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3387 | 2 | null | 3386 | 10 | null | The UCLA website has a bunch of great tutorials for every procedure broken down by the software type that you're familiar with. Check out [Annotated SPSS Output: Logistic Regression](http://www.ats.ucla.edu/stat/spss/output/logistic.htm) -- the SES variable they mention is categorical (and not binary). SPSS will auto... | null | CC BY-SA 2.5 | null | 2010-10-07T15:18:08.673 | 2010-10-07T15:18:08.673 | null | null | 1499 | null |
3388 | 2 | null | 3386 | 8 | null | Logistic regression is a pretty flexible method. It can readily use as independent variables categorical variables. Most software that use Logistic regression should let you use categorical variables.
As an example, let's say one of your categorical variable is temperature defined into three categories: cold/mild/h... | null | CC BY-SA 2.5 | null | 2010-10-07T15:56:05.430 | 2010-10-07T15:56:05.430 | null | null | 1329 | null |
3389 | 2 | null | 3377 | 3 | null | There is also a parametric approach. Ignoring the vector nature of your data, and looking only at the marginals, it suffices to solve the problem: find an online algorithm to compute the mean absolute deviation of scalar $X$. If (and this is the big 'if' here) you thought that $X$ followed some probability distribution... | null | CC BY-SA 2.5 | null | 2010-10-07T16:27:19.653 | 2010-10-07T16:27:19.653 | null | null | 795 | null |
3390 | 1 | 4057 | null | 11 | 2726 | The [Cornish-Fisher Expansion](http://www.riskglossary.com/link/cornish_fisher.htm) provides a way to estimate the quantiles of a distribution based on moments. (In this sense, I see it as a complement to the [Edgeworth Expansion](http://en.wikipedia.org/wiki/Edgeworth_expansion#Edgeworth_series), which gives an estima... | Why Use the Cornish-Fisher Expansion Instead of Sample Quantile? | CC BY-SA 2.5 | null | 2010-10-07T17:00:40.833 | 2017-09-28T18:28:02.507 | 2017-09-28T18:28:02.507 | 60613 | 795 | [
"distributions",
"quantiles",
"finance"
] |
3391 | 2 | null | 3331 | 7 | null | You could look at the work of [Eamonn Keogh](http://www.cs.ucr.edu/~eamonn/) (UC Riverside) on time series clustering. His website has a lot of resources. I think he provides Matlab code samples, so you'd have to translate this to R.
| null | CC BY-SA 2.5 | null | 2010-10-07T17:42:05.903 | 2010-10-07T17:45:59.027 | 2010-10-07T17:45:59.027 | 930 | 1436 | null |
3392 | 1 | 3398 | null | 53 | 3656 | It seems that lots of people (including me) like to do exploratory data analysis in Excel. Some limitations, such as the number of rows allowed in a spreadsheet, are a pain but in most cases don't make it impossible to use Excel to play around with data.
[A paper by McCullough and Heiser](http://www.pages.drexel.edu/~b... | Excel as a statistics workbench | CC BY-SA 2.5 | null | 2010-10-07T17:44:32.840 | 2022-12-02T14:26:39.963 | null | null | 666 | [
"software",
"computational-statistics",
"excel"
] |
3393 | 2 | null | 3294 | 12 | null | There is also a really good book by Oliver Cappe et. al: [Inference in Hidden Markov Models](http://rads.stackoverflow.com/amzn/click/0387402640). However, it is fairly theoretical and very light on the applications.
There is another book with examples in R, but I couldn't stand it - [Hidden Markov Models for Time Ser... | null | CC BY-SA 2.5 | null | 2010-10-07T17:59:07.497 | 2010-10-07T17:59:07.497 | null | null | 1499 | null |
3394 | 2 | null | 3392 | 7 | null | Incidently, a question around the use of Google spreadsheets raised contrasting (hence, interesting) opinions about that, [Do some of you use Google Docs spreadsheet to conduct and share your statistical work with others?](https://stats.stackexchange.com/questions/3244/do-some-of-you-use-google-docs-spreadsheet-to-cond... | null | CC BY-SA 4.0 | null | 2010-10-07T18:15:35.337 | 2022-12-02T14:26:39.963 | 2022-12-02T14:26:39.963 | 362671 | 930 | null |
3395 | 1 | 3396 | null | 4 | 339 | I am currently working on a model which takes two parameters and produces a measurement statistic. Think of it as Z = f(X,Y).
Z is a matrix of my statistics and I am creating a surface plot of it in matlab. Basically, I am looking for a mathematical/analytical way of determining if the surface is smooth, or if it is j... | Smoothness of a surface | CC BY-SA 2.5 | null | 2010-10-07T19:21:25.180 | 2010-10-08T02:43:53.970 | 2010-10-07T19:23:34.423 | 930 | null | [
"clustering",
"smoothing",
"matlab",
"spatial",
"autocorrelation"
] |
3396 | 2 | null | 3395 | 4 | null | One model for this situation is to view $Z$ as a realization of a stationary 2D stochastic process. The limiting behavior at zero (distance) of its empirical [variogram](http://en.wikipedia.org/wiki/Variogram) or correlogram provides information about its smoothness: if the limiting correlation is less than one, the p... | null | CC BY-SA 2.5 | null | 2010-10-07T19:52:10.740 | 2010-10-08T02:43:53.970 | 2010-10-08T02:43:53.970 | 8 | 919 | null |
3397 | 2 | null | 3392 | 11 | null | Well, the question whether the paper is correct or biased should be easy: you could just replicate some of their analyses and see whether you get the same answers.
McCullough has been taking different versions of MS Excel apart for some years now, and apparently MS haven't seen fit to fix errors he pointed out years ag... | null | CC BY-SA 2.5 | null | 2010-10-07T19:57:40.057 | 2010-10-07T19:57:40.057 | null | null | 1352 | null |
3398 | 2 | null | 3392 | 47 | null | Use the right tool for the right job and exploit the strengths of the tools you are familiar with.
In Excel's case there are some salient issues:
- Please don't use a spreadsheet to manage data, even if your data will fit into one. You're just asking for trouble, terrible trouble. There is virtually no protection aga... | null | CC BY-SA 3.0 | null | 2010-10-07T20:15:27.567 | 2012-04-03T11:18:05.973 | 2012-04-03T11:18:05.973 | 9007 | 919 | null |
3399 | 2 | null | 3392 | 7 | null | The papers and other participants point out to technical weaknesses. Whuber does a good job of outlining at least some of its strengths. I personally do extensive statistical work in Excel (hypothesis testing, linear and multiple regressions) and love it. I use Excel 2003 with a capacity of 256 columns and 65,000 ro... | null | CC BY-SA 3.0 | null | 2010-10-07T21:36:51.820 | 2016-05-26T15:52:14.530 | 2016-05-26T15:52:14.530 | 1329 | 1329 | null |
3400 | 1 | 3411 | null | 27 | 6961 | Question: From the standpoint of statistician (or a practitioner), can one infer causality using [propensity scores](http://en.wikipedia.org/wiki/Propensity_score) with an observational study (not an experiment)?
Please, do not want to start a flame war or a fanatical debate.
Background: Within our stat PhD program, we... | From a statistical perspective, can one infer causality using propensity scores with an observational study? | CC BY-SA 2.5 | null | 2010-10-07T23:27:47.727 | 2016-12-01T09:49:04.243 | null | null | 1499 | [
"causality",
"propensity-scores"
] |
3402 | 1 | 3403 | null | 9 | 699 | I'm a newbie at stats, so if I make any mistaken assumptions here please tell me.
There's a population `N` of people. (For example `N` can be 1,000,000.) Some of the people are redheads. I take a sample `n` of people (say 10,) and find that `j` of them are redheads.
What can I say about the general proportion of redhea... | What's the accuracy of data obtained through a random sample? | CC BY-SA 2.5 | null | 2010-10-08T00:51:55.783 | 2010-10-08T23:30:44.833 | 2010-10-08T02:39:46.587 | 8 | 5793 | [
"standard-deviation",
"sample-size",
"binomial-distribution",
"standard-error"
] |
3403 | 2 | null | 3402 | 8 | null | You can think of this as a binomial trial -- your trials are sampling "redhead" or "not readhead". In which case, you can build a confidence interval for your sample proportion ($j/n$) as documented on Wikipedia:
- Binomial proportion confidence interval
A 95% confidence interval basically says that, using the same... | null | CC BY-SA 2.5 | null | 2010-10-08T01:01:57.537 | 2010-10-08T01:12:34.190 | 2010-10-08T01:12:34.190 | 251 | 251 | null |
3404 | 2 | null | 3400 | 8 | null | Only a prospective randomized trial can determine causality. In observational studies, there will always be the chance of an unmeasured or unknown covariate which makes ascribing causality impossible.
However, observational trials can provide evidence of a strong association between x and y, and are therefore useful fo... | null | CC BY-SA 2.5 | null | 2010-10-08T01:39:26.280 | 2010-10-08T01:39:26.280 | null | null | 561 | null |
3405 | 2 | null | 3392 | 20 | null | An interesting paper about using Excel in a Bioinformatics setting is:
>
Mistaken Identifiers: Gene name errors
can be introduced inadvertently when
using Excel in bioinformatics, BMC
Bioinformatics, 2004 (link).
This short paper describes the problem of automatic type conversions in Excel (in particular [date... | null | CC BY-SA 2.5 | null | 2010-10-08T02:35:37.343 | 2010-10-08T13:01:56.017 | 2010-10-08T13:01:56.017 | 919 | 8 | null |
3407 | 1 | 3410 | null | 15 | 4555 | I am building an android application that records accelerometer data during sleep, so as to analyze sleep trends and optionally wake the user near a desired time during light sleep.
I have already built the component that collects and stores data, as well as the alarm. I still need to tackle the beast of displaying and... | Smoothing time series data | CC BY-SA 4.0 | null | 2010-10-08T07:59:32.177 | 2019-01-11T19:04:52.047 | 2019-01-11T19:04:52.047 | 79696 | 1520 | [
"time-series",
"smoothing",
"signal-processing",
"java"
] |
3408 | 2 | null | 3407 | 10 | null | There are many nonparametric smoothing algorithms including splines and loess. But they will smooth out the sudden changes too. So will low-pass filters. I think you might need a wavelet-based smoother which allows the sudden jumps but still smooths the noise.
Check out [Percival and Walden (2000)](http://rads.stackove... | null | CC BY-SA 2.5 | null | 2010-10-08T09:35:30.260 | 2010-10-08T09:35:30.260 | null | null | 159 | null |
3409 | 2 | null | 3407 | 3 | null | This is somewhat tangential to what you're asking, but it may be worth taking a look at the Kalman filter.
| null | CC BY-SA 2.5 | null | 2010-10-08T09:54:24.820 | 2010-10-08T09:54:24.820 | null | null | 439 | null |
3410 | 2 | null | 3407 | 16 | null | First up, the requirements for compression and analysis/presentation are not necessarily the same -- indeed, for analysis you might want to keep all the raw data and have the ability to slice and dice it in various ways. And what works best for you will depend very much on what you want to get out of it. But there are ... | null | CC BY-SA 2.5 | null | 2010-10-08T09:57:22.490 | 2010-10-08T10:11:31.953 | 2010-10-08T10:11:31.953 | 174 | 174 | null |
3411 | 2 | null | 3400 | 17 | null | At the beginning of an article aiming at promoting the use of PSs in epidemiology, Oakes and Church (1) cited Hernán and Robins's claims about confounding effect in epidemiology (2):
>
Can you guarantee that the results
from your observational study are
unaffected by unmeasured confounding?
The only answer an ep... | null | CC BY-SA 3.0 | null | 2010-10-08T11:30:29.323 | 2013-10-30T21:28:34.690 | 2013-10-30T21:28:34.690 | 930 | 930 | null |
3412 | 1 | 3415 | null | 17 | 3674 | I have an experiment that I'll try to abstract here. Imagine I toss three white stones in front of you and ask you to make a judgment about their position. I record a variety of properties of the stones and your response. I do this over a number of subjects. I generate two models. One is that the nearest stone to... | Comparing mixed effect models with the same number of degrees of freedom | CC BY-SA 2.5 | null | 2010-10-08T12:34:11.673 | 2022-09-18T20:11:05.137 | 2011-03-13T16:27:57.167 | 601 | 601 | [
"r",
"mixed-model",
"model-selection"
] |
3413 | 1 | 3414 | null | 4 | 3327 | I am looking for the Hurst exponent calculation methodology. Please suggest online materials / methodology papers.
| Hurst exponent calculation methodology | CC BY-SA 2.5 | null | 2010-10-08T13:24:33.177 | 2015-11-18T14:26:04.613 | 2015-11-18T14:26:04.613 | 22468 | 1250 | [
"references",
"fractal"
] |
3414 | 2 | null | 3413 | 8 | null | The calculation is covered on [the related wikipedia page](http://en.wikipedia.org/wiki/Hurst_exponent).
R has several implementations for this:
- The fArma package provides 10 different functions to estimate the Hurst exponent (see LrdModelling).
- The Rwave package has the hurst.est() function.
- The fractal packa... | null | CC BY-SA 2.5 | null | 2010-10-08T13:33:08.560 | 2010-10-08T15:05:16.480 | 2010-10-08T15:05:16.480 | 5 | 5 | null |
3415 | 2 | null | 3412 | 9 | null | Still, you can compute confidence intervals for your fixed effects, and report AIC or BIC (see e.g. [Cnann et al.](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.133.2052&rep=rep1&type=pdf), Stat Med 1997 16: 2349).
Now, you may be interested in taking a look at [Assessing model mimicry using the parametric ... | null | CC BY-SA 2.5 | null | 2010-10-08T13:41:22.563 | 2010-10-08T13:49:37.653 | 2010-10-08T13:49:37.653 | 930 | 930 | null |
3416 | 2 | null | 3412 | 3 | null | I do not know R well enough to parse your code but here is one idea:
Estimate a model where you have both center and near as covariates (call this mBoth). Then mCenter and mNear are nested in mBoth and you could use mBoth as a benchmark to compare the relative performance of mCenter and mNear.
| null | CC BY-SA 2.5 | null | 2010-10-08T13:53:52.570 | 2010-10-08T13:53:52.570 | null | null | null | null |
3417 | 2 | null | 3287 | 5 | null | I'm an ecologist, so I apologise in advance is this sounds a bit strange :-)
I like to think of these plots in terms of weighted averages. The region points are at the weighted averages of the smoking status classes and vice versa.
The problem with the above figure is the axis scaling and the fact that you can't displa... | null | CC BY-SA 2.5 | null | 2010-10-08T16:16:58.473 | 2010-10-08T16:22:10.900 | 2010-10-08T16:22:10.900 | 1390 | 1390 | null |
3419 | 1 | 3673 | null | 7 | 452 | There are umpteen million research papers regarding relationships between various patient attributes (e.g. how does gene x affect condition y?). What I am interested in though is a distance metric between patients in toto. Sort of like if I were constructing a dating site, I'd want to know how similar two people are. (... | Patient distance metrics | CC BY-SA 2.5 | null | 2010-10-08T17:52:20.097 | 2017-11-16T13:21:24.017 | 2010-10-15T16:18:12.350 | 900 | 900 | [
"clustering",
"biostatistics"
] |
3420 | 2 | null | 3296 | 1 | null | Addressing the issue mentioned under Update 2. You are dealing with outliers. Those outliers have a significant impact on your Logistic Regression coefficients. By removing them, you found that your models performed better on the validation set.
Does it mean that the outliers are "bad"? No. It means that they a... | null | CC BY-SA 2.5 | null | 2010-10-08T19:38:39.063 | 2010-10-08T19:38:39.063 | null | null | 1329 | null |
3421 | 2 | null | 3419 | 3 | null | The whole field of [Cluster Analysis](http://www.amazon.com/s/ref=nb_sb_noss?url=search-alias%3Dstripbooks&field-keywords=Cluster+analysis&x=0&y=0) is relevant to your concept of multi-variable statistical distance. The linked book on the subject is very short and pretty good.
| null | CC BY-SA 2.5 | null | 2010-10-08T20:05:14.780 | 2010-10-08T20:05:14.780 | null | null | 1329 | null |
3422 | 2 | null | 3412 | 12 | null | Following ronaf's suggestion leads to a more recent paper by Vuong for a Likelihood Ratio Test on nonnested models. It's based on the KLIC (Kullback-Leibler Information Criterion) which is similar to the AIC in that it minimizes the KL distance. But it sets up a probabilistic specification for the hypothesis so the u... | null | CC BY-SA 4.0 | null | 2010-10-08T20:58:17.027 | 2022-06-19T15:54:27.863 | 2022-06-19T15:54:27.863 | 361019 | 251 | null |
3423 | 1 | null | null | 8 | 412 | I'm working on a web app, and I'm creating some data viz tools for it. For one particular series, I've got an extremely wide variance in data values (0 to millions). We're using a column chart to view the data now, which of course results in some columns that are a pixel high or smaller. We already have some ways to sl... | Recommendations for visualization type when data has an extremely wide variance | CC BY-SA 2.5 | null | 2010-10-08T21:01:20.033 | 2010-10-09T15:40:21.837 | 2010-10-09T15:40:21.837 | null | 1531 | [
"data-visualization"
] |
3424 | 2 | null | 3423 | 9 | null | A standard approach to dealing with data that has a wide variance is to use a [log scale](http://en.wikipedia.org/wiki/Logarithmic_scale) (or some other kind of scaling approach) regardless of the visualization itself. This could be applied in any graphical package (including a JS library like [Protovis](http://vis.st... | null | CC BY-SA 2.5 | null | 2010-10-08T21:20:22.510 | 2010-10-08T21:36:33.643 | 2010-10-08T21:36:33.643 | 5 | 5 | null |
3425 | 1 | 3433 | null | 44 | 61699 | I am not sure how this should be termed, so please correct me if you know a better term.
I've got two lists. One of 55 items (e.g: a vector of strings), the other of 92. The item names are similar but not identical.
I wish to find the best candidates in the 92 list to the items in the 55 list (I will then go through ... | How to quasi match two vectors of strings (in R)? | CC BY-SA 4.0 | null | 2010-10-08T21:31:00.867 | 2020-10-16T16:12:09.383 | 2018-12-15T23:43:20.467 | 11887 | 253 | [
"r",
"text-mining"
] |
3426 | 2 | null | 2948 | 2 | null | I've an java implementation for non-overlapping, weighted/unweighted network that could probably handle 3 million nodes (I've tested it for a million node dataset). However, it works like k-means, and needs the number of partitions to be detected as an input (k in kmeans). You can find more info [here](http://www.googl... | null | CC BY-SA 3.0 | null | 2010-10-08T21:42:44.397 | 2017-03-01T19:47:15.617 | 2017-03-01T19:47:15.617 | -1 | null | null |
3427 | 2 | null | 3425 | 15 | null | There are many ways to measure distances between two strings. Two important (standard) approaches widely implemented in R are the Levenshtein and the Hamming distance. The former is avalaible in package 'MiscPsycho' and the latter in 'e1071'. Using these, i would simply compute a 92 by 55 matrix of pairwise distances, ... | null | CC BY-SA 2.5 | null | 2010-10-08T21:45:29.480 | 2010-10-09T20:14:40.313 | 2010-10-09T20:14:40.313 | 603 | 603 | null |
3428 | 2 | null | 3412 | 4 | null | there is a paper by [d.r.cox](https://projecteuclid.org/ebooks/berkeley-symposium-on-mathematical-statistics-and-probability/Proceedings%20of%20the%20Fourth%20Berkeley%20Symposium%20on%20Mathematical%20Statistics%20and%20Probability,%20Volume%201:%20Contributions%20to%20the%20Theory%20of%20Statistics/chapter/Tests%20of... | null | CC BY-SA 4.0 | null | 2010-10-08T22:27:14.820 | 2022-09-18T20:11:05.137 | 2022-09-18T20:11:05.137 | 79696 | 1112 | null |
3429 | 2 | null | 346 | 12 | null | I re-direct you to my answer to a similar [question](https://stats.stackexchange.com/questions/3372/is-it-possible-to-accumulate-a-set-of-statistics-that-describes-a-large-number-of/3376#3376). In a nutshell, it's a read once, 'on the fly' algorithm with $O(n)$ worst case complexity to compute the (exact) median.
| null | CC BY-SA 2.5 | null | 2010-10-08T22:49:46.743 | 2010-10-08T22:49:46.743 | 2017-04-13T12:44:55.360 | -1 | 603 | null |
3430 | 2 | null | 3402 | 0 | null | if your sample size $n$ is not such a tiny fraction of the population size $N$ as in your example, and if you sample without replacement [Sw/oR], a better expression for the [estimated] SE is
$$\hat{SE} = \sqrt{\frac{N - n}{N}\frac{\hat p \hat q}{n}},$$
where $\hat p$ is the estimated proportion $j/n$ and $\hat q = 1- ... | null | CC BY-SA 2.5 | null | 2010-10-08T23:11:33.593 | 2010-10-08T23:30:44.833 | 2010-10-08T23:30:44.833 | 1112 | 1112 | null |
3431 | 2 | null | 3413 | 3 | null | [Octave](http://www.gnu.org/software/octave/) has a built-in Hurst Exponent function.
| null | CC BY-SA 2.5 | null | 2010-10-08T23:53:06.367 | 2010-10-08T23:53:06.367 | null | null | 226 | null |
3432 | 2 | null | 3425 | 7 | null | To supplement Kwak's useful answer, allow me to add some simple principles and ideas. A good way to determine the metric is by considering how the strings might vary from their target. "Edit distance" is useful when the variation is a combination of typographic errors like transposing neighbors or mis-typing a single... | null | CC BY-SA 2.5 | null | 2010-10-09T00:12:17.553 | 2010-10-09T00:12:17.553 | null | null | 919 | null |
3433 | 2 | null | 3425 | 22 | null | I've had similar problems. (seen here: [https://stackoverflow.com/questions/2231993/merging-two-data-frames-using-fuzzy-approximate-string-matching-in-r](https://stackoverflow.com/questions/2231993/merging-two-data-frames-using-fuzzy-approximate-string-matching-in-r))
Most of the recommendations that I received fell ar... | null | CC BY-SA 2.5 | null | 2010-10-09T02:41:55.300 | 2010-10-09T20:00:20.550 | 2017-05-23T12:39:26.167 | -1 | 776 | null |
3434 | 2 | null | 3377 | 4 | null | I've used the following approach in the past to calculate absolution deviation moderately efficiently (note, this a programmers approach, not a statisticians, so indubitably there may be clever tricks like [shabbychef's](https://stats.stackexchange.com/questions/3377/online-algorithm-for-mean-absolute-deviation-and-lar... | null | CC BY-SA 2.5 | null | 2010-10-09T03:27:11.417 | 2010-11-09T05:33:11.947 | 2017-04-13T12:44:36.923 | -1 | 179 | null |
3435 | 2 | null | 3425 | 3 | null | I would also suggest you check out [N-grams](http://en.wikipedia.org/wiki/N-gram) and the [Damerau–Levenshtein](http://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance) distance besides the other suggestions of Kwak.
This [paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.15.178&rep=rep1&type=p... | null | CC BY-SA 2.5 | null | 2010-10-09T03:32:47.390 | 2010-10-09T03:32:47.390 | null | null | 1036 | null |
3436 | 2 | null | 3381 | 3 | null | I would see each histogram as a different model (parametrized by the width). Fitting a smoothing spline or some other kind of smoother for each of the models is simple.
You can then do model selection (such as cross-validation) to choose the histogram width that gives the best results, or do model stacking to fit least... | null | CC BY-SA 2.5 | null | 2010-10-09T09:16:11.677 | 2010-10-09T09:16:11.677 | null | null | 1526 | null |
3437 | 2 | null | 3419 | 3 | null | The simple idea is to make PCA and base distance of few first components (yet I don't like this technique because of assumptions it makes).
The complex idea is to use machine learning; the resulting distances will expose the classifier structure, so will be about as good as the classification accuracy. The simplest app... | null | CC BY-SA 2.5 | null | 2010-10-09T11:59:06.653 | 2010-10-09T11:59:06.653 | null | null | null | null |
3438 | 1 | 3440 | null | 14 | 55594 | See this Wikipedia page: [Binomial proportion confidence interval](http://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Agresti-Coull_Interval).
To get the Agresti-Coull Interval, one needs to calculate a percentile of the normal distribution, called $z$. How do I calculate the percentile? Is there a re... | Calculating percentile of normal distribution | CC BY-SA 4.0 | null | 2010-10-09T13:34:40.713 | 2020-08-23T04:02:16.183 | 2020-08-23T04:02:16.183 | 236645 | 5793 | [
"python",
"normal-distribution"
] |
3439 | 2 | null | 3438 | 4 | null | Well, you didn't ask about R, but in R you do it using ?qnorm
(It's actually the quantile, not the percentile, or so I believe)
```
> qnorm(.5)
[1] 0
> qnorm(.95)
[1] 1.644854
```
| null | CC BY-SA 2.5 | null | 2010-10-09T13:40:55.500 | 2010-10-09T13:40:55.500 | null | null | 253 | null |
3440 | 2 | null | 3438 | 3 | null | For Mathematica `$VersionNumber > 5` you can use
```
Quantile[NormalDistribution[μ, σ], 100 q]
```
for the `q`-th percentile.
Otherwise, you have to load the appropriate Statistics package first.
| null | CC BY-SA 3.0 | null | 2010-10-09T14:08:55.643 | 2017-01-17T09:38:14.200 | 2017-01-17T09:38:14.200 | 830 | 830 | null |
3441 | 2 | null | 3438 | 4 | null | In Python, you can use the [stats](http://www.scipy.org/SciPyPackages/Stats) module from the [scipy](http://www.scipy.org/) package (look for `cdf()`, as in the following [example](http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.norm.html)).
(It seems the [transcendantal](http://bonsai.hgc.jp/~mdehoon/s... | null | CC BY-SA 2.5 | null | 2010-10-09T14:20:58.783 | 2010-10-09T14:20:58.783 | null | null | 930 | null |
3442 | 2 | null | 97 | 28 | null | For basic summaries, I agree that reporting frequency tables and some indication about central tendency is fine. For inference, a recent article published in PARE discussed t- vs. MWW-test, [Five-Point Likert Items: t test versus Mann-Whitney-Wilcoxon](http://pareonline.net/pdf/v15n11.pdf).
For more elaborated treatmen... | null | CC BY-SA 3.0 | null | 2010-10-09T15:03:51.913 | 2014-01-21T21:33:29.013 | 2014-01-21T21:33:29.013 | 2921 | 930 | null |
3443 | 2 | null | 3400 | 11 | null | Propensity scores are typically used in the matching literature. Propensity scores use pre-treatment covariates to estimate the probability of receiving treatment. Essentially, a regression (either just regular OLS or logit, probit, etc) is used to calculate the propensity score with treatment as your outcome and pre-t... | null | CC BY-SA 2.5 | null | 2010-10-09T16:02:58.157 | 2010-10-09T18:23:45.387 | 2010-10-09T18:23:45.387 | 930 | 401 | null |
3444 | 2 | null | 3438 | 21 | null | John Cook's page, [Distributions in Scipy](http://www.johndcook.com/distributions_scipy.html), is a good reference for this type of stuff:
```
In [15]: import scipy.stats
In [16]: scipy.stats.norm.ppf(0.975)
Out[16]: 1.959963984540054
```
| null | CC BY-SA 2.5 | null | 2010-10-09T16:09:00.780 | 2010-10-09T16:09:00.780 | null | null | 251 | null |
3445 | 1 | null | null | 3 | 10854 | I have the following data, which is the output from the [MS Hudson](http://bioinformatics.oxfordjournals.org/content/18/2/337.full.pdf+html) software.
```
segsites: 6
positions: 0.1256 0.3122 0.3218 0.4970 0.5951 0.7943
001010
110101
010100
001010
010100
```
I want to make an R function to calculate the R-Squa... | How to calculate the pairwise LD for the given data? | CC BY-SA 3.0 | null | 2010-10-09T16:28:24.350 | 2017-02-02T21:14:02.180 | 2013-04-14T10:54:52.443 | null | null | [
"r",
"correlation",
"genetics"
] |
3446 | 1 | 3454 | null | 9 | 2543 | I've been looking at some of the packages from the High perf task [view](http://cran.r-project.org/web/views/HighPerformanceComputing.html) dealing with GPU computations, and given that most GPU seem to be an order of magnitude stronger at performing single precision arithmetics than DP [ones](http://en.wikipedia.org/w... | Significance of single precision floating point | CC BY-SA 4.0 | null | 2010-10-09T17:47:05.413 | 2020-11-15T18:55:57.833 | 2020-11-15T18:55:57.833 | 265676 | 603 | [
"r",
"python",
"gpu"
] |
3447 | 2 | null | 3445 | 4 | null | I know the `LDheatmap` function/package can calculate the pairwise LDs, see `ldhm$LDmatrix` in the example below. I'm not familiar with the software you mention or how to get data into the required format for `LDheatmap`.
```
> library(LDheatmap)
> data(CEUData)
> ldhm <- LDheatmap(CEUSNP, genetic.distances=CEUDist... | null | CC BY-SA 2.5 | null | 2010-10-09T18:14:22.367 | 2010-10-09T18:14:22.367 | null | null | 251 | null |
3448 | 2 | null | 3445 | 3 | null | There are various R/Bioconductor packages that allow you to compute pairwise correlation for SNPs in linkage disequilibrium, see the CRAN Task View [Statistical Genetics](http://cran.r-project.org/web/views/Genetics.html). As I worked directly with whole genome scan, I've been mainly using `snpMatrix`, but [LDheatmap](... | null | CC BY-SA 2.5 | null | 2010-10-09T18:14:36.530 | 2010-10-10T14:05:27.840 | 2010-10-10T14:05:27.840 | 930 | 930 | null |
3449 | 2 | null | 134 | 14 | null | #Edit:
As @Hunaphu's points out (and @whuber below in his answer) the original answer I gave to the OP (below) is wrong. It is indeed quicker to first sort the initial batch and then keep updating the median up or down (depending on whether a new data points falls to the left or to the right of the current median).
--... | null | CC BY-SA 4.0 | null | 2010-10-09T19:02:09.717 | 2021-08-19T04:28:21.460 | 2021-08-19T04:28:21.460 | 603 | 603 | null |
3450 | 2 | null | 3400 | 7 | null | The question seems to involve two things that really ought to be considered separately. First is whether one can infer causality from an observational study, and on that you might contrast the views of, say, Pearl (2009), who argues yes so long as you can model the process properly, versus the view @propofol, who will ... | null | CC BY-SA 2.5 | null | 2010-10-09T19:17:12.947 | 2010-10-09T19:17:12.947 | null | null | 96 | null |
3451 | 2 | null | 2849 | 4 | null | I guess the current (econometrics) industry standard for this setting is fixed effects regression. Take a look at the section on panel data in [this paper](http://www-personal.umich.edu/~nicholsa/ciwod.pdf) by Austin Nichols for a concise discussion. For these kinds of analyses you want larger N, typically, though. ... | null | CC BY-SA 2.5 | null | 2010-10-09T19:40:28.550 | 2010-10-09T19:40:28.550 | null | null | 96 | null |
3452 | 2 | null | 3446 | 6 | null |
- Because before GPUs there was no practical sense of using single reals; you never have too much accuracy and memory is usually not a problem. And supporting only doubles made R design simpler. (Although R supports reading/writing single reals.)
- Yes, because Python is aimed to be more compatible with compiled lang... | null | CC BY-SA 2.5 | null | 2010-10-09T20:12:24.353 | 2010-10-09T20:12:24.353 | null | null | null | null |
3453 | 2 | null | 3446 | 4 | null | I presume that by GPU programming, you mean programming nvidia cards? In which case the underlying code calls from R and python are to C/[CUDA](http://en.wikipedia.org/wiki/CUDA).
---
The simple reason that only single precision is offered is because that is what most GPU cards support.
However, the new nvidia [Fe... | null | CC BY-SA 2.5 | null | 2010-10-09T20:40:43.653 | 2010-10-09T20:47:49.703 | 2010-10-09T20:47:49.703 | 8 | 8 | null |
3454 | 2 | null | 3446 | 5 | null | From the [GPUtools help file](http://cran.r-project.org/web/packages/gputools/gputools.pdf), it seems that `useSingle=TRUE` is the default for the functions.
| null | CC BY-SA 2.5 | null | 2010-10-09T22:56:45.643 | 2010-10-09T22:56:45.643 | null | null | 251 | null |
3455 | 2 | null | 3446 | 1 | null | The vast majority of GPUs in circulation only support single precision floating point.
As far as the title question, you need to look at the data you'll be handling to determine if single precision is enough for you. Often, you'll find that singles are perfectly acceptable for >90% of the data you handle, but will fai... | null | CC BY-SA 2.5 | null | 2010-10-10T05:22:38.977 | 2010-10-10T05:22:38.977 | null | null | 1539 | null |
3456 | 2 | null | 3419 | 3 | null | There is a subfield called Distance Metric Learning. One such method is Information Theoretic Metric Learning (ITML).
| null | CC BY-SA 2.5 | null | 2010-10-10T06:12:37.237 | 2010-10-10T06:12:37.237 | null | null | 1540 | null |
3457 | 2 | null | 138 | 4 | null | One more: R bloggers has many posts with tutorials materials:
[http://www.r-bloggers.com/?s=tutorial](http://www.r-bloggers.com/?s=tutorial)
| null | CC BY-SA 2.5 | null | 2010-10-10T06:27:50.547 | 2010-10-10T06:27:50.547 | null | null | 253 | null |
3458 | 1 | 3459 | null | 25 | 12284 | I am looking for an alternative to Classification Trees which might yield better predictive power.
The data I am dealing with has factors for both the explanatory and the explained variables.
I remember coming across random forests and neural networks in this context, although never tried them before, are there another... | Alternatives to classification trees, with better predictive (e.g: CV) performance? | CC BY-SA 2.5 | null | 2010-10-10T09:27:49.817 | 2013-10-09T17:51:28.310 | 2010-10-10T13:24:22.520 | null | 253 | [
"r",
"machine-learning",
"classification",
"cart"
] |
3459 | 2 | null | 3458 | 31 | null | I think it would be worth giving a try to Random Forests ([randomForest](http://cran.r-project.org/web/packages/randomForest/index.html)); some references were provided in response to related questions: [Feature selection for “final” model when performing cross-validation in machine learning](https://stats.stackexchang... | null | CC BY-SA 3.0 | null | 2010-10-10T09:50:16.577 | 2012-05-01T10:48:25.300 | 2017-04-13T12:44:29.923 | -1 | 930 | null |
3460 | 1 | 3464 | null | 45 | 1916 | For some of us, refereeing papers is part of the job. When refereeing statistical methodology papers, I think advice from other subject areas is fairly useful, i.e. [computer science](https://cstheory.stackexchange.com/questions/1893/how-do-i-referee-a-paper) and [Maths](https://mathoverflow.net/questions/36596/referee... | Reviewing statistics in papers | CC BY-SA 2.5 | null | 2010-10-10T09:55:00.890 | 2010-10-12T09:49:10.387 | 2017-04-13T12:58:32.177 | -1 | 8 | [
"references",
"referee"
] |
3461 | 2 | null | 3458 | 8 | null | For multi-class classification, support vector machines are also a good choice. I typically use the the R kernlab package for this.
See the following JSS paper for a good discussion: [http://www.jstatsoft.org/v15/i09/](http://www.jstatsoft.org/v15/i09/)
| null | CC BY-SA 2.5 | null | 2010-10-10T10:19:27.863 | 2010-10-10T10:19:27.863 | null | null | 5 | null |
3462 | 2 | null | 3296 | 3 | null | I think you are suffering from the presence of outliers in your design matrix.
The remedy is to detect them using a multivariate robust estimator of location/scale (just as you can use the median to detect outliers in an univariate setting but you can't use the mean because the mean itself is sensitive to the presence... | null | CC BY-SA 2.5 | null | 2010-10-10T11:10:14.780 | 2010-10-12T15:02:24.580 | 2010-10-12T15:02:24.580 | 603 | 603 | null |
3463 | 1 | null | null | 15 | 9228 | I have two time series S, and T. they have the same frequency and the same length.
I would like to calculate (using R), the correlation between this pair (i.e. S and T), and also be able to calculate the significance of the correlation), so I can determine whether the correlation is due to chance or not.
I would like t... | Computing correlation (and the significance of said correlation) between a pair of time series | CC BY-SA 2.5 | null | 2010-10-10T11:11:52.523 | 2010-10-13T06:37:12.110 | null | null | 1216 | [
"r",
"time-series",
"correlation"
] |
3464 | 2 | null | 3460 | 23 | null | I am not sure about which area of science you are referring to (I'm sure the answer would be really different if dealing with biology vs physics for instance...)
Anyway, as a biologist, I will answer from a "biological" point of view:
>
How much effort should we put in to understand the application area?
I tend at l... | null | CC BY-SA 2.5 | null | 2010-10-10T11:27:35.330 | 2010-10-10T11:33:27.747 | 2010-10-10T11:33:27.747 | 582 | 582 | null |
3465 | 2 | null | 3458 | 3 | null | As already mentioned Random Forests are a natural "upgrade" and, these days, SVM are generally the recommended technique to use.
I want to add that more often than not switching to SVM yields very disappointing results. Thing is, whilst techniques like random trees are almost trivial to use, SVM are a bit trickier.
... | null | CC BY-SA 2.5 | null | 2010-10-10T12:08:40.070 | 2010-10-10T17:31:45.723 | 2010-10-10T17:31:45.723 | 300 | 300 | null |
3466 | 1 | 3467 | null | 63 | 69981 | Imagine the following common design:
- 100 participants are randomly allocated to either a treatment or a control group
- the dependent variable is numeric and measured pre- and post- treatment
Three obvious options for analysing such data are:
- Test the group by time interaction effect in mixed ANOVA
- Do an AN... | Best practice when analysing pre-post treatment-control designs | CC BY-SA 2.5 | null | 2010-10-10T13:04:18.347 | 2022-08-21T17:03:50.260 | 2022-08-21T17:03:50.260 | 121522 | 183 | [
"ancova",
"clinical-trials",
"pre-post-comparison",
"faq"
] |
3467 | 2 | null | 3466 | 43 | null | There is a huge literature around this topic (change/gain scores), and I think the best references come from the biomedical domain, e.g.
>
Senn, S (2007). Statistical issues in
drug development. Wiley (chap. 7 pp.
96-112)
In biomedical research, interesting work has also been done in the study of [cross-over trials]... | null | CC BY-SA 4.0 | null | 2010-10-10T13:59:47.777 | 2020-09-14T18:03:39.107 | 2020-09-14T18:03:39.107 | 930 | 930 | null |
3471 | 1 | 3493 | null | 0 | 159 | Is it possible to load an S-PLUS Linux workspace in Windows?
If I try it I get this error: "Problem in exists(name, where = db):
This directory has both Unix style __nonfile and Windows style
__nonfi"
The __nonfi file is created when I first try to load that Linux
workspace in Windows.
Is there any way to convert it to... | Load Linux workspace in S-PLUS for Windows | CC BY-SA 2.5 | null | 2010-10-10T20:35:54.560 | 2010-10-11T20:27:59.543 | null | null | 749 | [
"splus"
] |
3472 | 2 | null | 3307 | 4 | null | I doubt you're going to find a single answer to this, given the space of fractal dimensions. Most papers (in physics, geology) looking at correlation simply stick to a Pearson correlation with fractal math reserved for identifying dimension/self-similarity, etc.
But you might be interested in the following papers w... | null | CC BY-SA 2.5 | null | 2010-10-10T21:16:38.417 | 2010-10-10T21:16:38.417 | null | null | 251 | null |
3474 | 1 | 3481 | null | 9 | 2462 | As the title says, I'm looking for the marginal densities of $$f (x,y) = c \sqrt{1 - x^2 - y^2}, x^2 + y^2 \leq 1.$$
So far I have found $c$ to be $\frac{3}{2 \pi}$. I figured that out through converting $f(x,y)$ into polar coordinates and integrating over $drd\theta$, which is why I'm stuck on the marginal densities ... | Finding marginal densities of $f (x,y) = c \sqrt{1 - x^2 - y^2}, x^2 + y^2 \leq 1$ | CC BY-SA 3.0 | null | 2010-10-11T03:48:14.103 | 2014-03-13T21:55:48.787 | 2014-03-13T21:55:48.787 | 919 | 1545 | [
"self-study",
"marginal-distribution",
"multivariable"
] |
3475 | 2 | null | 2467 | 7 | null | Caution: I'm assuming that when you said "classification", you are rather referring to cluster analysis (as understood in French), that is an unsupervised method for allocating individuals in homogeneous groups without any prior information/label. It's not obvious to me how class membership might come into play in your... | null | CC BY-SA 3.0 | null | 2010-10-11T06:30:02.287 | 2012-02-03T12:00:08.003 | 2012-02-03T12:00:08.003 | 930 | 930 | null |
3476 | 1 | 3477 | null | 20 | 180938 | Python [matplotlib](http://matplotlib.sourceforge.net/) has a [boxplot command](http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.boxplot).
Normally, all the parts of the graph are numerically ticked. How can I change the ticks to names instead of positions?
For illustration, I mean the Mon Tue We... | How to name the ticks in a python matplotlib boxplot | CC BY-SA 2.5 | null | 2010-10-11T06:39:50.770 | 2016-01-27T20:05:46.097 | null | null | 190 | [
"python",
"matplotlib"
] |
3477 | 2 | null | 3476 | 30 | null | Use the second argument of `xticks` to set the labels:
```
import numpy as np
import matplotlib.pyplot as plt
data = [[np.random.rand(100)] for i in range(3)]
plt.boxplot(data)
plt.xticks([1, 2, 3], ['mon', 'tue', 'wed'])
```
edited to remove `pylab` bc [pylab is a convenience module that bulk imports matplotlib.pypl... | null | CC BY-SA 3.0 | null | 2010-10-11T07:12:12.017 | 2016-01-27T20:05:46.097 | 2016-01-27T20:05:46.097 | 94986 | 251 | null |
3478 | 2 | null | 3460 | 12 | null | My POV would be reviewing a paper in psychology or forecasting on its statistical merits. I'll mostly second Nico's very good remarks.
>
How much effort should we put in to
understand the application area?
Quite a lot, actually. I wouldn't trust myself to comment on more than the most basic statistical problems wi... | null | CC BY-SA 2.5 | null | 2010-10-11T08:09:36.607 | 2010-10-12T09:49:10.387 | 2010-10-12T09:49:10.387 | 1352 | 1352 | null |
3479 | 1 | 3482 | null | 11 | 5005 | What is the rationale, if any, to use Discriminant Analysis (DA) on the results of a clustering algorithm like k-means, as I see it from time to time in the literature (essentially on clinical subtyping of mental disorders)?
It is generally not recommended to test for group differences on the variables that were used d... | Cluster Analysis followed by Discriminant Analysis | CC BY-SA 2.5 | null | 2010-10-11T08:37:31.890 | 2010-10-11T15:10:57.870 | null | null | 930 | [
"clustering",
"discriminant-analysis"
] |
3480 | 2 | null | 3471 | 2 | null | I don't know for certain, but that won't stop me from wildly speculating:
The __nonfi file lists what's in the workspace. You can open it with a text editor and look at the contents. It might be possible to either manipulate the unix version (e.g. using dos2unix) or else copy the contents over into your new file.
Tha... | null | CC BY-SA 2.5 | null | 2010-10-11T13:11:51.507 | 2010-10-11T13:11:51.507 | null | null | 5 | null |
3481 | 2 | null | 3474 | 15 | null | Geometry helps here. The graph of $f$ is a spherical dome of unit radius. (It follows immediately that its volume is half that of a unit sphere, $(4 \pi /3)/2$, whence $c=3/(2 \pi)$.) The marginal densities are given by areas of vertical cross-sections through this sphere. Obviously each cross-section is a semicirc... | null | CC BY-SA 2.5 | null | 2010-10-11T14:55:07.220 | 2010-10-11T14:55:07.220 | null | null | 919 | null |
3482 | 2 | null | 3479 | 5 | null | I don't know of any papers on this. I've used this approach, for descriptive purposes. DFA provides a nice way to summarize group differences and dimensionality with respect to the original variables. One might more easily just profile the groups on the original variables, however, this loses the inherently multivar... | null | CC BY-SA 2.5 | null | 2010-10-11T15:10:57.870 | 2010-10-11T15:10:57.870 | null | null | 485 | null |
3483 | 2 | null | 3460 | 17 | null | This addresses the new question #6: "What's the maximum number of papers you would review in a year?" I'm responding as a member of several editorial boards. The perennial problem is finding enough reviewers. Depending on the journal, every submitted paper needs one to three peer reviewers, usually three. If the jo... | null | CC BY-SA 2.5 | null | 2010-10-11T15:35:26.567 | 2010-10-11T15:56:43.347 | 2010-10-11T15:56:43.347 | 919 | 919 | null |
3484 | 1 | 3487 | null | 8 | 7209 | I have been looking at analyst job postings and one of the most common requirement is experience of SAS.
- Unless your organisation currently uses SAS, how can you train as a SAS user?
- What programming language would be equivalent to SAS that employers might be happy to accept?
| Obtaining SAS experience | CC BY-SA 2.5 | null | 2010-10-11T15:42:06.377 | 2017-02-22T22:44:58.320 | null | null | 1077 | [
"sas",
"careers"
] |
3485 | 2 | null | 3199 | 4 | null | Good question. A trivial way to find "cluster of high values in the upper left"
(as opposed to correlations)
is to split the image into tiles and look at tile means. For example,
```
means of 100 x 100 tiles:
[[ 82 78 80 94 99 100]
[ 80 53 66 62 80 100]
[ 82 61 65 64 72 98]
[ 87 83 99 81 80 100]
[... | null | CC BY-SA 2.5 | null | 2010-10-11T15:46:43.540 | 2010-10-11T15:46:43.540 | null | null | 557 | null |
3487 | 2 | null | 3484 | 5 | null | I would recommend going through a self-study course such as the [UCLA website](http://www.ats.ucla.edu/stat/sas/) and specifically the [SAS Starter Kit](http://www.ats.ucla.edu/stat/sas/sk/default.htm). If you learn better within an interactive environment, I would suggest checking out online course offerings such as ... | null | CC BY-SA 2.5 | null | 2010-10-11T17:08:44.130 | 2010-10-11T17:37:01.560 | 2010-10-11T17:37:01.560 | 1499 | 1499 | null |
3488 | 2 | null | 3463 | 6 | null | You can use the ccf function to get the cross-correlation, but this will only give you a plot. If the estimated cross correlations fall outside the dash red line, then you can conclude that there is a statistically significant cross-correlation. But I do not know of a package with a formally encapsulated test. Examp... | null | CC BY-SA 2.5 | null | 2010-10-11T17:17:26.393 | 2010-10-11T17:17:26.393 | 2017-04-13T12:44:40.807 | -1 | 1499 | null |
3489 | 1 | 3508 | null | 6 | 10089 | I have a biometric system that outputs a distribution of scores that resembles a Gaussian distribution (similar to the example graph in the following link: [LINK](http://support.bioid.com/sdk/docs/About_EER.htm)). My point of confusion is how I calculate the False Acceptance Rate. How does threshold factor into the who... | Calculating False Acceptance Rate for a Gaussian Distribution of scores | CC BY-SA 2.5 | null | 2010-10-11T17:21:37.217 | 2010-10-20T21:12:08.117 | 2010-10-20T21:12:08.117 | 8 | 1224 | [
"bioinformatics"
] |
3490 | 2 | null | 3484 | 5 | null | As far as SAS goes, getting [certified is resume gold](http://support.sas.com/certify/). The SAS Institute offers [classes and exams](http://support.sas.com/certify/creds/prep.html) to receive the certification. There are also books you can use if you are self-motivated.
Getting SAS is quite difficult if your company d... | null | CC BY-SA 2.5 | null | 2010-10-11T17:27:37.583 | 2010-10-11T17:27:37.583 | null | null | 1118 | null |
3491 | 2 | null | 3489 | 5 | null | I'm not certain. I'm curious as to the other responses you get. However, I think you'll need to clarify a bit:
Does your Gaussian distribution represent the scores for a population of individuals which should be rejected by your biometric system?
If so, then I think you simply need to compute a cumulative probability... | null | CC BY-SA 2.5 | null | 2010-10-11T17:33:16.783 | 2010-10-11T17:33:16.783 | null | null | 1499 | null |
3492 | 2 | null | 3484 | 1 | null | The programming language most similar to SAS is... SAS. Which you can interpret using [WPS, which will run SAS code and evidently costs substantially less than a SAS license](http://en.wikipedia.org/wiki/World_Programming_System) and has [a 30 day free trial](http://www.teamwpc.co.uk/tryorbuy). I haven't used it myse... | null | CC BY-SA 2.5 | null | 2010-10-11T18:07:43.963 | 2010-10-11T18:07:43.963 | null | null | 71 | null |
3493 | 2 | null | 3471 | 3 | null | Tibco support gave me a solution:
- Create a new Windows workspace
- Attach the Linux workspace
attach("C:\\Linux\\Workspace\\Path")
- Copy the contents of the Linux workspace to the Windows workspace
objs <- objects(2)
for (i in objs) assign(i, value=get(i, where=2), where=1)
objs <- objects(2, meta=1)
for (i in ob... | null | CC BY-SA 2.5 | null | 2010-10-11T20:27:59.543 | 2010-10-11T20:27:59.543 | null | null | 749 | null |
3495 | 2 | null | 3377 | 1 | null | The following provides an inaccurate approximation, although the inaccuracy will depend on the distribution of the input data. It is an online algorithm, but only approximates the absolute deviance. It is based on a [well known algorithm](http://www.johndcook.com/blog/2008/09/26/comparing-three-methods-of-computing-s... | null | CC BY-SA 2.5 | null | 2010-10-11T23:15:33.223 | 2010-11-09T05:36:27.843 | 2010-11-09T05:36:27.843 | 179 | 179 | null |
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