	Extrapolating a lowess model Voted off-topic as this is a statistics question and can hence be better asked at crossvalidated.com. Short answer: You can't extrapolate from a lowess curve, as that is a LOCAL regression. You need to use different models, and data that fits your curve perfectly or the uncertainty on the Y value at X=500 will be so large that you can't draw any conclusions.