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7679 | 1 | 7682 | null | 9 | 1201 | I've scoured Google and this site and I am still confused about the lmer function in the lme4 library.
I have some data collected from different psychiatric wards, which have a multilevel structure. To simplify, I'll pick two level 2 and two level 1 variables, although I actually have a few more.
Level 2- WardSize [thi... | Am I specifying my lmer model correctly? | CC BY-SA 2.5 | null | 2011-02-27T16:16:32.887 | 2013-04-04T03:55:09.250 | null | null | 199 | [
"r",
"mixed-model",
"lme4-nlme"
] |
7680 | 2 | null | 2213 | 18 | null | What George Dontas writes is correct, however the use of RNNs in practice today is restricted to a simpler class of problems: time series / sequential tasks.
While feedforward networks are used to learn datasets like $(i, t)$ where $i$ and $t$ are vectors, e.g. $i \in \mathcal{R}^n$, for recurrent networks $i$ will alw... | null | CC BY-SA 4.0 | null | 2011-02-27T16:29:58.647 | 2020-01-07T17:45:25.723 | 2020-01-07T17:45:25.723 | 156277 | 2860 | null |
7681 | 1 | null | null | 8 | 608 | Say we have the following 5 cities, each with the same population
- CityA with 20% each of 5 ethnicities
- CityB with 99% of one ethnicity, but 100 different ethnicities in the remaining 1%
- CityC with 40% of one ethnicity and the remaining 60% distributed evenly over 10 different ethnicities
How can one measure... | Is there a way to compute diversity in a population? | CC BY-SA 2.5 | null | 2011-02-27T19:05:14.440 | 2018-10-13T08:57:24.400 | 2018-10-13T08:57:24.400 | 11887 | 276 | [
"distributions",
"population",
"diversity"
] |
7682 | 2 | null | 7679 | 12 | null | Your model specification is fine.
The varying intercept for Ward, specified in lmer as you've done with (1 | Ward), is saying that subjects within each ward might be more similar to each other on Selfreject for reasons other than WardSize or Gender, so you are controlling for between-ward heterogeneity.
You can think... | null | CC BY-SA 2.5 | null | 2011-02-27T19:38:14.600 | 2011-02-27T19:38:14.600 | null | null | 1916 | null |
7683 | 1 | 7685 | null | 15 | 6046 | I'm struggling to find a method for reducing the number of categories in nominal or ordinal data.
For example, let's say that I want to build a regression model on a dataset that has a number of nominal and ordinal factors. While I have no problems with this step, I often run into situations where a nominal feature is... | Methods for merging / reducing categories in ordinal or nominal data? | CC BY-SA 3.0 | null | 2011-02-27T21:02:23.573 | 2017-02-09T13:36:51.180 | 2017-02-09T13:36:51.180 | 11887 | 3401 | [
"r",
"categorical-data",
"dimensionality-reduction",
"many-categories"
] |
7684 | 2 | null | 213 | 12 | null | I novel approach I saw was by IT Jolliffe Principal Components Analysis. You run a PCA on your data (Note: PCA can be quite a useful data exploration tool in its own right), but instead of looking at the first few Principal Components (PCs), you plot the last few PCs. These PCs are the linear relationships between yo... | null | CC BY-SA 3.0 | null | 2011-02-27T21:22:37.633 | 2013-11-21T23:21:08.853 | 2013-11-21T23:21:08.853 | 17230 | 2392 | null |
7685 | 2 | null | 7683 | 12 | null | This is a response to your second question.
I suspect the correct approach to these kinds of decisions will be determined largely by disciplinary norms and the expectations of the intended audience of your work. As a social scientist, I often work with survey (or survey-like) data and I always try to balance substantiv... | null | CC BY-SA 2.5 | null | 2011-02-27T21:52:51.347 | 2011-03-01T03:12:29.883 | 2020-06-11T14:32:37.003 | -1 | 3396 | null |
7686 | 1 | 7696 | null | 1 | 1829 | if range of gaussian pdf is not a probability then how come it is used in Bayes rule in the same way as pmf?
| Bayes rule and gaussian PDF | CC BY-SA 2.5 | null | 2011-02-27T22:04:16.123 | 2011-02-28T16:23:11.257 | 2011-02-27T22:18:27.367 | null | 3456 | [
"bayesian",
"normal-distribution"
] |
7687 | 2 | null | 7681 | 8 | null | How about the [Shannon index](http://en.wikipedia.org/wiki/Shannon_index)?
| null | CC BY-SA 2.5 | null | 2011-02-28T00:42:13.393 | 2011-02-28T00:42:13.393 | null | null | 1050 | null |
7688 | 1 | 7693 | null | 2 | 535 | I searched on the internet for books on statistics (particularly 4shared.com), and most of the books I found do not cover multivariate statistics in detail. Are there any good books which cover these topics in detail and with sufficient examples?
| Books with good coverage of joint distributions, multivariate statistics, etc? | CC BY-SA 3.0 | null | 2011-02-28T01:24:26.660 | 2013-07-03T15:05:26.567 | 2013-07-03T15:05:26.567 | 17230 | null | [
"multivariate-analysis",
"references",
"joint-distribution"
] |
7689 | 2 | null | 7681 | 2 | null | You may be interested in [this paper](http://arxiv.org/abs/1101.5305): "A new axiomatic approach to diversity" from Chris Dowden.
| null | CC BY-SA 2.5 | null | 2011-02-28T03:15:48.423 | 2011-02-28T03:15:48.423 | null | null | 3459 | null |
7690 | 2 | null | 7686 | 1 | null | The posterior distribution derived using continuous distributions in Bayes Theorem can always be integrated (although maybe not be hand) to give a probability. If you want to convince yourself "caveman style," run the desired probabilities through Bayes Theorem using a Gaussian CDF, then take the derivative to get the ... | null | CC BY-SA 2.5 | null | 2011-02-28T03:36:47.303 | 2011-02-28T03:36:47.303 | null | null | 5792 | null |
7691 | 2 | null | 6155 | 8 | null | Aside from what has been said, for the vusualization task alone (and outside from R), you might be interested in checking [Gephi](http://gephi.org).
| null | CC BY-SA 2.5 | null | 2011-02-28T06:14:53.413 | 2011-02-28T06:14:53.413 | null | null | 892 | null |
7692 | 2 | null | 7688 | 1 | null | Despite @whuber's sound comment--covering all advances in MV analysis for the last 30 years is also outside the scope of e.g. the famous [Handbook of Statistics](http://www.elsevier.com/locate/inca/BS_HS) series--, I would like to recommend
>
Izenman, Modern Multivariate
Statistical Techniques, Springer
2008.
A... | null | CC BY-SA 2.5 | null | 2011-02-28T07:39:54.400 | 2011-02-28T07:39:54.400 | null | null | 930 | null |
7693 | 2 | null | 7688 | 2 | null | Last year, I spent every lunchtime for a week going to the Waterstones University bookshop in London looking for a good book on multivariate statistics (sad I know!). I also endorse Izenman, Modern Multivariate Statistical Techniques, Springer 2008, as it really was the stand-out book. It starts every chapter with an e... | null | CC BY-SA 2.5 | null | 2011-02-28T07:57:23.627 | 2011-02-28T07:57:23.627 | null | null | null | null |
7694 | 1 | null | null | 16 | 693 | I am doing time series data analysis by state space methods. With my data the stochastic local level model totally outperformed the deterministic one. But the deterministic level and slope model gives better results than with stochastic level and stochastic/deterministic slope. Is this something usual?
All methods in ... | How to check which model is better in state space time series analysis? | CC BY-SA 3.0 | 0 | 2011-02-28T08:38:16.633 | 2013-08-18T22:49:08.440 | 2013-01-17T12:27:44.190 | 17230 | null | [
"time-series",
"state-space-models"
] |
7695 | 1 | null | null | 2 | 345 | The Pareto distribution can be used to give a pdf for the wealth of a person chosen randomly from a population. (In fact, this was its origin. See, for instance, [http://en.wikipedia.org/wiki/Pareto_principle](http://en.wikipedia.org/wiki/Pareto_principle) ).
I would like to explore the reciprocal question: Given the... | Understanding the Pareto distribution as applied to wealth | CC BY-SA 2.5 | null | 2011-02-28T08:55:17.277 | 2011-02-28T09:26:52.597 | 2011-02-28T09:26:52.597 | null | null | [
"distributions",
"modeling",
"predictive-models",
"application",
"pareto-distribution"
] |
7696 | 2 | null | 7686 | 3 | null | @Ahmed - you are definitely correct in thinking that something is not quite right here.
Conditioning on "point values" which have probability/measure 0 can be "dangerous" and can lead to what is called a [Borel and Kolmogorov Paradox](http://en.wikipedia.org/wiki/Borel%E2%80%93Kolmogorov_paradox). The lesson from this... | null | CC BY-SA 2.5 | null | 2011-02-28T09:26:48.403 | 2011-02-28T16:23:11.257 | 2011-02-28T16:23:11.257 | 919 | 2392 | null |
7697 | 2 | null | 672 | 3 | null | Bayes theorem in its most obvious form is simply a re-statement of two things:
- the joint probability is symmetric in its arguments $P(HD|I)=P(DH|I)$
- the product rule $P(HD|I)=P(H|I)P(D|HI)$
So by using the symmetry:
$$P(HD|I)=P(H|I)P(D|HI)=P(D|I)P(H|DI)$$
Now if $P(D|I) \neq 0$ you can divide both sides by $P(D... | null | CC BY-SA 2.5 | null | 2011-02-28T09:55:19.267 | 2011-02-28T09:55:19.267 | null | null | 2392 | null |
7698 | 1 | null | null | 6 | 9472 | I want to approximate a non-linear function with a limited value range by an artificial neural network (feed forward, back propagation).
Most tools and literature availabe suggest linear functions for the output neurons when doing regressions. However, I know a priori that my goal function is of limited range, therefor... | Output layer of artificial neural networks when learning non-linear functions with limited value range | CC BY-SA 4.0 | null | 2011-02-28T10:53:14.850 | 2018-10-11T10:59:28.927 | 2018-10-11T10:58:59.127 | 128677 | 3465 | [
"neural-networks"
] |
7699 | 1 | 7700 | null | 1 | 2744 | I have just done a Chow test on a regression in order to see whether there is a structural break. I am a bit stumped however as my Chow test returns a negative number. Now what do I do?
More specifically, this expression (from the Wikipedia [entry](http://en.wikipedia.org/wiki/Chow_test) for the Chow test):
$\frac{(S_c... | What to do with negative Chow test? | CC BY-SA 3.0 | null | 2011-02-28T11:36:50.210 | 2017-11-01T11:31:04.367 | 2017-11-01T11:31:04.367 | 28666 | 3086 | [
"change-point",
"chow-test"
] |
7700 | 2 | null | 7699 | 4 | null | You've made a mistake somewhere in your calculations. It's not possible for the sum of the squared residuals from a single regression using the combined data to be less than the sum of the sums of squared residuals from the regressions using the two separate sets of data.
| null | CC BY-SA 2.5 | null | 2011-02-28T12:09:40.833 | 2011-02-28T12:09:40.833 | null | null | 449 | null |
7701 | 1 | null | null | 3 | 8633 | I have data from human participants in a study. There are more females in the study (60%) and males are older. I have a binary categorical variable $O$. If those who are $True$ for $O$ are older, do I need to correct for sex and/or age? Maybe those $True$ for $O$ contain more men. What concepts or methods should I use ... | Adjusting for Confounding Variables | CC BY-SA 2.5 | null | 2011-02-28T12:10:44.647 | 2011-02-28T14:33:46.443 | 2011-02-28T14:12:06.410 | 2116 | 2824 | [
"r",
"regression",
"categorical-data"
] |
7702 | 2 | null | 7681 | 3 | null | [Tree diversity analysis](http://www.worldagroforestry.org/units/library/books/PDFs/Kindt%20b2005.pdf) book will get you up to speed with common diversity indices, along with some useful packages in R and their usage. While the book talks about trees, it can be used with marine fauna (which I did for my thesis) or even... | null | CC BY-SA 2.5 | null | 2011-02-28T12:32:27.213 | 2011-02-28T12:32:27.213 | null | null | 144 | null |
7703 | 2 | null | 7698 | 2 | null | If you use a logistic activation function in the output layer it will restrict the output to the range 0-1 as you require.
However if you have a regression problem with a restricted output range the sum-of-squares error metric may not be ideal and maybe a beta noise model might be more appropriate (c.f. beta regressi... | null | CC BY-SA 4.0 | null | 2011-02-28T13:01:10.830 | 2018-10-11T10:59:28.927 | 2018-10-11T10:59:28.927 | 128677 | 887 | null |
7704 | 2 | null | 7681 | 4 | null | This paper by [Massey and Denton 1988](http://dx.doi.org/10.2307/2579183) is a fairly prolific overview of commonly used indices in Sociology/Demography. It would also be useful for some other key terms used for searching articles. Frequently in Sociology the indices are labelled with names such as "heterogeneity" and ... | null | CC BY-SA 3.0 | null | 2011-02-28T13:07:11.443 | 2013-06-27T13:15:47.193 | 2013-06-27T13:15:47.193 | 22047 | 1036 | null |
7705 | 2 | null | 7698 | 0 | null | If you know an absolute range for the output, but there is no reason to expect it to have the non-linear characteristic of the typical logistic activation function (i.e. a value in the middle is just as likely as a value near 0 or 1), then you can just transform the output by dividing by the absolute maximum. If the m... | null | CC BY-SA 2.5 | null | 2011-02-28T13:45:26.013 | 2011-02-28T13:45:26.013 | null | null | 2917 | null |
7706 | 1 | 7710 | null | 3 | 1063 | If my dataset comprises few censored variables (<1%) and I fit the OLS regression using a heteroscedastic resistant estimator (the residuals are not terribly heteroscedastic to begin with)- are the results valid?
| What is the magnitude of bias in censored regression when OLS is applied? | CC BY-SA 2.5 | null | 2011-02-28T14:03:40.473 | 2011-02-28T19:50:18.567 | 2011-02-28T14:41:16.510 | 2116 | 1291 | [
"survival",
"least-squares",
"censoring"
] |
7707 | 2 | null | 7701 | 1 | null | Edited several times to reflect comments
I realized I should give an example of what I meant by "what your model looks like now." From what you've said, I'm assuming that $Variant$ or $O$ is your dependent or outcome variable and that you're starting with something like the following:
$Variant = \beta_0 + \beta_1(Femal... | null | CC BY-SA 2.5 | null | 2011-02-28T14:05:58.000 | 2011-02-28T14:33:46.443 | 2011-02-28T14:33:46.443 | 3396 | 3396 | null |
7709 | 2 | null | 6809 | 5 | null | UPDATE:
Now on CRAN:
[http://cran.r-project.org/web/packages/C50/index.html](http://cran.r-project.org/web/packages/C50/index.html)
ORIGINAL:
We've been working on this for a bit now (starting with Cubist then working on C5.0).
If you'd like to contribute:
[https://r-forge.r-project.org/projects/rulebasedmodels/](https... | null | CC BY-SA 3.0 | null | 2011-02-28T14:09:51.923 | 2012-08-30T18:48:56.600 | 2012-08-30T18:48:56.600 | 3468 | 3468 | null |
7710 | 2 | null | 7706 | 5 | null | Suppose you observe $(y_i,x_i)$, which come frome censored regression model:
\begin{align}
y^*_i&=x_i\beta+u_i \\
y_i&= \max(y_i^*,0)
\end{align}
with $u_i|x_i\sim N(0,\sigma^2)$,
Then this model is equivalent to:
\begin{align}
y_i=x_i\beta+\sigma\lambda(x_i\beta/\sigma)+e_i,
\end{align}
where $E(e_i|x_i,y_i>0)=0$ and... | null | CC BY-SA 2.5 | null | 2011-02-28T14:38:32.627 | 2011-02-28T19:50:18.567 | 2011-02-28T19:50:18.567 | 930 | 2116 | null |
7712 | 1 | 7713 | null | 2 | 211 | I am not sure if this is an instance of vectorizing the operations in R, but this is where I am stuck:
I want to get:
```
dpois(1, 0.1)
dpois(2, 0.2)
dpois(3, 0.3)
```
and I tried:
```
dpois(1:3, 0.1:0.3)
```
and
```
do.call(dpois, list(x = 1:3, lambda = 0.1:0.3))
```
both do not work.
It there a R-ish way of doin... | How do I "vectorize" calls to dpois? | CC BY-SA 2.5 | null | 2011-02-28T16:30:13.000 | 2018-10-19T02:12:21.990 | null | null | 1307 | [
"r"
] |
7713 | 2 | null | 7712 | 6 | null | From `help(dpois)` it looks like you need `x` and `lambda` to be vectors (read more about object classes in the R Intro or any other R documentation to understand what this means).
The following works:
`dpois(1:3, c(seq(0.1, 0.3, .1)))`
Your first attempt fails because you are not concatenating (see: `help(c)`) the va... | null | CC BY-SA 2.5 | null | 2011-02-28T16:48:15.783 | 2011-02-28T17:16:06.103 | 2011-02-28T17:16:06.103 | 3396 | 3396 | null |
7714 | 1 | null | null | 3 | 870 | What inter-rate reliability test is best for continuous data? I am doing a study with one variable with continuous data, now the measurement involves measurements done by two people. I would wish to do inter-rater reliability test for the data, so far I have collected a few samples and a sample data I have given below... | Interater reliability | CC BY-SA 2.5 | null | 2011-02-28T17:00:48.593 | 2011-02-28T19:08:43.810 | 2011-02-28T18:59:08.360 | 3472 | 3472 | [
"reliability",
"agreement-statistics"
] |
7716 | 2 | null | 2957 | 20 | null | Unbiased estimates are typical in introductory statistics courses because they are: 1) classic, 2) easy to analyze mathematically. The Cramer-Rao lower bound is one of the main tools for 2). Away from unbiased estimates there is possible improvement. The bias-variance trade off is an important concept in statistics ... | null | CC BY-SA 2.5 | null | 2011-02-28T18:06:45.623 | 2011-03-01T01:07:08.683 | 2011-03-01T01:07:08.683 | 1670 | 1670 | null |
7717 | 2 | null | 7714 | 1 | null | I'd suggest you [plot the difference against the mean ](http://en.wikipedia.org/wiki/Bland-Altman_plot) then quantify things using the mean difference and the standard deviation of the difference. Seven samples is rather few though.
| null | CC BY-SA 2.5 | null | 2011-02-28T19:08:43.810 | 2011-02-28T19:08:43.810 | null | null | 449 | null |
7718 | 1 | 7725 | null | 12 | 7232 | Is there any standard method to determine an "optimal" operation point on a [precision recall](http://en.wikipedia.org/wiki/Precision_and_recall) curve? (i.e., determining the point on the curve that offers a good trade-off between precision and recall)
Thanks
| How to choose a good operation point from precision recall curves? | CC BY-SA 2.5 | null | 2011-02-28T19:56:26.907 | 2020-12-26T19:35:38.027 | 2011-02-28T21:22:53.433 | 930 | 2798 | [
"machine-learning",
"precision-recall"
] |
7719 | 1 | null | null | 3 | 890 | The Data:
The observed probability (proportions) of three mutually exclusive events for five species.
What is the best way to plot these data in R along with their standard errors? I'd like to avoid a "beside" bar plot with error bars (3 bars for each species). I was hoping to use a stacked bar plot, but I'm unsure how... | Plotting Multiple Proportions With Standard Error | CC BY-SA 2.5 | null | 2011-02-28T20:21:29.907 | 2011-02-28T20:21:29.907 | null | null | 3474 | [
"r",
"data-visualization",
"proportion"
] |
7720 | 1 | null | null | 31 | 48740 | I am new to R, ordered logistic regression, and `polr`.
The "Examples" section at the bottom of the help page for [polr](http://stat.ethz.ch/R-manual/R-patched/library/MASS/html/polr.html) (that fits a logistic or probit regression model to an ordered factor response) shows
```
options(contrasts = c("contr.treatment", ... | How to understand output from R's polr function (ordered logistic regression)? | CC BY-SA 2.5 | null | 2011-02-28T20:51:28.700 | 2018-01-08T16:24:10.643 | 2011-03-01T21:22:49.080 | 2849 | 2849 | [
"r",
"logistic"
] |
7721 | 1 | 7770 | null | 8 | 412 | I need to do a high dimensional biological data analysis. My data consists of hundreds of thousands of dimensions. I am looking for an implementation of multinomial logistic regression that will scale well to data of this size.
Ideally, it should allow me to also do Ridge and Lasso regressions also. Which software shou... | Scalable multinomial regression implementation | CC BY-SA 3.0 | null | 2011-02-28T21:24:23.323 | 2017-07-26T13:50:10.523 | 2017-07-26T13:50:10.523 | 128677 | 3301 | [
"regression",
"lasso",
"ridge-regression",
"multinomial-logit"
] |
7723 | 1 | null | null | 6 | 8453 | We are learning pivot functions, test statistics, and hypothesis testing at university but it makes no sense. I've tried reading my text book/notes, going through examples, etc., but the concepts seem like a random guess and I'm clueless about how to even start guessing what the answer could be.
### 1st part
Can yo... | Pivotal quantities, test statistics and hypothesis tests | CC BY-SA 2.5 | null | 2011-02-28T21:58:04.160 | 2011-03-08T20:04:13.470 | 2011-03-08T20:04:13.470 | null | null | [
"hypothesis-testing",
"pivot"
] |
7725 | 2 | null | 7718 | 14 | null | The definition of "optimal" will of course depend on your specific goals, but here are a few relatively "standard" methods:
- Equal error rate (EER) point: the point where precision equals recall. This feels to some people like a "natural" operating point.
- A refined and more principled version of the above is to sp... | null | CC BY-SA 4.0 | null | 2011-02-28T22:11:04.873 | 2019-11-08T15:10:57.790 | 2019-11-08T15:10:57.790 | 11032 | 3369 | null |
7726 | 2 | null | 7554 | 1 | null | As @rolando2 mentioned, the histogram might be a tool for displaying the variations; and also, as @ashaw stated, you might need to find where 95% percentage of number go to, then, you can probably just use box plot to generate the basic features of your dataset, and put the data to the dashboard that is shown in the co... | null | CC BY-SA 2.5 | null | 2011-02-28T23:04:16.287 | 2011-02-28T23:04:16.287 | null | null | 3296 | null |
7727 | 1 | null | null | 11 | 11332 | I asked [this question](https://stackoverflow.com/questions/5130808/how-to-correlate-two-time-series-with-gaps-and-different-time-bases) over on StackOverflow, and was recommended to ask it here.
---
I have two time series of 3D accelerometer data that have different time bases (clocks started at different times, w... | How to correlate two time series with gaps and different time bases? | CC BY-SA 3.0 | null | 2011-03-01T01:13:29.410 | 2019-07-23T22:34:49.777 | 2019-07-23T22:34:49.777 | 11887 | 3479 | [
"time-series",
"correlation",
"unevenly-spaced-time-series"
] |
7728 | 2 | null | 7683 | 6 | null | The kinds of approaches ashaw discusses can lead to a relatively more systematic methodology. But I also think that by systematic you mean algorithmic. Here data mining tools may fill a gap. For one, there's the chi-squared automated interaction detection (CHAID) procedure built into SPSS's Decision Tree module; it ... | null | CC BY-SA 2.5 | null | 2011-03-01T01:29:23.287 | 2011-03-01T01:29:23.287 | null | null | 2669 | null |
7730 | 1 | 7731 | null | 8 | 2095 | If I have a dependent variable and $N$ predictor variables and wanted my stats software to examine all the possible models, there would be $2^N$ possible resulting equations.
I am curious to find out what the limitations are with regard to $N$ for major/popular statistic software since as $N$ gets large there is a com... | What are the software limitations in all possible subsets selection in regression? | CC BY-SA 2.5 | null | 2011-03-01T03:09:39.890 | 2011-03-02T14:06:57.093 | 2011-03-01T12:53:58.627 | 10633 | 10633 | [
"regression",
"model-selection",
"multivariable"
] |
7731 | 2 | null | 7730 | 12 | null | I suspect 30--60 is about the best you'll get. The standard approach is the leaps-and-bounds algorithm which doesn't require fitting every possible model. In $R$, the [leaps](http://cran.r-project.org/web/packages/leaps/index.html) package is one implementation.
The documentation for the `regsubsets` function in the le... | null | CC BY-SA 2.5 | null | 2011-03-01T03:19:54.343 | 2011-03-01T04:09:02.487 | 2011-03-01T04:09:02.487 | 2970 | 2970 | null |
7732 | 1 | null | null | 1 | 2962 | I have 2 acceleration vectors, each represented by a matrix with its first column corresponding to the magnitude of acceleration and second column corresponding to the time (in ms) They both represent the same data, but one sensor is started a little later than the other, so I'm trying to remove the time lag using corr... | Calculate Cross Correlation of two matrices of the 'Values Vs. Time' representation | CC BY-SA 2.5 | null | 2011-03-01T03:21:16.320 | 2011-03-01T04:27:36.000 | 2011-03-01T04:27:36.000 | 2116 | null | [
"correlation",
"matlab",
"autocorrelation",
"cross-correlation",
"fourier-transform"
] |
7734 | 1 | null | null | 23 | 1365 |
### Context:
In an effort to structure the center pieces that I have came across in probability theory and statics, I created a reference document focussing on the mathematical essentials (available [here](https://github.com/mavam/stat-cookbook)).
By sharing this document, I hope to give statistics students a compre... | Suggestions for improving a probability and statistics cheat sheet | CC BY-SA 3.0 | null | 2011-03-01T06:45:55.910 | 2012-11-28T06:50:32.550 | 2012-11-28T06:50:32.550 | 1537 | 1537 | [
"teaching"
] |
7735 | 2 | null | 7734 | 4 | null | My favorite is the [R Inferno](http://www.burns-stat.com/pages/Tutor/R_inferno.pdf) by Patrick Burns.
| null | CC BY-SA 2.5 | null | 2011-03-01T07:40:34.397 | 2011-03-01T07:40:34.397 | null | null | 3309 | null |
7736 | 2 | null | 7734 | 6 | null | [Tom Short's R Reference Card](http://cran.r-project.org/doc/contrib/Short-refcard.pdf) is excellent.
| null | CC BY-SA 2.5 | null | 2011-03-01T08:01:54.057 | 2011-03-01T08:01:54.057 | null | null | 183 | null |
7737 | 2 | null | 7723 | 5 | null | The first thing you should do is challenge your lecturer to explain these things clearly. If anything whatsoever seems counter-intuitive or backwards, them demand that he/she explains why it is intuitive. Statistics always makes sense if you think about it in the "right" way.
Calculating pivotal quantities is a very ... | null | CC BY-SA 2.5 | null | 2011-03-01T09:34:17.290 | 2011-03-06T12:13:16.900 | 2011-03-06T12:13:16.900 | 2392 | 2392 | null |
7739 | 2 | null | 7730 | 3 | null | As $N$ gets big, your ability to use maths becomes absolutely crucial. "inefficient" mathematics will cost you at the PC. The upper limit depends on what equation you are solving. Avoiding matrix inverse or determinant calculations is a big advantage.
One way to help with increasing the limit is to use theorems for ... | null | CC BY-SA 2.5 | null | 2011-03-01T10:17:03.143 | 2011-03-01T10:17:03.143 | null | null | 2392 | null |
7741 | 2 | null | 7698 | 0 | null | "Would it work to use the linear function and simply cut all values below 0 to 0, and values above 1 to 1?"
I believe in many cases the cut-off value should be the percentage split of the training data. Eg if your training data has 13% - 0's and 87% - 1's, then the cut-off would be 0.13; For example anything 0.13 and ... | null | CC BY-SA 2.5 | null | 2011-03-01T10:48:20.840 | 2011-03-01T10:48:20.840 | null | null | null | null |
7742 | 1 | null | null | 10 | 1925 | Suppose that the quantity which we want to infer is a probability distribution. All we know is that the distribution comes from a set $E$ determined, say, by some of its moments and we have a prior $Q$.
The maximum entropy principle(MEP) says that the $P^{\star}\in E$ which has least relative entropy from $Q$ (i.e., $... | Bayesian vs Maximum entropy | CC BY-SA 2.5 | null | 2011-03-01T12:01:31.540 | 2017-06-05T23:00:38.103 | 2017-06-05T23:00:38.103 | 11887 | 3485 | [
"bayesian",
"estimation",
"maximum-entropy"
] |
7743 | 2 | null | 7730 | 10 | null | Just a caveat, but feature selection is a risky business, and the more features you have, the more degrees of freedom you have with which to optimise the feature selection criterion, and hence the greater the risk of over-fitting the feature selection criterion and in doing so obtain a model with poor generalisation ab... | null | CC BY-SA 2.5 | null | 2011-03-01T13:01:35.607 | 2011-03-01T13:01:35.607 | null | null | 887 | null |
7744 | 1 | null | null | 2 | 493 | Can anyone give some advice on how to start proving this algebraically?
Define the residual from a regression (one independent variable) algebraically and show that:
- the mean of the residuals is zero
- the correlation of the residuals and the independent variable is zero
| Algebraic definition of a residual from a regression | CC BY-SA 2.5 | null | 2011-03-01T13:42:42.827 | 2011-03-01T14:35:34.517 | 2011-03-01T13:51:30.480 | 8 | null | [
"regression",
"self-study",
"residuals"
] |
7745 | 1 | 7752 | null | 2 | 131 | hopefully you can help me with the meaning of the following, I don't really understand the terminology:
"regression of a vector of ones on the matrix $W$",
where $W$ is something like $(W_t)' = (w_{1t},w_{2t},w_{3t}, w_{4t})$.
I don't understand, which regression I actually have to compute. If it is of help for you, I'... | Terminology question concerning regression | CC BY-SA 2.5 | null | 2011-03-01T14:04:45.603 | 2011-03-01T15:05:06.350 | 2011-03-01T14:34:47.363 | 449 | 3104 | [
"regression",
"hypothesis-testing",
"terminology"
] |
7746 | 2 | null | 7744 | 2 | null | Suppose you have the following regression model:
$$
y_i=\alpha+\beta x_i+\varepsilon_i
$$
Least squares problem looks for $\alpha$ and $\beta$ which minimize the following function:
$$g(\alpha,\beta)=\sum_{i=1}^n(y_i-\alpha-\beta x_i)^2$$
Solution for this problem will satisfy
$$\frac{\partial g}{\partial \alpha}=0... | null | CC BY-SA 2.5 | null | 2011-03-01T14:10:29.967 | 2011-03-01T14:35:34.517 | 2011-03-01T14:35:34.517 | 2116 | 2116 | null |
7747 | 2 | null | 5292 | 119 | null | You can also try the brand-new [RStudio](http://www.rstudio.org/). Reasonably full-featured IDE with easy set-up. I played with it yesterday and it seems nice.
Update
I now like RStudio even more. They actively implement feature requests, and it shows in the little things getting better and better. It also includes... | null | CC BY-SA 3.0 | null | 2011-03-01T14:19:56.700 | 2014-03-06T17:03:55.387 | 2014-03-06T17:03:55.387 | 36515 | 3488 | null |
7748 | 2 | null | 7698 | 2 | null | I am opposed to cutting values of, since this will lead to an undifferentiable transfer function and your gradient based training algorithm might screw up.
The sigmoid function at the output layer is fine: $\sigma(x) = \frac{1}{1 + e^{-x}}$. It will squash any output to lie within $(0, 1)$. So you can get arbitrarily c... | null | CC BY-SA 2.5 | null | 2011-03-01T14:31:43.597 | 2011-03-01T14:31:43.597 | null | null | 2860 | null |
7749 | 1 | null | null | 2 | 284 | "Under what condition (or conditions if you think it necessary) would one observe no change in the regression coefficient (e.g., b-hat Y on X1) for some variable when another variable is added to the regression equation?"
I think the answer is when the exogenous variables are perfectly uncorrelated - is that correct?
| Changes in the regression coefficient | CC BY-SA 2.5 | null | 2011-03-01T14:36:24.950 | 2011-03-01T17:15:36.960 | null | null | null | [
"regression"
] |
7750 | 2 | null | 7749 | 5 | null | Basically yes. This follows from the [omitted variable bias](http://en.wikipedia.org/wiki/Omitted_variable_bias) problem. As you can see the bias depends on crossproduct of the variables in regression (in this case the intercept and your variable of interest) and the omitted variable. If the sample correlation of the v... | null | CC BY-SA 2.5 | null | 2011-03-01T14:55:17.340 | 2011-03-01T17:15:36.960 | 2011-03-01T17:15:36.960 | 2116 | 2116 | null |
7751 | 2 | null | 7730 | 5 | null | I was able to generate all possible subsets using 50 variables in SAS. I do not believe there is any hard limitation other than memory and CPU speed.
### Edit
I generated the 2 best models for N=1 to 50 variables for 5000 observations.
@levon9 - No, this ran in under 10 seconds. I generated 50 random variables from... | null | CC BY-SA 2.5 | null | 2011-03-01T14:56:19.553 | 2011-03-02T14:06:57.093 | 2020-06-11T14:32:37.003 | -1 | 3489 | null |
7752 | 2 | null | 7745 | 4 | null | Ordinary least squares regression of $y$ on $X$ involves solving the normal equations
$$X'X\hat{\beta} = X'y$$
for $\hat{\beta}$, so I'd assume OLS regression of a vector of ones on $W$ implies solving
$$W'W\hat{\beta} = W'\bf{1},$$
where $\bf{1}$ is a vector of ones. If the matrix $X$ itself contained a column of ones... | null | CC BY-SA 2.5 | null | 2011-03-01T15:05:06.350 | 2011-03-01T15:05:06.350 | null | null | 449 | null |
7753 | 2 | null | 5292 | 2 | null | Despite all of the good recommendations, I've not found anything radically better than the default Mac GUI. R-Studio shows promise, but it's not currently that much more customizable or featureful than R and, say, BBEdit to edit.
| null | CC BY-SA 2.5 | null | 2011-03-01T16:16:25.957 | 2011-03-01T16:16:25.957 | null | null | 1764 | null |
7754 | 1 | 7755 | null | 7 | 41787 |
## Background
I have two estimates of variance and their associated standard errors calculated from sample sizes of $n=500$ and $n=10,000$ the results are $\hat{\sigma^2} (sd_{\hat{\sigma^2}})$:
$$\hat{\sigma^2}_{n=500}=69 (6.4)$$
$$\hat{\sigma^2}_{n=10,000}=72 (1.5)$$
## Question
If I say that variance increased... | How to calculate the difference of two standard deviations? | CC BY-SA 2.5 | null | 2011-03-01T16:37:19.447 | 2011-03-01T19:10:57.737 | 2020-06-11T14:32:37.003 | -1 | 1381 | [
"standard-deviation",
"variance"
] |
7755 | 2 | null | 7754 | 8 | null | The standard deviation of the difference between two independent random variables is the square root of the sum of the squares of their individual standard deviations (easier to express as variances) so in this case
$$\sqrt{6.4^2 + 1.5^2} \approx 6.6$$
| null | CC BY-SA 2.5 | null | 2011-03-01T17:00:15.870 | 2011-03-01T17:00:15.870 | null | null | 2958 | null |
7757 | 1 | 7759 | null | 67 | 140962 | I am trying to predict the outcome of a complex system using neural networks (ANN's). The outcome (dependent) values range between 0 and 10,000. The different input variables have different ranges. All the variables have roughly normal distributions.
I consider different options to scale the data before training. One ... | Data normalization and standardization in neural networks | CC BY-SA 2.5 | null | 2011-03-01T18:53:04.537 | 2020-11-10T12:35:37.593 | 2019-11-05T12:36:28.050 | 219619 | 1496 | [
"machine-learning",
"neural-networks",
"normalization",
"standardization"
] |
7758 | 1 | 7767 | null | 4 | 1348 | I play a lot with [PyBrain](http://pybrain.org) -- Artificial Neural Network implementation in Python. I have noticed that in all the models that I receive the weights of the connections are roughly normally distributed around zero with a pretty low standard deviation (~0.3), which means that they are effectively limit... | On connection weights in an Artificial Neural Network | CC BY-SA 2.5 | null | 2011-03-01T19:27:34.413 | 2012-02-09T04:00:27.067 | null | null | 1496 | [
"neural-networks"
] |
7759 | 2 | null | 7757 | 50 | null | A standard approach is to scale the inputs to have mean 0 and a variance of 1. Also linear decorrelation/whitening/pca helps a lot.
If you are interested in the tricks of the trade, I can recommend [LeCun's efficient backprop paper.](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)
| null | CC BY-SA 2.5 | null | 2011-03-01T20:27:31.693 | 2011-03-01T20:27:31.693 | null | null | 2860 | null |
7763 | 1 | 7764 | null | 4 | 163 | I'm examining correlations in a data set with a large number of variables but small sample sizes. To get a feel for how these quantities behave, I generated some random data and looked at the distribution of correlations:
```
n = 4
y = matrix(rnorm(1000 * n), 1000, n)
x = matrix(rnorm(1000 * n), 1000, n)
p = as.numeric... | Curious Sample Correlation Property | CC BY-SA 2.5 | null | 2011-03-01T21:10:20.670 | 2011-03-01T21:37:34.823 | null | null | 2111 | [
"distributions",
"correlation"
] |
7764 | 2 | null | 7763 | 12 | null | For independent Normal variates, the distribution of the correlation coefficient $r$ is proportional to $(1 - r^2)^{{1\over2} (n-4)}dr$. When $n=4$, that's uniform.
### Reference
R. A. Fisher, [Frequency-distribution of the values of the correlation coefficient in samples from an indefinitely large population](http... | null | CC BY-SA 2.5 | null | 2011-03-01T21:37:34.823 | 2011-03-01T21:37:34.823 | 2020-06-11T14:32:37.003 | -1 | 919 | null |
7766 | 1 | 7773 | null | 11 | 2988 | How should I syntax the `rma` function from [metafor](http://cran.r-project.org/web/packages/metafor/index.html) package in order to get results in the following real-life example of a small meta-analysis?
(random-effect, summary statistic SMD)
```
study, mean1, sd1, n1, mean2, sd2, n2
Foo2000, 0.78, ... | Meta-analysis in R using metafor package | CC BY-SA 3.0 | null | 2011-03-01T22:38:49.620 | 2018-02-02T16:11:34.790 | 2018-02-02T16:11:34.790 | 101426 | 3333 | [
"r",
"meta-analysis"
] |
7767 | 2 | null | 7758 | 6 | null | I just took a look at some of my neural networks; the weights in those look normally distributed.
One possible argument is that each weight is the sum of IID delta values during backpropagation, so they will be Gaussian (due to the central limit theorem). This argument involves making some simplifications; for example... | null | CC BY-SA 2.5 | null | 2011-03-01T23:01:28.537 | 2011-03-02T01:00:12.740 | 2011-03-02T01:00:12.740 | 2965 | 2965 | null |
7768 | 1 | 7804 | null | 12 | 1178 | I've never really found any good text or examples on how to handle 'non-existent' data for inputs to any sort of classifier. I've read a lot on missing data but what can be done about data that cannot or doesn't exist in relation to multivariate inputs. I understand this is a very complex question and will vary dependi... | How to handle non existent (not missing) data? | CC BY-SA 2.5 | null | 2011-03-01T23:04:01.467 | 2012-03-08T21:46:38.083 | 2011-03-02T11:37:02.397 | 930 | null | [
"missing-data"
] |
7769 | 2 | null | 7718 | 2 | null | Following up on SheldonCooper's second and third bullet points: The ideal choice is to have somebody else make the choice, either in the form of a threshold (point 3) or a cost benefit tradeoff (point 2). And perhaps the nicest way to offer them the choice is with an [ROC curve](http://en.wikipedia.org/wiki/Receiver_o... | null | CC BY-SA 2.5 | null | 2011-03-01T23:35:48.853 | 2011-03-01T23:35:48.853 | null | null | 1739 | null |
7770 | 2 | null | 7721 | 4 | null | I've had good experiences with Madigan's and Lewis's [BMR and BBR](http://www.bayesianregression.org) packages for multiple category dependent variables, lasso or ridge priors on parameters, and high dimensional input data. Not quite as high as yours, but it might still be worth a look. Instructions are here: [http:/... | null | CC BY-SA 2.5 | null | 2011-03-01T23:49:23.417 | 2011-03-02T20:44:34.913 | 2011-03-02T20:44:34.913 | 1739 | 1739 | null |
7771 | 1 | 7849 | null | 7 | 6483 | is there a way of calculating an effect size for the Kolmogorov-Smirnov Z statistic (in SPSS or by hand)? Or should I stick to the Mann-Whitney test, even though my group sizes are less than n=25?
| How do I calculate the effect size for the Kolmogorov-Smirnov Z statistic? | CC BY-SA 2.5 | null | 2011-03-02T00:54:11.730 | 2017-11-19T13:13:10.180 | 2011-04-28T20:23:56.830 | 919 | 2025 | [
"effect-size",
"kolmogorov-smirnov-test"
] |
7772 | 1 | 7819 | null | 6 | 521 | I'm trying to fit a multivariate multiple regression model where the independent variable X is latent but I don't know where to start (I have prior information about the coefficient matrix so I can use some iterative method).
The dependent variable Y is a NxM matrix denoting N observations each from M variables. The la... | Is it possible to fit a multivariate regression model where the independent variable is latent? | CC BY-SA 2.5 | null | 2011-03-02T03:05:21.403 | 2011-03-02T20:42:51.933 | 2011-03-02T20:20:02.473 | 3499 | 3499 | [
"regression",
"multivariate-analysis",
"latent-variable"
] |
7773 | 2 | null | 7766 | 11 | null | Create a proper `data.frame`:
```
df <- structure(list(study = structure(c(1L, 5L, 3L, 4L, 2L), .Label = c("Foo2000",
"Pete2008", "Pric2005", "Rota2008", "Sun2003"), class = "factor"),
mean1 = c(0.78, 0.74, 0.75, 0.62, 0.68), sd1 = c(0.05, 0.08,
0.12, 0.05, 0.03), n1 = c(20L, 30L, 20L, 24L, 10L), mean2 = c(0... | null | CC BY-SA 2.5 | null | 2011-03-02T03:34:49.143 | 2011-03-02T04:38:14.870 | 2011-03-02T04:38:14.870 | 307 | 307 | null |
7774 | 1 | 7917 | null | 22 | 4284 | In the classic [Coupon Collector's problem](http://en.wikipedia.org/wiki/Coupon_collector%27s_problem), it is well known that the time $T$ necessary to complete a set of $n$ randomly-picked coupons satisfies $E[T] \sim n \ln n $,$Var(T) \sim n^2$, and $\Pr(T > n \ln n + cn) < e^{-c}$.
This upper bound is better than t... | What is a tight lower bound on the coupon collector time? | CC BY-SA 2.5 | null | 2011-03-02T03:58:17.613 | 2021-12-31T16:14:38.127 | 2016-02-27T20:19:54.317 | 919 | 3500 | [
"probability",
"probability-inequalities",
"coupon-collector-problem"
] |
7775 | 1 | 9708 | null | 10 | 9891 | Does anyone have suggestions or packages that will calculate the coefficient of partial determination?
The coefficient of partial determination can be defined as the percent of variation that cannot be explained in a reduced model, but can be explained by the predictors specified in a full(er) model. This coefficient i... | R implementation of coefficient of partial determination | CC BY-SA 2.5 | null | 2011-03-02T04:13:28.443 | 2021-08-09T20:43:58.457 | null | null | 696 | [
"r",
"regression",
"anova"
] |
7776 | 2 | null | 5292 | 9 | null | At least on linux, [RKWard](http://rkward.sourceforge.net/) offers the best functionality. The new [RStudio](https://www.rstudio.com/) appears quite promising as well.
| null | CC BY-SA 3.0 | null | 2011-03-02T05:24:33.013 | 2016-08-13T09:35:40.303 | 2016-08-13T09:35:40.303 | 2461 | null | null |
7777 | 2 | null | 7720 | 24 | null | I would suggest you look at books on categorical data analysis (cf. Alan Agresti's Categorical Data Analysis, 2002) for better explanation and understanding of ordered logistic regression. All the questions that you ask are basically answered by a few chapters in such books. If you are only interested in `R` related ex... | null | CC BY-SA 3.0 | null | 2011-03-02T05:54:08.080 | 2015-03-04T12:06:56.747 | 2015-03-04T12:06:56.747 | 8413 | 1307 | null |
7780 | 1 | 7781 | null | 12 | 5295 | I have a question about group sequential methods.
According to Wikipedia:
>
In a randomized trial with two treatment groups, classical group sequential testing is used in the following manner: If n subjects in each group are available, an interim analysis is conducted on the 2n subjects. The statistical analysis is pe... | Overall type I error when repeatedly testing accumulating data | CC BY-SA 2.5 | null | 2011-03-02T07:31:29.113 | 2013-02-16T10:44:22.070 | null | null | 3019 | [
"multiple-comparisons",
"clinical-trials",
"type-i-and-ii-errors"
] |
7781 | 2 | null | 7780 | 12 | null | The following slides, through 14, explain the idea. The point, as you note, is that the sequence of statistics is correlated.
The context is a z-test with known standard deviation. The first test statistic $z_1$, suitably standardized, has a Normal(0,1) distribution with cdf $\Phi$. So does the second statistic $z_2... | null | CC BY-SA 2.5 | null | 2011-03-02T08:23:13.177 | 2011-03-02T08:36:21.063 | 2011-03-02T08:36:21.063 | 919 | 919 | null |
7782 | 1 | null | null | 8 | 1116 |
### Context
I have got some problems with my doctoral dissertation. My thesis is Investigating Secondary Primary School Teachers' Organizational citizenship behaviours through their perceptions about organizational culture and their organizational trust levels.
I have a sample of 871 teachers.
I have three instrum... | What to do following poor fit statistics for a confirmatory factor analysis? | CC BY-SA 2.5 | null | 2011-03-02T10:01:07.933 | 2011-03-02T14:30:59.527 | 2011-03-02T11:08:14.763 | 930 | null | [
"factor-analysis",
"structural-equation-modeling"
] |
7783 | 2 | null | 7782 | 1 | null | Instead of looking for statistical solutions that directly solve this problem, I would look for solutions that improve the diagnosis.
First, I'd compare the different samples used in the different studies.
Then, if you have the data, I'd look at the correlation patterns among the variables in the different samples. ... | null | CC BY-SA 2.5 | null | 2011-03-02T10:59:44.943 | 2011-03-02T10:59:44.943 | null | null | 686 | null |
7784 | 1 | null | null | 9 | 2763 | I am training an artificial neural network (backpropagation, feed-forward) with non-normal distributed data. Beside the root mean squared error, literature does often suggest the Pearson correlation coefficient for evaluating the quality of the trained net. But, is the Pearson correlation coefficient reasonable, if the... | Measuring correlation of trained neural networks | CC BY-SA 2.5 | null | 2011-03-02T11:04:04.407 | 2011-11-18T16:52:12.623 | 2011-11-18T16:52:12.623 | 919 | 3465 | [
"correlation",
"neural-networks",
"spearman-rho"
] |
7785 | 1 | 7789 | null | 14 | 698 | I am looking for good references on using directional data (measure of direction in degrees) as an independent variable in regression; ideally, it would also be useful for hierarchical nonlinear models (the data are nested). I am also interested in directional data more generally.
I have found a text by Mardia, which ... | Logistic regression with directional data as IV | CC BY-SA 2.5 | null | 2011-03-02T11:06:55.250 | 2011-03-05T11:29:56.993 | 2011-03-05T11:29:56.993 | 686 | 686 | [
"circular-statistics"
] |
7786 | 1 | null | null | 8 | 1033 | I am looking for some suggestions about assessing the representativeness of a particular dataset I am analyzing.
In this dataset I am looking at the relationship between two variables (e.g., X and Y) in a population that is split into five distinct blocks. The main problem is that the data is based upon reports from t... | Assessing the representativeness of population sampling | CC BY-SA 2.5 | null | 2011-03-02T11:19:11.983 | 2023-04-17T20:07:45.987 | 2011-03-02T11:41:04.033 | 930 | 3136 | [
"sampling",
"survey",
"dataset",
"resampling"
] |
7787 | 2 | null | 7768 | 8 | null | For a logistic regression fitted by maximum likelihood, as long as you have both (1) and (2) in the model, then no matter what "default" value that you give new runners for (2), the estimate for (1) will adjust accordingly.
For example, let $X_1$ be the indicator variable for "is a new runner", and $X_2$ be the variabl... | null | CC BY-SA 2.5 | null | 2011-03-02T11:48:13.157 | 2011-03-02T11:48:13.157 | null | null | 495 | null |
7788 | 1 | 7793 | null | 4 | 180 | Some edits made...
I have a dataset which other researchers have used mixed effects modelling with to come up with a nice set of associations. I also have a much smaller dataset which is the same variables but from a different country. The first dataset is plenty powerful enough (350 individuals from 30 locations) but ... | How to replicate large well powered mixed effects model with a smaller sample? | CC BY-SA 2.5 | null | 2011-03-02T11:54:29.660 | 2011-03-03T09:46:22.250 | 2011-03-03T09:46:22.250 | 199 | 199 | [
"bayesian",
"mixed-model",
"statistical-power"
] |
7789 | 2 | null | 7785 | 8 | null | I would suggest applying a transform which deals with periodicity. i.e. $\lim_{x \to 360} f(x) = f(0)$. An easy option is to take the sin and cos, and put them both as covariates in the model.
| null | CC BY-SA 2.5 | null | 2011-03-02T11:55:26.133 | 2011-03-02T11:55:26.133 | null | null | 495 | null |
7790 | 1 | 7807 | null | 3 | 2002 | I have two exclusive groups of people and a counter of how many events happened for each group.
Lets say group 1 has 7000 people and group 2 has 3000 people.
group 1 had 50 events and group 2 had 40 events.
I'm calculating the event percentage for each group for example for group1 its 50/7000. for group 2 its 40/3000.... | How to determine statistical validity of results | CC BY-SA 2.5 | null | 2011-03-02T12:03:36.580 | 2011-03-02T17:14:47.650 | 2011-03-02T13:32:53.977 | 3506 | 3506 | [
"statistical-significance",
"chi-squared-test"
] |
7791 | 1 | 7808 | null | 9 | 345 | Some edits made...
This question is just for fun, so if it isn't fun then please feel free to ignore it. I already get a lot of help from this site so I don't want to bite the hand that feeds me. It's based on a real life example and it's just something I've wondered about a lot.
I visit my local dojo to train on an es... | Can I estimate the frequency of an event based on random samplings of its occurrence? | CC BY-SA 2.5 | 0 | 2011-03-02T12:04:56.220 | 2011-03-02T17:09:01.147 | 2011-03-02T17:09:01.147 | 919 | 199 | [
"probability",
"estimation",
"sampling"
] |
7792 | 2 | null | 168 | 3 | null | For univariate kernel density estimation, the bandwidth can be estimated by Normal reference rule or Cross Validation method or plug-in approach.
For multivariate kernel density estimation, a Bayesian bandwidth selection method may be utilized, see [Zhang, X., M.L. King and R.J. Hyndman (2006), A Bayesian approach to ... | null | CC BY-SA 3.0 | null | 2011-03-02T12:11:19.977 | 2016-04-12T20:24:11.920 | 2016-04-12T20:24:11.920 | 10416 | null | null |
7793 | 2 | null | 7788 | 2 | null | Sounds like what you want is a hierarchical model: you would have a country-level effect, and nested within that would be a location-level effect. I've only ever skimmed through it, but the Gelman and Hill book seems to be quite highly regarded in this area:
[http://www.stat.columbia.edu/~gelman/arm/](http://www.stat.c... | null | CC BY-SA 2.5 | null | 2011-03-02T12:20:21.893 | 2011-03-02T12:20:21.893 | null | null | 495 | null |
7795 | 1 | null | null | 13 | 12603 | I would like to find out the values `(x, y)` used in plotting `plot(b, seWithMean=TRUE)` in mgcv package. Does anyone know how I can extract or compute these values?
Here is an example:
```
library(mgcv)
set.seed(0)
dat <- gamSim(1, n=400, dist="normal", scale=2)
b <- gam(y~s(x0), data=dat)
plot(b, seWithMean=TR... | How to obtain the values used in plot.gam in mgcv? | CC BY-SA 3.0 | null | 2011-03-02T13:16:19.200 | 2015-04-27T09:54:09.650 | 2014-04-29T14:26:27.517 | 7290 | null | [
"r",
"time-series",
"smoothing",
"mgcv"
] |
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