tttjjj/elt_bench huggingface dataset:
Also, I can tell with the naked eye that it's shaved, if I compare it to another die of the same size.
Package `coin` provides the function `spearman_test()` which implements a permutation test for Spearman's $\\rho$. It also provides additional information about the permutation distribution, see `?support`.
You can do this for individual subjects, but frankly the sample size of 3 measurements would strike many statistical people as too small. You can do much more if you pool the subjects.
@Peter: That's true, but there is no essential difference in this case and this is discussed in the answer there. There was no option to close as an *essentially* exact duplicate. :-)
Can you give the reference of the paper you mention?
+1, eminently sensible answer! I'd add another item to your list of spreadsheet fields: nature of intervention.  In the two examples given, the interventions differ (clopidogrel vs. clopidogrel plus aspirin).  Esther, I'd advise thinking carefully about how heterogeneous the interventions are across your studies - is it appropriate to treat them all as the same thing?
@DilipSarwate I moved my response up again, because it needed some additional fixes ... and then got too long.
@Thias is correct, but it appears that first one series should be resampled so they have common intervals.
Thanks, Greg! I will check out `outliers`.
Consider A & D to be biological replicates and D & F to be technical replicates.
At first blush, to get something meaningful, I think you will need to put further restrictions of the function $\\sigma$. Differentiability is not much of a constraint, and the function can be essentially arbitrarily wild. (Can you construct a differentiable function $\\sigma$ such that $|\\sigma(t_i)| > B$ for all $t_i$ and any fixed $B$? Alternatively, think about the implications of [Stone--Weierstrass](http://en.wikipedia.org/wiki/Stone%E2%80%93Weierstrass_theorem) to the problem as stated.)
Note that it might be better to evaluate the quality of a set of abilities based on their total point buy score, rather than their sum. The annoying thing about that is that you can't point-buy a score below 7, so it's not always well-defined....
This is awesome, I cannot thank you enough! My one upvote feels so puny. Optimizing m was my next step if it was in fact possible to do, so thanks for the additional guidance on that as well.
How do you conclude these values are inconsistent with normality? Your sample size is *ten*; there will usually  be lumpiness and curvature in the Q-Q plot. I just now looked at ten Q-Q plots of simulated normals at that sample size and three of the ten looked less normal than that. (I don't think your values *are* normal - they never are with real data - I just think you have no basis in that plot on which to think that they aren't)
(+1) very useful. The goal is prediction. I wanted to hard code in the linear relationship, unpenalized, because I thought it would help prediction since general nonparametric methods wouldn't "look for" this.
I've updated your question to incorporate simple slopes and I've updated my answer to incorporate simple slopes.
NB: Although I pointed to a similar-looking question, there is a basic difference: in this one, a "subsequence" need not be *contiguous*. This becomes apparent in the examples: *e.g.*, 4568 is not a contiguous subsequence of 4521368, but the digits 4568 do appear within the latter in that order (separated in the middle by the string 213). I do find it a little strange that subsequences would be referred to as "patterns," though. I guess I tend to think of a "pattern" as being some kind of coherent local feature. This (nonlocal) sense of pattern might not be amenable to statistical methods.
Perhaps  http://getthedata.org/  might yield some useful suggestions. It's specifically designed to answer questions about where to locate datasets.
Absolutely; although I would add that the recommendations in the above links could really apply to any statistical project (regardless of the language).
I believe you refer to my answer, but you do not quite correctly characterize it. It suggests *re-expressing* the data and redrawing the boxplot based on the re-expressed data. That means that you write down the *logarithms* of the data and proceed with the usual computations *based on the logs*. Although the medians and hinges will be the logs of the original medians and hinges, the step (which determines the fences) will change. That is different than merely drawing the original boxplot on a logarithmic scale.
No, that only follows from that particular form of the joint distribution you mentioned. I suggest you edit your question to make it more clear.
"*I have read about the importance of checking normality for this type of analysis*" -- really?  I'm curious to discover why this unnamed source feels is it important to check *normality* for a one-sided interval on a quantile, and further, why a hypothesis test in particular was thought to be of value for that purpose. (In any case, your data are quite plainly non-normal.) Do you recall where you saw this advice?
Maybe it would be a good idea to add more detail to your question. Explain clearly what it is you're planning on doing w.r.t the bootstrapping - I for one am confused.
Maybe you could start by writing down the expressions that gave the 0.35 and 0.40.
First, I'm sure that topicmodels is an excellent package, and I agree that it is very well documented. Regarding "trusting" the authors of the LDA package, Jonathan Chang was my PhD student, and I completely trust his code. He is both an excellent software engineer and scholar. His package is easy, scales well to fairly large collections, and it is nice to explore the results in R. (I like and I'm used to the sparse matrix representation of documents.) Of note, LDA implements other models, including relational topic models, supervised LDA (with GLM), and mixed-membership stochastic blockmodel.
@G_H Ah, yes. I see what you're saying. That's absolutely correct.
I think the OP is after some kind of *treatment interference* (non-interference is defined as "The effects of actions of each drug in the combination should be as good as or superior to its actions when used alone", e.g. FDA guidelines http://bit.ly/dIxYQH).
and http://stats.stackexchange.com/questions/66304/how-do-soms-reduce-dimensionality-of-data
(+1) It might help to recognize that the volume of the $K-1$-simplex (the support of this distribution) is $\\Gamma(K)$. With $K=1000$, the (natural) *logarithm* of that volume is $-5905.22$.  Accordingly, a *typical* log of any continuous PDF defined on that simplex should be around $6000$.
Nice follow-up. I didn't catch that your question was only about static graphs.
Thanks for your answer, it is indeed something basic, however this is not a typical way of using splines.  It simply works like this:     ns(x<-1:256, df=12)
After thinking through this for a while, you've convinced me that this approach is the best we can do if we want to stay nonparametric. I'm still also playing with the idea of fitting distributions to each subpopulation and then creating a mixture distribution, but in the meantime this approach clearly shows the bounds of the problem. Thanks for the graphs and R snippets too.
Do some reading about generalized models and look at the `family` parameter of the `gam` function.
By $x$ & $y$ in the ROC curve, I gather you mean the *False Positive Rate* & the *True Positive Rate*, right?
By ADF, do you mean [Augmented Dickey–Fuller](https://en.wikipedia.org/wiki/Augmented_Dickey%E2%80%93Fuller_test)? What do you mean by EG?
I've seen _blunder_ for points that are impossible or implausible and are considered the result of errors in measurement or recording.  I wouldn't agree with @user603 that outliers are points you do not want to model; to the contrary, one often wants a model on which they do not seem outliers. The simplest and most common example is that outliers can look acceptable on log scale. But you could always invert that by saying that if they look acceptable, they are not really outliers.
Thanks for the suggestion ACD! Actually, I would like to identify which countries have the same characteristics. So I have info for around 30 countries, and I would like to group these countries by similar characteristics. Not sure if this helps understand the problem. Thanks again!
The variation of the "Chinese baby" joke I've heard had the expectant mother being afraid that her baby might be considered as an illegal alien and thus deported...
Without some indication of quite what you intend by 'really useful' this is going to be tricky to answer.
"One line per individual" is sort-of-correct in an introductory course. More precisely, it should be "one line per observation, and don't forget to model dependencies between observations, such as multiple observations coming from the same individual." Note how this is a bit longer than the original suggestion... SPSS is certainly able to work with so-called *repeated measurements*. I suggest reading up on this topic (there are roughly a bazillion introductory hits on Google) and posting any specific remaining questions on StackExchange with the SPSS tag.
The lasso has not been demonstrated to find the "right" variables in many cases.  The reason for choosing an approach that yields parsimony instead of one that maximizes predictive accuracy (e.g., L2 norm instead of lasso's L1) is the belief that the parsimoneous solutions is "correct" in a certain sense.  If you run 5 samples with replacement of size $n$ from the original sample of size $n$ and the lasso finds roughly the same non-zero coefficients each time, you can have more faith that the search for parsimony is meaningful.
I think the meaning is exactly what I said.  At the three different times the time spent using the technologies changes significantly.  But it doesn't for the other variables (abuse and frequency of use).  That is all there is to interpret.  Regarding the importance of the interaction, it is important because it says that time spent using technologies will not have the same effect at different times.
Yes, if someone wants to be somewhat ungrateful to Bernoulli, they can note that the WLLN is essentially a straightforward application of Chebyshev's inequality as long as all $X_i \\in L_2$. Then, one sees that as long as $\\mathrm{Var}(S_n) = o(n^2)$, everything works out. This doesn't even require the means or variances of the $X_i$ to be constant if we interpret the relevant statement of interest as $\\bar X_n - \\bar{\\mu}_n \\to 0$ in probability. Of course, more general forms of even the WLLN exist. (+1, by the way.)
I think its important to note that both estimators are biased estimators, even if you divide it by n-h.
As a first stab, I would guess you should normalize the vectors you are projecting onto, otherwise their magnitude could screw things up.
you have 4 observations and 5 variables....you'll notice that nothing is really working in the regression above: you just have too few observations.
@Macro, apologies, I want to predict future values of the first vector, the stock volume.
@Abe - Ah, I get it now.  Y is distributed as an inverted beta distribution, with parameters (2,1), sometimes known as the beta-prime distribution and sometimes as the inverted beta 2 distribution (in those cases, "inverted beta" refers to the dist'n of 1/X).  A good general reference is Johnson, Kotz, and Balakrishnan, "Continuous Univariate Distributions" Vol. 2.  The R package GB2 has the d,p,q,r functions for the beta prime as a special case of the generalized beta (which is what it really has in it); the documentation will make clear what parameters need to be set to what values.
I am not sure that it is possible to come up with such an interpretation. Simply because what you provided are images in the original space of features and responses. And penalized regression involves the space of coefficients, which is very different.
I think you need to give a bit more detail about what 'appearance' means here.
The K-W test (like the Wilcoxon-Mann-Whitney test) does not test medians.  It tests concordance probabilities or Hodges-Lehmann locations (like pseudomedians).  See http://en.wikipedia.org/wiki/Mann-Whitney_U.  These tests are special cases of the proportional odds ordinal logistic model, which allows for various generalizations, including multiway layouts and covariate adjustment.
:) Thanks. As I look back I comment, it's clear.
@David, oh, it looks like I've misunderstood your problem. I'll post the fix later.
Improv is the $y$, PriorImp is the $x$. RDExp is a factor (nominal). The other one is actually a replicate number but I have treated it kind of like it was a factor in the second plot. As for what the data measure, I have no clue. The fourth plot (lower right) combines the two plots on the left. Don't worry about it - just try to comprehend the general idea of doing a separate y vs x for each level of a factor (that's called a coplot). The second plot is called an interaction plot. If you gave some sample data, someone might be able to generate one for you.
Thank a lot for your answer Henry.
More specifically, what is a pivotal quantity?
Although your calculations look correct to me, independence of increments $X_{t+1}-X_{t}$ and $X_t-X_{t-1}$ is a common assumption in the construction of [many sorts](http://en.wikipedia.org/wiki/L%C3%A9vy_process#Independent_increments) of stochastic processes. This assumption is common in finance for instance, where flexible distributions are typically used for modelling log-returns ([see](http://web.econ.ku.dk/fru/conference/Programme/Sunday/F4/Aas_226.pdf)).
Certainly, under the whole set of assumptions. Added it to complete.
@Procrastinator Thank you very much for your help!
0.04993 < 0.05, so it's just lower. Your instinct is good that no P-value can be trusted to several decimal places, but if the program says less than 0.05, people generally take it as delivered. The real issue here is making a fetish of fixed-level significance testing so that < 0.05 means "real", "publishable",  "cause for happiness" and the opposite means "illusory", "not publishable", "cause for misery". Most good introductory texts on statistics discuss this to some extent. One good one is Freedman, Pisani, Purves, _Statistics_. New York: W.W. Norton, any edition.
You write: "...when I am not given a particular density function. ...". Is this homework, since you say "not given"? To compute a likelihood ratio test, you need a model. What is your model? If your model is "non-parametric", you could read http://en.wikipedia.org/wiki/Empirical_likelihood
Yeah, I actually feared optimization because of the reasons you mentioned, but it indeed looks like it's working quite well. I'll try this on my real data later. Thank you!
I this kjetil is talking abt sign of x1 only...coz you can see x1 as a component of vector x. So when u switch you actually switch the sign of x1 only.
What about this? Stack up all the X and Y data as a single vector. Create predictor P1 that is -1 for X values and +1 for Y values. Create predictor P2 that is -1 for X1/Y1 values and +1 for X2/Y2 values. Do a regression including a constant, P1, P2, and P1*P2. See if the interaction is significant.
please give exact descriptions for $\\mathcal C$ and $\\mathcal C^c$ ... etc. It would prevent me from guessing. **NO**, not all LRT are UMP. Most of the times LRT are UMP same, and this is true when MLR holds (that's why MLR property is an important one!). A very typical example, two-sided two sample t-test ($H_0: \\Delta \\mu=0 \\text{ vs } H_1: \\Delta \\mu\\neq0$).
Two ideas:
1) Code them as don't know and then use the average (of nonmissing) rather than the sum

2) If you are using this sum for something else (e.g. as part of a regression) try multiple imputation.
Do you *only* want to predict the rank of something, or do you want to ultimately predict the odds of, say, each horse winning, & see getting the ranks as a step along the way?
The OP did add a tag "regression", but there is no need to focus on linear regression. It's true for any kind of model that you want predicted values to be close to actual, assuming that they are measured in the same units.
I think you're right though I'm having trouble understanding your notation - dp for variance looks odd and adding a variance to an estimate is wrong on purely dimensional grounds.
Leaves are not related to reducibility. Here, if there are leaves, the chain is **periodic**. Note that periodicity would not change the fact that the proportion of time spent in each state converges, hence the addition of loops is (slightly artificial, if you ask me, and) not needed to solve the question.
Exactly why won't random sampling work?  It satisfies all your criteria.
(didn't get a notifier from SA of your comment) Well, i wasn't referring to any papers when i wrote that, rather just informally summing pieces of my experience relevant to your Question. I'll look through my files and see what i have that is relevant though.
It's quite possible for the effect of task is significant in the treatment group but non-significant in the control group when there is *zero* interaction. (Consider, for example, the case where the control group is smaller.) You might get some value from [this article](http://www.stat.columbia.edu/~gelman/research/published/signif4.pdf).
I assume that for the case where we are comparing to a known good file (data set), the known good would be declared the mean from the start.  For the case where we don't have a known good file, then I would get the mean of each data point across all files and then compare each file to that mean file?

We will probably need to experiment a bit to figure out what the best threshold values would be.  But that is a matter of lab experiments once we get the code in place.

Thanks a bunch for your help,
Bill
Agreed and thanks for the ref to Schervish. However I attend biomedical informatics seminars (UCSD) where p-values are often the only quantitative results presented. My background is in engineering where even in stochastic contexts, p-values are basically unheard of.  Similarly significance and p-values are absent from the majority of books in the QA-QD section of the library. So I agreed that sensible results should be based on measurements whose units are universally understood.
+1 First, of course, you have to fit a variogram model to the empirical variogram shown.  Note that `geoR` easily performs simulations too.
(1)I don't get why you say "including an interaction without the main effects". For the three models i built, I never include an interaction without the main effects. The model without time is without interaction terms as well. (2) the design is longitudinal because it is on a course, and we did measurements 4 times during a semester, but our main aim is not to examine the effect of time, but to test the effect of the variable "sync". You think it also make sense to do "quality ~  sync  + (1|group.name/student.id) + (1|session.week)", where the time variable is included as a random effect?
Why not call it a trapezoidal distribution? That's the shape we see.
Are you comfortable with the following ideas: All items are treated on their own. You want to estimate discrete changes of the people on the latent scale over time for each item plus a general trend. Perhaps you even have groups? If you can also assert that all changes on items/persons are independent of each other and there is no unobserved heterogeneity, there may be a way with linear logistic models with relaxed assumptions, see here: http://statmath.wu-wien.ac.at/~hatz/psychometrics/11s/RM_handouts_11.pdf
What is a "[CRF](http://en.wikipedia.org/wiki/CRF)"? Critical repetition frequency?  Conditional random field?  Cardiovascular risk factor?  Capital recovery factor?  Case report form?  Etc... .  What is the reason for creating "binary features"?
Editing the question so that now stimuli is also a within variable.
Is this for some subject? If so, please add the `self-study` tag. ("Is there a bug in this code" questions are generally off topic on CV.) Yes, if the constant term is in your model, it should appear in your design matrix.
When you say "decay", do you mean as a function of time (number of iterations) or as a function of network depth? I interpreted it as the latter. Where did you read about it? Maybe a little bit more context will help.
note that `logit` and `invlogit` are availabe in base R as `plogis`, `qlogis` ...
(+1) For a well-posed and interesting question. (Did you mean to call $X_i$ a dependent or independent variable?)
Firstly @Peter Flom, thank you so much for your comment. I am not sure that I understand you right ( likely due to my limited knowledge in stats).  I also think that I may not have given the full picture. 
 I am doing a retrospective review of patients in my clinic. 
They were treated with one of two treatment options A vs B. 
Kaplan Meier analysis for survival between the two  treatment options and the p value was 0.03. I did not expect this. I was only able to collect data on a few of the factors that others have identified as predictors of survival in other reviews.
The earth is an oblate spheroid. So I guess that depends on what degree of accuracy you care about..
Classification is usually not a good idea, but AUROC (AUC; $c$-index) is a good index of predictive discrimination for a binary logistic model.  It requires no arbitrary cutpoints.
It's not clear what data you have. Do you have multiple time points? If so, how many? And did the survey go to the same people each time?
Sorry, @caracal, I'm not R user. That's _why_ I'm asking it. Since you've used not original Pearson r but tetrachoric r you've lost straightforward linear-algebraic link between original binary data and the loading matrix. I imagine that in this case some special algo would be used (e.g. based on EM approach) in place of classic regression/Bartlett one. So does `psych` give its due to the fact that we were dealing with tetrachoric r, not usual r, when it computes factor scores, or doesn't?
and Bernd:  Ditto! Great acronym and I hope it catches on.
It *is* a *minor* point I am making :) ... but the language "test *for* equality" implies that we are looking for evidence to support the null, rather than looking for evidence to support the alternative. And yes my raising this is me tilting at the windmills of common (linguistic) laxity.
This question provides additional details for http://stats.stackexchange.com/questions/16287/assessing-error-of-a-spatial-interpolation-algorithm .  It would be best, then, to keep them the same.  You can edit your old question to include this new information.
As a Russian, I think it is an OK joke, I don't see why people get so pissed off about it. Smirnov (whichever way you want to spell it) is a very common last name (according to http://ru.wikipedia.org/wiki/%D0%A1%D0%BF%D0%B8%D1%81%D0%BE%D0%BA_%D0%BE%D0%B1%D1%89%D0%B5%D1%80%D1%83%D1%81%D1%81%D0%BA%D0%B8%D1%85_%D1%84%D0%B0%D0%BC%D0%B8%D0%BB%D0%B8%D0%B9, THE most common last name), so in a way it was unavoidable that there'd be a statistician by that name and a vodka producer by that name.
Now that is a great answer!
If you could disclose the purpose of the classification, Rachel, that would likely reveal an appropriate way to compare two classifications.  For instance, if the purpose is to estimate costs, then you should be comparing the precision of cost estimates made with the classifications.  By the way, what is this "variance" you refer to?  Variance of what?
Analogously, if asked "what is chemistry?" we could reply that it's a set of differential equations.  A description of the mathematical theory can give us a small idea of what a subject is about, but it is not the subject itself.
Are these the same statistical units that are measured at the two time points?
@Tivie, if your data are non-stationary, ignoring their time series data generating process will not change the fact that they are not i.i.d.. Of course, you could *test* for stationarity and if you find it proceed with some form of non-time series analysis. Also: if my answer works feel free to upvote and/or click the check-mark.