tttjjj/elt_bench huggingface dataset:
That's ok, this is not TCS.SE (-;
I am interested in ANOVA-type structural modelling for a meta-analysis of sample correaltions. If you could please read my articles on meta-analysis published in 2004 and 2006 and then respond. Thanks for the answer and raising the querry why the variance should be one. I can not answer it.
Closely related: http://stats.stackexchange.com/questions/27512/how-best-to-communicate-uncertainty
B is essentially the weights. You can use ec50*B to get estimates for the fit of Y.
@asjohnson That's not a limitation of Cross Validated, but I believe you didn't reach the [min reputation level](http://stats.stackexchange.com/faq#reputation) (15) to  vote up at the time you wrote the above comment.
Are you looking to control for latitudinal effects by detrending  for instance (i.e. estimating the latitudinal temperature trend, then removing it from the data before identifying clusters)?
+1 Awesome answer. Thanks for all the great references.
although I am not an expert on empirical process theory, it is my impression that the use of outer probabilities is not really based on a desire to assign  probabilities to non-measurable sets, but because you don't want to go through the hassle of actually proving measurability all the time. And if you can live without things like Fubini's theorem then you basically don't loose anything by just computing outer probabilities.
@Bakaburg Publish in *Epidemiology* and you don't have to. Problem solved :)
Your example uses a discontinuous improper scoring rule, which is in general not a good basis for comparing sets of predictions (it's arbitrary and lacks power and precision).
Answered [here](http://math.stackexchange.com/questions/186959/correlation-matrix-from-covariance-matrix/300775#300775).
What is the basis for the "In that case, we also know..." statement?
Hi roover, welcome to the site! There *was* the `R` package [COZIGAM](http://www.jstatsoft.org/v35/i11/paper) which could fit such models. Unfortunately, it is no longer available (I don't know why). Perhaps it will be available again in the future. You could also contact the package maintainer directly: Hai Liu, email: liuhai at iupui.edu.
That's an interesting idea, treat each pair of subjects as one subject. It might be OK. I'd be concerned that you run into a power imbalance because likely the most correlated part is what's paired within S and then between S within a pairing has low power, which results in an inordinately lower powered interaction measure. There's no reason to think the two subjects run together will be correlated much, if at all.
@Greg Snow Is there any reason to prefer a multi-state to competing risk model in this case? Unless they want to make the account termination reversible.
@DarrenCook: There's isn't a version of `align.time` that does that.  Regardless, it seems like a bad idea to align observations with a time _prior_ to their occurrence.
Due to some confusion in these statements, it would be good if you could edit this question to include an explanation of what analyses you intend to perform on these variables. For instance, no amount of iid data from a non-Normal population will produce a Normal sample, but the *sampling distribution* of some *specific statistics,* such as the sample mean, often will become approximately Normal. Which statistic is involved really does matter: for instance, the sampling distribution of the range will never approach a Normal distribution, even when suitably standardized.
Glad I could help. In the Rasmussen & Williams' book, the second paragraph of Ch. 2.8 *History and Related Work*, mentions exactly this connect between GP's and kriging/spatial statistics. They actually mention two meteorology references about that also.
The end result being a value from the centroid of the cluster? So that all cases predicted from the same cluster would have the same value?
ayush...  You are right.  That is basically it.  And, this is because your standard error will become so small (due to the large sample size).  This in turn overstates the statistical distance between your test and control groups.  And, causes you to ultimately run into a Type I Error (uncover a difference that is so small as to be immaterial).  This is a common problem in hypothesis testing with large samples.
When you say that there is a 1% fitted difference, do you mean that the predicted probability increases by 1%? That is not necessarily a small difference. Only .5% of your cases are successes, so are you saying your predicted probabilities change from something like .25% to 1.25% with some predictor x?
Outliers have been discussed many times here, try searching for that tag and see what you can find.
I took this information and have tried to build a model in SAS; you may find the question I've asked about it relevant: "[How do I ensure PROC ARIMA is performing the correct parameterization of input variables?](http://stats.stackexchange.com/q/10604/1583)"
@Anony-Mousse, in this simple example, the clusters produced in the second run are the same as in the first run.  I would not expect this to be true for all cases in general.  But the question remains, what are the calculations behind the distances 1, 2.2361, 3.7933, and 5.0593?  I can't figure out what equations are used to generate those numbers.  I tried playing around with squares and square roots ... and different weightings ... but I am stumped.
This may or may not be a hmm problem.  I personally thought it was a neural network problem and another person suggested particle filter issue.  Although the majority of people I spoke with said hmm, I have no attachment to any specific solution(just want something that works).
I never observe (X, Y, Z). Sorry for the confusing diagram, I tried to fix that.
On second thoughts my answer may not make much sense. I will leave it up in case it gives you some ideas.
Article by Reshef might answer this question "Quite a lot, really." See Reshef, D., Reshef, Y., Finucane, H., Grossman, S., McVean, G., Turnbaugh, P., Lander, E., Mitzenmacher, M., and Sabeti, P. (2011). Detecting novel associations in large data sets. *Science*, 334(6062):1518–1524.
@Glen_b thanks I corrected it
If your problem is that you fail to replicate their results despite that you believe you know how to do it - then I think you should post (or link to) the data, leave your code (syntax) and, desirably, to cite or link to those authors paper easily downloadable.
@Macro - "What is the most appropriate statistic and corresponding test to optimize my parameters and determine if the outcome can be considered normally distributed?"  This isn't quite asking how to do the transform, it's instead asking how to evaluate whether the transform is effective, i.e., (in this case) a statistic and corresponding test for normality.
And statistical software has syntax that can be saved and shows an audit trail of everything done to the data, unlike Excel. :)
@mbq A clarification posted on the SO version makes it abundantly clear that the OP is about error propagation.  There is no longer any question that this is relevant to stats.  Apparently there's no longer any option for us to vote to migrate it back, but it definitely belongs here and should be re-opened.
+1 for a good answer but it seems misleading to me to suggest that we should hope that our algorithm's previous success was luck rather than skill.
(ctd) ... however, the particular thing with mediation is it is quite explicitly about establishing not only a graphical model (heads on arrows) but the changing magnitudes of effects, and yet the care required to even begin to establish what is claimed generally seems to be absent. At least in regression on observational studies it's not hard to find people making it very clear why it's usually just establishing association and why things like Simpson's paradox must be kept in mind. With mediation studies, there doesn't seem to have been the same density of caveats in what I've seen.
(+1), very clear and to the point.
If it is possible, a small self contained example would be great, i.e. a small data frame with some data and code to create the plots based on this data.
A small p-value does not tell anything about the size of the effect, so I wonder why you are interested in p-values anyway.
@whuber extremely sorry, I realised an edit: unit prices are continuous. Should I record some statistics measuring bivariate dependence on nominal variables {One way ANOVA, mutual information, Kruskal Wallis etc.} ? Would that make sense as an apriori to regression analysis?
1000 is plenty in most straightforward situations, but it depends on the complexity of the test statistic/s you create (particularly if you want to observe how often a rare coincidence of events takes place).  You don't want the answer to depend on how many replications you do.  10,000 should be stacks...
I am not sure what you mean by "all these variables" other than "these variables separately". The way to find a relationship between some independent variables and a dependent variable is to model them one at a time. Sometimes you can do data reduction (e.g. with principal component analysis) but that doesn't seem right here (distance walked and time spent in school don't go together in any obvious way). 

I don't think there's a way to get a single curve that represents all this.
@Andy W : First define "permutation test". for me, permutation tests are exact tests, using every permutation possible. That is impossible on larger datasets. The "approximate permutation tests" are in fact the simples Monte Carlo method around, and should be adressed that way. Next to that, the central limit theorem assures in most cases that the assumptions regarding the distribution of the test statistics are met when using large datasets. In complex testing, the use of permutation tests on large datasets makes calculation times unbearably long without adding any significant value. my2cents
In case this help: http://stats.stackexchange.com/questions/29121/intuitive-explanation-of-unit-root The whole thread is pretty epic.
So many thanks for this nice reply. :)
Well, you place your points independently of each other with a uniform probability over the ball. Hence you get a constant, position independent density and have met the Poisson-requirements of constant rate (=density) and independence of the events (points).
@ttnphns: I suspect as Non-negative Matrix Factorization.
Do you mean mental age of 19, or physical age?
But there **is** something called purposive sampling... read this one. http://www.sfu.ca/~palys/Purposive%20sampling.pdf
Can you give some more details, or a simple example corresponding to what you're specifically doing?
Have a look at the Barr et al. paper. They explain what random slopes are extensively. If you then still have question, I am glad to help.
I definitately prefer "chi-squared" but so many people (especially in certain application areas, and also moreso in the US) seem to call it "chi-square" that I sometimes do so myself.
I think you're right to question the definitions, but there is no paradox if we take a "fair" die to be one that *when rolled in a standard way* has the statistical properties we expect. The point is that one needs to distinguish between a physical object and an experimental setup that yields repeated observations, both of which might be referred to briefly as "a fair die"--whence the need for clear definitions.  As Marc Claesen points out, it is not terribly difficult to create an experimental setup with a physically "fair" die that produces a non-uniform distribution of results.
If you are working with python, you can directly use the variable importance calculated in sklearn when calling a random forest.
Agree with all your points. However, the OP has revealed that their goal is not scale construction but cross-cultural comparison. In this context dropping a complex (cross-loaded, hence multisemantic) item will rather impoverish the analysis.
A very informative read!
Great answer! Note also recommended publication in that site with [exhaustive diagram](http://www.math.wm.edu/~leemis/2008amstat.pdf).
@mljrg Wrong conclusion! Doing a normality test is meaningless, but that does not mean that normality-based tests cannot be used. However, time-to-event variables such as elapsed time are usually quite skewed and heteroskedastic (external information!) It is easy to misinterpret even non-parametric tests under these conditions. Other options such as a log-rank test might be a good alternative. But, to reiterate, you need to incorporate your knowledge about the outcome variable.
I am confused by your question because there don't seem to be any interactions that include your two covariates of interest.
+1, a similar plot is a [population pyramid](http://en.wikipedia.org/wiki/Population_pyramid), the reflected distribution is just a different category (and it uses more typical histogram type estimators instead of kde).
... I am sorry, I pressed ENTER accidentally.but this does not solve the problem, because if I assign the value of "2" to one of the objects, then I cannot observe the same value again... still, even leaving one variable out, there is always some relationship between the others...
Hi Luca - Any additional explanation of what's going on is likely to result in more relevant and useful responses, so certainly, clarify the circumstances. We can't take into account what you don't tell us.
Changing your model in response to hypothesis tests you've run on the components tends to lead to overfitting (for more on this, see my answer here: [algorithms-for-automatic-model-selection](http://stats.stackexchange.com/questions/20836//20856#20856)). Beyond that, can you paste your model into your question? I'm not sure if I understand what you are describing.
Can you explain the first two lines of math a little more?  This would make more sense to me without the second line of math.  Maybe, it is a typo?
Thanks @mbq, but I'm not sure I follow. I **do** N-fold cross-validation for each point value of my grid search in the  hyperparameter space. The average result of the N-folds gives me the approximation that you mention, which I use to compare models and do model selection by selecting the model the best fits the validation set. 

My question is about what happens when I train with the full dataset. I think the learned model changes (the $\\vec\\beta$ parameters of the learned model change), and in principle I have no way to know if I suffer from overfitting.
@D L Dahly, Thank you. Actually I wanted to do path analysis, but I'm afraid my sample size (66 subjects) is too small for that analysis, so I used regression instead.
It would be all too easy (and natural) for many readers to draw incorrect conclusions from this answer. In the absence of further clarification, it appears to say that "amount of remaining variance explained" is the same thing as the coefficient in a linear model as mentioned in the question. To avoid such an unhappy ambiguity, this question needs an explanation of how we are meant to interpret "influence."
possibly related http://stats.stackexchange.com/questions/421/what-book-would-you-recommend-for-non-statisticians
> diss1vs <- seqdist(bhpsfup.seqw, method = "OM", indel = .1*maxsub, sm = upsubcostsb)
 [>] 536 sequences with 30 distinct events/states
 [>] 536 distinct sequences
 [>] min/max sequence length: 10/10
 [>] computing distances using OM metric
 [>] total time: 0.17 secs
Warning messages:
1:  [!] at least, one substitution cost doesn't respect the triangle inequality.
 [!] replacing 1 with 11 (cost=1) and then 11 with 4 (cost=1)
 [!] costs less than replacing directly 1 with 4 (cost=3)
 [!] total difference ([1=>11] + [11=>4] - [1=>4]): -1
Unfortunately, no. I mostly forecast unit sales per SKU and location. There *are* approaches to this involving forecasting traffic and basket share separately, but it's never been our approach, and since I'm rather constrained by a lot of architectural decisions made for me, I never had the opportunity to explore traffic forecasting. Sorry and good luck!
My first thought would be logistic regression.  I don't know but if GLM is being used here for for generalized linear models and the logit function is the link function then we are talking about the same thing. Is there any reason to think that dose is related to the outcome in a way that logit(p) or any other suitable link function would be related to the dose in a way that is nonlinear in the parameters?
Ahh I see, I re-read the question and understand a bit better what you want. You are already doing weighted least squares, but don't know what the weights should be. In that case there is no real answer. You assert that these points have a much smaller error than the others, but then want the data to tell you by how much. In the situation you describe there is no way to do this. From a Bayesian perspective, you want to apply your prior knowledge that these points are know with greater certainty that the others. But you need to specify that information quantitatively. You can't get it from
@MudPhud: Modelling a variable as a fixed or as a random effect should actually be decided before the analysis, when the study is planned. It depends, in particular, on the scope of your conclusions. Random effects allow more generalisability. It could also avoid some technical difficulties. For example, the asymptotics might break down when the number of parameters grow up, as it is the case when a categorical variable with a lot of levels is considered as a fixed variable.
thanks. Anything else, guys?
Thanks COOLSerdash & B Williams.                               @BWilliams: One of my requirement is inclusion of smoothing terms for some of the predictors. Do these models allow these function? I have tried this code, but I am not quite sure whether this is a proper additive model:                        m1 <-zeroinfl(hosp1~ s(trend,df=56) + pm10 +  s(temp,df=6) + s(humid, df = 6)  , dist = "negbin", data = data1)
Welcome to this site! As stated, your question is a little vague. Could you please indicate what an "SVM Type1" could be, and what you mean with *automatically defining classes*, assuming this is done prior to running the SVM?
There is also contradiction of your title with your question, In title you say "categorical response on ordinal response" and in question "in-group or out group has an impact on the ratings"
To me standardization makes interpretation much more difficult.
I am confused because that is not what your `IIvar` function appears to be computing.  That variance of the eigenvalues would correctly be computed using a denominator of `p` rather than `p*(p-1)`.  However, that's unimportant because this is just a constant factor applied regardless of the sample size.  Are you asking how to estimate the standard error of this index?  If so, I would expect the SE to be large, because the index ought to be unstable when sample sizes are smaller than or on the order of the number of variables, as is the case here.
Another conservative approach is just to divide weekly counts by the number of days. I know there's a presupposition that the real process will be smoother than that, but it will preserve the mean.
(+1) The edited question is exemplary in how well it explains what is needed and provides a context for answers.
If I understand correctly, your question encompasses at least two different topics: (1) use of FA in attitude or motivational scales, and (2) how to handle 'extreme' patterns of responses (ceiling/floor effects) in such scales?
Interesting. My understanding is that Shrout and Fleiss described 6 ICCs which are selected on the basis of the experimental design.  You can't use a convenience sample of 3-raters and then analyze the data as if the observers were randomly selected from a population of all possible observers.  The psych package in R is quite descriptive about how this is done (https://personality-project.org/r/html/ICC.html). Prior to doing any analysis, you may want to determine appropriate sample sizes required to find a difference between observers. Five observations per rater might be a little thin.
Nice find; Unfortunately, the CV has a discontinuity when $\\mu = 0$, which is a real possibility in my application.
"*Also, f must be a one-to-one function.*" -- [must it](https://en.wikipedia.org/wiki/Maximum_likelihood#Functional_invariance)? Why?
@mpiktas thank you for the response ! I often make the mistake to mix "precisely equivalent" with "close enough for practical purpose" which was the case here. I am going to reformulate this question in a few minutes since it is, well, rather stupid :).
I assume you want to know how long it takes to accelerate to a given speed?
Welcome to the site, @user24788. Note that you can't post figures (plots) until your rep reaches 10. You can also post the images elsewhere on the internet & post a link here, and another user can load them for you.
What kind of software is that?
Also, since presumably the goal is to measure if there is an impact of training on test scores - having the post-test have the same questions as the pre-test will cloud the interpretation (is it really the training that improved scores or familiarity with the test?)
In that case I think a Markov model or a hidden Markov model would be my first choice.
@ttnphns I have edited the question accordingly.
Because I'd predict the missing values in y (in my real case) for multiple times.
I've changed the acronym, since this makes the internet the better place :)
In general there are many possibilities consistent with these data, depending on the size of $A\\cap B\\cap C$. However, the limited data you provide determine a *unique* such (Venn) diagram because the set sizes and their intersection sizes determine a triangle formed by the circle centers (at least when all pairwise intersections have positive sizes). Thus what you are requesting is (in general, not in this particular case) likely (a) to be inconsistent with your actual data yet (b) to be impossible to do anyway.
This remind me of a poster I saw at the Human Brain Mapping 2010 conference. Tom Nichols used parametric bootstrap to assess the stability of the cophenetic correlation and silhouette in hierarchical clustering, but see his poster: http://j.mp/9yXObA.
@Elpezmuerto Very briefly, to complement @ars answer, question 1 can be answered with a conditional or trellis plot, e.g. sth like `dist ~ occ | isLand` using Lattice, or see the `coplot()` function in the `vcd` package -- this is for exploratory purpose; question 2 calls for a prediction model; depending on the variable you consider as your outcome, it may be logistic regression (e.g. if Y=isLand), a linear regression (e.g. if Y=distance), or directly a log-linear model providing you categorize your continuous measurement; question 3 is clearly a log-linear model as suggested by @ars.
Nice response, plenty in there that I'm not familiar with, so I need to try it out. Still, looking at my data with your methods, I'm thinking I need to show the high points in my data as well.  Thanks
george, you can comment on your preceding question if you need further clarification or if you want to add details. You could then vote to reopen your post rather than reposting the same question. Please, tell us what's not clear with the possible duplicate, and we'll merge this question with your other one.
I am currently struggling to understand a paper by Airoldi et al called "Mixed Membership Stochastic Blockmodels" in which they do exactly this (I think.) However, I am not very good with variational methods, so you might want to look at the paper yourself. See Figure 5 in their paper. A question I have been asking for a while is: Why is this EM approach not the same as trying to find a global maximum of the lower bound? And if it is, how can it succeed? It seems like I end up trying to optimise a function with way too many parameters and the answers I get depend too much on starting values.