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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3281 | 1 | 3282 | null | 12 | 744 | I am looking for books / textbooks on statistics based on fractal mathematics. I know it is not a very well known area and it is rather difficult to find good literature. Any suggestions are welcome (books, textbooks, online materials).
| Statistics based on fractal mathematics | CC BY-SA 2.5 | null | 2010-10-03T12:58:13.803 | 2015-11-04T11:20:18.663 | 2015-11-04T11:20:18.663 | 22468 | 1250 | [
"references",
"fractal"
] |
3282 | 2 | null | 3281 | 10 | null |
- Tutorials in Contemporary Nonlinear Methods for the Behavioral Sciences Web Book
It has several chapters discussing fractals.
| null | CC BY-SA 2.5 | null | 2010-10-03T13:15:14.493 | 2010-10-03T13:15:14.493 | null | null | 183 | null |
3283 | 2 | null | 3275 | 7 | null | Let $p_W$ be the probability of a win, $p_T$ that of a tie, and $p_L$ of a loss. Because the probabilities sum to 1, so does their tenth power. Expanding this out expresses the tenth power as a linear combination of the monomials $p_W^W p_T^T p_L^L$ where each term picks out the probability of $W$ wins, $T$ ties, and... | null | CC BY-SA 2.5 | null | 2010-10-03T13:58:51.183 | 2010-10-03T14:08:45.343 | 2010-10-03T14:08:45.343 | 919 | 919 | null |
3284 | 2 | null | 3271 | 3 | null | It seems you just want to make a fair standard analysis, so I am not a best person to answer your question; yet I would suggest you to dive deeper into Bioconductor; it has a lot of useful stuff, nevertheless finding what you want is painful. For instance [Mfuzz](http://www.bioconductor.org/packages/release/bioc/html/M... | null | CC BY-SA 2.5 | null | 2010-10-03T16:49:21.400 | 2010-10-03T16:49:21.400 | null | null | null | null |
3285 | 2 | null | 3271 | 5 | null | In complement to @mbq's response (`Mfuzz` looks fine), I'll just put some references (PDFs) about clustering of time-course gene expression data:
- Futschik, ME and Charlisle, B (2005). Noise robust clustering of gene expression time-course data. Journal of Bioinformatics and Computational Biology, 3(4), 965-988.
- L... | null | CC BY-SA 2.5 | null | 2010-10-03T20:34:43.010 | 2010-10-03T22:59:40.033 | 2010-10-03T22:59:40.033 | 930 | 930 | null |
3286 | 1 | 3288 | null | 7 | 5331 | I'm not a statistician, but I sometimes need to play around with data. I have two data sets, lists of values in the unit interval. I've plotted them as histograms, so I have an intuitive idea of how "far apart" they are. But I want to do something a little more formal.
My first thought was to just sum the differences o... | Distance between empirically generated distributions (in R) | CC BY-SA 4.0 | null | 2010-10-03T21:10:14.903 | 2021-09-16T13:39:41.300 | 2021-09-16T13:39:41.300 | 11887 | 5141 | [
"r",
"distributions",
"distance"
] |
3287 | 1 | 3417 | null | 6 | 1119 | I'd like to ensure that I understand the process correctly. This is a follow-up question to [Interpreting 2D correspondence analysis plots](https://stats.stackexchange.com/questions/3270/interpreting-2d-correspondence-analysis-plots)
```
library(reshape)
library(ca)
df <- read.csv(file="http://www.bertelsen.ca/R/smo... | Interpreting 2D correspondence analysis plots (Part II) | CC BY-SA 3.0 | null | 2010-10-03T21:24:06.223 | 2011-06-15T20:09:27.880 | 2017-04-13T12:44:26.710 | -1 | 776 | [
"r",
"biplot",
"correspondence-analysis"
] |
3288 | 2 | null | 3286 | 7 | null | You can do a Kolmogorov-Smirnov test using the `ks.test` function. See `?ks.test`.
In general, when you are looking for a function in R (and you don't know its name) try using `??`. For instance, `??"Kolmogorov Smirnov"`. If nothing comes up `RSiteSearch("whatever you're looking for")` should help :)
| null | CC BY-SA 2.5 | null | 2010-10-03T21:59:50.240 | 2010-10-03T21:59:50.240 | null | null | 582 | null |
3289 | 1 | null | null | 3 | 195 | a recent posting on [math.stackexchange](https://math.stackexchange.com/questions/5922/probability-of-card-decks-being-in-the-same-order-for-n-shufflers-over-x-amount-o) reminds me of a somewhat less ambitious$^1$ question i have been meaning to ask.
suppose 3 people each have a deck of $M$ cards numbered from 1 to $M... | A matching problem for 3 decks of cards | CC BY-SA 2.5 | null | 2010-10-03T22:28:59.097 | 2010-10-04T09:07:35.723 | 2017-04-13T12:19:38.447 | -1 | 1112 | [
"probability",
"games"
] |
3290 | 2 | null | 3268 | 5 | null | What you describe in your example is not only a network of relationships, but a network of "flows" between all groups.
Like you suggested in a) (and as Jeromy said as well) your graphic will likely be a visualization of one group (or node) linked to other groups. Most of my knowledge of this subject is visualizing flo... | null | CC BY-SA 2.5 | null | 2010-10-04T02:22:18.003 | 2010-10-04T03:08:41.037 | 2017-04-13T12:33:47.693 | -1 | 1036 | null |
3291 | 1 | 3312 | null | 11 | 523 | Suppose you had $200 US to build a (very) small library of statistics books. What would your choices be? You may assume free shipping from Amazon, and any freely available texts from the internet are fair game, but assume a 5 cent charge per page to print.
(I was inspired by a mailing from Dover books, but most of thei... | Statistics library with knapsack constraint | CC BY-SA 3.0 | null | 2010-10-04T04:31:35.903 | 2015-11-04T01:34:18.227 | 2015-11-03T22:34:24.613 | 22468 | 795 | [
"references",
"puzzle"
] |
3292 | 2 | null | 3291 | 1 | null | A tad pricey, but [Bruning and Kintz's Computational Handbook of Statistics](http://rads.stackoverflow.com/amzn/click/0673990850) ($95.80) would certainly fit in your knapsack.
| null | CC BY-SA 2.5 | null | 2010-10-04T04:48:07.010 | 2010-10-04T20:29:00.157 | 2010-10-04T20:29:00.157 | 795 | 830 | null |
3293 | 2 | null | 3291 | 5 | null |
- Harrell, FE. Regression Modeling Strategies (Springer, 2010, 2nd ed.)
- Izenman, AJ. Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning (Springer, 2008)
You should have money left to print part of The [Handbook of Computational Statistics](http://rads.stackoverflow.com/a... | null | CC BY-SA 2.5 | null | 2010-10-04T07:15:14.340 | 2010-10-04T15:49:02.887 | 2017-04-13T12:44:48.803 | -1 | 930 | null |
3294 | 1 | null | null | 59 | 14461 | I am looking for resources (tutorials, textbooks, webcast, etc) to learn about Markov Chain and HMMs. My background is as a biologist, and I'm currently involved in a bioinformatics-related project.
Also, what are the necessary mathematical background I need to have a sufficient understanding of Markov models & HMMs?
... | Resources for learning Markov chain and hidden Markov models | CC BY-SA 3.0 | null | 2010-10-04T08:33:05.990 | 2021-04-14T14:31:30.577 | 2015-11-03T10:25:08.487 | 22468 | 1495 | [
"references",
"markov-process",
"hidden-markov-model",
"bioinformatics"
] |
3295 | 2 | null | 3271 | 3 | null | Just to add to the other answers (which look like they should solve your problem), did you try using standard clustering algorithms for you data when constructing your dendrogram? For example,
```
heatmap.2(dataset, <standard args>,
hclustfun = function(c){hclust(c, method= 'average')}
)
```
Inste... | null | CC BY-SA 2.5 | null | 2010-10-04T09:04:29.530 | 2010-10-04T21:13:21.687 | 2010-10-04T21:13:21.687 | 8 | 8 | null |
3296 | 1 | 3308 | null | 8 | 391 | I have a pretty large data set (~300 cases with ~40 continuous attributes, binary labeled) which I used to create several alternative predictive models. To do this, the set was divided to training and validation subsets (~60:40%, respectively).
I have noticed that there are several samples (both in the training and the... | Dealing with "trouble maker" samples | CC BY-SA 3.0 | null | 2010-10-04T09:36:25.393 | 2016-08-10T13:56:49.377 | 2016-08-10T13:56:49.377 | 22468 | 1496 | [
"hypothesis-testing",
"logistic",
"modeling",
"outliers",
"large-data"
] |
3297 | 2 | null | 3286 | 4 | null | A standard way to compare distributions is to use the [Kullback-Leibler divergence](http://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence). As usual, there's an R package that does this for you! From the `?KLdiv` help page in the [flexmix](http://cran.r-project.org/web/packages/flexmix/) package, we get the ... | null | CC BY-SA 2.5 | null | 2010-10-04T09:49:33.380 | 2010-10-04T09:49:33.380 | null | null | 8 | null |
3298 | 2 | null | 3294 | 19 | null | Here are some tutorials (available as PDFs):
- Dugad and Desai, A tutorial on hidden markov models
- Valeria De Fonzo1, Filippo Aluffi-Pentini2 and Valerio Parisi (2007). Hidden Markov Models in Bioinformatics. Current Bioinformatics, 2, 49-61.
- Smith, K. Hidden Markov Models in Bioinformatics with Application to G... | null | CC BY-SA 3.0 | null | 2010-10-04T10:16:23.660 | 2012-10-11T01:28:00.303 | 2012-10-11T01:28:00.303 | 12318 | 930 | null |
3299 | 2 | null | 2956 | 3 | null | Although the reasoning about calculating the LR from the SS values is quite fair, a least-squares method is equivalent but not the same as a likelihood estimate. (The difference can be illustrated eg in the calculation of the se, which is divided by (n-1) in a least-squares approach and divided by n in a maximum-likeli... | null | CC BY-SA 2.5 | null | 2010-10-04T12:45:23.117 | 2010-10-04T13:04:25.893 | 2010-10-04T13:04:25.893 | 1124 | 1124 | null |
3300 | 2 | null | 3262 | 7 | null | I teach undergraduate biology students, and The Fear is rife among them. I generally start by telling them three things:
1) Statistics is not maths, it's logic. And if you're doing a science degree at a respected university, you eveidently don't have any problems with using logic to solve problems.
2) If you can add,... | null | CC BY-SA 2.5 | null | 2010-10-04T13:02:30.140 | 2010-10-04T13:02:30.140 | null | null | 266 | null |
3301 | 2 | null | 3291 | 1 | null | What topics are you interested in? I learned from [KNNL](http://rads.stackoverflow.com/amzn/click/007310874X) ($157.50), but oh gosh I couldn't imagine carrying this thing around -- you'd be asking for a reading list on scoliosis correction.
"General Statistics" is certainly an area of interest, but I'm curious if you... | null | CC BY-SA 2.5 | null | 2010-10-04T14:08:55.490 | 2010-10-04T20:26:32.840 | 2010-10-04T20:26:32.840 | 795 | 1499 | null |
3302 | 2 | null | 3291 | 3 | null | As a social scientist I would have to vouch for the [Sage Green Books](http://www.sagepub.com/booksSeries.nav?forceIgnore=True&sortBy=bookPublishDate&prodTypes=books&sizeBooks=&reverseFlag=true&series=Series486&&start=1&_requestid=381729). If you are a bargain shopper you would be able to get between 10 to 20 books for... | null | CC BY-SA 2.5 | null | 2010-10-04T15:04:32.177 | 2010-10-04T15:04:32.177 | null | null | 1036 | null |
3303 | 2 | null | 3262 | 10 | null | [Frederick Mosteller said:](http://www.edwardtufte.com/tufte/mosteller_p1)
>
When I think of teaching a class, I think of five main components, not all ordinarily used in one lecture. They are
Large-scale application
Physical demonstration
Small-scale application (specific)
Statistical or probabilistic principle
Proo... | null | CC BY-SA 2.5 | null | 2010-10-04T15:40:44.507 | 2010-10-04T15:40:44.507 | null | null | 666 | null |
3306 | 2 | null | 3286 | 4 | null | First thing: define "distance". That sounds like a stupid question, but what do you mean by distance? Is the data paired? Then -and only then- it makes sense to look at the sum of (squared) differences to decide about the distance between two datasets. If not, you have to resort to other means.
The next question is: is... | null | CC BY-SA 4.0 | null | 2010-10-04T16:33:09.597 | 2021-09-16T12:57:18.510 | 2021-09-16T12:57:18.510 | 265676 | 1124 | null |
3307 | 1 | null | null | 7 | 647 | I am looking for a fractal-based statistical measure which could be used as alternative to correlation between two variables (I know that hurst exponent can be used for auto-correlation).
Is anyone aware of such measures?
| Fractal alternative to correlation | CC BY-SA 2.5 | null | 2010-10-04T16:44:53.327 | 2010-10-17T15:59:56.937 | 2010-10-07T22:52:00.950 | 1499 | 1250 | [
"correlation",
"fractal"
] |
3308 | 2 | null | 3296 | 9 | null | I think this will require domain expertise. If I were you, I would spend time examining these samples and their provenance, to figure out what (if anything) is wrong with them. If the samples were collected by a colleague working in some application domain, they may be able to help you with this.
Sometimes, samples can... | null | CC BY-SA 2.5 | null | 2010-10-04T17:39:16.307 | 2010-10-04T17:39:16.307 | null | null | 1436 | null |
3309 | 1 | 3310 | null | 5 | 764 | For example:
```
k <- 100
R <- 10000
max.g <- numeric(R)
for(i in 1:R) max.g [i] <- max(rnorm(k))
hist(max.g) # We can see it's right tailed...
```
I remember once encountering that there is a name for this type of distributions, but the name alludes me.
| Is there an analytical expression for the distribution of the max of a normal k sample? | CC BY-SA 2.5 | null | 2010-10-04T17:43:27.613 | 2010-10-04T20:03:08.383 | 2010-10-04T20:03:08.383 | null | 253 | [
"distributions",
"extreme-value",
"normal-distribution"
] |
3310 | 2 | null | 3309 | 6 | null | Properly normalized, it's closely approximated by a [Gumbel distribution](http://en.wikipedia.org/wiki/Gumbel_distribution) as shown by [Extreme value theory](http://en.wikipedia.org/wiki/Extreme_value_theory). Alternative names are provided in the links.
| null | CC BY-SA 2.5 | null | 2010-10-04T17:47:51.937 | 2010-10-04T17:47:51.937 | null | null | 919 | null |
3311 | 2 | null | 3252 | 14 | null | I just drop some references about data dredging and clinical studies for the interested reader. This is intended to extend [@onestop](https://stats.stackexchange.com/users/449/onestop)'s fine answer. I tried to avoid articles focusing only on multiple comparisons or design issues, although studies with multiple endpoin... | null | CC BY-SA 2.5 | null | 2010-10-04T17:56:18.403 | 2010-11-01T10:11:29.257 | 2017-04-13T12:44:42.893 | -1 | 930 | null |
3312 | 2 | null | 3291 | 11 | null | [All of Statistics: A Concise Course in Statistical Inference](http://rads.stackoverflow.com/amzn/click/1441923225) - US$ 79.11
[Statistical Models: Theory and Practice](http://rads.stackoverflow.com/amzn/click/0521743850) - US$ 40.00
[Data Analysis Using Regression and Multilevel/Hierarchical Models](http://rads.stack... | null | CC BY-SA 2.5 | null | 2010-10-04T18:24:36.603 | 2010-10-05T01:56:29.007 | 2010-10-05T01:56:29.007 | 666 | 666 | null |
3313 | 1 | 3319 | null | 5 | 745 | I have a S-Plus [library](https://home.zhaw.ch/~brw/index.html?tools) which I'd like to convert to R. I am a programmer, but I don't know anything about S-Plus or R. From my research it seems that they are highly compatibile. Is that true? The code I want to convert only uses core S-Plus libraries.
I have attached a pi... | How hard is to convert a library from S-PLUS 8.0 to R? | CC BY-SA 2.5 | null | 2010-10-04T19:47:23.197 | 2010-12-15T08:30:42.657 | 2010-10-04T21:15:49.663 | 8 | 749 | [
"r",
"splus"
] |
3314 | 2 | null | 3309 | 7 | null | You will find exact expressions for the full pdf of the $n^{th}$ order statistics (as a function of $n$, the sample size) in the following paper:
Percentage Points and Modes of Order Statistics from the Normal Distribution
Shanti S. Gupta
Source: Ann. Math. Statist. Volume 32, Number 3 (1961), 888-893.
Also includes e... | null | CC BY-SA 2.5 | null | 2010-10-04T19:52:45.160 | 2010-10-04T19:52:45.160 | null | null | 603 | null |
3315 | 2 | null | 3313 | 2 | null | Your S+ functions should work in R, except if they have dependencies on some Libraries that are not currently supported on R (most of the time, it is the reverse, though). About your "global" variables, the easiest way is to use single assignment in your script file (although you can explicitely can write to the global... | null | CC BY-SA 2.5 | null | 2010-10-04T20:09:29.433 | 2010-10-04T20:09:29.433 | null | null | 930 | null |
3316 | 1 | 3325 | null | 15 | 2414 | Supposing one has a time series from which one can take various measurements such as period, maximum, minimum, average etc. and then use these to create a model sine wave with the same attributes, are there any statistical approaches one can use that could quantify how closely the actual data fit the assumed model? The... | Statistical similarity of time series | CC BY-SA 2.5 | null | 2010-10-04T20:21:16.290 | 2013-02-09T06:17:31.850 | 2010-10-07T01:12:03.433 | 226 | 226 | [
"time-series",
"classification"
] |
3317 | 2 | null | 3200 | 20 | null | I think this is a very good question; it gets to the heart of the contentious multiple testing "problem" that plagues fields ranging from epidemiology to econometrics. After all, how can we know if the significance we find is spurious or not? How true is our multivariable model?
In terms of technical approaches to offs... | null | CC BY-SA 3.0 | null | 2010-10-04T20:40:30.743 | 2012-06-05T19:04:53.177 | 2012-06-05T19:04:53.177 | 7290 | 1501 | null |
3318 | 2 | null | 175 | 13 | null | Sharpie,
Taking your question literally, I would argue that there are no statistical tests or rules of thumb can be used as a basis for excluding outliers in linear regression analysis (as opposed to determining whether or not a given observation is an outlier). This must come from subject-area knowledge.
I think the... | null | CC BY-SA 2.5 | null | 2010-10-04T21:29:50.943 | 2010-10-04T21:29:50.943 | null | null | 1501 | null |
3319 | 2 | null | 3313 | 4 | null | I would say that your functions will mostly work in R, but that is far from being universally true. I went through the same conversion before myself, and it was actually quite painful. Especially given all the functions that are actually writing in a foreign language (like C).
As an example: it looks like you have ... | null | CC BY-SA 2.5 | null | 2010-10-04T21:31:19.277 | 2010-10-04T21:31:19.277 | null | null | 5 | null |
3321 | 1 | null | null | 52 | 71031 | I am looking to rank data that, in some cases, the larger value has the rank of 1. I am relatively new to R, but I don't see how I can adjust this setting in the rank function.
```
x <- c(23,45,12,67,34,89)
rank(x)
```
generates:
```
[1] 2 4 1 5 3 6
```
when I want it to be:
```
[1] 5 3 6 2 4 1
```
I assume this is... | Rank in R - descending order | CC BY-SA 2.5 | null | 2010-10-04T21:57:45.903 | 2010-10-04T22:43:34.287 | null | null | 569 | [
"r"
] |
3322 | 2 | null | 3321 | 98 | null | You could negate `x`:
```
> rank(-x)
[1] 5 3 6 2 4 1
```
| null | CC BY-SA 2.5 | null | 2010-10-04T22:43:23.163 | 2010-10-04T22:43:23.163 | null | null | 1390 | null |
3324 | 1 | null | null | 12 | 18705 | I have inherited some data analysis code that, not being an econometrician, I am struggling to understand. One model runs an instrumental variables regression with the following Stata command
```
ivreg my_dv var1 var2 var3 (L.my_dv = D2.my_dv D3.my_dv D4.my_dv)
```
This dataset is a panel with multiple sequential obse... | Why use a lagged DV as an instrumental variable? | CC BY-SA 3.0 | null | 2010-10-04T22:44:57.123 | 2013-07-20T22:59:43.043 | 2013-07-20T22:59:43.043 | 22047 | 1007 | [
"regression",
"stata",
"instrumental-variables"
] |
3325 | 2 | null | 3316 | 7 | null | The Euclidean distance is a common metric in machine learning. The following slides provide a good overview of this area along with references:
- Making Time-series Classification More Accurate Using Learned Constraints
- Introduction to Machine Learning Research on Time Series
Also see the references on Keogh's b... | null | CC BY-SA 2.5 | null | 2010-10-05T00:45:41.003 | 2010-10-05T00:45:41.003 | null | null | 251 | null |
3326 | 2 | null | 3109 | 2 | null | I believe that your use of the word "tracking" may be leading your web queries off topic. Are you interested in time-series? Could you provide a simple example of "tracks clusters of datapoints"?
Using the word "tracks" do you mean over time, through space, or perhaps in reference to a chemical or physical process?
F... | null | CC BY-SA 2.5 | null | 2010-10-05T03:20:46.547 | 2010-10-05T03:20:46.547 | null | null | 1499 | null |
3327 | 2 | null | 3316 | 5 | null | If you have a specific model you wish to compare against: I would recommend Least-squares as a metric to minimize and score possible parameter values against a specific dataset. All you basically have to do is plug in your parameter estimates, use those to generate predicted values, and compute the average squared de... | null | CC BY-SA 2.5 | null | 2010-10-05T03:34:51.777 | 2010-10-05T03:34:51.777 | null | null | 1499 | null |
3328 | 1 | 3334 | null | 11 | 1695 | Question: With a 10 dimensional MCMC chain, let's say I'm prepared to hand you a matrix of the draws: 100,000 iterations (rows) by 10 parameters (columns), how best can I identify the posterior modes? I'm especially concerned with multiple modes.
Background: I consider myself a computationally savvy statistician, but... | Given a 10D MCMC chain, how can I determine its posterior mode(s) in R? | CC BY-SA 2.5 | null | 2010-10-05T04:00:11.767 | 2011-04-18T19:47:30.297 | 2011-01-17T23:59:07.313 | 449 | 1499 | [
"r",
"bayesian",
"markov-chain-montecarlo",
"k-nearest-neighbour",
"mode"
] |
3329 | 2 | null | 2635 | 12 | null | A Gamma distribution is not a conjugate prior for a Gamma distribution. There is a conjugate prior for the Gamma distribution developed by Miller (1980) whose details you can find on [Wikipedia](http://en.wikipedia.org/wiki/Conjugate_prior) and also in the pdf linked in footnote 6. Checkout section 3.2 on page 25 of ... | null | CC BY-SA 3.0 | null | 2010-10-05T04:15:12.117 | 2013-08-02T02:39:59.817 | 2013-08-02T02:39:59.817 | 1499 | 1499 | null |
3330 | 2 | null | 3324 | 5 | null | I don't know Stata, so I can't comment on the specific model. But the use of lagged variables is a fairly common approach when dealing with simultaneity bias in general and creating instrumental variables in particular.
Say you have a feedback between two variables in your model: the independent variable (such as pr... | null | CC BY-SA 2.5 | null | 2010-10-05T05:28:39.143 | 2010-10-05T05:39:47.353 | 2010-10-05T05:39:47.353 | 159 | 251 | null |
3331 | 1 | 3363 | null | 56 | 27955 | I have sales data for a series of outlets, and want to categorise them based on the shape of their curves over time. The data looks roughly like this (but obviously isn't random, and has some missing data):
```
n.quarters <- 100
n.stores <- 20
if (exists("test.data")){
rm(test.data)
}
for (i in 1:n.stores){
interv... | Is it possible to do time-series clustering based on curve shape? | CC BY-SA 2.5 | null | 2010-10-05T07:45:19.550 | 2022-07-04T14:33:43.710 | 2010-10-05T23:05:43.947 | 179 | 179 | [
"r",
"time-series",
"clustering"
] |
3332 | 2 | null | 3328 | 2 | null | Have you considered 'PRIM / bump hunting' ? (see e.g. Section 9.3. of 'The Elements of Statistical Learning' by Tibshirani et al. or ask your favourite search engine). Not sure whether that's implemented in R though.
[ As far as I understood are you trying to find the mode of the probability density from which your 100... | null | CC BY-SA 2.5 | null | 2010-10-05T07:54:19.357 | 2010-10-05T07:54:19.357 | null | null | 961 | null |
3333 | 1 | null | null | 2 | 8270 | I asked a question earlier on [here](https://stats.stackexchange.com/questions/2483/how-to-model-and-test-a-decision-support-system-e-g-a-terrorist-warning-system).
Essentially, I am trying to evaluate a warning system that consists of a light bulb (of a specific color) being switched on, to indicate a predicted warnin... | Calculating posterior probabilities in R? | CC BY-SA 2.5 | null | 2010-10-05T07:54:48.897 | 2010-10-05T19:41:51.290 | 2017-04-13T12:44:24.947 | -1 | 1216 | [
"r",
"bayesian"
] |
3334 | 2 | null | 3328 | 9 | null | Have you considered using a nearest neighbour approach ?
e.g. building a list of the `k` nearest neighbours for each of the 100'000 points and then consider the data point with the smallest distance of the `kth` neighbour a mode. In other words: find the point with the 'smallest bubble' containing `k` other points aro... | null | CC BY-SA 2.5 | null | 2010-10-05T07:58:19.880 | 2010-10-05T07:58:19.880 | null | null | 961 | null |
3335 | 2 | null | 3328 | 4 | null | This is only a partial answer.
I recently used [figtree](http://www.umiacs.umd.edu/~morariu/figtree/) for multidimensional kernel density estimates. It's a C package and I got it to work fairly easily. However, I only used it to estimate the density at particular points, not calculate summary statistics.
| null | CC BY-SA 2.5 | null | 2010-10-05T09:39:19.830 | 2010-10-05T09:39:19.830 | null | null | 8 | null |
3336 | 2 | null | 3324 | 7 | null | Edit: Given the clarification on the stata code provided by Andy W below, i changed my answer to better adress the question. You will find the old version of my answer below the current one.
It seems your code is a clumsy attempt at DIY'ing the Arellano-Bond estimator (assuming ivreg estimates with 2SOLS). You can fin... | null | CC BY-SA 2.5 | null | 2010-10-05T10:31:48.563 | 2010-10-07T08:45:27.100 | 2010-10-07T08:45:27.100 | 603 | 603 | null |
3337 | 1 | 247768 | null | 15 | 4110 | After reading [one of the "Research tips"](http://robjhyndman.com/researchtips/crossvalidation) of R.J. Hyndman about cross-validation and time series, I came back to an old question of mine that I'll try to formulate here. The idea is that in classification or regression problems, the ordering of the data is not impor... | Ordering of time series for machine learning | CC BY-SA 3.0 | null | 2010-10-05T10:47:46.470 | 2016-11-25T06:17:43.577 | 2016-05-09T10:40:48.363 | 22228 | 1504 | [
"time-series",
"machine-learning",
"cross-validation"
] |
3338 | 1 | null | null | 4 | 124 | I'm using a genetic algorithm to generate a string that produces certain results I map into a line/bar graph. I'm trying to rate how closely the results produced by the genetic string compares to a graph I have produce manually by hand.
Currently I am comparing the graphs be finding the area of difference between the t... | Rating how closely one graph models another | CC BY-SA 2.5 | null | 2010-10-05T11:28:09.010 | 2010-10-05T11:51:35.757 | null | null | 1505 | [
"data-visualization",
"model-comparison",
"genetic-algorithms"
] |
3339 | 2 | null | 3338 | 3 | null | You question is very closely related to this [question](https://stats.stackexchange.com/questions/3286/distance-between-empirically-generated-distributions-in-r) about differences between distributions.
Have a look at the three answers. That should give you some R code to get started.
BTW, I don't think your question... | null | CC BY-SA 2.5 | null | 2010-10-05T11:51:35.757 | 2010-10-05T11:51:35.757 | 2017-04-13T12:44:52.277 | -1 | 8 | null |
3340 | 2 | null | 2348 | 1 | null | I use a slice sampler - originally proposed by Neal(2003), which I tune through heuristic optimization.
| null | CC BY-SA 2.5 | null | 2010-10-05T12:11:53.590 | 2010-10-05T12:11:53.590 | null | null | 1499 | null |
3341 | 2 | null | 725 | 4 | null | Not an R package, but D. A. Landgrebe from Purdue (author of Signal theory methods in multispectral remote sensing) has sponsored the [MultiSpec](https://engineering.purdue.edu/~biehl/MultiSpec/) freeware. Its a rather clunky GUI but gets the job done for most of the common hyperspectral algorithms.
| null | CC BY-SA 2.5 | null | 2010-10-05T12:18:20.193 | 2010-10-05T12:18:20.193 | null | null | 179 | null |
3342 | 1 | 3343 | null | 7 | 6869 | I have some data that I've built histograms out of in R. Now I want to play around with the data, but first I want to summarise it in the same way the histogram does. That is, I want to take my data vector, and count how many points fall in each bin interval in the same way that R's `hist` function does.
I was about to... | Recreating R's hist function's bin counting | CC BY-SA 2.5 | null | 2010-10-05T12:32:27.393 | 2010-10-06T14:31:44.757 | 2010-10-06T14:31:44.757 | 8 | 5141 | [
"r",
"histogram"
] |
3343 | 2 | null | 3342 | 10 | null | Perhaps I've misunderstood what you want, but `hist()` returns all the details required to produce the histogram that is plotted. But you don't need to plot it and you can capture the returned object for subsequent use. So if the histogram contains the relevant summary you are after, this should be all you need. Here's... | null | CC BY-SA 2.5 | null | 2010-10-05T12:38:30.410 | 2010-10-05T12:38:30.410 | null | null | 1390 | null |
3345 | 2 | null | 3333 | 1 | null | By the tone of your question, it may be handy to read a little bit about generating draws from a posterior. Two introductory springer books (full of R codes) comes to mind (in order of preference):
a) Bayesian computation with R.
Springer, 2007. J. Albert.
b) Introducing Monte Carlo Methods with R.
Springer 2010. C. P... | null | CC BY-SA 2.5 | null | 2010-10-05T13:21:50.150 | 2010-10-05T13:21:50.150 | null | null | 603 | null |
3346 | 2 | null | 125 | 7 | null | I simply must to include [MCMC in Practice](http://rads.stackoverflow.com/amzn/click/0412055511). It provides an excellent introduction to MCMC, perhaps not as general as other books mentioned, but excellent for gaining insight and intuition. I would recommend reading it after (or in parallel with) [Bayesian Computat... | null | CC BY-SA 2.5 | null | 2010-10-05T13:54:13.333 | 2010-10-05T13:54:13.333 | null | null | 1499 | null |
3347 | 2 | null | 1904 | 1 | null | Though it hasn't been around for decades, I'm currently looking forward to [MCMSki](http://madison.byu.edu/mcmski/). One could also mention the [Valencia Meetings](http://www.uv.es/valenciameeting), but those only happen every four years (and you've already missed the 2010 meeting).
| null | CC BY-SA 2.5 | null | 2010-10-05T15:08:21.867 | 2010-10-05T15:08:21.867 | null | null | 1499 | null |
3348 | 2 | null | 3199 | 5 | null | You could also consider [Moran's I](http://en.wikipedia.org/wiki/Moran%27s_I) which is available in the R package "ape". And then simply use a weighting based on distance:
```
nRows <- 30
nCols <- 15
nPixels <- nRows * nCols
# Create a Random Image
image <- matrix(sample.int(256, nPixels, replace=TRUE),
... | null | CC BY-SA 4.0 | null | 2010-10-05T17:06:35.777 | 2019-07-30T10:42:07.273 | 2019-07-30T10:42:07.273 | 189336 | 1499 | null |
3349 | 2 | null | 3064 | 2 | null | This is not a complete answer but I hope it gives you some ideas as to how to model the situation in a coherent manner.
Assumptions
- The values at the lower end of the scale follow a normal distribution truncated from below.
- The values at the upper end of the scale follow a normal distribution truncated from abov... | null | CC BY-SA 2.5 | null | 2010-10-05T17:08:52.073 | 2010-10-06T23:46:47.417 | 2010-10-06T23:46:47.417 | null | null | null |
3350 | 2 | null | 2611 | 3 | null | Disclaimer: This idea might be foolish & I'm not going to pretend to understand the theoretical implications of what I'm proposing.
"Suggestion" : Why don't you simply impute 100 (I know you normally do 5) datasets, run the lme4 or nmle, get the confidence intervals (you have 100 of them) and then:
Using a small int... | null | CC BY-SA 2.5 | null | 2010-10-05T17:34:51.493 | 2010-10-05T17:34:51.493 | null | null | 1499 | null |
3351 | 2 | null | 3333 | 2 | null | This should actually be a fairly simple operation to do in R.
```
bulbs <- read.csv("bulbs.csv")
bulbs <- table(bulbs)
```
Now just sum along the rows or columns to get the marginal values.
```
sum(bulbs[,1]) # counts of no event occurring
sum(bulbs[1,]) # counts of green light
```
Now just take these frequencies an... | null | CC BY-SA 2.5 | null | 2010-10-05T19:41:51.290 | 2010-10-05T19:41:51.290 | null | null | 1392 | null |
3352 | 1 | 3353 | null | 5 | 540 | If $X$ is a categorical variable, and I am interested in the posterior distributions of $\beta_1$, where $\beta_1$ is a vector of coefficients, one for each level of X, are these equivalent models?
Model 1:
$$Y \sim ( \beta_0 + \beta_1X_1,\sigma^2)$$
$$\beta_1 \sim N(0,\tau)$$
Model 2:
$$Y \sim(\beta_1X_1,\sigma^2)$$
$... | Are these equivalent representations of the same hierarchical Bayesian model? | CC BY-SA 3.0 | null | 2010-10-05T20:18:07.143 | 2015-04-15T14:19:02.867 | 2015-04-15T14:19:02.867 | 1381 | 1381 | [
"modeling",
"bayesian",
"multilevel-analysis"
] |
3353 | 2 | null | 3352 | 6 | null | Updated Response: You still don't have a full specification for model #2. However, I can sort of guess what you mean -- correct me if I'm wrong. The trouble is that the statements $Y = \beta_1 X$ & $Y = \beta_0 + \beta_1 X$ are not probabilistic.
[ Aside: In a mathematical sense, you're defining a set of linear equ... | null | CC BY-SA 2.5 | null | 2010-10-05T20:37:30.390 | 2010-10-06T03:49:19.403 | 2010-10-06T03:49:19.403 | 1499 | 1499 | null |
3354 | 2 | null | 3337 | 6 | null | Interesting question!
The approach you describe is certainly very widely used by people using standard ML methods that require fixed-length feature vectors of attributes, to analyse time series data.
In the post that you link to, Hyndman points out that there are correlations between the reshaped data vectors (samples... | null | CC BY-SA 3.0 | null | 2010-10-05T21:16:56.510 | 2016-05-09T13:18:46.970 | 2016-05-09T13:18:46.970 | 109618 | 1436 | null |
3355 | 2 | null | 3352 | 4 | null | The only similarity in the two models is the general type of models they belong to, otherwise they are not similar in general as pointed out by M. Tibbits.
Both these models belong the class of hierarchical models with varying slope (cf [Gelman and Hill 2006](http://rads.stackoverflow.com/amzn/click/052168689X) for det... | null | CC BY-SA 2.5 | null | 2010-10-05T21:56:23.063 | 2010-10-05T21:56:23.063 | null | null | 1307 | null |
3356 | 2 | null | 3324 | 5 | null | For those not familiar with the following code snippet from Stata the OP provided
`ivreg my_dv var1 var2 var3 (L.my_dv = D2.my_dv D3.my_dv D4.my_dv)`
this equation can be read as
$Y_t = \alpha + \beta_1 (Var1) + \beta_2 (Var1) + \beta_3 (Var1) + \beta_4 (\tilde{Y}_{t-1})$
where $\tilde{Y}_{t-1}$ is estimated by
$\tild... | null | CC BY-SA 2.5 | null | 2010-10-06T02:22:05.297 | 2010-10-07T03:14:40.720 | 2017-04-13T12:44:49.837 | -1 | 1036 | null |
3357 | 2 | null | 3275 | -1 | null | I've figured out how to do this in O(n^3) time (where n is the number of games):
Use a 4-dimensional array:
probabilityOfOutcome[gamesLookedAt][wins][losses][ties]
Initially, we have "looked at" zero games. After looking at zero games, there is a 100% probability of being 0-0-0, so set:
probabilityOfOutcome[0][0][... | null | CC BY-SA 2.5 | null | 2010-10-06T02:23:39.773 | 2010-10-06T02:23:39.773 | null | null | null | null |
3358 | 2 | null | 2611 | 7 | null | Repeated comment from above:
i'm not sure that a proper analytical solution to this problem even exists. I've looked at some additional literature, but this problem is elegantly overlooked everywhere. I've also noticed that Yucel & Demirtas (in the article i mentioned, page 798) write:
>
These multiply imputed dataset... | null | CC BY-SA 2.5 | null | 2010-10-06T09:35:15.300 | 2010-10-06T14:31:47.217 | 2010-10-06T14:31:47.217 | 930 | 1266 | null |
3359 | 1 | 3360 | null | 9 | 6171 | I am looking at trigger efficiencies, meaning that I have some device that fires on $k$ out of $n$ events. In the end I am interested in some estimate of the efficiency $\epsilon$ which is the probability to fire on a randomly given event. Using a Bayesian approach with a uniform prior over $[0,1]$ I can model the prob... | Product of beta distributions | CC BY-SA 2.5 | null | 2010-10-06T11:52:36.327 | 2022-05-06T22:37:45.897 | 2017-04-13T12:44:25.243 | -1 | 1512 | [
"distributions",
"beta-distribution"
] |
3360 | 2 | null | 3359 | 9 | null | According to the abstract of [this paper](http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=SMJMAP000018000004000721000001&idtype=cvips&gifs=yes&ref=no),
>
The density function of products of
random beta variables is a Meijer
$G$-function which is expressible in
closed form when the parameters are
i... | null | CC BY-SA 4.0 | null | 2010-10-06T12:19:12.380 | 2022-05-06T22:37:45.897 | 2022-05-06T22:37:45.897 | 79696 | 319 | null |
3361 | 2 | null | 3291 | 8 | null | You might want to spend $1.20 printing out Matthias Vallentin's [probability and statistics cheat sheet](http://statistics.zone/).
| null | CC BY-SA 3.0 | null | 2010-10-06T12:22:32.077 | 2015-11-04T01:34:18.227 | 2015-11-04T01:34:18.227 | 805 | 319 | null |
3362 | 1 | 3375 | null | 6 | 3474 | I'd like to assess the impact of an upcoming policy implementation, as measured by changes in questionnaire response to a Likert-scale question.
I understand I could use a difference-in-difference approach. However, in my situation there is no single obvious comparison, non-treated population. I think I'd like to u... | Can I use Synthetic Control Method for Comparative Case Studies with survey data? | CC BY-SA 2.5 | null | 2010-10-06T13:27:08.537 | 2010-10-07T12:59:02.717 | 2010-10-07T12:59:02.717 | 644 | 644 | [
"econometrics",
"survey",
"causality",
"panel-data"
] |
3363 | 2 | null | 3331 | 32 | null | Several directions for analyzing longitudinal data were discussed in the link provided by @Jeromy, so I would suggest you to read them carefully, especially those on functional data analysis. Try googling for "Functional Clustering of Longitudinal Data", or the PACE Matlab toolbox which is specifically concerned with m... | null | CC BY-SA 2.5 | null | 2010-10-06T14:00:52.570 | 2010-10-06T15:18:24.760 | 2010-10-06T15:18:24.760 | 930 | 930 | null |
3364 | 1 | 3365 | null | 4 | 1570 | I have conducted an IRT analysis using package [ltm](http://cran.r-project.org/web/packages/ltm/index.html) in R 2.10.
```
fit1 <- grm(data)
```
When producing plots of item response characteristic curves (ICCs),
```
plot(fit1)
```
the text "Item response characteristic curves - Item:NAME" is printed as the main ti... | R package ltm: How to manipulate title on item response category characteristic curve plot | CC BY-SA 2.5 | 0 | 2010-10-06T15:15:25.997 | 2010-10-18T07:50:52.467 | 2010-10-18T07:50:52.467 | 449 | 913 | [
"r",
"data-visualization",
"psychometrics"
] |
3365 | 2 | null | 3364 | 3 | null | Try using the argument `main=` when calling `plot()`, e.g.
```
dat <- Science[c(1,3,4,7)]
fit1 <- grm(dat)
plot(fit1, items=1, main=paste("Item", names(dat)[1], sep=": "))
```
See `help(plot.grm)`.
Also, you can embed all ICC curves in the same figure by using `par()`, e.g.
```
opar <- par(mfrow=c(2,2))
for (i in 1:4... | null | CC BY-SA 2.5 | null | 2010-10-06T15:31:00.033 | 2010-10-06T15:31:00.033 | null | null | 930 | null |
3367 | 2 | null | 152 | 9 | null | [Gilles Celeux](https://web.archive.org/web/20070219055605/http://www.math.u-psud.fr/select/people/celeux/Welcome.html) also worked on the problem of label switching, e.g.
>
G. Celeux, Bayesian inference for
Mixture: the label switching problem.
Proceedings Compstat 98, pp. 227-232, Physica-Verlag (1998).
As a compl... | null | CC BY-SA 4.0 | null | 2010-10-06T16:57:44.897 | 2022-11-25T10:01:05.537 | 2022-11-25T10:01:05.537 | 362671 | 930 | null |
3368 | 1 | null | null | 4 | 293 | Suppose I have three time series with the same length and the same temporal interval. If I upsample each of the three time series through interpolation, that will cause aliasing. However, if I'm interested in the information embedded in the average of the three time series, is such an upsampling justified? In other wor... | Does averaging across multiple time series render higher Nyquist frequency? | CC BY-SA 2.5 | null | 2010-10-06T19:52:48.773 | 2010-10-06T21:44:56.557 | null | null | 1513 | [
"time-series"
] |
3369 | 1 | null | null | 0 | 841 | >
Possible Duplicate:
What are the differences between Factor Analysis and Principal Component Analysis
I am trying to understand the difference between PCA and FA. Through google research, I have come to understand that PCA accounts for all variance, while FA accounts for only common variance and ignores unique va... | Difference between FA and PCA | CC BY-SA 2.5 | 0 | 2010-10-06T20:05:29.200 | 2010-10-06T21:51:18.863 | 2017-04-13T12:44:29.013 | -1 | 1514 | [
"pca",
"factor-analysis"
] |
3370 | 2 | null | 1611 | 11 | null | I think Bayesian statistics come into play in two different contexts.
On the one hand, some researchers/statisticians are definitely convinced of the "Bayesian spirit" and, acknowledging the limit of the classical frequentist hypothesis framework, have decided to concentrate on Bayesian thinking. Studies in experiment... | null | CC BY-SA 3.0 | null | 2010-10-06T20:10:59.000 | 2013-08-06T10:41:15.660 | 2013-08-06T10:41:15.660 | 6029 | 930 | null |
3371 | 2 | null | 1576 | 33 | null | You are right about your first point, although in FA you generally work with both (uniqueness and communality).
The choice between PCA and FA is a long-standing debate among psychometricians. I don't quite follow your points, though. Rotation of principal axes can be applied whatever the method is used to construct lat... | null | CC BY-SA 3.0 | null | 2010-10-06T20:33:36.393 | 2013-06-25T00:17:50.460 | 2017-04-13T12:44:37.420 | -1 | 930 | null |
3372 | 1 | 3376 | null | 25 | 8816 | I must clarify immediately that I am a practicing software developer, not a statistician, and that my college stats class was a very long time ago…
That said, I would like to know if there is a method for accumulating a set of descriptive statistics that could then be used to produce a boxplot, that does not entail sto... | Is it possible to accumulate a set of statistics that describes a large number of samples such that I can then produce a boxplot? | CC BY-SA 2.5 | null | 2010-10-06T21:16:06.060 | 2012-12-17T21:54:45.480 | 2010-10-07T08:54:50.567 | null | 1515 | [
"algorithms",
"median",
"quantiles"
] |
3373 | 2 | null | 3368 | 3 | null | Simple Answer: No. (At least I don't think so -- It's been 10 years since my EE classes).
Suggested Reading: You might check out the Wikipedia page on [Oversampling](http://en.wikipedia.org/wiki/Oversampling) or also this page on [Reducing the effects of noise](http://zone.ni.com/devzone/cda/tut/p/id/3947).
In general... | null | CC BY-SA 2.5 | null | 2010-10-06T21:29:23.210 | 2010-10-06T21:44:56.557 | 2010-10-06T21:44:56.557 | 1499 | 1499 | null |
3374 | 2 | null | 1576 | 54 | null | From my response here:
[Is PCA followed by a rotation (such as varimax) still PCA?](https://stats.stackexchange.com/questions/612/is-psychprincipal-function-still-pca-when-using-rotation)
Principal Component Analysis (PCA) and Common Factor Analysis (CFA) are distinct methods. Often, they produce similar results and PC... | null | CC BY-SA 3.0 | null | 2010-10-06T21:51:18.863 | 2017-03-05T05:08:09.727 | 2017-04-13T12:44:51.060 | -1 | 485 | null |
3375 | 2 | null | 3362 | 3 | null | [Caveat: I have not read the paper so the below may be nonsense for all I know ...]
Based on the summary of the R package I would venture to guess that you could use the proposed methodology for the survey data provided the following conditions are met:
- You have survey data from control groups during pre-interventio... | null | CC BY-SA 2.5 | null | 2010-10-07T00:02:32.620 | 2010-10-07T00:02:32.620 | 2020-06-11T14:32:37.003 | -1 | null | null |
3376 | 2 | null | 3372 | 32 | null | For 'on the fly' boxplot, you will need 'on the fly' min/max (trivial) as well as 'on the fly' quartiles (0.25,0.5=median and 0.75).
A lot of work has been going on recently in the problem of online (or 'on the fly') algorithm for median computation.
A recent developements is binmedian. As a side-kick, it also enjoy ... | null | CC BY-SA 2.5 | null | 2010-10-07T00:33:55.410 | 2010-10-07T08:41:44.510 | 2010-10-07T08:41:44.510 | 603 | 603 | null |
3377 | 1 | null | null | 17 | 5029 | I have a little problem that is making me freaking out.
I have to write procedure for an online acquisition process of a multivariate time series.
At every time interval (for example 1 second), I get a new sample, which is basically a floating point vector of size N.
The operation I need to do is a little bit tricky:
... | Online algorithm for mean absolute deviation and large data set | CC BY-SA 2.5 | null | 2010-10-07T03:26:23.003 | 2010-11-09T05:36:27.843 | 2010-10-08T16:04:53.503 | 8 | 667 | [
"algorithms",
"quantiles",
"online-algorithms",
"large-data"
] |
3378 | 2 | null | 3377 | 6 | null | If you can accept some inaccuracy, this problem can be solved easily by binning counts. That is, pick some largeish number $M$ (say $M = 1000$), then initialize some integer bins $B_{i,j}$ for $i = 1\ldots M$ and $j = 1\ldots N$, where $N$ is the vector size, as zero. Then when you see the $k$th observation of a percen... | null | CC BY-SA 2.5 | null | 2010-10-07T05:17:09.267 | 2010-10-07T16:44:47.327 | 2010-10-07T16:44:47.327 | 795 | 795 | null |
3379 | 2 | null | 3377 | 2 | null | MAD(x) is just two concurrent median computation, each of which can be made online through the binmedian algorithm.
You can find the associated paper as well as C and FORTRAN code online [here](http://www-stat.stanford.edu/~ryantibs/median/).
(this is just the use of a clever trick on top of Shabbychef's clever trick, ... | null | CC BY-SA 2.5 | null | 2010-10-07T08:40:07.437 | 2010-10-07T08:40:07.437 | null | null | 603 | null |
3380 | 1 | null | null | 2 | 77 | I work with amino acid sequences and I want to use a self-made model to tell me something about it, lets call it $f(\text{seq})$. Now i want to know the contribution of every position in the sequence onto the model, i.e. my question is what is the importance/effect of amino acid $A$ occuring at position $I$ in the sequ... | Analysis of variables of varying numbers | CC BY-SA 2.5 | null | 2010-10-07T10:50:24.480 | 2010-10-07T11:27:06.060 | 2010-10-07T11:10:56.880 | 930 | 1516 | [
"machine-learning",
"feature-selection",
"bioinformatics"
] |
3381 | 1 | null | null | 4 | 1718 | Say I make histograms H1, H2, H4 ...of the same set of data
with bins 1, 2, 4 ... wide.
Then the bins containing a given $x$ have counts and averages
```
n1 av1 in H1,
n2 av2 in H2,
n4 av4 in H4 ...
```
How should one weight these to estimate data(x) ?
One possibility would be $\Sigma w_j \text{av}_j / \Sigma w_j$
wit... | How to smooth histograms with bins 1, 2, 4, ... wide? | CC BY-SA 3.0 | null | 2010-10-07T10:59:51.483 | 2013-06-07T09:25:31.843 | 2013-06-07T09:25:31.843 | 557 | 557 | [
"smoothing",
"histogram"
] |
3382 | 2 | null | 3380 | 1 | null | This is a tricky topic in general; because of the size variability you mentioned it rather boils down to motif finding. Yet I can propose four ideas:
- Supposing that you can align sequences (ok, you always can, but let's say it yields non nonsense consensus), you can reduce the problem to the conserved region and mak... | null | CC BY-SA 2.5 | null | 2010-10-07T11:27:06.060 | 2010-10-07T11:27:06.060 | null | null | null | null |
3383 | 1 | 3639 | null | 4 | 3375 | This sounds like an easy setup, and I'm absolutely sure it's not too complicated to do, but somehow I can't figure out how to approach this.
The setting is as follows : There are 6 treatments with 16 subjects in each treatment. Weight is measured for every subject before and after treatment.
The hypothesis to be tested... | Compare treatments on mean difference between two times | CC BY-SA 2.5 | null | 2010-10-07T12:30:23.340 | 2010-10-15T16:34:25.457 | null | null | 1124 | [
"anova",
"mean",
"repeated-measures"
] |
3384 | 2 | null | 3316 | 3 | null | Your "simplistic first thought" of qualitatively representing just the directional movement is similar in spirit to Keogh's SAX algorithm for comparing time series. I'd recommend you take a look at it: [Eamonn Keogh & Jessica Lin: SAX](http://www.cs.ucr.edu/~eamonn/SAX.htm).
From your edit, it sounds like you're now th... | null | CC BY-SA 2.5 | null | 2010-10-07T13:15:02.057 | 2010-10-07T13:15:02.057 | null | null | 1436 | null |
3385 | 2 | null | 3383 | 2 | null | First since you do not know which subjects are which, I think it makes more sense to treat each group of observations (before treatment and after treatment) as seperate observations (i.e. as if you randomly assigned treatment to 96 out of 192 subjects). I know this seems obvious but for heuristic purposes I think it he... | null | CC BY-SA 2.5 | null | 2010-10-07T13:21:46.283 | 2010-10-07T13:21:46.283 | null | null | 1036 | null |
3386 | 1 | null | null | 15 | 151473 | I have to do binary logistic regression with a lot of independent variables. Most of them are binary, but a few of the categorical variables have more than two levels.
What is the best way to deal with such variables?
For example, for a variable with three possible values, I suppose that two dummy variables have to be ... | How to deal with non-binary categorical variables in logistic regression (SPSS) | CC BY-SA 3.0 | null | 2010-10-07T14:48:10.447 | 2017-07-12T12:07:47.197 | 2017-07-12T12:07:47.197 | 3277 | 1219 | [
"logistic",
"categorical-data",
"spss",
"ordinal-data",
"categorical-encoding"
] |
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