tttjjj/elt_bench huggingface dataset:
Could you elaborate on the models that you are considering, and how the error term assumptions deviate from the data?
Hint: write down the prediction equation using deviations from means (which is what the question asks). Then remember how the constant term in a simple regression is calculated.
Thanks, that's good info.  It looks like something like this may work; I just don't know if it's the best approach for this circumstance http://stackoverflow.com/questions/7863942/save-a-random-forest-object
By *resampling*, I understandood pick them in a random order.... but I guess that ***with replacement*** means that one of the "resamples" could possibly be 25 times the same value (possibly). I simply take my 25 values, and randomly $n$ of them to have the "resample" and to that 1000 times right?
You have a problem of reverse causality, which is a form of endogeneity. Since I don't have much time: google "treatment effect identification". Techniques to establish causation are, among others, instrumental variables (that's what you should look into), difference-in-differences, matching, quasi-experiments,..
+1 for example of spurious regression. Will employ it in the lectures :)
I came to a similar conclusion for daily repeated measues data here: http://stats.stackexchange.com/questions/39211/regression-with-repeated-measures-in-matlab
Just to let you know, we welcome questions like this but we treat them differently. Instead of giving you answers, we provide hints to help you figure it out for yourself. For more about our policy here, read [this](http://stats.stackexchange.com/tags/self-study/info).
As mentioned in the above answer, you could try fitting a mixture distribution.  Here's a paper that uses these hybrids for wind speed -- I think some of the distributions are combinations of 3 other distributions.  http://www.journal-ijeee.com/content/3/1/27/
Thank you so much, Tristan!
(1) As you're expecting the coefficients to change somewhat, why *wouldn't* you expect some small positive coefficients to become small negative ones, or some barely significant coefficients to become nearly significant, even if the changes in magnitude are quite small? (2) Don't know what "using categorical variables as is" means - sum-to-zero coding? Doesn't matter, so long as you're not inadvertently treating categorical variables as continuous.
Thanks so much, I see where I went wrong.
@Nick That is consistent with Barnett and Lewis, whom I quote in a comment to [John's answer](http://stats.stackexchange.com/a/7157/919).
A lot of information to think about, thanks! You are absolutely right! If the variables were normalized, the number of training examples were larger and the number of neurons on one hidden layer equal to one, it will look much better. Of course, not a multiplication function but something similar. Ah, if I got such a great response 2 weeks ago, things would be easier. However, very grateful for your reply, thank you!
And for good reason.  You're asking for a solution to a physical problem where you record a signal without knowing the input or the medium, and you want to invert for both; simultaneously.  If you could solve this problem uniquely, you would be famous: this problem (or variants of it) is one of the most important classes of problems in many physical sciences.
thanks for your input.  I may not have been clear: right now, I'm just interested in finding the gradient for the prior. The gradient of the log-posterior is just the gradient of the log-likelihood plus the gradient of the log-prior, so finding these two gradients separately should be okay.
I'd also recommend the car package (http://cran.r-project.org/web/packages/car/index.html)
(1) What range would you rather use for x otherwise? I assumed the range of your data as an estimate (2) I don't decide. You could keep looping till you collect enough samples. How many samples do **you** need? (3) Using density is the way I thought of. Bet there are other ways. Can you elaborate your concerns about using density?
Do you know what the definition of the p-value is? e.g. see the first sentence [here](http://en.wikipedia.org/wiki/P-value)
It seems that as.mids2() does not produce a proper mids-object. It returns 5 imputations instead of 15.
Welcome to our site! The assessment that more than 10 or so variables would not be appropriate needs more nuance and better justification. And could you be more precise about what you mean by "performance" in the last paragraph?
If I type <significance of difference of two related correlations> into Google, the first link is: http://www.quantitativeskills.com/sisa/statistics/corrhlp.htm 

Which provides a link to a web page that does the calculation for you.

The references at the bottom of the page provide the formulas that are used (and I imagine that some of the other links from that search will give you the formulas too.)
The reference given by CDX is: Foster Provost, David Jensen, and Tim Oates. 1999. Efficient progressive sampling. In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '99). ACM, New York, NY, USA, 23-32.
Sensitivity and specificity are not hypotheses themselves. They are indices, or statistics. The wikipedia page says that their 2X2 table-form definition resembles the definition of H0 and H1 probabilities (type I and II errors).
Thanks a lot Zen.
+1. In the example, the uniform is the *exact* answer, by the way.  (The apparent dropoff at the ends of the plot is an artifact of how R draws histograms.)
Bear in mind that they typically have access to extensive [exit polling](http://en.wikipedia.org/wiki/Exit_poll) data, & other data that they can use to predict the result. They only need enough confirmation from the incoming count to ensure that they're not off the mark due to sampling error. There certainly are complexities involved & the incoming counts are generally a biased sample, but exit polls go a long way towards helping them address some of those issues.
I had assumed that the frequencies of names in your tables are representative of those found in the US, so it doesn't change anything for me. And the likelihood ratio you calculated is identical to mine, so I think the table sizes cancel out. The point is that a ratio of ~2 is never going to overturn your prior odds. Your independence assumption weakens the value of the evidence.
@user603 Thank you; that's an insightful distinction.  I don't think it changes any of the present conclusions in important ways but it certainly helps the intuition and points to an important difference between even and odd moments.
I think there is some good, useful information here, @IrishStat, but I think some caution is in order re suggesting your software to the OP (even if you state that there is "no obligation" & I fully trust that your intentions are noble), as it violates our [site's policies](http://stats.stackexchange.com/faq#promotion).
Not for each variable but jointly for all the terms making up each variable.
"I am interested in how precise my GPS phone is." I believe a more precise experiment would be: (1) measure the distance $D$ of a certain route as precisely as possible (perhaps 4km is not the best choice), (2) walk/run $n$ times this distance and record the observed distances in your GPS $(d_1,...,d_n)$, (3) analyse *for example* the values $D-d_j$ in order to assess the distribution of the errors. If you only have the output of the GPS, then there is not much information about how precise it is. Suppose that you are actually running 5 km, this could not be detected with only this data set.
Please don't crosspost to multiple [se] sites. See [so] http://stackoverflow.com/q/21266984/429846
is right about the assumptions, but there are variations of the t-test that relax the assumption about equal variances (or standard deviations). SAS gives the Satterthwaite approximations for such cases.
"*Since the data is not normally districted if I use ttest I will not get any significant value*" -- how does the argument behind this assertion go? I'd say something else (that the t-test may be unsuitable for what sounds like 0-1 data, but you should clarify what the response actually consists of). If it is 0-1, the Mann-Whitney may be *less* suitable even than the t-test. I'd think you would probably look at a model suitable for Bernoulli responses.
@ttnphns:  your request for a mathematical, rather than an intuitive, answer seems incompatible with your request for something less technical than what people have offered.
Actually, we no longer think fitting against all points instead of the average is better for us since we *want* to fit the average runtime (in order to guess the expected runtime). It's still an interesting question.
Glen_b's reference sounds promising, but the Hölder inequality lets you relate the sum of Weibull random variables with shape $k \\gt 1$ to the sum of exponential random variables, for which you have exponential bounds http://stats.stackexchange.com/questions/4816/what-are-the-sharpest-known-tail-bounds-for-chi-k2-distributed-variables. For $k \\lt 1$, you can't get exponential bounds because the tail of each term decays too slowly.
Thanks for the corrections, Glen ! :-)
Welcome to the site, @VictoriaGeelong. I have taken the liberty of editing your question to use $\\LaTeX$. Please make sure it still says what you want it to.
"*why does one report statistical power only when results are non significant?*" -- that's a new one on me. You would do the calculation before you know whether the result is significant.  Who says to leave it out if the result is significant?
I vote all one dependent variable with a random intercept by person, a time independent variable possibly as a random slope (with the possibility of higher order time IVs, e.g., time^2) and any other observation or person level predictors. If you lack person level predictors, a fixed effect type of approach may be better.
So, what's the question? Instead of posting your reasoning formulate the problem in an understandable way and then you'll get the answer.
To cover the case when $j=k$, you may add a Kronecker delta to Macros's answer: $\\mathrm{Cov}[Y_{ij},Y_{ik}]=\\sigma^2_u+\\sigma^2_e\\,\\delta_{jk}$.
My intuition is that it is more prone to overfitting than a random forest. However, the algorithm is designed to avoid overfitting, and it usually doesn't seem to be a problem.  I have no references to back this up, but you can use the `caret` package to cross-validate adaboost, and I've found that it usually generalizes well.
This is not a simple question. Have you derived the conditional density by the Jacobian formula?
Thanks! What is the definition of "high-order of co-occurrences"?
Thank you very much.I get it
I don't think you would divide by 7 unless you were trying to make the series a lot smaller. Consider what happens to a long series of all-1's after you apply the filter.
ok, now I really looked at your data. The above still applies: in lme*, DO NOT address variables by `data$var`. Also from your data it seems that `choicenum` is nested within `ipnum` and not the other way around (i.e. for every `ipnum` there are several `choicenums`). Definitely you have to read some more basic information on lme*, like the Gelman & Hill book or similar.
Welcome to the site. You can read the [FAQ here](http://stats.stackexchange.com/faq). What do you mean exactly by "redondant"? If your independent variable and your interaction term always have the same value, you can't put the two of them in a model, because they provide exactly the same information, so any modern statistical package is going to exclude one of the variable from the estimation. But maybe I am misunderstanding what you mean by redondant, in which case you will need to develop your question a bit to make it possible to answer it.
@ttnphns has provided an outstanding explanation below (no surprise there, this seems to be his specialty). For a simple example of a situation where you can get a singular data matrix, it might help to read my answer here: [qualitative-variable-coding-in-regression-leads-to-singularities](http://stats.stackexchange.com/questions/70699//70700#70700).
@chl: Yes, I do, if needed, I could forward it by mail...
1. You can't prove a negative. 2. It can be *disproven* and has been by @heropup's counterexample.
I'm using only 4 features. All of them are continuous. Actually even if I use a single feature (size of the house) knn still outperforms others significantly.
Could you give some arguments for this claim?
(+1) Welcome to the site, which I'm glad you've found. I look forward to your participation here.
I've added some code to my previous post.
Can you provide more information on your design: what are 'concepts', how many subjects, context, etc. Also, shall we assume that this is standard 'metric'-scaling (equivalent to PCA for the first two PCs), or did you rely on non-metric multidimensional scaling?
+1. Did not know about this Formula feature. Thanks
Thank you!  I just couldn't figure out how to change the lm command.
Cross-posted at http://stackoverflow.com/questions/14007771/kernel-svm-primal-with-stochastic-gradient-descent
What exactly do you want confidence intervals for? For the estimate of the mean of your data? What do you want to be able to test about 'some experimental data you have'?
The part where it says "change relative to a 23% increase in prices" is key.  It means that the zero line is a 23% increase, a 100% is a 2*23%=46% increase and -100% is a -23% change in price.
Surely that would just be the same test, but in reverse? If you have another specific distribution in mind, you could test for fit to that.
I was just mentioning parameters that can be estimated by bootstrap.  The bootstrap is used for medians but not often for means since the central limit theorem usually applies to the sample mean.
Thanks for the insight and references! The Gary King piece is indeed very interesting though not directly on topic.
Note that when $k$ & $N$ are the same, comparing the AIC & BIC amount to simply comparing the deviances (which is valid in this case).
Your post is on a border of quotation abuse; please give attribution to the book author.
This seems well-suited to multiple imputation.  There are plenty of textbooks on it, I find Andrew Gelman's [chapter][1] on it to be the most readable.  

I'd reply as an answer, but I'm not immediately sure how one would use an MI framework to combine multiple variance-covariance matrices in a logistic regression setting.


www.stat.columbia.edu/~gelman/arm/missing.pdf
(+1) Well done.  I hadn't even thought to approach this question from that direction.
What the OP didn't say, unfortunately, is that the kind of model he is interested in is generally of the form $f(\\text{response}\\mid\\text{trait level},\\text{group})=\\beta_0+\\beta_1\\cdot\\text{trait level}+\\beta_2\\cdot\\text{group}$, meaning that response is conditioned on individual trait level (usually, assumed to be a latent variable). This amounts to consider a mixed-effect logistic regression, as our (psychometric) measurement model. An alternative approach is to use raw scores stratified by levels of total score, which amounts to rely on some variant of the Mantel-Haenszel test.
I'm currently in the process of reading An introduction to statistical learning, by James et al. According to that book, choosing K has an impact the flexibility of the model. The larger the K, the model becomes less flexible. The opposite is true as well, smaller the K the model is needlessly more flexible. The recommended approach from that book is cross validation.
Please could you add more details?  Explain what S and E and x etc. are in your statements and put the problem into context (statistics is a big field, so what is standard in your area may not be familiar to everyone)
"Var" is a standard abbreviation for the [variance](http://en.wikipedia.org/wiki/Variance).  In the notation of the second link, "E" stands for the arithmetic mean and "Cov" stands for the [covariance](http://stats.stackexchange.com/questions/29713). You don't say you have covariances, **which is the crux of the matter**: if your data are correlated from one time to the other, this should affect the SEs of the ratios as shown in the formulas.  And yes, percentage changes are just ratios multiplied by 100.
Naive bayes classifiers are often horribly uncalibrated, they tend to make very extreme predictions, even though they are simple/useful as a first or baseline implementation for a classification task.  It's possibly you've messed something up, but I wouldn't be too worried about a naive bayes classifier sometimes being horribly off.  What does your overall accuracy look like?
We still don't know enough detail about the experimental set-up. What was the stimulus exactly (e.g. number of pixels presented in 300ms on a computer screen, pressure applied to a subject's hand in mmHg, windspeed of a puff of air blown onto the subject's eyeball)? What question was the subject asked in order to produce the response, and how was this measured (e.g. visual analog scale of pain intensity, a number they clicked on a computer screen). I believe that is what @chl meant by "describe very precisely" in his comment above.
@Maurizio, for some reason, I missed this post until now. Is there anyway you can edit it and add a plot similar to the last one in my post, but using your observed data? That might help diagnose the problem. I think I saw another question of yours where you were having trouble producing a uniform distribution, so maybe that is carrying over into these analyses as well. (?) Regards.
Is your restricted model nested within your unrestricted model?
Please explain the downvotes: this should be part of good culture here...
Your question is a little confusing to me. Is missingness equal to the response with an "I don't know" answer? Are the percentages in your question from the figures? Subject specific estimate is at individual level, and population average estimate is at population level, so you cannot interpret both with "average" in the second paragraph. According to your cause, the population average estimate fits your need.
Have you plotted your data? Extreme percentiles are necessarily sensitive to the details of the tails of the data.
Thanks (+1). You can achieve some notational simplification by re-expressing the covariates $(x,y)$ in terms of $\\xi=\\log(\\rho)/\\rho^2$ and $\\eta=\\rho^{-3}$ where $\\rho^2 = x^2+y^2$. It then becomes a weirdly-parameterized linear regression model with coefficients $c^3/(1+\\log(c))$ and $5.3/r^2$.
Dear whuber, thanks for your answer. In the example you posted it is possible to track a single subject through time. In my case this is not possible: in order to acquire a number I need to dissect the fly (killing it), so at the next timeline I have to dissect another fly from the same population and acquire new data. This sort of remind me of the difference between a One sample T-test and a paired T-test. I will now edit my original post adding more info on the experiment, maybe that can help
I have regression problem, not classification. Before using eps-svm I'm made such transform A = log(A). After prediction - inverse transform for predicted values. As result better MAPE and a bit worse MSE. Your example is still relevant in this case? I can't figure it out.
@Miroslav For some reasons the stackexchange servers discarded my answer (and put only temporary edit). Fortunately I have backups, so here it is.
@furtivefelon, you can always port the code from R, o figure out the algorithm from the code and reimplement it.
@Matt Dropping a zero is usually not a good idea unless the dataset is so large that losing an extreme value will make no difference in the analysis.  Your implicit suggestion of treating it as very small is good, but then the result can be sensitive to the value chosen.  One could check for that in various ways.  A more formal approach is to treat the zero as a left-censored value (censored perhaps at the second lowest value) and use methods that handle censored or interval-valued data (which includes ML).
Hi @FrankHarrell I think I can find my way provided that you can give me some nice hints!
I do not think that really answers the question, which is about SPSS capabilities to run GLM and mixed-effect models, and how it handles missing values. Was this intended to be a comment instead? Otherwise, please clarify.
@AndyMcKenzie: Good idea. Although, you should probably move the note from the lede to the body of the article since the lede should have the highest level information rather than details.
The model appears self-contradictory. Written more explicitly, it makes an assertion about the random variable $\\epsilon$: $f_1(x-\\epsilon)=\\beta f_2(x-\\epsilon)+\\alpha$. In general, given $f_1$ and $f_2$, there will not exist any constants $\\alpha$ and $\\beta$ that can make this true as statement about random variables unless $f_1$ and $f_2$ already enjoy such a functional relationship for all real numbers--in which case you scarcely need to make any observations at all, because you can just solve for $\\alpha$ and $\\beta$! What am I misunderstanding?
You can apply the answer at http://stats.stackexchange.com/questions/1444/how-should-i-transform-non-negative-data-including-zeros/1795#1795, *mutatis mutandis,* to this situation.
(+1) nice spider fish anecdote!
I'd say most published articles using ERGM models either used the Statnet package, or used PNet. I don't even know of other well developed packages for ERGM (of course, that doesn't imply there aren't any!). If you're estimating parameters, I don't see how you could simply supply a few statistics and expect useful parameters in return. However, you could specify some parameters yourself and simulate graphs which can be used for study. Maybe I misunderstand your circumstances and goal, though.
G[4:6] is rows 4:6 *and* columns 4:6 of G?  It is up to you to fix G; you could endow your system with a random walk dynamics, an AR dynamics or whatever. Nothing is wrong "a priori". You might want to print your matrices, rather than give the code, and state your assumptions, so other people can check whether assumptions and specification match.
In the spirit of [this](http://stats.stackexchange.com/questions/29121/intuitive-explanation-of-unit-root/29129#comment55107_29129), I wish you would have started this answer with "It is *plane* ..." (+1)
$P\\left(C\\right)$ represents the probability of picking either $C_1$ or $C_2$.
I tried to apply this method on my second scenario (1d3, sides 0,1,2) but I must be setting it up incorrectly. `e = 1 + 1/3 * 1e + 1/3 * 2e` which reduces to `e = 1 + e` which is putting me in jeopardy of breaking some mathematical laws. Anyone fix my blunder?
But the problem is that when you permute the labels, your  sampling under the null makes the population variances *equal* - you are simply sampling from a mixture of the two distributions for both samples. The whole point of a permutation test is to get an exact test, and this *doesn't* come close to doing that, because it throws out the conditioning on the different population variances - if the null were true but the population variances actually differed, you *cannot* reasonably treat the observed sample as a random sample from your permutation distribution.
This is one of the Kolmogorov axioms of probability: see "Second Axiom" at http://en.wikipedia.org/wiki/Probability_axioms.  If you want a "proof," please stipulate what alternative axiomatization you are using.
I´ll look into the matching package. My first analysis does control for the same factors as implemented in the match procedure in a multivariate linear regression model. Thx for the feedback..
Just an idea (probably what @gung had in mind): Couldn't you simply test your subsample(s) against the rest of the data (instead of the whole data set)? Then you would be back to a simple 2xN contingency  table.
The integral does not converge: presumably you intend for the argument of $\\exp$ to be negated.
Please use dput() to share your example data.