tttjjj/elt_bench huggingface dataset:
as for the answer to your question: it may be a little bit too vague.  The 'effect sizes' are the parameters of the linear mixed models that are fitted to the data. In your case the interaction term is the difference in the slope with respect to the continuous predictor (saying "factor" might be confusing to R users since a `factor` is a representation of a categorical predictor) between the two groups, i.e. (slope of group 2) - (slope of group 1).  The effect size tells you the magnitude of the effect, which should be interpretable in subject-area terms.
p-values are uniformly distributed under the null hypothesis, therefore they should not be used as a measure of the effectiveness of a treatment. The quantity $P(X<Y)$ is often used as a measure of effectiveness ([see](http://ba.stat.cmu.edu/journal/2011/vol06/issue03/ventura.pdf)). You might also consider estimating the differences of a quantity of interest, (e.g. the amount of contaminants in each sample, the difference of means, the ratio of means, ...) and give confidence intervals for this quantity in order to quantify a possible improvement.
Regardless of the algorithm used, I would take the *best* result because it cannot be any worse than the average and likely is better. As far as SA is concerned, one way to frame the question is in terms of computational effort: would it be better to expend a given amount of computing resources on a *single* SA run with a very slow cooling schedule or on multiple faster SA runs with fast cooling schedules?
You should explain in what way the linked post wasn't helpful. The likely reason that `coeftest` wasn't found would be that you didn't load the package that has that function in it. I presume that's the `coeftest` function in `lmtest`
Thank you, @gung! I still cannot grok reshape, but feeling that it is very powerful I'll have to look more closer at it.
I am relatively new to Stata, so i could be totally wrong on xtlogit not being able to handle binomial logit- if so let me know. Thanks
No, you cannot use just one coefficient. There is no meaningful way to do this. 

You need to look at summary() of your model. You might also want to look at the xlevels.
It is certainly wrong to group the data based on results without some thought as to why this is happening. That's called fishing. If you do that, all your results will be biased. 

You can only legitimately do something like that if you do it on a training set and then test it on a testing set. 

"If you torture the data long enough, it will confess to anything" - Ronald Coase
Hi Stats and welcome to the site! With an outcome taking only two values, 0 and 1, [logistic regression](http://www.ats.ucla.edu/stat/r/dae/logit.htm) is one possibility. Your outcome would be customer satisfaction and your other factors are the independent variables. How to do it concretely depends on your software. Just ask if you have further/more specific questions.
I discuss such a distinction in a reply (to an unrelated question) at http://stats.stackexchange.com/a/17148.
I investigated deeper the 'logistf' function, because it appears it contains the method the test weighted models terms significance. The coefficients I get with the 'logistf' function are very close from the coefficients I get with the 'glm' (when back transforming to odd ratios and plotting, i get a x=y line).
It seems to me the first component will merely point out a strong direction of commonality among the experts.  How could it possibly tell us who is better and who is worse, though?  That requires additional information concerning the relationships between these variables and the quality of being a "good" or "bad" expert.  If we believe all the variables are monotonically associated with goodness or badness, then perhaps PCA can help us explore the frontier of extreme (or maybe just outlying!) experts.  Watch out though--even the monotonicity assumption is suspect.
@Michael Mayer: of course. I just pointed out the wrong step.
Thank you Jeromy. 
I looked up G power 3, ANOVA repeated measures within-between interaction:
Only the total sample size is reported assuming equal sample size for the two groups.
1. How would it work if the sample sizes are slightly different, for example: N1/ N2 = 1.16. 
 (2) I have to input correlation between repeated measures. Is this the correlation between repeats after merging the data from the two groups?
This was my general impression, but I wanted to be sure as I am a trained fish ecologist and not a statistician - though I fit models more than I see fish.
@ Matt: Glancing at your data, it looks like they are both upward trends.  So are you essentially interested in the hypothesis that one cohort is increasing more quickly than the other?
Generally in terms of image processing, DL methods will act as feature extractors which can then be paired with SVMs to do classification. These methods are generally comparable to hand-engineered approaches like SIFT, SURF, and HOG. DL methods have been extended to video with gated CRBMs, and ISA. Hand-engineered methods include HOG/HOF, HOG3d, and eSURF (see Wang et al. 2009 for a good comparison).
I haven't seen anything in the literature, just a lot of informal hand wringing and attempts to guess what the reviewer du jour will think.
A Kruskal-Wallis test can only be used when you have independant groups, which is not the case in this example.
BTW you do not simulate a true Poisson regression model here, or you drop a predictor variable: the one generating by `rnorm(N,0,1)` when you define `mu`.
Which R packages' `likelihood.test` is this? The one in `Deducer`?
I just want to comment that anyone familiar with the Binomial theorem and multinomial coefficients can easily use this generating function to write down a closed-form formula for any specified pattern (a partition of the number of dice).  It is a product of two multinomial coefficients.  The point of this response is that you can go pretty far without any such knowledge, solely by exploiting generating functions.
It appears to me you are misusing `ks.test`.  According to its manual page, you need to supply parameters for the named distribution. Without them, `pnorm` will default to a standard normal distribution and `pexp` will default to an exponential with rate $1$. But beware! It is invalid to supply parameters that have been estimated from the data themselves--see the last paragraph of the "Details" section.
As to your other question, could you cut the data in half, I would be very cautious in doing so -- because that would double the minimum frequency you could consider.  I think you may end up needing to look at Fourier coefficients (take an [FFT](http://en.wikipedia.org/wiki/Fast_Fourier_transform) or a [DCT](http://en.wikipedia.org/wiki/Discrete_cosine_transform) and regress on them?!? -- __Not sure__).  Or perhaps periodic regression as mentioned above.
You provided no context, especially with regard why it is relevant to cascade sets of predictors.  How many regression courses have you taken?
And the question is...?
Your approach appears to be misguided - you're transforming in order to make a regression 'significant' - and you will almost certainly succeed, but the significance will be meaningless. Perhaps you'd be better served by a nonparametric regression approach, perhaps a local polynomial in the logs, or a logspline fit. "I want to confirm my intuition" relates to your preferred outcome, not what the actual point of the activity is. What is $y$ measuring?
@gung This is true but the $2x2$ case doesn't generalize well to larger dimension. I am more interested in $3x3$ and larger
@Scortchi I understand what you mean - it can be a good approximation in the same way that the normal distribution can approximate test scores and other quantities that have finite ranges. I had in mind situations where the actual service times had an extremely long right tail and most of the times were occurring at the floor. I guess in light of your response it's fair to say that in that case an exponential distribution would just be a poor assumption to make.
Awesome! Thanks a lot! I really appreciate it.
This does not provide an answer to the question. To critique or request clarification from an author, leave a comment below their post - you can always comment on your own posts, and once you have sufficient [reputation](http://stats.stackexchange.com/help/whats-reputation) you will be able to [comment on any post](http://stats.stackexchange.com/help/privileges/comment).
This index is widely known as **Calinski–Harabasz** criterion. I recommend you read posts on this site which mention this index.
Indeed, the same thing happens if I just use glmnet. I don't think I understand what you mean by "If λ is small enough, you should get more non-zero coefficients in the model without an intercept as well." Can you please elaborate?
(1) For a 4X faster simulation use `s <- replicate(10^5, {
  delta <- floor(runif(100, 0, 6)) - floor(runif(100, 0, 6));
  max(sum(delta[delta > 0]), -sum(delta[delta < 0]))
}); sum(s > 100)/ length(s)`. (2) I believe the answer is 19024559499426570037611458829573021458074826188755762634838975523692331755102046755611268880392383888911950488196749907935871387917415599926170449751285047 / 26676576488251712549810930719923358271436796584655589343467442433444558096851233688692008708797318581050800966667258114434654077532349702649000974494990336, approximately equal to $0.713155959.$
I can't understand what you're saying in the paragraphs starting with "I've" and "Also".
Then you want to focus on Definition 3, Ahsan, not on the first two!  ("Compute" == "implement", BTW.)  The reason why "Gaussian" is synonymous with "Normal" is that the probability density function (PDF) of a Normal distribution is Gaussian: they are one and the same thing, but differ distinctly from the Inverse Gaussian.  I wanted to make sure you weren't just looking for a Normal (Gaussian) CI, which is easier to compute.
A "gaussian kernel" and the probability density function (PDF) for a Normal distribution are mathematically the same.
Entropy of continuous distributions behaves quite differently than that of discrete distributions, because it is defined in terms of probability *densities* rather than probabilities themselves. @Pat We can still make sense of (differential) entropy of delta functions; as intimated in the link in the question, it can be understood as the limiting entropy of a sequence of functions whose (compact) supports shrink to a point. Regardless of what functions are used, the entropy indeed drops to $-\\infty$. This actually makes sense as "a value smaller than all real numbers."
Welcome to the site, @Ashwini. I took the liberty of using CV's $\\LaTeX$ markup features to make your post more readable. Please make sure it still says what you want it to.
**Closely related**: http://stats.stackexchange.com/questions/5747.  It's not quite a duplicate, but its answers provide some results for the general trivariate correlation matrix that are easy to specialize to this case. The two points they do not cover are sufficiency--namely, showing that any matrix whose coefficients satisfy the conditions actually is a correlation matrix for some distribution--and tightness (there are no better bounds).
@Frank This is a straightforward, if messy, application of methods of decision theory described in textbooks on the subject by E.S. Lehman (*The Theory of Point Estimation*) and Jack Kiefer (*Introduction to Statistical Inference*), among others.
@ttnphns: "It is you, who decides whether a likert-type rating scale **is** interval". I disagree, it **is** ordinal. Period. However, you may decide to **treat it as interval** which is called making the [assumption of equal intervals](http://www.spss-tutorials.com/assumption-of-equal-intervals/).
Adding to the packages, in SAS you just need to use a CLASS statement in PROC LOGISTIC.
I don't use SPSS, so how does SPSS represent missing? Your code appears to mean: if the value is -2 or -1, code the new variable as 1; otherwise code it as 0. All new values being 0 implies that no values of the original were -2 or -1. That is something you can check.
Sometimes I don't the forest for all the trees ... Thanks!
@January thank you. Yes, by accuracy I mean correctly predicted classes. I will look at the poorly predicted classes to check. I am not familiar with the test you suggested, can you suggest a reference where I can read more.
It's not totally clear what kind of model you're fitting - is this a linear regression model?
@Wayne the paper you linked is excellent. Thanks for sharing
What happens when you add the indicators for the two $\\mu$-contrast you mentioned?
possible duplicate of [When should you center your data & when should you standardize?](http://stats.stackexchange.com/questions/29781/when-should-you-center-your-data-when-should-you-standardize), [Variables are often standardised before making a model - when is this a good idea?](http://stats.stackexchange.com/questions/19216/variables-are-often-adjusted-e-g-standardised-before-making-a-model-when-is) or [What's the difference between Normalization and Standardization?](http://stats.stackexchange.com/questions/10289/whats-the-difference-between-normalization-and-standardization).
@Ga Oh, I see, you had an answer and then edited it.  I'm always sorry when interesting answers are deleted, even when it turns out upon further clarification that they might be less relevant, but it sounds like you have hit on a reasonable solution here.
Please explain why exactly you are thanking @JeromyAnglim. I am sure he deserves the thanks, but how is this related to your question?
@Nick, I did not mean to discourage you from answering, either. Please consider submitting an answer detailing your favorite technique (or two) for deriving the results. It may be instructive for the OP and others. :)
What is the purpose of your analysis?
A similar case would be like this: I want to estimate the variance of the weight of all infants at birth given the assumption that body weight fluctuates throughout the day. After taking measurements like the ones above, I do some calculations to obtain an estimate for the true variance of weight of all infants at birth. What I'm trying to figure out is what the calculations are and how I can make sure that it's devoid of measurement and intra-subject error.
3x7=21. Too few degrees of freedom, too few test subjects, the effects can not be identified. Please go back to the study design phase and plan for at least a hundred (am not a regulatory consultant, so the number is true only on the order of magnitude!) donors. If you give this stuff to the FDA be prepared to hear a bunch of unpleasant opinions.
(+1) This is not an elementary question because a correct solution requires understanding how the standard deviation of the mean of a spatial process changes with the support of the mean. (I would somewhat cynically guess that the intended answer pays no attention to this critical issue.) If this is a typical exercise from your source, I warmly recommend learning basic probability from a different one. Please [search our site](http://stats.stackexchange.com/search?q=probability+book) for references.
if you don't get an answer here, try the recently (09/2010) opened [JAGS forum on sourceforge](http://sourceforge.net/projects/mcmc-jags/forums/forum/610037). The developer, Martyn Plummer, is very helpful.
(and sorry I can't vote-up yet, I'm still new here :))
There is good discussion below on why one might not want to do hypothesis testing with the C-index. However, I still would like to know whether my script is correct. Is this the way to bootstrap in R?
Just as "bad" as rejecting H0 based on low p-value.
What variables would you like to multiply with what weights?  kNN does not rely on class centers - please be clearer with what you want to achieve in your question.
So the question turns out to: how to explore unknown regions of the space in order to query the label of points lying in such regions ?
@user603 There are papers (possibly Sen's paper) which discuss what's it's equivariant to and what it's not. This is the most common form I have seen.
OK, thanks. As a side note: Unless I am missing something, I don't think that is a valid approach. You need to have the sample size fixed before you conduct your test, else you invalidate the statistical analysis imo, as you now found the p value was to high and need new data.  However, you could think of using a sequential test procedure that adds the result from one additional dataset, than calculates the test than tells you H0, Ha or look for more data.
Sure, table for converting D stat to p-value: http://www.soest.hawaii.edu/wessel/courses/gg313/Critical_KS.pdf
Can you please help build an example that shows a simple dataset clustered using each of the these methods will give different dendogram?
I'm not sure I agree w/ this. The correlation is r=-.41, which is a reasonable magnitude for a correlation, I would think. Given the likely N (based on a glance at the plot) I would expect the r to be highly 'significant'. Whether or not Table & Depth are correlated enough to be called "collinear" is going to be a matter of definition (although I wouldn't call it problematic collinearity, either). Lastly, I would be wary of simply eliminating one of the variables unless the r were *very* close to |1| (e.g., ~.99)--I can't tell if that's what you mean.
@Nick, thanks for your amendments (shame on me (facepalm)).
@IsabelleMazerie Mild means slight.  So i am saying that if the distribution is only slightly skewed for example the standard inference assuming normality will work okay.
Order matters in ANOVAs with unbalanced designs, i.e., when there are unequal cell sizes. This topic is often treated under the headline of "types of sums of squares". See http://epm.sagepub.com/content/38/3/621.full.pdf+html and chl's answer to http://stats.stackexchange.com/questions/11209/the-effect-of-the-number-of-replicates-in-different-cells-on-the-results-of-anova
@JMS: Absolutely! Good some good points/directions...pretty much what I was looking for! Thanks.
@DLDahly Hmm, I am not familiar with that paper, but the idea seems to be to bootstrap _first_, and then perform multiple imputation. The OP appears to be bootstrapping estimates from MI datasets.
@whuber Good.  Then the OP, Fomb can use it given that the formula is explained and Fomb knows what covariances and/or correlations are.  This OP is a PhD student in biology who may not be well-versed in statistics. Your explanatory comment may help.
Thank you for your quick response. I found several other similar questions here on CrossValidated and it seems that their answers all point in the same direction.
@Rob: OK, thanks for the discussion.  I agree with the first part, but doubt the last, and would include that size only plays a role because normal asymptotics don't apply.  For example, Kowalski 1972 has a pretty good survey of the history around this, and concludes that the Pearson's correlation is not as robust as thought.  See: http://www.jstor.org/pss/2346598
I think you misinterpret what exact means. Calculate the exact p-value for a given a model means it's exact relative to a model. You need to find the exact sampling distribution of the test statistic, as opposed to approximating it with the CLT as isalmost always done. 

Uncertainty about whether the poisson is a good model of traffic accidents or any other model assumption (your concern) is never captured in a p value.
Please refer to [this](http://srmo.sagepub.com/view/the-sage-encyclopedia-of-social-science-research-methods/n525.xml) as a possible solution. Because dichotomous variables can't be differentiated, it is suggested to simply use the regression coefficient as the marginal effect.
Thanks! I'll investigate that. Looks like a really great tool for my task!
I took the problem to not be actually writing a usable file, but to transporting the value labels to SAS. I don't have a copy of SAS handy to see if the export to SAS7dat files normally saves this or not.
It's not inaccurate at all.
Looks pretty linear
With that large a sample size it is not surprising to find really small p-values.  I think it raises the question of practical vs statistical significance here and I would be more interested in the confidence intervals than p-values.
Do you get any error messages when using ezANOVA? Whenever I use it, I get a nice table of sphericity tests and GG/HF corrections.
It does not work for ANCOVA models, for this you will need to use the lme4 package, and there are other things to account for variance.
The nugget actually is not the extrapolation to zero: it is the (necessarily nonnegative) jump at zero.  When extrapolation of a *fitted* ("model") variogram based on the *data* is negative, this suggests that the spatial process is smooth, which for certain kinds of images might be no surprise, and you should be using a smooth model. This behavior also arises from data with finite (rather than point) support, which is usually the case with digital images, whose values are pixel averages. The true underlying variance is "smeared" out, frequently resulting in what you describe.
Hi, I have drawn both series by hand as a visual example that's the reason for some noise appearing. I did try to emulate my series that are a sum of sine-cosine function without noise
@chutsu Advising against something is different from saying that it is impossible. Given a set of potential predictor variables you can certainly automate a procedure for ruling out variables that don't correlate at all with the response. Stepwise selection methods are examples of automated procedures to start with a collection of variables and systematically add and subtract from the list until a final set is selected. This does not include interaction terms but you could automate the introduction of interaction terms also. It is possible. But the connotation is mindless manipulation.
@PeterFlom, Sir may I ask for your suggestion? I guess if Runs test is appropriate, I can perform this for proportions too. But I am just getting a little bit confused!
I found a sample session for ChoiceModelR (copied below). Does anyone have a sample session for prefmod?
You've fitted a model with period $2\\pi$. What's the period of your data? And what's the frequency of the observations?
Why would you think your data should follow the gamma distribution, which has an infinite support (all $x>0$)? To find the right distribution, tell us about the data generating process: what makes it restricted to the $(-4,4)$ range, and why is it skewed?
I think Rosenblatt invented kde several years before Parzen.
Ah thanks! Missed the most obvious way!
You may be interested in reading this related question, [Inter-rater reliability for ordinal or interval data](http://stats.stackexchange.com/questions/3539/inter-rater-reliability-for-ordinal-or-interval-data/3546#3546). About your point 2, are you speaking of *raw agreement* or *corrected agreement*?
+1. Related questions: [Prerequisites for AIC model comparison](http://stats.stackexchange.com/q/48714/5509), http://stats.stackexchange.com/q/65390/5509
@Zach, there's some discussion here, in Harrell's book: http://tinyurl.com/92fsmuv (look at the last paragraph in page 93 and the first paragraph in page 94.) I'll try to remember other references that are more explicit.
The relationship between significance of the coefficients and that of the regression is complicated.  For examples, please explore some of the [related questions](http://stats.stackexchange.com/search?q=regression+significant+coefficient) that have been asked about this.  For instance, your regression could--in principle--still have $R^2=0.38$ after removing $x_2$ (and then $\\beta_3$ might become highly significant).
@Jeremy It's not just the skewness that matters for the t-test. If the original data are skew, the differences are likely to be heavy-tailed.
Assuming $\\Sigma_{ii} = 1$ for $i=1,2$, of course: Generate $(X,Y) \\sim \\mathcal N(0,\\Sigma)$. Take $F^{-1}(\\Phi(X))$ and $G^{-1}(\\Phi(Y))$. All done.
Good question. I have been always confused about their uses. Thanks for asking.
In addition to statistical methods, I would urge you  to look at the substantive meaning: Which clustering is more useful to you?