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3060 | 2 | null | 2686 | 1 | null | Buzzwords: interpolation, resampling, smoothing.
Your problem is similar to one encountered frequently in demography: people might have census counts broken down into age intervals, for example, and such intervals are not always of constant width. You want to interpolate the distribution by age. What this shares with... | null | CC BY-SA 2.5 | null | 2010-09-24T17:57:25.143 | 2010-09-24T17:57:25.143 | null | null | 919 | null |
3061 | 1 | null | null | 7 | 2155 | Does Lin's [concordance correlation coefficient](http://en.wikipedia.org/wiki/Concordance_correlation_coefficient) assume that the 2 data sets have linear or monotonic tendencies? Or, can I measure the concordance between 2 data sets that have a sinusoidal tendency?
| Does the concordance correlation coefficient make linearity or monotone assumptions? | CC BY-SA 3.0 | null | 2010-09-24T18:40:39.993 | 2017-06-01T11:19:26.733 | 2017-06-01T11:19:26.733 | 11887 | 1417 | [
"correlation"
] |
3062 | 1 | null | null | 1 | 456 | Is there a statistical procedure that can measure the concordance between two sets of data that have, say, a sinusoidal tendency?
| How do we find concordance between two sets of non-linear, non-monotonic data | CC BY-SA 2.5 | null | 2010-09-24T19:09:28.577 | 2010-09-24T19:21:21.743 | null | null | 1417 | [
"correlation"
] |
3063 | 2 | null | 3062 | 2 | null | short answer (the question is rather imprecise). Say $y_{1t}$ and $y_{2t}$ are your two sets, then $z_t=y_{1t}-y_{2t}$. Then it amounts to checking whether $z_t$ is bounded (in your example it will be, if you sample $y_{1}$ and $y_{2}$ over a long enough amount to time).
| null | CC BY-SA 2.5 | null | 2010-09-24T19:21:21.743 | 2010-09-24T19:21:21.743 | null | null | 603 | null |
3064 | 1 | 3349 | null | 10 | 462 | I have the test results of a blood test administered to 2500 people four times at six-month intervals. The results primarily consist of two measures of immune response - one in the presence of certain tuberculosis antigens, one in the absence. Currently, each test evaluates to either positive or negative based on the... | Longitudinal comparison of two distributions | CC BY-SA 2.5 | null | 2010-09-24T19:54:13.507 | 2010-10-06T23:46:47.417 | 2010-09-29T20:51:01.840 | 71 | 71 | [
"repeated-measures"
] |
3065 | 2 | null | 3052 | 5 | null | You could use some sort of Monte Carlo approach, using for instance the moving average of your data.
Take a moving average of the data, using a window of a reasonable size (I guess it's up to you deciding how wide).
Throughs in your data will (of course) be characterized by a lower average, so now you need to find some... | null | CC BY-SA 2.5 | null | 2010-09-24T20:12:41.937 | 2010-09-24T21:37:23.963 | 2010-09-24T21:37:23.963 | 582 | 582 | null |
3066 | 2 | null | 3048 | 11 | null | Lets assume mat_pages[] contains pages in the columns (which you want to cluster) and individuals in the rows. You can cluster pages based on individual data in Rby using the following command:
```
pc <- prcomp(x=mat_pages,center=TRUE,scale=TRUE)
```
The loadings matrix is the matrix of eigenvectors of the SVD deco... | null | CC BY-SA 2.5 | null | 2010-09-25T00:23:28.127 | 2010-09-28T16:05:34.673 | 2010-09-28T16:05:34.673 | 1307 | 1307 | null |
3067 | 2 | null | 175 | 7 | null | There are two statistical distance measures that are specifically catered to detecting outliers and then considering whether such outliers should be removed from your linear regression.
The first one is Cook's distance. You can find a pretty good explanation of it at Wikipedia: [http://en.wikipedia.org/wiki/Cook%27s... | null | CC BY-SA 2.5 | null | 2010-09-25T00:38:39.800 | 2010-09-25T00:38:39.800 | null | null | 1329 | null |
3068 | 2 | null | 2966 | 0 | null | One could view the recorded times as biased estimates of the runner's latent ability. Many factors would cause the time to be worse than the latent best time, such as a bad start, headwind, a stumble, mis-judgement of pace, etc, while very few would cause the recorded times to be better than the latent best, such as a ... | null | CC BY-SA 2.5 | null | 2010-09-25T03:51:59.883 | 2010-09-25T03:51:59.883 | null | null | 795 | null |
3069 | 1 | 3070 | null | 18 | 13346 | Among Matlab and Python, which language is good for general statistical data analysis? What are the pros and cons, other than accessibility, for each?
| Among Matlab and Python, which language is good for statistical analysis? | CC BY-SA 3.0 | null | 2010-09-25T04:09:33.167 | 2016-07-13T21:41:22.603 | 2016-07-11T23:31:00.323 | 28666 | null | [
"matlab",
"python"
] |
3070 | 2 | null | 3069 | 30 | null | As a diehard Matlab user for the last 10+ years, I recommend you learn Python. Once you are sufficiently skilled in a language, when you work in a language you are learning, it will seem like you are not being productive enough, and you will fall back to using your default best language. At the very least, I would sugg... | null | CC BY-SA 2.5 | null | 2010-09-25T04:53:48.313 | 2010-09-25T04:53:48.313 | null | null | 795 | null |
3071 | 2 | null | 3069 | 12 | null | Lets break it down into three areas (off the top of my head) where programming meets statistics: data crunching, numerical routines (optimization and such) and statistical libraries (modeling, etc).
On the first, the biggest difference is that Python is a general purpose programming language. Matlab is great as long... | null | CC BY-SA 2.5 | null | 2010-09-25T05:25:21.730 | 2010-09-25T05:25:21.730 | 2017-04-13T12:44:21.160 | -1 | 251 | null |
3072 | 2 | null | 3051 | 3 | null | I can do this easily in Matlab and duck while you downvote me:
```
%given vector x, windowsize, slide
idx1 = 1:slide:numel(x);
idx2 = min(numel(x) + 1,idx1 + windowsize); %sic on +1 here and no -1;
cx = [0;cumsum(x(:))]; %pad out a zero, perform a cumulative sum;
rv = (cx(idx2) - cx(idx1)) / windowsize; %tada! the a... | null | CC BY-SA 2.5 | null | 2010-09-25T05:51:58.807 | 2010-09-26T16:00:42.393 | 2010-09-26T16:00:42.393 | 795 | 795 | null |
3073 | 2 | null | 3051 | 3 | null | [shabbychef's answer](https://stats.stackexchange.com/questions/3051/mean-of-a-sliding-window-in-r/3072#3072) in R:
```
slideMean<-function(x,windowsize=3,slide=2){
idx1<-seq(1,length(x),by=slide);
idx1+windowsize->idx2;
idx2[idx2>(length(x)+1)]<-length(x)+1;
c(0,cumsum(x))->cx;
return((cx[idx2]-cx[idx1])/windowsi... | null | CC BY-SA 2.5 | null | 2010-09-25T08:31:02.847 | 2010-09-25T08:39:03.437 | 2017-04-13T12:44:33.237 | -1 | null | null |
3075 | 2 | null | 3064 | 3 | null | Tricky Matt, as many real-world stats problems are!
I would start be defining your study aims/objectives.
Without knowing the true status of the subjects it will be hard to define the probability distributions for the TB+ and TB- test. Do you have questionairres regarding previous TB infection (or better, medical histo... | null | CC BY-SA 2.5 | null | 2010-09-25T09:39:40.313 | 2010-09-25T09:39:40.313 | null | null | 521 | null |
3076 | 2 | null | 2910 | 8 | null | This overlaps with Shane's answer, but in my view there are two main piers:
- Reproducibility; not
only because you won't end with
results that are made "somehow" but
also be able to rerun the analysis
faster (on other data or with
slightly changed parameters) and have
more time to think about the results. For a huge ... | null | CC BY-SA 2.5 | null | 2010-09-25T15:45:57.583 | 2010-09-25T15:45:57.583 | null | null | null | null |
3077 | 2 | null | 1735 | 2 | null | One answer is to do a seemingly unrelated regression. Suppose that you only have a single predictor plus an intercept. Create a data set (or data matrix) like
```
wo 1 wp 0 0
so 0 0 1 sp
```
where 'wo' is the outcome in the winter season and 'wp' is the winter predictor/"X" value and 'so' is the summer outcome value ... | null | CC BY-SA 2.5 | null | 2010-09-25T16:17:28.180 | 2010-09-25T16:17:28.180 | null | null | 401 | null |
3078 | 2 | null | 1001 | 7 | null | For measuring the bin frequencies of two distributions, a pretty good test is the Chi Square test. It is exactly what it is designed for. And, it is even nonparametric. The distribution don't even have to be normal or symmetric. It is much better than the Kolmogorov-Smirnov test that is known to be weak in fitting ... | null | CC BY-SA 2.5 | null | 2010-09-25T16:27:42.863 | 2010-09-25T16:27:42.863 | null | null | 1329 | null |
3079 | 2 | null | 3059 | 5 | null | There is a wrinkle that you need to worry about. In the case of matching, you are throwing away observations (i.e., those that aren't matched and don't make it into your analysis) and some might be replicated. These decisions aren't random; they are a function of covariates. As a result, creating confidence intervals i... | null | CC BY-SA 2.5 | null | 2010-09-25T16:30:42.857 | 2010-09-25T16:30:42.857 | null | null | 401 | null |
3080 | 1 | 3099 | null | 11 | 778 | You so often come across in the press various studies that conclude directionally opposite results. Those can be related to the testing of a new prescription drug or the merit of a specific nutrient or anything else for that matter.
When two such studies arrive at conflicting results how can you tell which one of the... | How to detect which one is the better study when they give you conflicting results? | CC BY-SA 2.5 | null | 2010-09-26T04:25:32.470 | 2010-09-27T03:26:45.223 | 2010-09-27T00:44:35.023 | 183 | 1329 | [
"hypothesis-testing",
"clinical-trials"
] |
3081 | 2 | null | 3080 | 8 | null | The [meta analysis](http://jeromyanglim.blogspot.com/2009/12/meta-analysis-tips-resources-and.html) literature is relevant to your question. Using meta-analytic techniques you could generate an estimate of the effect of interest pooled across studies. Such techniques often weight studies in terms of their sample size.
... | null | CC BY-SA 2.5 | null | 2010-09-26T06:18:11.867 | 2010-09-26T11:09:08.063 | 2010-09-26T11:09:08.063 | 183 | 183 | null |
3082 | 1 | null | null | 24 | 5585 | This is a follow-up to a Stackoverflow [question about shuffling an array randomly](https://stackoverflow.com/questions/3796786/random-number-generator-without-dupes-in-javascript/3796821#3796821).
There are established algorithms (such as the [Knuth-Fisher-Yates Shuffle](http://en.wikipedia.org/wiki/Fisher%E2%80%93Yat... | What is wrong with this "naive" shuffling algorithm? | CC BY-SA 2.5 | null | 2010-09-26T07:37:14.583 | 2018-09-11T01:54:32.910 | 2017-05-23T12:39:26.167 | -1 | 1421 | [
"combinatorics",
"randomness"
] |
3083 | 2 | null | 3082 | 2 | null | Bear in mind I am not a statistician, but I'll put my 2cents.
I made a little test in R (careful, it's very slow for high `numTrials`, the code can probably be optimized):
```
numElements <- 1000
numTrials <- 5000
swapVec <- function()
{
vec.swp <- vec
for (i in 1:numElements)
{
i <- sampl... | null | CC BY-SA 2.5 | null | 2010-09-26T09:41:18.333 | 2010-09-26T21:29:29.830 | 2010-09-26T21:29:29.830 | 582 | 582 | null |
3084 | 2 | null | 1875 | 3 | null | When the forecast says "X percent chance of rain in (area)", it means that the numerical weather model has indicated rain in X percent of the area, for the time interval in question. For example, it would normally be accurate to predict "100 percent chance of rain in North America". Bear in mind that the models are g... | null | CC BY-SA 2.5 | null | 2010-09-26T10:48:15.957 | 2010-09-26T10:48:15.957 | null | null | 1054 | null |
3085 | 2 | null | 3082 | 8 | null | I think your simple algorithm will shuffle the cards correctly as the number shuffles tends to infinity.
Suppose you have three cards: {A,B,C}. Assume that your cards begin in the following order: A,B,C. Then after one shuffle you have following combinations:
```
{A,B,C}, {A,B,C}, {A,B,C} #You get this if choose the sa... | null | CC BY-SA 2.5 | null | 2010-09-26T13:04:04.267 | 2010-09-26T17:28:00.303 | 2010-09-26T17:28:00.303 | 8 | 8 | null |
3086 | 1 | 3135 | null | 9 | 12714 | Currently I'm analysing around 300 items in the field of education.
I'm interested in the dimensionality of the dataset. So I compute a matrix of tetrachoric correlation. The goal is to do a factor analysis on this matrix.
By curiosity I compare to a matrix of Pearson correlation, and the results are different. When I ... | Differences between tetrachoric and Pearson correlation | CC BY-SA 2.5 | null | 2010-09-26T13:40:48.663 | 2012-06-27T19:27:33.020 | 2010-09-27T10:27:38.730 | 183 | 1154 | [
"correlation",
"factor-analysis",
"psychometrics",
"psychology"
] |
3087 | 2 | null | 3082 | 12 | null | It is broken, although if you perform enough shuffles it can be an excellent approximation (as the previous answers have indicated).
Just to get a handle on what's going on, consider how often your algorithm will generate shuffles of a $k$ element array in which the first element is fixed, $k \ge 2$. When permutations... | null | CC BY-SA 2.5 | null | 2010-09-26T16:23:19.830 | 2010-09-27T15:21:15.717 | 2020-06-11T14:32:37.003 | -1 | 919 | null |
3088 | 2 | null | 3080 | 5 | null | I would hold off on considering meta-analysis until you've scrutinized sources if potential bias or variation in the target populations. If these are studies of treatment effects, was treatment randomly assigned? Were there deviations from the protocol? Was there noncompliance? Is there missing outcome data? Were the s... | null | CC BY-SA 2.5 | null | 2010-09-26T16:26:44.410 | 2010-09-26T16:26:44.410 | null | null | 1424 | null |
3089 | 2 | null | 3086 | 7 | null | Tetrachoric coefficient and Phi coefficient are indeed different. The tetrachoric coefficient is suitable for the following problem: Suppose there are two judges who judge cakes, say, on some continuous scale, then based on a fixed, perhaps unknown, cutoff, pronounce the cakes as "bad" or "good". Suppose the latent con... | null | CC BY-SA 2.5 | null | 2010-09-26T16:30:04.203 | 2010-09-28T00:12:55.770 | 2010-09-28T00:12:55.770 | 795 | 795 | null |
3090 | 2 | null | 725 | 3 | null | The best place to look for free/open source capabilities of this nature is GRASS GIS. The image processing manual is [here](https://grass.osgeo.org/gdp/imagery/grass4_image_processing.pdf). Because this is constantly undergoing development, it would be worthwhile posting an inquiry on one of the GRASS user lists (fou... | null | CC BY-SA 4.0 | null | 2010-09-26T16:36:50.307 | 2022-08-28T14:53:06.907 | 2022-08-28T14:53:06.907 | 79696 | 919 | null |
3091 | 1 | 3095 | null | 7 | 1815 | I've seen a little bit here about the difference between statistical inference for random samples, and what happens when we actually have population data. Most arguments seem to suggest you "never actually have the population" and the population data you think you have represents some unobservable super population whic... | Regression analysis and parameter estimates with populations | CC BY-SA 2.5 | null | 2010-09-26T18:17:03.283 | 2010-09-26T20:32:55.893 | 2010-09-26T19:17:10.030 | null | null | [
"hypothesis-testing",
"regression",
"logistic"
] |
3092 | 2 | null | 3091 | 1 | null | I work in a related area using what is called the Population Approach, in pharmacometrics. Basically you have a sample of individuals from the general population, and a moderate-size sample of observations of each individual, plus demographic covariates for these individuals.
To model them, we want to make levels of mo... | null | CC BY-SA 2.5 | null | 2010-09-26T18:47:29.700 | 2010-09-26T18:47:29.700 | null | null | 1270 | null |
3093 | 2 | null | 3091 | 2 | null | What you describe seems to refer to a particular case of multilevel modeling, where data are organized into a hierarchical structure; in your case, forests (1st level unit) nested in counties nested in states, but see [Under what conditions should one use multilevel/hierarchical analysis?](https://stats.stackexchange.c... | null | CC BY-SA 2.5 | null | 2010-09-26T19:46:28.427 | 2010-09-26T19:46:28.427 | 2017-04-13T12:44:40.807 | -1 | 930 | null |
3094 | 2 | null | 3052 | 2 | null | Some of the [Bioconductor](http://www.bioconductor.org)'s packages (e.g., [ShortRead](http://www.bioconductor.org/packages/2.3/bioc/html/ShortRead.html), [Biostrings](http://www.bioconductor.org/packages/2.2/bioc/html/Biostrings.html), [BSgenome](http://www.bioconductor.org/packages/2.3/bioc/html/BSgenome.html), [IRang... | null | CC BY-SA 2.5 | null | 2010-09-26T20:15:09.507 | 2010-09-26T20:15:09.507 | null | null | 930 | null |
3095 | 2 | null | 3091 | 5 | null | I like what chl has written. Despite that, I am moved to discuss whether this situation necessarily requires a complicated model. But first, let's begin with responses to some comments.
(1) You don't lose any degrees of freedom due to spatial correlation of forest cover. This is an explanatory variable, not the one ... | null | CC BY-SA 2.5 | null | 2010-09-26T20:32:55.893 | 2010-09-26T20:32:55.893 | null | null | 919 | null |
3096 | 2 | null | 2299 | 6 | null | Actually, Benford's Law is an incredibly powerful method. This is because the Benford's frequency distribution of first digit is applicable to all sorts of data set that occur in the real or natural world.
You are right that you can use Benford's Law in only certain circumstances. You say that the data has to have a ... | null | CC BY-SA 2.5 | null | 2010-09-26T22:37:18.040 | 2010-09-26T22:53:23.677 | 2010-09-26T22:53:23.677 | 1329 | 1329 | null |
3098 | 2 | null | 2948 | 2 | null | I just came across the tnet package for R. The creator seems to be researching on community discovery in weighted and bipartite (two-mode) graphs.
[http://opsahl.co.uk/tnet/content/view/15/27/](http://opsahl.co.uk/tnet/content/view/15/27/)
I have not yet use it.
| null | CC BY-SA 2.5 | null | 2010-09-27T01:33:45.997 | 2010-09-27T01:33:45.997 | null | null | null | null |
3099 | 2 | null | 3080 | 3 | null | I think [Jeromy's answer](https://stats.stackexchange.com/questions/3080/how-to-detect-which-one-is-the-better-study-when-they-give-you-conflicting-result/3088#3088) is sufficient if you are examining two experimental studies or an actual meta-analysis. But often times we are faced with examining two non-experimental s... | null | CC BY-SA 2.5 | null | 2010-09-27T03:26:45.223 | 2010-09-27T03:26:45.223 | 2017-04-13T12:44:29.013 | -1 | 1036 | null |
3100 | 1 | null | null | 4 | 369 | I previously asked how to estimate the latent potential of a runner who [ran the 100 metres each day for 200 days](https://stats.stackexchange.com/questions/2966/estimating-latent-performance-potential-based-on-a-sequence-of-observations). Latent skill was defined as "the latent time it would take the individual to run... | Quantifying the degree of consistency of two fitted curves | CC BY-SA 2.5 | null | 2010-09-27T06:24:37.780 | 2017-11-03T15:20:24.033 | 2017-11-03T15:20:24.033 | 11887 | 183 | [
"regression",
"nonlinear-regression",
"curve-fitting",
"isotonic"
] |
3101 | 1 | 3760 | null | 7 | 1172 | When trying to assess a validity of a claim relying on statistics, I was taught (in the school of epidemiology) that the scale to use is “[the pyramid of evidence](http://www.r-statistics.com/2010/02/for-fun-correlation-of-us-state-with-the-number-of-clicks-on-online-banners/)“
![alt text](https://i.stack.imgur.com/EOI... | How much can the "pyramid of evidence" be applied to economics and political sciences? | CC BY-SA 2.5 | null | 2010-09-27T07:25:32.120 | 2010-10-19T14:34:44.807 | 2017-04-13T12:44:33.237 | -1 | 253 | [
"causality",
"inference"
] |
3102 | 2 | null | 3100 | 3 | null | My understanding is that your sample size is 200 observations for each running length (i.e. the sample size is such that doubling the number of observations has a non trivial effect on accuracy).
Denote $y_1$ the 100 meters series ($y_2$ the 400 meters series). I suggest pooling both series unto $y$ and adding a dummy... | null | CC BY-SA 2.5 | null | 2010-09-27T08:59:59.863 | 2010-09-27T11:42:29.113 | 2010-09-27T11:42:29.113 | 603 | 603 | null |
3104 | 1 | 3108 | null | 12 | 8095 | I like introducing probability by discussing the [Boy or Girl](http://en.wikipedia.org/wiki/Boy_or_Girl_paradox) or [Bertrand](http://en.wikipedia.org/wiki/Bertrand_paradox_%28probability%29) paradox.
What other (short) problem/game provides a motivating introduction to probability? (One answer per response, please)
P.... | What is your favorite problem for an introduction to probability? | CC BY-SA 2.5 | null | 2010-09-27T10:11:37.493 | 2015-04-11T17:17:06.273 | 2011-05-20T18:22:17.697 | 930 | 930 | [
"teaching"
] |
3105 | 1 | null | null | 8 | 4114 | I wanted to find out what kind of different usages of stochastic processes theory in EE & CS are out there. For example, I find these kinds of usages interesting:
- using stochastic signal as carrier that is modulated by the information signal for communication
- using stochastic process analysis for improving photog... | What are the examples for stochastic processes in Electrical Engineering and Computer Science? | CC BY-SA 2.5 | null | 2010-09-27T10:19:33.873 | 2010-09-28T01:25:32.580 | null | null | null | [
"stochastic-processes"
] |
3106 | 2 | null | 3105 | 4 | null | What about monitoring network congestion. An underlying stochastic process is assumed to be driving the congestion, and the process has reached an equilibrium state and is stationary. That is, process characteristics such as the mean level does not depend on time. You can then use this the stochastic process to give yo... | null | CC BY-SA 2.5 | null | 2010-09-27T10:36:49.463 | 2010-09-27T10:36:49.463 | null | null | 8 | null |
3107 | 2 | null | 3104 | 9 | null | A standard example is the [Monty-Hall](http://en.wikipedia.org/wiki/Monty_Hall_problem) game.
Here's how I approach this example:
- Give the class sets of three cards and get them to play the game in pairs.
- Each pair plays the game following a particular strategy, i.e. always switching doors.
- Afterwards, I use ... | null | CC BY-SA 2.5 | null | 2010-09-27T10:45:17.213 | 2010-09-27T10:45:17.213 | null | null | 8 | null |
3108 | 2 | null | 3104 | 11 | null | A good example of showing how people are non-random is to get the class to write down a number between 1 and 10. You then ask the 1's, 2's, .. to stand up.
What happens is that the majority of the class choose 7 and very few choose 1 and 10. This leads on to interesting questions, such as:
- How should you choose a ra... | null | CC BY-SA 2.5 | null | 2010-09-27T10:48:47.553 | 2010-09-27T10:48:47.553 | null | null | 8 | null |
3109 | 1 | 3326 | null | 5 | 140 | For my thesis I am implementing a tracking algorithm that tracks clusters of datapoints.
However, I have a hard time finding research papers and/or an overview of commonly used algoritms in this subject.
| Tracking Algorithms | CC BY-SA 2.5 | null | 2010-09-27T11:03:45.737 | 2010-10-05T03:20:46.547 | 2010-09-27T11:12:02.683 | 183 | 1411 | [
"machine-learning"
] |
3110 | 2 | null | 3105 | 3 | null | Google's Pagerank [algorithm](http://en.wikipedia.org/wiki/PageRank#Simplified_algorithm) is a stochastic approximation (through Markov's stationary state) of the proportion of visits received asymptotically by website $x_i$.
| null | CC BY-SA 2.5 | null | 2010-09-27T11:48:54.790 | 2010-09-27T11:48:54.790 | null | null | 603 | null |
3111 | 2 | null | 3104 | 3 | null | The Drunkard's Walk by Leonard Mlodinow is full of such examples, including one on the meaning of a Positive HIV test that is 99.9% accurate. Using bayesian statistics, the actual odds of a positive test are less than 10% (a similar example is detailed in chapter two of Agresti's Introduction to Categorical Data Analys... | null | CC BY-SA 2.5 | null | 2010-09-27T12:00:06.260 | 2010-09-27T12:05:58.620 | 2010-09-27T12:05:58.620 | 603 | 603 | null |
3112 | 1 | 3127 | null | 8 | 11531 | I am using the epitools in R for calculating the confidence interval of relative risk.
[http://bm2.genes.nig.ac.jp/RGM2/R_current/library/epitools/man/riskratio.html](http://bm2.genes.nig.ac.jp/RGM2/R_current/library/epitools/man/riskratio.html)
There are three methods inside for calculations: namely Wald, Small and Bo... | Calculation of Relative Risk Confidence Interval | CC BY-SA 2.5 | null | 2010-09-27T13:18:11.573 | 2010-09-27T22:05:45.907 | null | null | 588 | [
"confidence-interval",
"relative-risk"
] |
3113 | 1 | null | null | 14 | 9544 | I'm curious to know if anyone has a specific reference (text or journal article) to support the common practice in the medical literature of performing sample size calculation using methods that are parametric (i.e. assuming a normal distribution and a certain variance of measurements) when the analysis of the primary ... | Parametric sample size calculation and non-parametric analysis | CC BY-SA 2.5 | null | 2010-09-27T13:29:01.003 | 2010-11-23T19:45:50.180 | 2010-11-23T15:23:13.617 | null | 561 | [
"nonparametric",
"sample-size"
] |
3114 | 2 | null | 3048 | 1 | null | Dimensionality reduction is basically applying clustering algorithm to the attributes (columns). Because of the fairly large dimensionality of your dataset, you might try to use SOM (self-organizing map/Kohonen net) to create a map for individuals or pages. You can then seen whether the are meaningful (interpretable) p... | null | CC BY-SA 2.5 | null | 2010-09-27T14:10:46.990 | 2010-09-27T14:10:46.990 | null | null | null | null |
3115 | 1 | null | null | 15 | 16854 | How do you tell if the correlations at different lags obtained from the cross-correlation (ccf function) of two time series are significant.
| Cross-correlation significance in R | CC BY-SA 2.5 | null | 2010-09-27T14:42:20.510 | 2015-06-04T17:53:00.033 | null | null | null | [
"r",
"statistical-significance",
"cross-correlation"
] |
3116 | 2 | null | 3104 | 6 | null | I really like any problem that has some result that is counter-intuitive to what we'd like to think. The problems thus far are classics in the field of probability, so I'll add my favorite classic problem:
[The Birthday Problem](http://en.wikipedia.org/wiki/Birthday_problem). I always found it amazing that there was su... | null | CC BY-SA 2.5 | null | 2010-09-27T15:13:38.850 | 2010-09-27T15:13:38.850 | null | null | 1118 | null |
3117 | 1 | 3122 | null | 11 | 196 | I've been studying hierachial reinforcement learning problems, and while a lot of papers propose algorithms for learning a policy, they all seem to assume they know in advance a graph structure describing the hierarchy of the actions in the domain. For example, [The MAXQ Method for Hierarchial Reinforcement Learning](h... | Learning the Structure of a Hierarchical Reinforcement Task | CC BY-SA 2.5 | null | 2010-09-27T16:31:58.150 | 2010-09-27T18:14:16.977 | 2010-09-27T16:54:53.537 | 741 | 741 | [
"machine-learning"
] |
3118 | 2 | null | 1557 | 11 | null | As mentioned by others, James-Stein is not often used directly, but is really the first paper on shrinkage, which in turn is used pretty much everywhere in single and multiple regression. The link between James-Stein and modern estimation is explained in detail [in this paper](http://www-stat.stanford.edu/~candes/paper... | null | CC BY-SA 2.5 | null | 2010-09-27T16:35:22.000 | 2010-09-27T16:35:22.000 | null | null | 30 | null |
3119 | 1 | 3124 | null | 3 | 146 | I have 2 classifiers with different scales. How can I adjust one classifier to the scale of the other without loss of quality?
On the scatter plot we have [2 solutions plotted (x1, x2) against each other](http://i55.tinypic.com/2v0fm28.png). I believe using linear combination as a link function is not appropriate in t... | How can I adjust classifier to the scale of the other | CC BY-SA 2.5 | null | 2010-09-27T16:53:52.353 | 2010-09-27T19:46:54.843 | 2010-09-27T19:42:06.567 | 919 | null | [
"classification",
"data-transformation",
"scales",
"exploratory-data-analysis"
] |
3120 | 2 | null | 1357 | 7 | null | More generally, mutual information is invariant under any smooth and uniquely invertible transformation of the variables.
See "Estimating mutual information" by A Kraskov, H Stögbauer, P Grassberger - Physical Review E, 2004 [[http://arxiv.org/pdf/cond-mat/0305641](http://arxiv.org/pdf/cond-mat/0305641)]
| null | CC BY-SA 2.5 | null | 2010-09-27T17:39:46.393 | 2010-09-27T17:39:46.393 | null | null | 439 | null |
3121 | 1 | null | null | 2 | 1365 | I would like to know the formula or method of calculating this.
For example, I want to know how many numbers I must pick in order to have 95% confidence that I will be within 2% of the average.
I'm also interested in doing a similiar analysis for the median as well.
Update: Numbers are not put back in the pool. I think... | Numbers 1-100, calculating how many I must pick to get x% of reliability | CC BY-SA 2.5 | null | 2010-09-27T17:50:10.193 | 2010-09-29T02:36:36.603 | 2010-09-29T02:36:36.603 | 521 | null | [
"sample-size",
"experiment-design"
] |
3122 | 2 | null | 3117 | 9 | null | According to this [paper](http://rlai.cs.ualberta.ca/papers/barto03recent.pdf)
>
In the current state-of-the-art, the
designer of an RL system typically
uses prior knowledge about the task to
add a specific set of options to the
set of primitive actions available to
the agent.
Also see section 6.2 Learnin... | null | CC BY-SA 2.5 | null | 2010-09-27T18:05:25.237 | 2010-09-27T18:14:16.977 | 2010-09-27T18:14:16.977 | 1075 | 1075 | null |
3123 | 2 | null | 3121 | 3 | null | I guess that you are looking for [experimental design for estimating the mean](http://books.google.gr/books?id=WK7l-csM0-gC&lpg=PA176&ots=8wTHuo4PrW&dq=%22experimental%20design%20for%20estimating%20the%20proportion%22&pg=PA175#v=onepage&q&f=false). I am not aware of something similar for the median though.
| null | CC BY-SA 2.5 | null | 2010-09-27T18:34:36.773 | 2010-09-27T18:34:36.773 | null | null | 339 | null |
3124 | 2 | null | 3119 | 3 | null | John Tukey described a simple method that works well in practice. Pick three representative points within the scatterplot: one near each end and one in the middle. Experiment with Box-Cox (power + log) transforms of either or both variables, applying them only to the coordinates of the three representatives. (Tukey ... | null | CC BY-SA 2.5 | null | 2010-09-27T19:41:30.373 | 2010-09-27T19:46:54.843 | 2010-09-27T19:46:54.843 | 919 | 919 | null |
3125 | 2 | null | 3121 | 3 | null | I did a little simulation in R, which resulted in 98. If you go to 5% of the mean then you're down to 93 and if you go to 10% then it's 76.
Similar results are obtained for the median.
Please, read whuber's comments to my answer
The code I used:
```
numbers <- 1:100
numTrials <- 1000
means <- matrix(nrow = numTrials, ... | null | CC BY-SA 2.5 | null | 2010-09-27T20:03:37.737 | 2010-09-28T21:11:22.577 | 2010-09-28T21:11:22.577 | 582 | 582 | null |
3126 | 2 | null | 3112 | 3 | null | I bookmarked this thread from r-help a while back:
- Summary, was Re: Confidence interval for relative risk
and you might find the referenced PDF by Michael Dewey helpful:
- confidence intervals for risk ratios
If you can though, get a copy of the following book. I know it covers the unconditional likelihood and... | null | CC BY-SA 2.5 | null | 2010-09-27T20:12:24.250 | 2010-09-27T20:12:24.250 | null | null | 251 | null |
3127 | 2 | null | 3112 | 10 | null | The three options that are proposed in `riskratio()` refer to an asymptotic or large sample approach, an approximation for small sample, a resampling approach (asymptotic bootstrap, i.e. not based on percentile or bias-corrected). The former is described in Rothman's book (as referenced in the online help), chap. 14, p... | null | CC BY-SA 2.5 | null | 2010-09-27T20:41:45.253 | 2010-09-27T22:05:45.907 | 2010-09-27T22:05:45.907 | 919 | 930 | null |
3128 | 2 | null | 3115 | 23 | null | The variance of the cross-correlation coefficient under the null hypothesis of zero correlation is approx $1/n$ where $n$ is the length of the series. The coefficients are also asymptotically normal. So approximate critical values (at the 5% level) are $\pm 2/\sqrt{n}$.
These critical values are plotted automatically i... | null | CC BY-SA 2.5 | null | 2010-09-27T22:23:31.053 | 2010-09-27T23:35:18.967 | 2010-09-27T23:35:18.967 | 159 | 159 | null |
3129 | 2 | null | 3113 | 6 | null | It sounds dodgy to me. Nonparametric methods almost always involve more degrees of freedom than parametric methods and so need more data. In your particular example, the Mann-Whitney test has lower power than the t-test and so more data are required for the same specified power and size.
A simple way to do sample size ... | null | CC BY-SA 2.5 | null | 2010-09-27T23:47:03.250 | 2010-09-27T23:47:03.250 | null | null | 159 | null |
3130 | 1 | 3131 | null | 9 | 158 | Suppose we have a set A and a subset B. If we know |A|, then we can calculate |B| by finding the probability p that an element chosen uniformly at random from A belongs to B. Specifically |A|p=|B|.
Suppose we generate n elements of A uniformly at random and use this data to estimate p (number of elements in B divided... | Error in estimating the size of a set? | CC BY-SA 2.5 | null | 2010-09-28T01:03:48.430 | 2010-09-28T01:39:19.667 | null | null | 386 | [
"estimation"
] |
3131 | 2 | null | 3130 | 8 | null | You are estimating proportions. For concreteness, imagine that A is the population of voters and B is the set of voters who vote for a particular candidate. Thus, p would be the percentage of voters who would vote for that candidate. Let:
$\pi$ be the true percentage of people who would vote for the candidate
In other ... | null | CC BY-SA 2.5 | null | 2010-09-28T01:21:27.053 | 2010-09-28T01:39:19.667 | 2010-09-28T01:39:19.667 | null | null | null |
3132 | 2 | null | 3105 | 2 | null | As you have alluded to, communications engineering (and signal processing in general) is filled with stochastic processes. See, for example:
- Chapter 9 and later of Communications Systems by Carlson and Crilly
- Chapter 13 of A Course in Digtial Signal Processing by Porat
This is only two textbook examples. You w... | null | CC BY-SA 2.5 | null | 2010-09-28T01:25:32.580 | 2010-09-28T01:25:32.580 | null | null | 352 | null |
3133 | 2 | null | 2513 | 2 | null | the short answer to your question is that with a sample size of n = 1280, a formal statistical test for equal variances is not really necessary. with that sample size, the variance 'estimates' you get are certainty-equivalents [almost certainly very close to the true population variances]. so you can just look at the t... | null | CC BY-SA 2.5 | null | 2010-09-28T02:27:31.433 | 2010-09-28T02:27:31.433 | null | null | 1112 | null |
3134 | 2 | null | 3121 | 1 | null | A couple of points first:
1) The answer will be very large, so we are safe to assume the [Central Limit Theorem](http://en.wikipedia.org/wiki/Central_limit_theorem) is in operation, even if the distribution is all 1s and 100s (i.e. Bernoulli).
2) We can use normal deviates (z-scores) rather than t-tests because the req... | null | CC BY-SA 2.5 | null | 2010-09-28T07:19:03.973 | 2010-09-29T02:35:18.350 | 2010-09-29T02:35:18.350 | 521 | 521 | null |
3135 | 2 | null | 3086 | 11 | null | My best bet is that you are facing large imbalance between your response categories, for some of your items.
If you assume that your binary responses reflect individual locations on an underlying latent (i.e., continuous) trait, then correlating the two variables is ok, provided the cutoff is close to the mean of the b... | null | CC BY-SA 3.0 | null | 2010-09-28T07:40:33.087 | 2012-06-27T19:27:33.020 | 2012-06-27T19:27:33.020 | 930 | 930 | null |
3136 | 1 | 3138 | null | 52 | 253356 | I have a data set with following structure:
```
a word | number of occurrence of a word in a document | a document id
```
How can I perform a test for normal distribution in R? Probably it is an easy question but I am a R newbie.
| How to perform a test using R to see if data follows normal distribution | CC BY-SA 2.5 | null | 2010-09-28T08:29:32.933 | 2018-03-13T23:12:56.417 | null | null | 1389 | [
"r",
"distributions",
"normality-assumption"
] |
3137 | 2 | null | 3136 | 16 | null | Assuming your dataset is called `words` and has a `counts` column, you can plot the histogram to have a visualization of the distribution:
```
hist(words$counts, 100, col="black")
```
where 100 is the number of bins
You can also do a normal Q-Q plot using
```
qqnorm(words$counts)
```
Finally, you can also use the Sha... | null | CC BY-SA 2.5 | null | 2010-09-28T09:10:33.713 | 2010-09-28T09:10:33.713 | 2017-04-13T12:44:37.420 | -1 | 582 | null |
3138 | 2 | null | 3136 | 50 | null | If I understand your question correctly, then to test if word occurrences in a set of documents follows a Normal distribution you can just use a shapiro-Wilk test and some qqplots. For example,
```
## Generate two data sets
## First Normal, second from a t-distribution
words1 = rnorm(100); words2 = rt(100, df=3)
## Ha... | null | CC BY-SA 3.0 | null | 2010-09-28T09:13:07.200 | 2018-03-13T23:12:56.417 | 2018-03-13T23:12:56.417 | 155460 | 8 | null |
3140 | 1 | null | null | 2 | 6583 | I am currently working on matlab and I am new to it. I would like to know how do i average the results from the folds (or otherwise combined) to produce a single estimation.
My dataset has 2 class labels +1 and -1. and k=5. I use the knnclassify() method in matlab in order to perform cross validation.
Kindly help!
| Knnclassify() matlab | CC BY-SA 2.5 | null | 2010-09-28T13:28:00.863 | 2011-01-04T19:11:00.380 | 2010-09-28T13:30:42.950 | 5 | null | [
"cross-validation",
"matlab"
] |
3141 | 2 | null | 3136 | 5 | null | In addition to the Shapiro-Wilk test of the stats package, the nortest package (available on CRAN) provides other normality tests.
| null | CC BY-SA 2.5 | null | 2010-09-28T14:30:29.593 | 2010-09-28T14:30:29.593 | null | null | 1355 | null |
3142 | 2 | null | 3140 | 6 | null | The knnclassify() function uses the k-nearest neighbours classification algorithm to perform classification. The crossvalind() function is a separate one to divide a data set in to folds to perform cross-validation. Both are part of the Bioinformatics toolbox. I suggest that you read the documentation associated with t... | null | CC BY-SA 2.5 | null | 2010-09-28T15:19:52.463 | 2010-09-28T15:19:52.463 | null | null | 1436 | null |
3143 | 1 | 3144 | null | 9 | 17467 | I'm quite new to R and I have a following problem:
I have a simple 2-factor linear model:
```
# factor1 has 8 categorical values, factor2 has 6 categories
Rate ~ factor1 + factor2
model1 <- lm(Rate~factor1+factor2, data=myData)
```
And want to put constraints SUM of factor1 coefficients = 0, the same for factor2.
Non... | Linear model with constraints | CC BY-SA 3.0 | null | 2010-09-28T17:03:17.213 | 2018-05-25T15:34:20.163 | 2011-08-09T22:34:02.213 | null | 1439 | [
"r",
"regression",
"contrasts"
] |
3144 | 2 | null | 3143 | 8 | null | You can do this using contrasts:
```
options(contrasts=c('contr.sum', 'contr.sum'))
```
See `?contr.sum` for more information.
UPDATE: A little googling reveals a page which might be a little clearer:
- Samuel E. Buttrey, Setting and Keeping Contrasts
| null | CC BY-SA 3.0 | null | 2010-09-28T17:47:40.680 | 2013-02-25T19:08:27.867 | 2013-02-25T19:08:27.867 | 919 | 251 | null |
3145 | 2 | null | 3143 | 22 | null | There is a trick to be had here. For simplicity, suppose you are trying to build a model of the form $$y = b_1 x_1 + b_2 x_2 + b_3 x_3,$$ subject to $b_1 + b_2 + b_3 = 0$. Simply re-express $b_3$ as $b_3 = - b_1 - b_2$, which is to say you are trying to build a model of the form
$$y = b_1 x_1 + b_2 x_2 - (b_1 + b_2) x_... | null | CC BY-SA 2.5 | null | 2010-09-28T17:48:38.293 | 2010-09-28T17:48:38.293 | null | null | 795 | null |
3147 | 1 | null | null | 6 | 105 | I have developed two differing methods in MATLAB which aim to analyse a pop song and then automatically create a 30 second audio thumbnail (a preview clip) containing part of the chorus section.
Both methods have varying results:
- The first method can create a thumbnail for each track, managing to find a chorus secti... | How should I objectively test my program results? | CC BY-SA 2.5 | null | 2010-09-28T19:02:38.067 | 2010-09-30T19:31:33.913 | 2010-09-30T19:31:33.913 | 1441 | 1441 | [
"model-comparison"
] |
3148 | 2 | null | 3147 | 1 | null | You can count the recognition of a chorus track as a 'Success' and the lack of recognition as a 'Failure'. Thus, you have the following data:
Method 1: Proportion of success (say, $p_1$) = $\frac{40}{50}$
Method 2: Proportion of success (say, $p_2$) = $\frac{21}{50}$
It seems that method 2 fails completely for 20 mus... | null | CC BY-SA 2.5 | null | 2010-09-28T19:29:16.157 | 2010-09-28T19:29:16.157 | null | null | null | null |
3149 | 2 | null | 3048 | 1 | null | If you do PCA, my advice would be to briefly screen other components than only the first two. Once you have the `prcomp` object `pc` (see suncoolsu's post), you can `plot(pc)` to see the amount of variance attributed to the different principal components. You can also easily visualise several (usually three or four) of... | null | CC BY-SA 2.5 | null | 2010-09-28T20:24:47.093 | 2010-09-28T20:24:47.093 | null | null | 1355 | null |
3150 | 2 | null | 3121 | 0 | null | ```
numbers <- 1:500
numTrials <- 1000
means <- matrix(nrow = numTrials, ncol = length(numbers))
for (n in 1:length(numbers))
{
for (t in 1:numTrials)
{
vals <- sample(numbers, n)
means[t, n] <- median(vals)
}
}
realAvg <- median(numbers)
uplim <- realAvg + 0.05*realAvg
do... | null | CC BY-SA 2.5 | null | 2010-09-28T21:04:50.340 | 2010-09-28T21:11:05.323 | 2010-09-28T21:11:05.323 | null | null | null |
3154 | 2 | null | 3136 | 14 | null | No test will show you that your data has a normal distribution - it will only be able to show you when the data is sufficiently inconsistent with a normal that you would reject the null.
But counts are not normal in any case, they're positive integers - what's the probability that an observation from a normal distribu... | null | CC BY-SA 3.0 | null | 2010-09-28T22:12:45.667 | 2017-04-04T01:47:44.910 | 2017-04-04T01:47:44.910 | 805 | 805 | null |
3156 | 1 | 3157 | null | 2 | 407 | My mean is `3.454992884900722e+008`, and my confidence intervals from three distributions (when compared to the given mean) are:
CI for dist. 1: 1.0e+008 * [4.084733001497999 4.097677503988565]
CI for dist. 2: 1.0e+008 * [5.424396063219890 5.586301025525149]
CI for dist. 3: 1.0e+008 * [2.429145282593182 2.8388971167... | Question on confidence interval | CC BY-SA 2.5 | null | 2010-09-28T23:04:13.890 | 2010-09-30T10:58:23.187 | 2010-09-28T23:25:18.567 | null | null | [
"confidence-interval",
"multiple-comparisons",
"mean"
] |
3157 | 2 | null | 3156 | 1 | null | I am afraid that there is something you are missing. The documentation says, p is p-value (which is definitely a probability under the null, but the probability of observing extreme observations if the null holds) and h=1 means you are rejecting the null in favor of the alternative hypothesis.
Here is a good quote from... | null | CC BY-SA 2.5 | null | 2010-09-29T00:45:26.520 | 2010-09-29T00:45:26.520 | null | null | 1307 | null |
3158 | 1 | 3159 | null | 23 | 15385 | Does this type of chart have a name? More importantly, is there any visualization library I can use to produce it?
[http://www.nytimes.com/interactive/2007/12/15/us/politics/DEBATE.html](http://www.nytimes.com/interactive/2007/12/15/us/politics/DEBATE.html)
| What is this type of circular-link visualization called? | CC BY-SA 2.5 | null | 2010-09-29T05:22:35.593 | 2022-04-27T13:37:30.303 | 2010-10-01T09:44:27.850 | 8 | 1106 | [
"data-visualization"
] |
3159 | 2 | null | 3158 | 13 | null | Take a look at [Circos](http://mkweb.bcgsc.ca/circos/?):
>
Circos is a software package for visualizing data and information. It visualizes data in a circular layout — this makes Circos ideal for exploring relationships between objects or positions.
The flowing data blog also had a post on this that you might find i... | null | CC BY-SA 2.5 | null | 2010-09-29T05:45:42.893 | 2010-09-29T05:45:42.893 | null | null | 251 | null |
3165 | 1 | 3170 | null | 3 | 5423 | Let's say we created a plot, with labels:
```
plot(1, xlab = "text", ylab = "text times three") # text too small
```
In this plot there is still a lot of free region to enlarge the text. We could do so by using cex.lab, like this:
```
par(cex.lab = 2)
plot(1, xlab = "text", ylab = "text times three")
```
Questions:
... | How to calculate cex.axis automatically so to fit to the maximal width possible (in R)? | CC BY-SA 2.5 | null | 2010-09-29T11:47:47.373 | 2010-09-30T21:28:23.743 | 2010-09-30T21:28:23.743 | 930 | 253 | [
"r",
"data-visualization"
] |
3166 | 2 | null | 3165 | 2 | null | I can't think of an answer to Q1, but Q2 can be done via separate calls to `title()`. Here's an example
```
x <- 1:10
y <- runif(10)
plot(y ~ x, ann = FALSE)
title(ylab = "my Y axis label", cex.lab = 2)
title(xlab = "my X axis label", cex.lab = 3)
```
But I don't see the value in doing this, as won't the user be drawn... | null | CC BY-SA 2.5 | null | 2010-09-29T12:42:50.793 | 2010-09-29T12:42:50.793 | null | null | 1390 | null |
3167 | 1 | 3168 | null | 5 | 492 | I have to incorporate a number of line plots in a Latex document and I want to get a very high quality.
Are there any website that show some templates or default plots styles that I can use. Myself I am not good in designing this kind of plot style, so I am searching some good basic settings for the tool I want to use.... | Where can I find a good resources for making publication quality line plots | CC BY-SA 2.5 | null | 2010-09-29T12:56:27.610 | 2010-09-29T20:33:02.683 | null | null | 190 | [
"data-visualization"
] |
3168 | 2 | null | 3167 | 6 | null | See [this related question](https://stats.stackexchange.com/questions/257/what-is-the-easiest-way-to-create-publication-quality-plots-under-linux/). All the same advice applies in your case. Just to highlight a few points:
- Using R is a good way to go, especially with the ggplot2 package. This is both flexible and... | null | CC BY-SA 2.5 | null | 2010-09-29T13:40:40.327 | 2010-09-29T15:23:59.080 | 2017-04-13T12:44:52.277 | -1 | 5 | null |
3169 | 2 | null | 3167 | 2 | null | You are probably aware of [TeXexample.net](http://www.texample.net/tikz/). The TeX package [pgfplots](http://www.ctan.org/tex-archive/help/Catalogue/entries/pgfplots.html) might also be of interest to you: it provides a pretty complete manual and allows to directly invoke gnuplot.
Personally, I use [Sweave](http://www.... | null | CC BY-SA 2.5 | null | 2010-09-29T13:50:25.693 | 2010-09-29T20:33:02.683 | 2010-09-29T20:33:02.683 | 930 | 1355 | null |
3170 | 2 | null | 3165 | 4 | null | The following uses calls to par(), strheight() and strwidth() to fit within the borders of the plot area and the edges of the margin for the text. They don't rescale together the same so this will give you a framework for redrawing your graph if you change the overall dimensions. If you want something different this ... | null | CC BY-SA 2.5 | null | 2010-09-29T13:55:03.430 | 2010-09-29T19:16:43.450 | 2010-09-29T19:16:43.450 | 601 | 601 | null |
3171 | 1 | null | null | 3 | 2194 | We have data from random polling within a group of people, sampled over a long period of time. As the pool is relatively small and anonymity is crucial there is a high likelihood that we have duplicate samples (though we can't tell for certain because we'd expect responses to vary over time).
Does this somehow invalida... | Does a high likelihood of duplicate samples invalidate my data or certain operations on it? | CC BY-SA 2.5 | null | 2010-09-29T15:55:04.150 | 2015-12-02T03:14:19.143 | null | null | 1343 | [
"statistical-significance",
"polling"
] |
3173 | 1 | 3174 | null | 7 | 3340 | There are occasions where I would like to fit log-linear models where the independence assumption between observations is violated. It is the normal case that I have multiple observations from each subject. Is there a mixed-effects extension to log-linear models like there are for linear and generalized linear models? ... | Mixed effects log-linear models | CC BY-SA 2.5 | null | 2010-09-29T18:22:58.343 | 2020-11-05T10:11:48.823 | null | null | 287 | [
"categorical-data",
"mixed-model",
"log-linear"
] |
3174 | 2 | null | 3173 | 11 | null | Log-linear or Poisson model are part of generalized linear models. Look at the [lme4](http://cran.r-project.org/web/packages/lme4/index.html) package which allows for mixed-effects modeling, with `family=poisson()`.
Here is an example of use:
```
> data(homerun, package="Zelig")
> with(homerun, table(homeruns, month))
... | null | CC BY-SA 4.0 | null | 2010-09-29T18:26:28.917 | 2020-11-05T10:11:48.823 | 2020-11-05T10:11:48.823 | 930 | 930 | null |
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