tttjjj/elt_bench huggingface dataset:
No. The folds are generated by random sampling and the state of the random number generator changes after each run. If you want reproducible results you should seed the random generator to a specific value beforehand using, for example, `set.seed(1)`.
Are you using all your data to fit the model? That's the impression I'm getting.
Hello IMA. Thank you. There's no set of dependent variables. Data are independent pairs $(x_i,y_i)$ from a bivariate Gaussian (the pair $(x_i,y_i)$ is independent of the pair $(x_j,y_j)$, but $x_i$ is not independent of $y_i$). In my example above, there's no dependent variable. I didn't have the time to study the method for the univariate case, but if it is likelihood-based it should be straightforward to generalize it to the multivariate case.
For posterity :)
@Wayne: You took your 500 detected peaks and averaged their profiles and get a peak. [YES]; You took 500 random locations and averaged their profiles and get something fairly flat. [YES]; Are you trying to compare these two averaged profiles to say that the one is significantly more peaked than the other? [YES]
@Pardis You're right, that's what I had in mind when I posted the question: if robust regressors are used to down-weight observations as the residuals grow larger, why wouldn't there be a technique to down-weight the smaller to null observations. As Frank observed in his answer, least square naturally overweights the larger outcomes. But I'm still a bit puzzled on why I haven't found this problem stated more often. It seems to me that focusing on large outcomes is a common problem, like in the example I described.
See if you find this useful - http://stats.stackexchange.com/questions/43727/given-a-set-of-points-in-two-dimensional-space-how-can-one-design-decision-func/43811#43811
Secondly, instead of batch filter, I used the FilterClassifier and put the same multi-filter as above to check
the cross-validation(CV) and supplied test(ST) set result respectively. 
Something is weird:
-If I put my multi-filter in "Preprocess" tab at first, and then run CV, i get 0.85 correlation
coefficient.(using additive regression)
-If I use filterclassifier and run CV, i get -0.11 correlation coefficient.BUT I checked
the models of additive regression, they are the same.
-The ST prediction result is obviously NOT confidence to say "I get 0.85 correlation 
coefficient".
I guess the hint was meant for the OP.
Presumably you will be referring the F-statistic to an F distribution. For that to be valid you need to assume (and verify) the usual assumptions on errors: zero mean, homoscedastic, uncorrelated, etc,--and that they are *additive*.  I bring up this point because it would be natural to take logarithms of both sides of these multiplicative models (which is tantamount to assuming multiplicative error in the original expressions), which would make them not quite so dreadfully nonlinear :-), so it is important to be as explicit about the error structure of your models as possible.
You say that your $X$ is the date of the test. That does not appear correct - your explanatory variables are the *results* of past tests, possibly together with the *dates* of past tests. Is that correct? Perhaps you could edit your question for clarity. As you correctly note, there may be ceiling effects, so it may make sense to convert test scores to the interval [0,1], then do some kind of logistic-type transformation. [Data Science](http://datascience.stackexchange.com) may be an alternative venue - consider flagging the question for migration if you don't get an answer here.
@MichaelChernick I remember a recent discussion between Procrastinaor and you about whether we assume or not a true value of $\\theta$ in a Bayesian model. Please do you have the link to this discussion ? This is somewhat related to the present post.
I'd rather say: the log hazard ratio estimates the difference between the two "log minus log'' survival functions (this difference is constant by the proportional hazards assumption). Do you mean that the log hazard ratio *is* a kind of average between the two ``log minus log''  Kaplan-Meier estimates ? Is it the maximum likelihood estimate in the Cox model ? Actually I don't know how it is defined.
See Penguin_Knight's answer, [here](http://stats.stackexchange.com/questions/73765/does-stand-alone-dummy-variables-in-linear-regression-models-make-sense).
Is there maybe a working paper version of this you have? I don't have access atm :-(
@Roji, If your demand process is IMA(1,1) then it *is* non-stationary.
@B_Miner, I suppose you could think of it as an interaction in some sense. It tells you how, in one particular way, the effect of `Doy` is different when `Loc` takes on different values. But, it is **not** the same as f(Doy*Loc). The space of functions defined by f(Doy)*Loc is different from that defined f(Doy*Loc) and they mostly do not overlap (except when, for example f(x) = x). A more general "interaction" would be the function f(Doy,Loc), which is far more general than both f(Doy*Loc) and f(Doy)*Loc
Thank you!
I found a significant main effect for time, but more importantly I found a significant interaction between group*time, but I'm struggling with how to interpret the interaction. I still don't get how I can prove that the change in brand attitudes (or purchase intentions) was significantly higher for people in condition1 than for the people in condition2. The change is clear in the profile plots.
Thanks again!
I'm using brain voyager. Anyway I wanted to know how to do it theoretically. Thank you.
I don't believe you've answered my original question: "...[I]s the basis for doing something like Bonferroni or Holm adjustments of $\\alpha$ applies only if the original question is: 'Will the null hypothesis be rejected for any of the tests'? If that's the case, are adjustments to $\\alpha$ irrelevant if the question is: 'Will the null hypothesis be rejected for all of the tests'?"
Thanks Benjamin!  I used MATLAB and LibLinear (not my choice, I was told to use them)
A general answer to this question appears at http://stats.stackexchange.com/questions/34134/interpreting-t-value-as-a-measure-of-evidence/34135#34135. A specific answer is buried in the text at http://stats.stackexchange.com/questions/37768/t-test-and-p-value/37770#37770.  A related discussion occurs at http://stats.stackexchange.com/questions/23800.
LOL at "tell everybody how awesome it is with its 99% accuracy"
(+1) Maybe you could add somewhere in the PCA part that you assume that in $\\mathbb{X}$ columns are variables/features and rows are observations/data-points (as often the convention is opposite).
Data from a continuous distribution should be a discrete uniform after transformation (what I presume you mean by 'flat'). You might note that this accords with what one would expect since for a continuous distribution, the result of the probability integral transform $F_X(X)$ is uniform. Discrete data won't have that property, and the more data you have in the mode the less uniform it will look.
What would be the distribution of $\\log(X)$ and/or $\\sqrt{X}$ when $X$is a normal random variable? Especially when the normal random variable has significant probability of taking on negative values?
Yeah they are fixed. Any idea how the algorithm would look like. The maximum amount of money I can receive is 204$(just the sum). With the listed probabilities it is 94.8(expected value). In the worst case I dont receive anything, so I get 0. So, if I win some cash prizes (I dont know which) I want to know the probability of their sum being greater than some k.
Try non-spherical clusters, too. Or you might "overfit" your analysis to gaussian clusters.
Only if sensitivity and specificity are helpful.  I find them not to be.  If we were regressing age on blood pressure we'd never create an ROC curve.  Why we do it when $Y$ is binary is beyond me.
+1 because everything in OP's reasoning is only valid when the data is normally distributed
If the value cannot take any value between two specified values (e.g. between 1 and 3), then it's not continuous. Just because it can take values up to infinity doesn't make it a continuous variable.
maybe losing the plotting characters altogether and increasing the line width (argument: lwd. takes positive real values) would made it reasonable. How does this example  x=ts(rnorm(100)); y=ts(rnorm(100)); plot(x, col=2, ylim=c(-4,4), lwd=4); lines(y, col=4, lwd=4) look? It would need to be suped-up a bit but I think that basic template makes both time series visible.
You have mischaracterized logistic regression: one does not take the logits of the data but instead models the data as outcomes of random variables whose *parameters* are expressed in terms of logits.  For more information see *inter alia* the [Wikipedia article on generalized linear models](http://en.wikipedia.org/wiki/Generalized_linear_model).
if you are wanting to test if the structure from sports is the same in the musicians id use a CFA. Test model fit by looking at coefficient estimates and model fit statistics. But 120 observations will not be enough to fit 10 factors and and 39 items with ordinal data.
Actually, in the above example $(Y_t)$ is a Markov chain. A special Markov chain with zero dependence but a Markov chain nonetheless!
Normality has nothing to do with it. Normality comes later, to endow the estimator with certain desirable properties. You may want to check the following post. http://stats.stackexchange.com/questions/70539/is-there-a-regression-method-for-fitting-general-n-order-polynomials-of-two-or-m/70647#70647
Can you list the levels of your factors, & paste the output from the model (ie, `summary(model)`)?
+1 and a short note for @static_rtti: see [here](http://stats.stackexchange.com/a/58792/21054) for a complete worked-out example using the $\\mathrm{Beta}$ for proportions.
Hi Tal, thanks! Bonferroni does not seem appropriate to me - if one of my SNPs *is* causal and others are associated with it, there should be a signal, and Bonferroni has always looked too conservative to me (I usually prefer Holm's stepwise correction). The FDR you link to and p.adjust do not consider *combined* evidence (and the FDR still requires me to understand the correlation of my tests, the original question). multcomp may help, though at first glance it seems like it deals more with multiple tests within a *single* model, whereas I have *multiple* models. I'll dig deeper...
At present, this is really a comment, @user1492268, not an answer. Would you care to expand on it somewhat?
It would be better to give an example of your code and/or your results if you want to have a good answer.
See also here: [how can a regression be significant yet all predictors be non-significant](http://stats.stackexchange.com/questions/14500/), & for a discussion of the opposite case, see here: [significant t-test vs non-significant F-statistic](http://stats.stackexchange.com/questions/24720/significance-of-coefficients-in-linear-regression-significant-t-test-vs-non-sig).
Ah, I just saw your edit. Changing the correlation should make a huge difference. I find gpower beyond me for repeated measures analysis (unless they're simple). I'm not very sure about the effect size - I wrote a paper about the method I use, here: http://www.biomedcentral.com/1471-2288/3/27/ , although it's pretty time consuming, you've got to really want to know the power to do it.
I was only wondering because I had a hypothesis that there would be an interaction, thus I should discuss it. But it's kind of obvious that if I start discussing that maybe after all these variables have no effect of recall then there would be no interaction
Thanks for your answer. I've checked the book, but it focuses on classification/regression. I'm aware there are feature selection methods designed for classification, but my question was about how relevant they can be for pattern mining. In other terms, how they would effect the results of the mining. And also, if there exist methods designed specifically for pattern mining, for example known to have good properties such as not affecting much the results. I've edited my question to make that clearer. There is no notion of class in my data (ie no nominal attribute is of particular importance).
You can't use this to determine how long you will have to wait for 13 concurrent users because the rates from moment to moment are autocorrelated. During high periods of usage, you may hit that peak pretty often. During low periods of usage, you may not hit this level for a long time. What MAY work is to segment your time periods (e.g. high/medium/low) and get separate statistics for each. But, frankly, lots of real-world applications are overdispersed (higher variance than the poisson) -- leading you to a gamma-poisson distribution.
For categorical predictor variables, how do you check linearity with the outcome variable?
I would like to suggest that a more fundamental reason precedes this one: the "matrix of interest" appears because we use *second order moments* of the data. Once we elect to do that, the original question amounts to asking why it is so useful to describe an ellipsoid in terms of its semi-axes--and the answer is patently obvious. Another interesting (and perhaps controversial) aspect of this reply is its implicit assumption that PCA is performed on a correlation matrix: if covariances are used instead, then the units of measurement matter and (therefore) so does the coordinate system.
@shabbychef: The page Srikant has referenced clearly documents the behavior you have noted.  In particular, it does not maintain that var() returns the 1/(n-1) version of the variance when n=1.  So the issue is not that Matlab is *wrong*, per se.  (From a software design point of view this implementation of var() is terrible because it tries to do too much in a single function and consequently risks misunderstanding and erroneous results by unvigilant users.)
Yes, I would say it should. You can also consider the median if you want to minimize the impact of outliers.
If you think there's a qualitative difference between drug users & abstainers, then heed @Nick's point & consider using a dummy variable as a predictor in addition to the count.
@Josh that's the vast and outstanding piece of literature :) Thank you very much. That was something I was looking for! CV is a great place.
Thanks. What about the bit that says "the t-distribution can be used to estimate how likely it is that the true mean lies in any given range."  This makes it sound like we should be able to take a sample, then for any range [A,B] we choose, calculate Prob(A<mu<B).
interopretable? Possibly you mean interpretable?
Do I understand the data right in that you have 3 variables (columns time1, time2, time3) with a common list of 5 possible categorical values (Nix, BV, etc). Each row of the data is an observer; at time1 he observes, say, Nix, at time2, say, BV, at time3, say, Nix again. If that is the case than why does amount of observations differ for 3 times (50 in time1, 44 in time2, 41 in time3)? Is this because of missing data? Or your data is another structure?
Thanks Chris! Yes, that is interesting, and very helpful.
That is a very broad question. Maybe we can narrow it down to something answerable if you tell us more about your model, and why you have doubts on whether you can add such a variable to your model.
Clearly also any strictly monotonic transformation of means being equal/unequal (or their difference being zero/nonzero) will be equivalent.
This is merely [Fubini's Theorem](http://en.wikipedia.org/wiki/Fubini%27s_theorem) together with the observation that the product of nonnegative values remains nonnegative.
Are you thinking of (1) experimental studies that demonstrate causation, or (2) observational studies that turned out on further investigation to have identified causal relationships, or (3) observational studies that in themselves provide strong evidence for causal relationships (having included all plausible confounders, &c.) - or something else?
@AdamO So the problem is with the possibility of trawling through a large list of possible transformations looking for 'significance' rather than with transformation of the data per se. In biomedical science there are many circumstances where transformation (mostly log transformation) makes sense because of the nature of the measurements. For example, the concentrations of biological molecules usually scale geometrically and so a log transform makes sense and controls heteroscedasticity.
@user4045 Although not used much in situations like these Fisher's combination test can be used. If you are not familiar with it what it does is to take the individual p-values for the different studies (so you don't even have to be applying the same test or have very similar designs) and computes a combined pvalue. I think really what is important is that they are basically testing the same hypothesis in a general sense in your case whether or not there is an association between gene expression for your particular gene and the presense of the disease in the subject.
When Diaconis did the coin flip for example, it looked like a genuine coin flip (i.e. it didn't just flip once). I imagine a die would be more difficult to control, but I am am just curious if it can be done
A few of the ways one might go about computing the distribution of a sum of uniform variates are described in my answer at http://stats.stackexchange.com/a/43075/919.  That question might even be a duplicate of this one, depending on what you mean by "that specific form."  What do you mean?
In case you find pleasure to program calculation of such coefficients yourself instead of using software, look [here](http://stats.stackexchange.com/q/49453/3277).
@Daniel Kessler, try http://quantitativeconservationbiology.wordpress.com/2013/07/02/confidence-interval-for-a-model-fitted-with-nls-in-r/
I don't know anything about `bayesglm` per se, but I suspect it is just a multinomial logistic regression from a Bayesian perspective rather than a frequentist one. If so, the interpretations of the betas would be more or less the same, & this thread: [interpreting-exp(b)-in-multinomial-logistic-regression](http://stats.stackexchange.com/questions/17196/) should help you.
Personally, I would be ok with code in pretty much any circumstance. Data is another issue. Unless it is already publicly available, I likely won't have permission to share it with anyone. I suspect this makes a data request unreasonable in many circumstances.
@MikeWierzbicki You're right in that if the full model contains only `IV1` (1st example), then the two expressions for the denominator are identical. However, when the full model contains additional effects, the denominator for testing $A$ changes even though the model comparison (`~ 1` vs. `~ IV1 + 1` for type 1 SS) does not. In the 3 examples, the mean square for $A$ does not change (same model comparison in all cases), but the mean square error does. I'm interested in what justifies the changing error term when the actual comparison stays the same.
Thanks! I tried the corrected code and it works well! What I do note, though, is that I think ff=mean(f,1), not ff=sum(f,1), as what was written in the code. Nevertheless, thank you very much!
@Weiwei: Right, but the first condition mainly requires that the function is invertible. Of course, f(x) = 0 also implies f(x + y) = f(x)f(y), but this is an uninteresting case. The third condition asks that the derivative at 1 is 1, which is only true for the logarithm to base $e$. Drop this constraint and you get logarithms to different bases, but still logarithms.
@Joris - Point taken (without offence), but it was my impression that SE sites were designed to foster problem/solution learning in a way not afforded by manuals. Additionally, this question will now be available for other beginners. Thanks for the links though.
"*MLE will pick the Gaussian that is the most likely one, given your data.*" Hmmm, isn't it actually: MLE will pick the Gaussian under which your data are most likely? Which is slightly different from picking the "most likely Gaussian"... wouldn't picking the most likely Gaussian require a consideration of *prior beliefs*?
I think yes. Then incorporating spline function of time into Cox model may help, however I'd prefer searching for some prior knowledge or considerations about the dependence of Sit effect on time.
either site, I think, is fine. So long as you don't cross post (as in post an active version to both sites at once). I would be happy to migrate it to Stats for you if that is what you want, but why not let it hang here for a couple days at least to see if you get a good answer?
Thanks for your fast response. I read about the Change-Point Analyzer, but because we made use of a control group isn't there a better way than using control charts?
I tried one of my favorite data challenges 1,9,1,9,1,9,1,9,1,5 on the Strategico link you provided and found that it did not pick up the clearly visually obvious pattern and identify the anomaly. It gave a forecast of 5,5,5,5,5,5,... which did not pass our test for identifying and adjusting for outliers.  See Ruey Tsay's work on outlier detection(see http://www.unc.edu/~jbhill/tsay.pdf) for the importance of handling these issues.
+1 But please note that boxplot whiskers conventionally--and by original design--do *not* necessarily extend to the extrema. They stop at the most extreme *non-outlying* data. Outliers--defined as those beyond invisible "fences" determined by the medians and quartiles (or "hinges")--are depicted separately with point-like symbols. In my experience, too, boxplots take a lot of explaining to people who are not educated in their use. That would be most older people and probably a lot of middle-aged ones, too.
It's next to impossible that the binary response is *actually* linear in the independent variable, unless the IV is very restricted in range --- and if it *were* actually linear, why would you use logistic regression? Isn't that nonlinear? Lastly, you state that it's linear with apparent certainty. Where does that certainty arise?
Analyses in R should be able to manage just fine with variables coded M/F and L/R; it has probably already made them factors (check by something like `is.factor(gender)` or even just use `str()` on them); if not, try `as.factor()`. You could analyze this data using ANOVA, yes
@Jeromy Nice reference! This one is worth looking too: Recommended effect size statistics for repeated measures designs (BRM 2005 37(3)), http://j.mp/cT9uEQ
@FTusell: I apologize, that was a typo and I can't edit my own comment to fix it, but your interpretation is correct.  Converge might not be the correct term, I mean that I am trying to understand why I'm getting those discontinuities as in plot 2.  The values are centered around 0, then at one point oscillate between -100 and 100 in only a few updates.  I also don't understand why my choice for `dV` doesn't matter as long as its not 0.  It looks like it's a crucial part of the Kalman filtering equation, but manually setting it to 0.00001 or using the MLE value 12 yields the same results.
Thank you very much ... :D
So you understand what it means to draw samples from two specific probability distributions (your two examples), but if we label one of them $P(x)$, you no longer understand how to draw from it? I'm not sure I understand where exactly your difficulty lies. Can you explain more clearly what you want?
In many situations with a composite null, the distribution of p-values depends on the true state of nature. The case of testing the mean of a Normal distribution with unknown mean and unknown SD provides a standard example.
Thanks, but even if the data is not independent, when you want to calculate the EMA return from t-m to t, you would ideally not want to have the EMA at t include information before t-m.