tttjjj/elt_bench huggingface dataset:
Typing `.paramGH` at the `R` command line shows you the code used to transform $\\zeta, \\rho, \\lambda$ to the `dgh` parameters.
Can you clarify what you mean by multivariate regression?
`But I need it for unsupervised clustering, instead of supervised classification` This key phrase alone is too brief and doesn't expain clearly what you want. Above it you described what seems to me to be a decision tree. Can you now give a similar passage about the algo you want?
Since we can reject the null but not prove it true the null is usually the opposite of what we want to prove or assume to be true.  If we believe that there is a difference then the null should be no difference so that you can disprove that.
Is it possible to compute the coherence for multiple signals?
@whuber: Do you mean that the test statistic can be chosen to be arbitrary? What I think is that the test statistic and critical value are subject to some forms. For example, in z-test, reject null iff $Z_n(X) \\geq z_{\\alpha}$, where $z_{\\alpha}$ is fixed for all $n$ values, and $Z_n(X)$ has a specific form for all $n$ values. Its power increases as $n$ increases and why? Is this also the same for other tests that reject null iff $T_n(X)\\geq t$ for some test statistic $T$ and fixed $c$?
The mysteries lie primarily in the details, which are carried out wholly without explanation. A look at the question shows the O.P.'s effort fell apart in not recognizing the importance of tracking the domains of the RVs, as evidenced by the appearances of "$\\min$" and "$\\max$" in this answer. Thus, although you have provided a *correct* answer, it does little to reveal what went wrong or to explain the methods you have used to break up the integrals and identify the proper ranges to use in each one.
On a side note, even though the Weka book is titled "Data mining", it actually is about regular machine-learning. You can even find a statement by the authors that it was supposed to be called "practical machine learning", and the publishing house changed it to "data mining" for marketing and sales reasons. All the classification material clearly belongs to AI and ML, not to data mining (no integration with database management!)
Appreciate if you can help me with the following. I have created categories High, medium and low and I need to transform them in ordinal numbers for further calculation. What is the best approach to keep in mind while selecting these numbers or deciding levels? How can one justify teh chosen numerical value?   I'm thinking of considering ordinal scale where one end represents minimum and the other end represents maximum value, so that any value between min and max can be selected. (do not remember the name of this scale which is used in medical especially). Appreciate your support.
Does the upper incomplete gamma function count as *closed form*? (To me, it does not.) This is just conveniently hiding an integral via notation.
If your purpose here is descriptive, and not to test a scientific hypothesis, you may report the value of the test statistics as a measure of which pairs seem most discordant, without correction. But, if you are doing tests of 105 different hypotheses, then you should probably do some correction. You could use the Bonferroni (or Sidak) correction to control the probability of incorrectly rejecting the null hypothesis due purely to the number of tests.
Data is not enough -- you need to have some hypothesis to test with a statistical test, like "People hate when administration website has magenta background" or "Younger people use adm. services at night more often".
Thus, each window has only one observation of score1 and score2. Therefore , a problem arises: how can one compute sample correlation with only one observation ?
Also http://stats.stackexchange.com/questions/38093/how-to-deal-with-high-correlation-among-predictors-in-multiple-regression
@Gavin I agree about having a script that processes a supplied data file. I guess I'm thinking about some relatively ad hoc files that I create (for example, I might create a little csv file that lists the variable names and corresponding labels that I'll use when preparing an R table).
Just as an aside: you can use xlab="predicted" and ylab="res" as arguments to plot() in R.
As I read it, he doesn't have the data for a 2 sample test.
Maybe you could expand the quote "to Tommy Franks:"... to give some context?
Is $\\lambda$ a parameter that is free to be chosen?
Thanks for all the comments and suggestions.
Hi @Sam Finegold and welcome to the site. As you have correctly said, the standard approach if you have a binary outcome is logistic regression. If you use OLS for this kind of data, this is called [Linear Probability Model](http://courses.umass.edu/pubp608/lectures/l22-2.pdf). The coefficients express the change in probability that $Y=1$ for a unit change in $X$. But this approach has shortcomings, e.g. that the predictions are outside the range of 0, 1. And there is heteroskedasticity by design.
@DilipSarwate Whatever you think of the pedagogical merits of the matrix algebra version of the prove, that is the question that Kyle asked. I answered it. You ignored it and made snide comments.
@chl; my opening line says I can't think of an answer to Q1, but Q2 *can* be done via my example, substituting in the maximum values for cex.lab in place of where I have `2` and `3` now. That was just an example of how to label the plot separately and never claimed to answer Q1. Maybe I misread/misinterpretted what Tal wanted with his Q2 though? Your reading of Q2's meaning seems obvious if Q1 is TRUE, hence I thought Tal was aksing the Q2 I answered...?
I see... Egger's test of the null hypothesis that intercept b=0 (or tests the null hypothesis that there is no funnel plot asymmetry). In this case the regression line will run through the origin. If the intercept b deviates from zero, the intercept b provides a meassure of asymmetry. The larger the interceptor's deviation from zero point, the larger the asymmetry. The two-sided p-value should be reported. (Synopsis from the book Publication bias in meta-analysis: prevention, assessment and adjustments. Po avtorjih A. J. Sutton). Thanks.
"Is there any inference that would be significant" is far too general - indeed by asking it, you're effectively data snooping, and thereby your significance levels no longer mean what they purport to mean. Be specific about what you have and what you're trying to find out.
Also posted here: http://quant.stackexchange.com/questions/1375/sample-size-for-actual-vs-backtested-performance
@Charlie - very very nice!
This question appears to be off-topic because it is about getting a thesis topic rather than a statistical question itself
It depends what you treat as independent observation. If you'll treat each slice as independent observation then it is number of slices. If you want to sum up all slices 'differences' from single exam then it will be the number of exams. The first variant is appropriate when all slices are near equally hard to recognize and between-exams variability is neglected (we make an assumption that all exams produce slices of equal quality). In other case summation of slices is preferred (though require much more data).
It is generally a bad idea to lump things like that, but if you wanted to, you could create a new variable GOODRATE with values 0 and 1 and then use regular logistic
@naught101 It's from Wikipedia, feel free to send an improved one there ;-)
kurtosis and skewness are not the third and fourth moments. Please be explicit about what you're comparing with what and if at all possible, show what the functions you call calculate (such as `moment`)
Thanks Tony for your explanation. FYI-my application area is web usage mining.
It appears the bat species you are studying is a "central-placed forager" where its central-place is the roost which the bat returns between foraging events.  The phenomenon of central-placed foraging could likely be accounted for in a model of habitat use by including a measure of distance to roost as one of the covariables in the model.
$$ \\hat{\\beta_1}=\\mathop{argmin}_{\\beta} \\sum\\limits_{i=1}^{n}(\\beta x_i-y_i)^2 \\\\ \\Rightarrow \\hat{\\beta_1}=\\frac{\\sum\\limits_{i=1}^{n} x_iy_i}{\\sum\\limits_{i=1}^{n}x_i^2}$$ v.s. $$\\min\\limits_{\\alpha,\\beta_1} \\sum\\limits_{i=1}^{n} (\\alpha+\\beta_1 x_i - y_i)^2 \\\\ \\Rightarrow \\hat{\\beta_1}=\\frac{\\sum\\limits_{i=1}^{n}(x_i-\\bar{x})(y_i-\\bar{y})}{\\sum\\limits_{i=1}^{n}\\left(x_i-\\bar{x}\\right)^2} \\\\ \\hat{\\alpha}=\\bar{y}-\\hat{\\beta_1}\\bar{x}$$ I don't think they lead to the same result when the intercept $\\alpha$ is different.
@Michael: maximum likelihood with a varying number of (gaussien) clusters — how do you make it not to overlearn, especially if the cluster width is a parameters as well?
Thank you very much for the answer. What can i do, if i have higly screwed numerical(!) data? Does weighting work there, too?
It seems like not enough detail is in the framing of your question. I would not have guessed from the way your question was asked that this was the kind of thing you were after. It feels like something relevant is missing.
The convexity of SVMs is IMHO a rather overstate benefit.  Once you include tuning the hyper-parameters (which is the key to their correct use), overall the problem is no longer convex.  The key benefit of kernel methods is simply that they encourage the user to deal with over-fitting by carefully tuning the regularisation parameter $C$.
Related: http://stats.stackexchange.com/q/6945/2970
Collinearity (singularity) among 3+ variables is not reduced only to high pairwise correlations. Search the site for questions tagged "multicollinearity". Also, I recommend you to read this my answer: http://stats.stackexchange.com/a/70910/3277.
Thank you so much, thats the best explanation I have ever seen about lmFit(0. I really appreciate your time.
This is pretty much a duplicate of [this question](http://stats.stackexchange.com/q/4040/12258) and [this question](http://stats.stackexchange.com/q/8225/12258). Maybe there should be a community wiki entry for this?
(1) What exactly would your data look like? (2) This looks like a physics problem rather than a statistics one.  Where is the uncertainty?
Does it work in a script mode? If you only have a GUI, then your results will be very difficult to reproduce. A script mode is a must for any serious software.
The main advantage of survival models such as Cox (as opposed to OLS regression) is that survival models deal well with censored data. 

I am not clear enough on what exactly you are trying to model. Is it something like "time to correct answer"? If so, and if some people never get the answer right, then survival analysis is likely what you want.

If it is something else, please elucidate.
Welcome to the site, @jmbejara. We welcome questions like these, but we treat them differently (see [here](http://stats.stackexchange.com/help/on-topic)); we mostly provide hints to help you get 'unstuck'. Let us know what you've understood so far & where you're stuck, & we'll see if we can get you going again.
How to do questions usually off topic, but this is admittedly mostly ignored
May I suggest @jerry (formerly michelle) and you, MsSnowy, and probably others, that you coordinate together in order to gather related homework questions and ask unique and precise questions as suggested in our [FAQ](http://stats.stackexchange.com/faq). Registering your account would be a plus :-)
I dont need an answer on the PAI, which is a well validated psychological questionnaire. My study is on a new questionnaire. My point is that on the PAI validation studies the factor analysis was only done (and several other studies replicated this) at scale level, not item level. This is a link to the PAI http://www.sigmaassessmentsystems.com/assessments/pai.asp http://en.wikipedia.org/wiki/Personality_Assessment_Inventory but my answer is not about the PAI, but another questionnaire, which is a new one.
Are subjects randomised into the three arms? Is it a valid assumption that here is no difference between the groups at baseline? Is it a valid assumption that the during-treatment value is between the pre-treatment and post-treatment values (a monotonic relationship)? Are the pre/during/post values available for each subject? What is your null hypothesis? What kind(s) of difference(s) are you interested in, what should the test be sensitive for?
Are you looking for the `stl()` function in the `stats` package? If not, please give a few more details.
+1, nice answer.
Not entirely right: the training complexity of kernel SVM is between $O(n^2)$ and $O(n^3)$ *depending on C*; See e.g. [Support Vector Machine Solvers
 by Bottou and Lin](http://140.112.30.28/~cjlin/papers/bottou_lin.pdf) (section 4.2).
@jank, I tried to explain better in the main text, but I can't undestand your last question. Can you give me more details?
Notice that "how can I do whatever in whichever software" is considered off topic here, you question may be moved to another site.
I am also looking for multiclass SVM code, LASVM if possible.
A mixed model can be expressed in several ways, one of this is
Y = Xb + Zc + E
I remember there is also the possibility to specifiy a model in the form of Yijk= ... but i have my notes at home (8000 Km from here), so not exactly easy to get them.
What do you think, is the manova equation right? I am not really sure to be honest.
@Cita, if this is homework then you should at a minimum add the 'self-study' tag.  This question might be better for your teacher or TA.  As written it is a poor question, very vague, there are many null hypotheses that could be considered.  Is there more context?  an example in the text or lecture notes that is similar?
The way I would proceed is to assume that there are $F_i$ that can be different across $i$ units, and to impose the restriction that $\\beta_i = \\beta$. Then I would simply find the vector of $\\hat\\beta$ that would max the RC summed over units. Would this make sense?
Many thanks for explaining growth mixture models :-)
+1 nice answer.
Thanks for the suggestion. I don't think this is quite what I'm after since my version of sampling starts at one tail (not random) and doesn't cover the whole distribution (stratified or otherwise). I'm calling this "priority sampling" for now.
(+1) An excellent article.  I also recommend MacCallum's Repairing Tom Swift’s Electric Factor Analysis Machine, http://kuscholarworks.ku.edu/dspace/bitstream/1808/1492/1/preacher_maccallum_2003.pdf, which is helpful as well as entertaining.
@vikkor Hmm, sounds strange. If yours does not work you need to post the data. I added a **working** example.
Are the $\\alpha$s the same in both equations or do they need a subscript?
Thanks Rolando.  I'm still not sure how to compare the questions.  Should I compare each response category in one question with the other.  In this case, since there are four answer choices in Question 1 and five in Question 2, I would have 20 chi square independence tests?
@cardinal: thanks, expanded the answer.
@AndrewGuenther That's right!
Although it does not matter for these data, because the difference is so obvious, `dgof::ks.test` is *not* applicable to this situation. Its [documentation](http://cran.r-project.org/web/packages/dgof/dgof.pdf) appears to state that when comparing discrete distributions, one of them must be a *known* reference and *not* estimated from data. Furthermore, the p-value computed for sets with more than $30$ data values is only approximate and "conservative." In such a case, one is likely better off with a permutation test or a chi-square test (available in base `R`).
I would be very much interested in the graph conversion method you have suggested.
I vote to keep this open. As to why, given the state of lme4, it may require theoretical as well as technical knowledge to get the diagonal of the hat matrix. Though the OP assumes that one can just extract this, I suspect the only way to get the desired result will be to compute it from other data included in the fitted model or from first principals that the OP could apply to the particular lme4 case.
I just went to the helps for the ez package where you are getting ezANOVA from.  From my quick read, it appears that this package does not support TukeyHSD.  However, you need to look at the provided ezPlot and ezStats functions for your problem. They both do post-hoc comparisons for you, among other things.
I'm curious what precisely you mean by "missing values are random" and how you know it.  I recommend Andrew Gelman's [chapter on imputation](http://www.stat.columbia.edu/~gelman/arm/missing.pdf)
I was thinking of fitting the ARMA first, then fitting the residuals to a GARCH model. Is this wrong?How can I "check the residuals for any linear time series properties which can then be modelled using ARMA processes."?Can the ljung-box test be used to detect ARCH effect?
yes this is a good example, although it seems that in this case the convolutional networks perform even better
+1, nice analytical explanation--very clear.
Thank you again ,i totally agree with you.And I try to reduce the levels number with a fake dummy method.For example,I replace a predictor with 600 levels with 4 predictors(as 600<5^4)After this transformation,I can run random forest algorithm.However,the RMSE result is strange,I will open two other question about how to reduce the level of factor feature and what's the relationship between 10-fold CV RMSE and test set RMSE score?
If x1 < .5 AND x2 < .1, then y="b". Is that the intended behaviour? (i.e., is the order of lines 7 and 8 significant?).
So if I understand your answer correctly, not only will forecasting not account for regression to the mean, it will actually exacerbate the problem by weighting the most recent cases more heavily, when we know that the design assures that the most recent cases will be at their highest point?
This might be useful: http://stackoverflow.com/questions/9666212/how-to-compute-error-rate-from-a-decision-tree
Thank you very much. I went with discrete models and multinomial logistic. The book "Discrete Choice Methods with Simulation" by Kenneth Train from here http://elsa.berkeley.edu/books/choice2.html helped me a lot.
@NickCox sure, I have updated the question.
@Steven If you can't vary the inputs to get various estimates of the temperature you have no way to see the distribution of the prediction accuracy.I do not know what you have in mind for the chi square test. You have 30 years of average temperatures and thirty model estimates. I fyou treat these as independent estimates of an unknown target temperature i could see using mean square error as a measure of accuracy but I din't see a chi suqare statistic entering in here. That would mean having some form of a contingency table where you would compare expected counts to observed counts.
+1 - I might also suggest since the OP is fitting ML estimates (and not say just estimating a KDE on the bootstrapped samples) an additional plot might be scatterplots of the bootstrapped parameter estimates. If a reviewer had a problem with this an easy fix is to just remove the quantile lines, the individual bootstrap estimates greyed out are valid PDF's and provide a good visual cue of the uncertainty.
I don't think so, no. Your validation error has not got worse. Take a look at the initial definition you give: "Overfitting is when we have a model which has memorized the training data *AND* does not perform well in real-world cases". The 'and' there is crucial. Yes, your model has memorized the training set - but it performs excellently on real world cases. So it has not overfit.
+1 Intelligible reference of important basics
Maybe not the most famous statistician, but when you put it like that, definitely the most important! ;o)
@user32454 I would advise reading this answer for your education but not necessarily trying to work through the calculations. Terry Therneau (author of pkg:survival) has already done all the hard work and the code is well tested,
Have you looked at an ANOVA table?  It clearly lays out variance estimates, which ought immediately to answer this question.
There's your problem! You don't 'solve for $x$'. You expand out, and complete the square in $x$ in the exponent, and pull out all the rest as constants out the front of the integration. If you organize it right, what's inside the integral is proportional to a normal density; you multiply and divide by the appropriate constants so the integral is 1 and what's left is now a density in $y$ with the various parameters ($\\mu_x$, $\\sigma_y$, $\\sigma_x$ etc) taking part.
Thanks for catching the typo! (I changed my variable names for clarity).  The error message says: Error in classifyVariables(model) : incorrectly specified model
@Aniko: yes, the process is, on average, effectively instantaneous. Assuming it is instantaneous will work for my purposes.
re #2: A different graphical method to explain covariance (and therefore, in an obvious way, the correlation coefficient) appears at http://stats.stackexchange.com/a/18200.  However, I used *Mathematica* to make the illustration, not R, and even *Mathematica* could not quite do what I wanted (which is to get complementary overlapping colors to cancel rather than add).
Of possible interest: [In genome wide association studies, what are principal components?](http://stats.stackexchange.com/q/8777/930).
you problem seems to be of hypothesis testing with condition variables such as length of employment, education, gender, etc.. Have you tried using Bayes Classification? A good introduction is [here](http://en.wikipedia.org/wiki/Naive_Bayes_classifier)!
The choice of tests is directly connected to your research questions. Could you please formulate them as precisely as possible? Btw. there is no such thing as a paired t test with unequal variance.
I'd approach this by first taking out a factor of $\\theta$ ($Y_i=X_i/\\theta$), and working with $-\\log Y$ (whereupon the GM will become the negative of an arithmetic mean of exponential(1) variables), and from which a simple manipulation gets you to a chi-square. You should be able to see how $\\theta$ comes into it after that, by going back to $X$ in terms of $Y$.
In the absence of any particular statistical procedure, "degrees of freedom" has little or no meaning. About the only meaning it could reasonably have is the dimension of the manifold of possible values of the sample conditioned on the weighted mean. That (obviously) is still $n-1$. Whether an "effective sample size"--however it may be computed--is useful, accurate, or even meaningful depends on how it will be used in a procedure.
What makes you think there is a problem?  If, say, main variables $x_1$ and $x_2$ both have positive coefficients, but their interaction a negative coefficient, that simply means there is "negative synergy" between the variables, so their presence at the same time dampens the effect. They are "inhibiting each other". That is a perfectly valid and often sensible situation!  There are often similar questions here, look at one of them : http://stats.stackexchange.com/questions/61506/nonsignificant-interaction-still-causes-main-effect-to-flip
Hint (which I will expand on in an answer if you seem to catch on): What happens when you work out the expected size of the complement (the fraction that *isn't* covered by your samples)?
Any reason to ask for a 'non-parametric' measure of inter-rater agreement. What's wrong with a weighted [Kappa](http://en.wikipedia.org/wiki/Cohen's_kappa), or some [ICC](http://stats.stackexchange.com/a/3546/930) or [generalizability](http://stats.stackexchange.com/a/64447/930) indices?
But, I do have the most significant baseline covariates in the model. What's throwing me is that the imbalance in baseline behavior is in *interaction* with baseline disease. Berger mentions in passing that one theoretical approach he uses applies to interactions (at least, I think that's his point), but it's over my head. In general, there seems to be a lot of discussion of covariate imbalance, e.g., too many old folks or too many men, but not of interacting imbalances, e.g., too many old men.
There are 6 categorical variables, where are the prices?