tttjjj/elt_bench huggingface dataset:
It's not 100% relevant, so I'm not posting this as an answer. But, J. P. A. Ioannidis, [Why most published research findings are false](http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0020124), *PLoS Med* **2** (8): e124, 2005. is worth a read.
What exactly is stumping you, merging the datasets or how to set up the analysis after the merge, or...?
But not all my classifiers are binary.
Then you need to clarify more, what you want to do and why you need this. Do you want to automatize this for thousands of different datasets? Do you want to find clusters of datasets? Or someone wants this number from you regardless of the reasonableness?
Thanks Drew and userZ/6. I'd suspected something like that.
+1, But I'm not convinced.  What's special about 10,000 is that it exactly equals the expected number of heads under the hypothesis that the coin is fair.  This fact is independent of any psychology or system of number representation.  The analysis in this response might provide some insight into a situation where, say, 20,005 coins were flipped and 10,000 heads (and therefore 10,005 tails) were noted and somebody's "intuition" suggested fakery took place.
What connection is there between the "correct" answer and these frequencies?
In this example the distinction needs to be made what losses mean. If the correlation is negative and if the market moves up, it is reasonable to expect losses - in a relative sense. You'd do better with the market PF and your returns would diminish, but that they become negative. Maybe it would be easier to view this as some sort of movement. Remember that the covariance is a measure of linear spread or divergence from a point (the mean), not a measure of what the absolute values will be!
Thanks! That was helpful.
Great, thank you. I've looked up the weights function and it seems that I can simply create a vector with the number of data points for each time point, and that will correctly weight them. Is that the best way to do this?
It's cool, but it looks to me like those all depend on having some additional information (prevalence) associated with each letter-letter pair.  Thus he's graphing 3 dimensions whereas we're mainly graphing 2....  I'd love to have the prevalence info for R, though.  But that's a data mining operation for another day.
The first thing is to learn when ROC curves are relevant/helpful.  I see them being used when it would be much better to use risk prediction.  They are not useful for determining a cutoff when a cutoff doesn't exist or when you are seeking a cutoff on an *independent* variable.  In general, ROC curves are at odds with individual decisionmaking.
I'll remember that answer, it's endlessly recyclable here. +1.
Neither. You would need an estimate of the standard error of the difference of the medians, and that is complicated. (It depends on the type of distribution, among other things.) Meanwhile, several nonparametric tests, among them the Mann-Whitney and the sign test, are very well suited for comparing locations of two distributions with the same shape.
The first Google hit for [spss fisher exact test](http://www.ats.ucla.edu/stat/spss/whatstat/whatstat.htm) says you need the "SPSS Exact Test Module".
This looks like standard bookwork. Please add the `self-study` tag and read its [tag wiki info](http://stats.stackexchange.com/tags/self-study/info)
Good! Just a few more followup questions. (1) What are these "point" frequencies? Are they counts or are they proportions? (2) What is the basis for selecting these particular 100 points: are they randomly selected? Were they chosen because some of them exhibited high frequencies? Something else? (3) Is there any relationship to be expected among the frequencies for adjacent points in the graph? (There appears to be strong serial correlation, as evidenced by the smoothness of the line.) (4) Does the *sequence* of points have any meaning?
(This reminds me of the legend about the only lock Houdini failed to pick: it was unlocked all the time!) In this case, your vectors $\\textbf 1 = (1,1,1,1,1)$, $\\textbf x=(-1/2,-1/4,0,1/4,1/2)$, and $\\textbf x^2=(1/4,1/16,0,1/16,1/4)$ are almost orthogonal already, as you can readily check, so there's not much left to do. You only need to make $\\textbf x^2$ orthogonal to $\\textbf 1$ by subtracting some multiple of $\\textbf 1$ from it.
Hopefully this clears things up?
But each of my time series is labelled (A,B,C) and I'm interested in using the motifs for predicting the labels not predicting the motifs. I'm trying to find rules involving the motifs and the labels
When you record temperature to the nearest degree, it can be considered discrete--and perhaps *has* to be so considered for certain forms of analysis. Also, in "regular" (OLS?) regression, $Y$ need not be continuous: many--and practically all its useful properties--apply to many types of discrete data, even binary responses.  What these points and counterpoints begin to suggest is that *data* are not necessarily discrete or continuous, but rather *statistical procedures* are discrete or continuous.
The valid ("physical") value for $p$ are within $[0,1]$, and estimating anything other than that is simple incorrect for the problem I am analyzing. More concretely, $p$ is a quantum expectation value of a positive operator, so it can never be negative, _i.e._ $p={\\mathrm{tr}}\\hat\\sigma\\hat\\rho$ where both $\\hat\\rho>0$ and $\\hat\\sigma>0$, and $\\hat\\rho^2=\\hat\\rho$.
Trivial observation to address the first question: when $Y=X$ the condition is satisfied for any non-negative $C$ and obviously $Y$ is normal.  **Suggestion**: draw pictures of the situation when $X$ is univariate.  They will make it easy to see that the answers to the questions are "Yes," "A little," and "A little" (in the sense of some bounds).
It's not clear what you mean by "9 questions in 3 different blocks" but the default choices for categorical response data are multinomial logistic or ordinal logistic. More information would be helpful.
I doubt fake electronic players exist but I think the idea that you might want to make your site more attractive by "adding some beginners luck" is harder to reject.
@Anony-Mousse For large sets, sampling with replacement can be computationally expensive (think all the shingles in all of the documents you've cataloged).  Also, for small `k`, the estimated error rate can skyrocket (e.g. `k = 5` then the estimated error rate is 0.4472).  You need fairly large values of `k` in order to get a respectable estimated error rate.
The idea of graphical depictions is good. By limiting your suggestions to the *univariate* distributions, though, you ignore all information that might be available from the relationship between CPU and RAM. There is also an outstanding question, as asked in a comment, concerning what a "pattern" might be: it could very well include the implicit *time* dimension in these data, too, which is also is ignored in these univariate graphics.
I would be more concerned about the apparent large deviation from 2.5 to 8 for a single thread.  Was this due to some other factor that isn't code dependent, e.g. memory dump or another program using the core at the same time?
I think if you give us more content (i.e., what do the groups mean) you will get better advice on what to do.
I'm playing around with the idea an index of 'stability' or 'solidity' of experimental evidence that might be based on how susceptible a result is to change upon the addition of an extra datum.
Thanks for your answer. I would like to share my dataset and script for analysis including output to see if what I did is indeed correct. Is it possible in stack exchange? Furthermore I think I ran the wrong random factor (1|rat/task), shouldn't it just be (1|rat)? I tested 60rats (30 of each sex) on three tasks.
Fixed.  Because the β^1 component of the regression parameter as a linear combination of the ys the xs appear as they do in the formula.  This is maybe handwavy rather than explicit but right nontheless.
I share your frustration about the vagueness of the question - it's just that the dataset is quite complex and I didn't want the details to detract form the general point. The data are sequence counts coming out of a slightly unconventional genomic analysis. Classically next-gen sequencing data are treated as Poisson or negative binomial, but this doesn't seem to be appropriate in my case.
Was going to write a comment response, but it got long. Going to put in the question now.
I meant independent variable, sorry for the confusion
This is not a "problem" and does not need to be "solved." As you already noted yourself, this apparent multicollinearity is a natural consequence of using dummy codes. If you use non-orthogonal codes, you get non-orthogonal parameter estimates. My advice: ignore it.
This is answerable generically, but to get the actual number you want you'll also need some estimate of how different is the alternative population the sample might have come from.  If you're hypothesising just one point out of population A is not in  population B but the other points all overlap you will need a very large number of draws from your sample (probably more than in the actual sample), whereas if populations A and B have no overlap at all you only need one draw.  So the actual answer will be a function of population size, sample size, and degree of overlap of populations.
@Daniel: Don't think you're missing a constant, but scaling might help.  Think your intuition about normality is correct though.  I updated my answer with some suggestions.
added R tag for appropriate syntax highlighting..
The above code doesn't display any grid or boundary as requested, or did I miss something (I've tested the code, btw)? Anyway, shouldn't this be put as a [new question](http://stats.stackexchange.com/questions/ask)? Cf. your last sentence.
@PatrickCoulombe I have research level work that uses the inverse uniform and it would be nice to have few lines in the introduction motivating its use as many are not familiar with it. I was hoping applied folks here would have many examples. I wish it was for homework: will assign it to my students next semester.
I have plotted the data and it's constantly trending. Residuals have been checked. Only the value of alpha I get is 1. I thought it should be less than 1.
As repeated measures anova is simply a special case of a factorial design, it should be possible to implement. However, as far as I can tell, the coding in matlab linked to above only works for balanced cases of 2x2 - so it might need some jiggery pokery to expand it to a repeated measures design, which is essentially a 3-way ANOVA.
I'm trying to do similar, using R. Its brain stretching!
Any intuition? yes. And your approach could work in general. However, in this case we can not have a lot of memory/computation. It is in a networking application where the device could see tens of thousands of items per second, and have VERY little processing left over for this purpose. Not the ideal/typical scenario, I know, but that is what makes it interesting!
Not sure I understand the question.  None of the methods you mention care whether your *inputs* are categorical or real valued.
Why would you consider 83.4% CIs?
There are $B$ sets of rules, each of which contributes to the final predicted values of the bagged tree. Perhaps you can explain what you *really* want to do and the statistical issue. Questions simply about using R packages are considered OT here.
very interesting will read hothorn
This is really useful and i adapted my Naive-Bayes implementation to do the TWCNB as said in [1] but it was not successful, i think that the problem at the end is that the data are too much unbalanced so i may not achieve anything better using a single model trained with NaiveBayes.
Please read [the policy about crossposting to multiple sites](http://meta.stackexchange.com/q/64068/158524).
1) don't focus on R^2. 2) It's not clear from what you've said that your variables include everything likely to have a strong impact on the outcome. If you've left out something major your error term will be large. 3) Could you explain more clearly what you're trying to do?
Oh no. You beat me to it. I will delete my answer!
@whuber and david, thank you for your help. I would like to receive an assessment of strength of a (yes, potentially linear) relationship. 'best' in terms of dealing with a small sample, non-normal distribution, and few extremes (or outliers), what is not achieved with Pearson or Spearman. so I found something about winsorizing...but am not sure whether this is the correct path to go?
Large-sample behavior is one way to show that a given estimator works, or whatever else, in the limit of infinite data. You're right that it doesn't necessarily tell us anything about how good an estimator is in practice, but it's a first step: you'd be unlikely to want to use an estimator that's *not* asymptotically consistent (or whatever). The advantage of asymptotic analysis is that it's often easier to figure out than a finite-sample one.
$\\log$ is considered an elementary function, so I think your explanation leaves a little bit to be desired. *In fact*, the form that the antiderivative solution of $e^{-x^2}$ must take is a sum of functions that are compositions of $\\log$ with other elementary functions!
I think we will need more information to be helfpul.  For example, what sort of variable is "interval number"?  Is there only one observation for each interval number?  What is width?  What other variables might be relevant?  What if anything would we expect theoretically or from other grounds the relationship of the two to be?
The statement in your second sentence sounds exactly like a p-values. A correctly calculated p-value does take into account the number of experiments, but the question in your last sentence is apparently about something other than what a p-value measures; you'd have to specify a criterion for 'valid'. Please try to figure out more clearly what you want to ask.
T distributions are an approximation of the normal distribution for small sample (n<=30) therefore a sample size n>30 would be best dealt with a normal distribution. it sounds like you want to do a one tail test to see if the current scores are significantly different from a nominated threshold level. You need to nominate this threshold level (maybe 3.97) then test for an increase in customer satisfaction score
Because links can go dead, why not copy the gist of it here?
One method either approximates the PDFs as histograms with constant-width bins or computes a kernel density estimate (that is, it *rasterizes* the PDFs in one dimension) and applies the FFT directly to obtain the PDF of the convolution and finally accumulates that to create the CDF. Although there are some obvious shortcomings in some cases (e.g., extreme outliers can be wasteful), this method looks appealing for any medium to large datasets. The technique I just described is used routinely in two and three dimensions for spatial analysis.
There's not enough information here to work out if there's enough information in your question to do it.
Welcome to the site, Tan! I encourage you to read the FAQ, focusing in particular on how to ask effective questions here. Please update your post accordingly so that we can then try to help you. Cheers.
@user3526125 By the way, take a look at the formatting help on this site. It's much more readable to use `fixed-width code formatting` and $\\LaTeX^{math}_{formatting}$ for stuff like this.
@DimitriyV.Masterov, I have Matlab/R/Excel/Mathematica... you name it. Actually I am more interested in the concepts rather than writing the actual code itself
How do I obtain Var[N] with the logistic transformation?
You could try multiobjective search optimization approaches such as evolutionary/genetic algorithms.
@Glen_b Even as it is, your comments seem to form a useful answer to a valid question, you could perhaps just copy them in an answer so that what could accept it and thus mark the question as “answered”.
This question is hard to understand. Can you revise to indicate the following: what do you know, what do you want to know, why do you think that what you know will help you with what you don't know, and what have you done so far. An example wouldn't hurt either. Right now, your post is too general.
Actually, I posted only few data points from my dataset. In all I have about 3000 observations per category. And when I checked the density plots of my return for different they are almost normal (maximum skewness is 1.6812 and min skewness is -0.3592)
I will try to rectify the table. I am new to this. I am trying
But you can never figure out what $\\nu$ actually is.
The most basic concept of modern geostatistics, kriging (GLS-type prediction), comes from the name Krige, who was a mining engineer. Books like <a href="http://www.amazon.com/Statistics-Spatial-Data-Wiley-Probability/dp/0471002550">Cressie</a>, although heavy (and not only technically, but in hand, too) would help.
Instead of the variance (distance from mean), try Sum(t*t*n) for the second moment, and Sum(t*t*t*n).  I've added some code and a graph to clarify.
In relation to Joris' 2nd comment above, my Update 2, shows that the difference is due to `um` in Joris' code not including the identifiability constraints in the basis functions. If you include these constraints into the basis function, `smoothCon()` returns the same thing as `Xp`
I am really happy it was helpfull for you.
This seems to be a dodge.  The question is probing whether one appreciates the distinction between "parametric" and "non-parametric," whether or not it is clear-cut.  A good answer will illuminate that distinction, not confuse it.
See this related question http://stats.stackexchange.com/questions/13382/how-to-define-a-distribution-that-correlates-with-a-draw-from-another-distributio/13384#13384 which directly addresses your question (at least the theory side of it).
Are the models nested (i.e. are the models the same except for the one variable in the four variable model?)
Feature selection when the selection is unstable will always be a recipe for frustration and error. Using only one sample cuts down on the frustration, but increases the likelihood of error as it encourages you to draw inferences about the relevant features for the problem based on what works best on the particular sample you look at - which is a form of over-fitting.  The re-sampling gives you an idea of the uncertainty in the feature selection - which is often just as important.  In this case we shouldn't draw any strong conclusions about the relevant features as there isn't enough data.
(cont'd) Rather than being parabolic, you've got a "hockey stick" kind of shape, which could mean that something abruptly changed in 2003, or that something is being affected by censoring or truncation at the end of your data set. I can't give you better advice (although someone else probably could) without knowing what you want to do with this data.
Hi, welcome to the site.  If you could make your question more specific I bet it'd draw more helpful answers.  For example, do you want to ask how a specific display could be improved?  If you're not being allowed by the site to post an image, feel free to email it to me.
Does the **median rating** work?
@Glen_b From the definition, if we have n input, that requires the matrix of nxn $[k_{i,j}]_{i,j=1}^n$ is psd.
Why not just propose $\\hat{n}$=17 and $\\hat{p}=1/2$ no matter what, then? :-)  But you have a point: the question doesn't even specify *what* is to be estimated.  If we only need an estimator for $np$, then there's an obvious good one available.
@thebigdog: There are simulations and examples in the paper, so probably the author has some R code (the plots look like R ones). If I were you, I would contact him.
@Flask I am interested in the actual data. And the `dput()` of the data makes it the easiest to read it into R.
@heropup re them being the same aside from one not being order-preserving -- correct, except for the additional issue that most rngs are on $[0,1)$, and $\\ln 0$ - while only occurring very rarely - is problematic, while $\\ln(1-1^-)$ might not be a problem, depending on the precise situation with the particular RNG and how it's dealt with.  [Where here $1^-$ is the largest value output by the uniform RNG.]
(-1) for "Rapidminer is mostly frontend to Weka when it comes to algorithms". This is sooo wrong. Please take a look at Rapidminer itself or see [this interview](http://www.kdnuggets.com/news/2007/n24/5i.html)
I'd try rewriting this much more concisely. Try standing back from this highly specific question, which is very difficult to follow. Tell us what you are trying to do and why. It sounds as if you want to class or bin measured data into intervals. Why is that even needed?
Just to avoid copy/paste, `sink()` or `capture.output()` might be your friends. Reporting utilities, like [Hmisc](http://cran.r-project.org/web/packages/Hmisc/index.html), [Sweave](http://bit.ly/ljRv4I), or [brew](http://cran.r-project.org/web/packages/brew/index.html) are worth to consider (your point 1). Versioning systems ([rcs](http://www.gnu.org/software/rcs/), [svn](http://subversion.apache.org/), or [git](http://git-scm.com/)) might help with point 2.
But the test here is **not** comparing means.
@gung: I (respectfully) up-voted to save the question for what, I think, is premature deletion: I think this O.P. is essentially searching for a statistical answer (difference between univariate and conditional models). I don't think the question will get *any* answer in S.O. and I think it could even get interesting/pedagogical ones here. I agree the question could be re-formulated to evince its statistical content, but I can also see how that could be hard for the OP (if my assumption is correct that the OP is missing part of the statistical content of the question).
[This](http://stats.stackexchange.com/questions/52585/what-happens-when-i-include-a-squared-variable-in-my-regression/52654#52654) may be useful to you.
@Scott, I have tried to give more details, but at the end of the day, for logistic regression, I see no need to center.
+1 for the pun.
"Independence" in this context refers to independence *in probability,* not in terms of some historical connection: the mean of the data from process $Y$ is indeed independent of the mean of the data from process $X$. Provided you have enough data or that the variation is approximately Gaussian, the sampling distributions of the means are for all reasonable purposes Normal: then http://stats.stackexchange.com/questions/16349/how-to-compute-the-confidence-interval-of-the-ratio-of-two-normal-means answers your question.
I am familiar with some of the books and for the ones I know I think Procrastinator has identified books that fit the criteria.  But Robert Kubrick what do you expect of us.  This is a difficult question and we serve you best by giving you a good list.  in the end you have personal choices to make and we don't know you well enough to pick for you.  We are not saying buy every book on the list .  But you can go to amazon and take a peek inside.  Read the customer reviews and the publisher descriptions.
Related to your questions, but not exactly answering them, sometimes it is useful to consider the method of moments through functions of the observations. For example, in *exponential families*, the maximum likelihood estimators are method of moments estimators *through* the sufficient statistics. For example, if $T_1(X),\\ldots,T_p(X)$ are the sufficient statistics, than the MLEs are found by solving the system of $p$ equations: $n^{-1} \\sum_j T_i(X_j) = \\mathbb E T_i(X)$.
@whuber I would vote against CW because even if there are different perspectives there is likely to be one *best* approach. This is similar to how the same hypothesis can be tested using different frameworks/models but there is likely to be one best approach.