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1731 | 2 | null | 1729 | 3 | null | One way that you can do the above task without using Sweave is as follows:
- Write a custom export function from R that takes the pairwise.t.test object and outputs latex code.
- Use \input{...} in your LaTeX document to input this file into your LaTeX document.
With regards to step 1, there are many functions tha... | null | CC BY-SA 2.5 | null | 2010-08-16T08:28:16.523 | 2010-08-16T08:28:16.523 | null | null | 183 | null |
1732 | 2 | null | 1485 | 3 | null | For (1), as ebony1 suggests, there are several incremental or on-line SVM algorithms you could try, the only thing I would mention is that the hyper-parameters (regularisation and kernel parameters) may also need tuning as you go along as well, and there are fewer algorithmic tricks to help with that. The regularisati... | null | CC BY-SA 2.5 | null | 2010-08-16T09:07:50.237 | 2010-08-16T09:07:50.237 | null | null | 887 | null |
1733 | 2 | null | 602 | 3 | null | For optimisation, you don't need to perform a grid search; a Nelder-Mead simplex(fminsearch in MATLAB) approach is just as effective and generally much faster, especially if you have a lot of hyper-parameters to tune. Alternatively you can use gradient descent optimisation - if your implementation doesn't provide grad... | null | CC BY-SA 2.5 | null | 2010-08-16T09:13:42.133 | 2010-08-16T09:23:45.913 | 2010-08-16T09:23:45.913 | 887 | 887 | null |
1734 | 2 | null | 36 | 8 | null | As a generalization of 'pirates cause global warming': Pick any two quantities which are (monotonically) increasing or decreasing with time and you should see some correlation.
| null | CC BY-SA 2.5 | null | 2010-08-16T11:00:11.680 | 2010-08-16T11:00:11.680 | null | null | 961 | null |
1735 | 1 | null | null | 5 | 5195 | I am regressing two butterfly richness variables (summer and winter)
against a set of environmental variables separately.
(variables with continuous numbers)
Environmental variables are identitcal in each model.
In the summer model,
the weight rank of coefficients is
temp > prec > ndvi.
The weight rank in winter is
... | Method to compare variable coefficient in two regression models | CC BY-SA 2.5 | null | 2010-08-16T11:36:42.863 | 2010-09-25T16:17:28.180 | 2010-09-24T14:01:07.313 | 930 | 962 | [
"regression"
] |
1736 | 1 | 1741 | null | 50 | 24771 | I am wondering if there are any packages for python that is capable of performing survival analysis. I have been using the survival package in R but would like to port my work to python.
| Survival Analysis tools in Python | CC BY-SA 2.5 | null | 2010-08-16T12:10:29.567 | 2020-12-04T14:42:13.057 | 2020-12-04T14:42:13.057 | 11887 | 172 | [
"python",
"survival",
"mortality"
] |
1737 | 1 | 1745 | null | 11 | 9407 | I want to calculate a summary of a variable in a data.frame for each unique combination of factors in the data.frame. Should I use plyr to do this? I am ok with using loops as opposed to apply() ; so just finding out each unique combination would be enough.
| How to find summary statistics for all unique combinations of factors in a data.frame in R? | CC BY-SA 3.0 | null | 2010-08-16T13:23:52.747 | 2013-01-21T07:01:48.660 | 2013-01-21T07:01:48.660 | 196 | null | [
"r",
"categorical-data",
"aggregation",
"plyr"
] |
1738 | 2 | null | 1713 | 12 | null | If you want inferences specifically about the mean of the original variable, then don't use Box-Cox transformation. IMO Box-Cox transformations are most useful when the transformed variable has its own interpretation, and the Box-Cox transformation only helps you to find the right scale for analysis - this turns out to... | null | CC BY-SA 2.5 | null | 2010-08-16T13:42:04.937 | 2010-08-16T13:42:04.937 | null | null | 279 | null |
1739 | 2 | null | 1737 | 11 | null | See `aggregate` and `by`. For example, from the help file for `aggregate`:
```
## Compute the averages according to region and the occurrence of more
## than 130 days of frost.
aggregate(state.x77,
list(Region = state.region,
Cold = state.x77[,"Frost"] > 130),
mean)
```
| null | CC BY-SA 2.5 | null | 2010-08-16T13:46:53.893 | 2010-08-16T13:46:53.893 | null | null | 279 | null |
1740 | 2 | null | 1737 | 3 | null | Here's the plyr solution, which has the advantage of returning multiple summary stats and producing a progress bar for long computes:
```
library(ez) #for a data set
data(ANT)
cell_stats = ddply(
.data = ANT #use the ANT data
, .variables = .(cue,flanker) #uses each combination of cue and flanker
, .fun = f... | null | CC BY-SA 2.5 | null | 2010-08-16T14:01:14.360 | 2010-08-16T14:01:14.360 | null | null | 364 | null |
1741 | 2 | null | 1736 | 23 | null | AFAIK, there aren't any survival analysis packages in python. As mbq comments above, the only route available would be to [Rpy](http://rpy.sourceforge.net/).
Even if there were a pure python package available, I would be very careful in using it, in particular I would look at:
- How often does it get updated.
- Does... | null | CC BY-SA 2.5 | null | 2010-08-16T14:05:44.193 | 2010-08-16T14:05:44.193 | null | null | 8 | null |
1742 | 2 | null | 1668 | 4 | null | I'd suggest Christopher Bishop's "Pattern Recognition and Machine Learning". You can see some of it, including a sample chapter, at [https://www.microsoft.com/en-us/research/people/cmbishop/#!prml-book](https://www.microsoft.com/en-us/research/people/cmbishop/#!prml-book)
| null | CC BY-SA 4.0 | null | 2010-08-16T14:38:47.827 | 2018-09-07T06:27:28.540 | 2018-09-07T06:27:28.540 | 131198 | 247 | null |
1743 | 2 | null | 36 | 6 | null | A correlation on its own can never establish a causal link. [David Hume](http://en.wikipedia.org/wiki/David_Hume) (1771-1776) argued quite effectively that we can not obtain certain knowlege of cauasality by purely empirical means. Kant attempted to address this, the Wikipedia page for [Kant](http://en.wikipedia.org/... | null | CC BY-SA 2.5 | null | 2010-08-16T15:29:24.607 | 2010-08-16T15:29:24.607 | null | null | 887 | null |
1744 | 2 | null | 1737 | 1 | null | In addition to other suggestions you may find the `describe.by()` function in the `psych` package useful.
It can be used to show summary statistics on numeric variables across levels of a factor variable.
| null | CC BY-SA 2.5 | null | 2010-08-16T15:32:04.733 | 2010-08-16T15:32:04.733 | null | null | 183 | null |
1745 | 2 | null | 1737 | 7 | null | While I think `aggregate` is probably the solution you are seeking, if you are want to create an explicit list of all possible factor combinations, `expand.grid` will do that for you. e.g.
```
> expand.grid(height = seq(60, 80, 5), weight = seq(100, 300, 50),
sex = c("Male","Female"))
height weight ... | null | CC BY-SA 2.5 | null | 2010-08-16T15:46:32.153 | 2010-08-16T15:46:32.153 | null | null | 729 | null |
1746 | 2 | null | 1736 | 9 | null | [python-asurv](http://sourceforge.net/projects/python-asurv/) is an effort to port the [asurv](http://www.astrostatistics.psu.edu/statcodes/sc_censor.html) software for survival methods in astronomy. Might be worth keeping an eye on, but cgillespie is right about the things to watch out for: it has a long way to go a... | null | CC BY-SA 2.5 | null | 2010-08-16T16:30:54.480 | 2010-08-16T16:30:54.480 | null | null | 251 | null |
1747 | 2 | null | 1517 | 0 | null | 2 great answers so far and I promise I'll accept one of them. But I wanted to post this link as an answer too:
[summary of how to compare two numbers](http://www.statsoft.com/textbook/elementary-concepts-in-statistics/#Two%20basic%20features%20of%20every%20relation%20between%20variables)
Yes, for me merely comparing t... | null | CC BY-SA 2.5 | null | 2010-08-16T17:13:58.303 | 2010-08-16T17:13:58.303 | null | null | 857 | null |
1748 | 2 | null | 980 | 3 | null | If BPM is staying the same over many samples (or changing infinitesimally in a way you aren't concerned about) you can truncate your data to a significant digit that you actually care about and then do Run Length Encoding.
For example, in R this data:
```
0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 ... | null | CC BY-SA 2.5 | null | 2010-08-16T17:20:25.063 | 2010-08-16T17:20:25.063 | null | null | 196 | null |
1749 | 1 | 1751 | null | 6 | 652 | I'm uncertain whether I should be able to intuit the answer to my question from a question that has [already been asked](https://stats.stackexchange.com/questions/1123/modeling-success-rate-with-gaussian-distribution) but I can't, so I am asking the question anyway. Thus, I am looking for a clear easy to understand an... | How can you approximate the number of trials to success given a particular Pr(Success)? | CC BY-SA 2.5 | null | 2010-08-16T17:27:28.967 | 2010-08-16T19:05:00.580 | 2017-04-13T12:44:29.013 | -1 | 196 | [
"probability",
"binomial-distribution",
"negative-binomial-distribution"
] |
1750 | 2 | null | 1737 | 1 | null | In [library(doBy)](http://cran.r-project.org/web/packages/doBy/vignettes/doBy.pdf) there is also the `summaryBy()` function, e.g.
```
summaryBy(DV1 + DV2 ~ Height+Weight+Sex,data=my.data)
```
| null | CC BY-SA 3.0 | null | 2010-08-16T17:31:42.483 | 2012-12-03T13:09:49.407 | 2012-12-03T13:09:49.407 | 1205 | 196 | null |
1751 | 2 | null | 1749 | 3 | null | If I understand your question correctly you want to compute the quantiles for the "No of failures before the first success" given that $p=\frac{1}{104}$.
The distribution you should be looking at is the [negative binomial distribution](http://en.wikipedia.org/wiki/Negative_binomial_distribution). The wiki discusses the... | null | CC BY-SA 2.5 | null | 2010-08-16T17:43:49.463 | 2010-08-16T17:43:49.463 | null | null | null | null |
1753 | 1 | 1754 | null | 2 | 3074 | I came across an example where standard deviation was being plotted on a Cartesian plot (standard 2D with X and Y axes.)
This seems like a valid thing to do but in this case the example only had a single line running across the graph to "indicate" standard deviation. This to me seems not very useful, possibly dangerous... | Visualizing standard deviation on a Cartesian plot | CC BY-SA 2.5 | null | 2010-08-16T18:01:43.127 | 2010-08-16T20:03:34.477 | 2010-08-16T19:36:49.437 | 968 | 968 | [
"data-visualization",
"standard-deviation"
] |
1754 | 2 | null | 1753 | 7 | null | Probably a line for the mean and a line for +/- twice the standard deviation. That would be the "default" plot for that.
That said, I think you may be missing the point of the plot with the single line for the standard deviation. If what you're trying to represent is change in the variability of Y over X (i.e., heter... | null | CC BY-SA 2.5 | null | 2010-08-16T19:59:01.507 | 2010-08-16T19:59:01.507 | null | null | 71 | null |
1755 | 2 | null | 1753 | 3 | null | What about plotting the point with error bars, say mean +/- sd. Here's what your example data would look like:
---
Here's the R code I used to generate the plot:
```
library(ggplot2)
df = data.frame(values=c(10, 20, 30, 35, 50, 20, 40, 5, 55, 10),
type=rep... | null | CC BY-SA 2.5 | null | 2010-08-16T20:03:34.477 | 2010-08-16T20:03:34.477 | null | null | 8 | null |
1756 | 2 | null | 841 | 8 | null | The most naive approach I can think of is to regress $Y_i$ vs $X_i$ as $Y_i \sim \hat{m}X_i + \hat{b}$, then perform a $t$-test on the hypothesis $m = 1$. See [t-test for regression slope](http://en.wikipedia.org/wiki/T-test#Slope_of_a_regression_line).
A less naive approach is the Morgan-Pitman test. Let $U_i = X_i - ... | null | CC BY-SA 2.5 | null | 2010-08-16T20:12:03.067 | 2010-08-16T20:12:03.067 | null | null | 795 | null |
1757 | 1 | null | null | 7 | 1559 | I am taking the time to learn how to analyze networks and want to test if there are differences between two networks over time. Since I am new to R and networks in general, I am hoping to get some help how to compare and analyze network graphs.
Simply, my dataset will contain information on flows between two geographi... | Significant Difference between two network graphs | CC BY-SA 2.5 | null | 2010-08-16T23:36:20.887 | 2010-08-17T07:51:41.177 | 2010-08-17T07:37:41.443 | 8 | 569 | [
"r",
"networks"
] |
1758 | 2 | null | 1681 | 0 | null | I will try and answer your question for only one of the bounds. Chernoff bounds are given by:
$$Pr\left[\frac{1}{m} \sum_{i=1}^{i=m} X_i \ge p+\epsilon\right] \le e^{-D(p+\epsilon\Vert p)m}$$
where
$D(\cdot )$ is the [Kullback-Leibler divergence](http://en.wikipedia.org/wiki/Kullback-Leibler_divergence)
For the sake of... | null | CC BY-SA 4.0 | null | 2010-08-17T00:50:11.027 | 2023-02-11T10:21:51.907 | 2023-02-11T10:21:51.907 | 362671 | null | null |
1759 | 2 | null | 1757 | 1 | null | I am not sure I will be able to provide a complete answer but here is how I would start.
Step 1: Model the data generating process for the flows through the network
For example, you may want to model the flows from one point to another point in the network as a [poisson distribution](http://en.wikipedia.org/wiki/Poiss... | null | CC BY-SA 2.5 | null | 2010-08-17T01:11:36.063 | 2010-08-17T01:11:36.063 | null | null | null | null |
1760 | 2 | null | 726 | 83 | null | >
He uses statistics like a drunken man uses a lamp post, more for support than illumination.
-- Andrew Lang
| null | CC BY-SA 2.5 | null | 2010-08-17T02:01:44.427 | 2010-10-02T17:11:05.487 | 2010-10-02T17:11:05.487 | 795 | 74 | null |
1761 | 1 | null | null | 28 | 3318 | There was already [a request for Mathematical Statistics Videos](https://stats.stackexchange.com/questions/485/mathematical-statistics-videos), but it explicitly asked from people for
>
videos that provide a rigorous
mathematical presentation of
statistics. i.e., videos that might
accompany a course that use a ... | Statistics/Probability Videos for Beginners | CC BY-SA 2.5 | null | 2010-08-17T02:52:02.637 | 2013-01-23T10:05:12.513 | 2017-04-13T12:44:37.583 | -1 | 253 | [
"references"
] |
1762 | 2 | null | 1687 | 1 | null | I doubt that you can get an analytical solution to this problem. About the only step that is doable is the probability that the average of the first three velocities is under some cutoff and it is not (probability the one velocity is under the cutoff)^3 as you stated. If $V_i \sim N (m, s^2)$, then $\bar{V}_3 \sim N (m... | null | CC BY-SA 2.5 | null | 2010-08-17T03:09:29.367 | 2010-08-17T03:36:03.013 | 2010-08-17T03:36:03.013 | 279 | 279 | null |
1763 | 2 | null | 1761 | 16 | null | I think a number of the suggestions put forward on the [mathematical statistics video question](https://stats.stackexchange.com/questions/485/mathematical-statistics-videos) probably fall in the stats 101 category:
- http://www.khanacademy.org/#Statistics: series of short videos on introductory statistics
- http://ww... | null | CC BY-SA 3.0 | null | 2010-08-17T03:47:24.627 | 2011-11-20T09:44:06.357 | 2017-04-13T12:44:48.803 | -1 | 183 | null |
1764 | 1 | 1792 | null | 28 | 10588 | Users are often tempted to break axis values to present data of different orders of magnitude on the same graph (see [here](http://peltiertech.com/Excel/Charts/BrokenYAxis.html)). While this may be convenient it's not always the preferred way of displaying the data (can be misleading at best). What are alternative ways... | What are alternatives to broken axes? | CC BY-SA 2.5 | null | 2010-08-17T05:22:31.340 | 2013-03-30T20:16:35.730 | 2010-08-17T05:25:15.750 | 159 | 144 | [
"data-visualization",
"logarithm"
] |
1765 | 2 | null | 1764 | 8 | null | Maybe it can be classified as lattice, but I'll try; plot all the bars scaled to the highest in one panel and put another panel showing zoom on lower ones. I used this technique once in case of a scatterplot, and the result was quite nice.
| null | CC BY-SA 2.5 | null | 2010-08-17T06:22:29.067 | 2010-08-17T06:22:29.067 | null | null | null | null |
1766 | 2 | null | 1649 | 1 | null | You can calculate SD for each person and its standard error (for example using bootstrap). Then you can use rmeta package to do analysis. I think you should use some transformation of SD for example log (or maybe better log of variance).
| null | CC BY-SA 2.5 | null | 2010-08-17T06:26:59.823 | 2010-08-17T06:26:59.823 | null | null | 419 | null |
1767 | 2 | null | 1378 | 3 | null | From your description I can't see any reason to distinguish the "baseline test" from the immediately drawn "second sample". They are simply 2 baseline measurements and the variance (at baseline) can be calculated on that basis. It would be better plotting the mean of the two baseline measurements versus the third "six ... | null | CC BY-SA 2.5 | null | 2010-08-17T06:29:42.563 | 2010-08-17T06:29:42.563 | null | null | 521 | null |
1769 | 2 | null | 1761 | 7 | null | I would suggest [Statistics 401](http://video.google.com/videoplay?docid=-3474013489970580510&hl=en&emb=1#) (from Iowa State University).
This is the link for Lesson 1. There are 64 lectures in total. (Here is the [last one](http://video.google.com/videoplay?docid=3835401745697888723&ei=coO0S9aOOJLF-Qb9kJnnBg&q=Statis... | null | CC BY-SA 2.5 | null | 2010-08-17T07:28:37.813 | 2010-08-17T07:36:52.710 | 2010-08-17T07:36:52.710 | 339 | 339 | null |
1771 | 2 | null | 1757 | 5 | null | I agree with Srikant, you need to model your process. You mentioned that you had already created some networks in R, what model did you assume?
The way I would tackle this problem, is to form a mathematical model, say an ODE model. For example,
\begin{equation}
\frac{dX_i(t)}{dt} = \lambda X_{i-1}(t) -\mu X_{i+1}(t)
\... | null | CC BY-SA 2.5 | null | 2010-08-17T07:51:41.177 | 2010-08-17T07:51:41.177 | null | null | 8 | null |
1772 | 2 | null | 726 | 4 | null | It's not really about statistics, but I think it applies to statistics:
>
It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.
Arthur Conan Doyle
| null | CC BY-SA 2.5 | null | 2010-08-17T08:29:54.487 | 2010-08-17T08:29:54.487 | null | null | 956 | null |
1773 | 1 | 1775 | null | 29 | 26143 | Precision is defined as:
```
p = true positives / (true positives + false positives)
```
Is it correct that, as `true positives` and `false positives` approach 0, the precision approaches 1?
Same question for recall:
```
r = true positives / (true positives + false negatives)
```
I am currently implementing a statist... | What are correct values for precision and recall in edge cases? | CC BY-SA 2.5 | null | 2010-08-17T09:11:30.343 | 2017-04-25T16:38:56.470 | 2010-09-16T07:01:47.313 | null | 977 | [
"precision-recall"
] |
1774 | 2 | null | 1773 | 2 | null | That would depend on what you mean by "approach 0". If false positives and false negatives both approach zero at a faster rate than true positives, then yes to both questions. But otherwise, not necessarily.
| null | CC BY-SA 2.5 | null | 2010-08-17T09:21:16.873 | 2010-08-17T09:21:16.873 | null | null | 159 | null |
1775 | 2 | null | 1773 | 21 | null | Given a confusion matrix:
```
predicted
(+) (-)
---------
(+) | TP | FN |
actual ---------
(-) | FP | TN |
---------
```
we know that:
```
Precision = TP / (TP + FP)
Recall = TP / (TP + FN)
```
Lets consider the cases where the denominator is zero:
... | null | CC BY-SA 3.0 | null | 2010-08-17T09:45:46.663 | 2016-03-05T12:13:53.743 | 2016-03-05T12:13:53.743 | 170 | 170 | null |
1776 | 1 | null | null | -2 | 323 | The purpose to run regressions for butterfly richness again 5 environmental variables is to show the importance rank of the independent variables mainly by AIC.
In non-full models, they reveal that variable A tends to be more influential than the others by delta AIC.
However, in the full model, the regression coefficie... | Shall I trust AIC (non-full model) or slope (full model)? | CC BY-SA 2.5 | null | 2010-08-17T11:23:18.773 | 2010-08-17T11:56:05.983 | 2010-08-17T11:36:26.987 | null | 962 | [
"regression",
"aic",
"feature-selection"
] |
1778 | 2 | null | 1776 | 3 | null | R-Squared can't be 1.43... and other errors make your question hard to interpret.
Here's a sort of generic response that might eventually lead to an answer.
The AIC score tells you how good the model is similar to R-squared but penalizes it based on how many components are in the model. You can theoretically always... | null | CC BY-SA 2.5 | null | 2010-08-17T11:56:05.983 | 2010-08-17T11:56:05.983 | null | null | 601 | null |
1779 | 2 | null | 1651 | 2 | null | You can try gamlss.cens package.
| null | CC BY-SA 2.5 | null | 2010-08-17T12:27:00.557 | 2010-08-17T12:27:00.557 | null | null | 419 | null |
1780 | 1 | 1785 | null | 8 | 1068 | (I'm a bit outside my comfort zone, so apologies if this is badly worded, or off-topic)
I have a bibliographic database, containign details of about 1200 different papers, books, web sites etc, all with various details, including keywords and an abstract. I want to somehow analyse this database and produce some graphi... | How to start an analysis of keywords from a bibliography and detect correlations? | CC BY-SA 2.5 | null | 2010-08-17T13:10:09.610 | 2010-11-10T20:53:29.547 | 2017-04-13T12:44:45.640 | -1 | 114 | [
"r",
"text-mining"
] |
1781 | 1 | null | null | 25 | 2761 | Chemical analyses of environmental samples are often censored below at reporting limits or various detection/quantitation limits. The latter can vary, usually in proportion to the values of other variables. For example, a sample with a high concentration of one compound might need to be diluted for analysis, resultin... | Unbiased estimation of covariance matrix for multiply censored data | CC BY-SA 2.5 | null | 2010-08-17T13:10:29.173 | 2012-10-20T19:34:07.840 | 2010-10-19T07:19:29.530 | 449 | 919 | [
"correlation",
"estimation",
"censoring",
"covariance-matrix",
"unbiased-estimator"
] |
1782 | 2 | null | 1651 | 2 | null | Another R package that seems to do what you want, is [pscal](http://cran.r-project.org/web/packages/pscl/). The associated [vignette](http://cran.r-project.org/web/packages/pscl/vignettes/countreg.pdf) has lots of examples.
| null | CC BY-SA 2.5 | null | 2010-08-17T13:18:59.637 | 2010-08-17T13:18:59.637 | null | null | 8 | null |
1783 | 2 | null | 1773 | 4 | null | I am familiar with different terminology. What you call precision I would positive predictive value (PPV). And what you call recall I would call sensitivity (Sens). :
[http://en.wikipedia.org/wiki/Receiver_operating_characteristic](http://en.wikipedia.org/wiki/Receiver_operating_characteristic)
In the case of sensitivi... | null | CC BY-SA 2.5 | null | 2010-08-17T13:20:07.530 | 2010-08-17T13:20:07.530 | null | null | 521 | null |
1784 | 2 | null | 1764 | 11 | null | Some additional ideas:
(1) You needn't confine yourself to a logarithmic transformation. Search this site for the "data-transformation" tag, for example. Some data lend themselves well to certain transformations like a root or a logit. (Such transformations--even logs--are usually to be avoided when publishing graph... | null | CC BY-SA 2.5 | null | 2010-08-17T13:30:03.980 | 2010-08-17T13:30:03.980 | null | null | 919 | null |
1785 | 2 | null | 1780 | 1 | null | I'm also outside my area of expertise, but assuming that you want to use R, here are a few thoughts.
- There is a bibtex package in R for importing bibtex files.
- Various character functions could be used to extract the key words.
- The data sounds a little like a two-mode network, which might mean packages like sn... | null | CC BY-SA 2.5 | null | 2010-08-17T13:52:29.993 | 2010-08-17T13:52:29.993 | null | null | 183 | null |
1786 | 2 | null | 1780 | 0 | null | You may want to take a look at the [phi coefficient](http://en.wikipedia.org/wiki/Phi_coefficient) which is a measure of association for [nominal variables](http://en.wikipedia.org/wiki/Level_of_measurement#Nominal_scale).
| null | CC BY-SA 2.5 | null | 2010-08-17T14:01:10.333 | 2010-08-17T14:01:10.333 | null | null | null | null |
1787 | 1 | null | null | 7 | 2451 | I am fitting a GLM model (in R), and would like to get an estimation of the variability of the coefficients estimated by the model.
If I understand it correctly the method to use in such a case is bootstraping (not, cross validation).
Am I correct that an easy way to do this is by using the boot command from the boot p... | Using bootstrap for glm coefficients variance estimation (in R) | CC BY-SA 2.5 | null | 2010-08-17T15:56:39.067 | 2010-08-18T16:38:48.443 | null | null | 253 | [
"r",
"confidence-interval",
"variance",
"generalized-linear-model",
"bootstrap"
] |
1788 | 2 | null | 726 | 10 | null | >
A man who ‘rejects’ a hypothesis provisionally, as a matter of habitual practice, when the significance is at the 1% level or higher, will certainly be mistaken in not more than 1% of such decisions. For when the hypothesis is correct he will be mistaken in just 1% of these cases, and when it is incorrect he will ne... | null | CC BY-SA 3.0 | null | 2010-08-17T16:17:00.330 | 2015-02-28T18:06:46.280 | 2020-06-11T14:32:37.003 | -1 | 561 | null |
1789 | 2 | null | 1780 | 5 | null | so you have a document x keyword matrix which basically represents a bipartite graph (or two-mode network depending on your cultural background) with edges between documents and tags. If you're not interested in individual documents - as I understand you -, you can create a network of keywords by counting the number of... | null | CC BY-SA 2.5 | null | 2010-08-17T16:27:30.400 | 2010-08-17T16:27:30.400 | null | null | 979 | null |
1790 | 1 | 1791 | null | 7 | 496 | I'm trying to compare several methods by their performance on a set of synthetic data samples. For each method, I obtain a performance value between 0 and 1 for each of those samples. Then I plot a graph with the average performance per method
The problem now is that the achievable quality per sample varies strongly be... | Meaningful deviation measure with strongly varying datapoints | CC BY-SA 3.0 | null | 2010-08-17T16:39:24.803 | 2012-12-04T06:46:40.940 | 2012-12-04T06:46:40.940 | 979 | 979 | [
"standard-deviation",
"statistical-significance",
"standard-error"
] |
1791 | 2 | null | 1790 | 3 | null | You need to use some paired test, maybe paired t-test or a sign test is the distribution is really weired.
| null | CC BY-SA 2.5 | null | 2010-08-17T16:49:24.107 | 2010-08-17T16:49:24.107 | null | null | null | null |
1792 | 2 | null | 1764 | 17 | null | I am very [wary of using logarithmic axes on bar graphs](http://www.graphpad.com/faq/viewfaq.cfm?faq=1477). The problem is that you have to choose a starting point of the axis, and this is almost always arbitrary. You can choose to make two bars have very different heights, or almost the same height, merely by changing... | null | CC BY-SA 2.5 | null | 2010-08-17T17:35:21.270 | 2010-08-17T17:35:21.270 | null | null | 25 | null |
1794 | 2 | null | 1661 | 2 | null | From my point of view, when there are two explanatory variables and both have just two levels, we have the famous two-by-two contingency table. Fisher’s exact test can take such a matrix as its sole argument. Alternatively you can use Pearson’s chi-squared test.
If your null hypothesis is not the 25:25:25:25 distribut... | null | CC BY-SA 2.5 | null | 2010-08-17T18:31:59.927 | 2010-08-17T18:31:59.927 | null | null | 339 | null |
1795 | 2 | null | 1444 | 51 | null | A useful approach when the variable is used as an independent factor in regression is to replace it by two variables: one is a binary indicator of whether it is zero and the other is the value of the original variable or a re-expression of it, such as its logarithm. This technique is discussed in [Hosmer & Lemeshow's ... | null | CC BY-SA 4.0 | null | 2010-08-17T18:48:40.177 | 2019-09-19T14:57:03.797 | 2019-09-19T14:57:03.797 | 919 | 919 | null |
1796 | 2 | null | 1790 | 1 | null | I am not at all sure if ignoring the performance spread is a good idea. Ideally, you would want a method to be both reliable (i.e., have low spread) and be valid (i.e., give a performance measure of close to 1). Consider the following two output measures:
Method 1. [0.80, 0.60]
Method 2. [0.71, 0.69].
Unlike your exa... | null | CC BY-SA 2.5 | null | 2010-08-17T19:08:05.987 | 2010-08-17T19:08:05.987 | null | null | null | null |
1797 | 1 | 1855 | null | 14 | 3976 | I was looking at this [page](http://glmm.wikidot.com/faq) and noticed the methods for confidence intervals for lme and lmer in R. For those who don't know R, those are functions for generating mixed effects or multi-level models. If I have fixed effects in something like a repeated measures design what would a confid... | What would a confidence interval around a predicted value from a mixed effects model mean? | CC BY-SA 2.5 | null | 2010-08-17T19:15:32.930 | 2012-01-08T21:32:24.613 | null | null | 601 | [
"r",
"confidence-interval",
"mixed-model",
"repeated-measures",
"sas"
] |
1798 | 2 | null | 1787 | 2 | null | Yes, you are right. What boot does is that it just generates new training sets by drawing with replacement from the original set. So about 2/3 of the original objects are present in each of the new sets, still the size is the same, so it does not influence model building.
| null | CC BY-SA 2.5 | null | 2010-08-17T19:30:14.080 | 2010-08-17T19:30:14.080 | null | null | null | null |
1799 | 1 | null | null | 9 | 2914 | I am seeking recommendations and/or best practices for analyzing non-independent data. In particular, I am curious about non-independent data that does not reflect typical repeated-measures time-based data in which data for the same question(s) or stimuli are collected at different time points. Rather the data collecte... | Recommendations - or best practices - for analyzing non-independent data. Specific example relating to pain perception data provided | CC BY-SA 2.5 | null | 2010-08-17T20:00:59.700 | 2010-09-16T06:34:25.647 | 2017-04-13T12:44:52.277 | -1 | 835 | [
"non-independent"
] |
1800 | 2 | null | 1023 | 5 | null | Tests and thousands of sample questions are available on the ARTIST ("Assessment Resource Tools for Improving Statistical Thinking") site, [https://app.gen.umn.edu/artist/tests/index.html](https://app.gen.umn.edu/artist/tests/index.html) . Most are appropriate for an intro stats course.
| null | CC BY-SA 2.5 | null | 2010-08-17T20:09:56.013 | 2010-08-17T20:09:56.013 | null | null | 919 | null |
1801 | 2 | null | 928 | 11 | null | John Tukey strongly and cogently argued for a proportion type of measurement in his book on EDA. One thing that makes proportions special and different from the classical "nominal, ordinal, interval, ratio" taxonomy is that frequently they enjoy an obvious symmetry: A proportion can be thought of as the average of a b... | null | CC BY-SA 4.0 | null | 2010-08-17T20:28:20.243 | 2022-03-10T12:48:17.403 | 2022-03-10T12:48:17.403 | 919 | 919 | null |
1802 | 2 | null | 887 | 7 | null | The second question seems to ask for a prediction interval for one future observation. Such an interval is readily calculated under the assumptions that (a) the future observation is from the same distribution and (b) is independent of the previous sample. When the underlying distribution is Normal, we just have to e... | null | CC BY-SA 3.0 | null | 2010-08-17T20:58:12.667 | 2017-01-14T18:15:23.173 | 2017-01-14T18:15:23.173 | 919 | 919 | null |
1803 | 2 | null | 1780 | 0 | null | You could try to employ the [theory](http://www-users.cs.umn.edu/~kumar/dmbook/ch6.pdf) and [praxis](http://cran.r-project.org/web/packages/arules/vignettes/arules.pdf) of association analysis or market basket analysis to your problem (just read "items" as "keywords" / "cited reference" and "market basket" as "journal ... | null | CC BY-SA 2.5 | null | 2010-08-17T21:25:25.937 | 2010-08-17T21:25:25.937 | null | null | 573 | null |
1804 | 2 | null | 1268 | 2 | null | Just a thought: you might not need the full SVD for your problem. Let M = U S V* be the SVD of your d by n matrix (i.e., the time series are the columns). To achieve the dimension reduction you'll be using the matrices V and S. You can find them by diagonalizing M* M = V (S*S) V*. However, because you are missing s... | null | CC BY-SA 2.5 | null | 2010-08-17T22:49:17.710 | 2010-08-17T22:49:17.710 | null | null | 919 | null |
1805 | 1 | 1806 | null | 35 | 77482 | I was taught to only apply Fisher's Exact Test in contingency tables that were 2x2.
Questions:
- Did Fisher himself ever envision this test to be used in tables larger than 2x2 (I am aware of the tale of him devising the test while trying to guess whether an old woman could tell if milk was added to tea or tea was add... | Fisher's Exact Test in contingency tables larger than 2x2 | CC BY-SA 3.0 | null | 2010-08-17T23:42:12.133 | 2022-02-13T15:25:32.843 | 2013-07-21T18:24:53.760 | 7290 | 561 | [
"spss",
"stata",
"contingency-tables",
"fishers-exact-test"
] |
1806 | 2 | null | 1805 | 23 | null | The only problem with applying Fisher's exact test to tables larger than 2x2 is that the calculations become much more difficult to do. The 2x2 version is the only one which is even feasible by hand, and so I doubt that Fisher ever imagined the test in larger tables because the computations would have been beyond anyt... | null | CC BY-SA 2.5 | null | 2010-08-18T00:10:56.507 | 2010-08-18T00:10:56.507 | null | null | 159 | null |
1807 | 1 | null | null | 28 | 15459 | Student's $t$-test requires the sample standard deviation $s$. However, how do I compute for $s$ when only the sample size and sample average are known?
For example, if sample size is $49$ and sample average is $112$, I will then attempt to create a list of $49$ identical samples with values of $112$ each. Expectedly, ... | How to perform Student's t-test having only sample size, sample average and population average are known? | CC BY-SA 2.5 | null | 2010-08-18T01:39:31.013 | 2015-05-16T19:36:06.987 | 2015-05-15T11:49:58.767 | 35989 | 850 | [
"t-test",
"standard-deviation",
"small-sample"
] |
1810 | 2 | null | 1807 | 2 | null | I presume you are referring to a one sample t test. Its goal is to compare the mean of your sample with a hypothetical mean. It then computes (assuming your population is Gaussian) a P value that answers this question: If the population mean really was the hypothetical value, how unlikely would it be to draw a sample w... | null | CC BY-SA 2.5 | null | 2010-08-18T02:11:10.360 | 2010-08-18T06:34:34.960 | 2010-08-18T06:34:34.960 | 25 | 25 | null |
1811 | 2 | null | 1807 | 13 | null | This does look to be a slightly contrived question. 49 is an exact square of 7. The value of a t-distribution with 48 DoF for a two-sided test of p<0.05 is very nearly 2 (2.01).
We reject the null hypothesis of equality of means if |sample_mean - popn_mean| > 2*StdError, i.e. 200-112 > 2*SE so SE < 44, i.e. SD < 7*44 =... | null | CC BY-SA 2.5 | null | 2010-08-18T05:40:49.263 | 2010-08-18T05:40:49.263 | null | null | 521 | null |
1812 | 1 | 2043 | null | 8 | 2261 | One of the most important issues in using factor analysis is its interpretation. Factor analysis often uses factor rotation to enhance its interpretation. After a satisfactory rotation, the rotated factor loading matrix L' will have the same ability to represent the correlation matrix and it can be used as the factor l... | FA: Choosing Rotation matrix, based on "Simple Structure Criteria" | CC BY-SA 2.5 | null | 2010-08-18T07:36:59.733 | 2011-01-22T11:35:40.123 | 2017-05-23T12:39:26.143 | -1 | 339 | [
"r",
"algorithms",
"factor-analysis",
"psychometrics",
"matlab"
] |
1813 | 1 | 1816 | null | 10 | 1043 | I have two implementations of a genetic algorithm which are supposed to behave equivalently. However due to technical restrictions which cannot be resolved their output is not exactly the same, given the same input.
Still I'd like to show that there is no significant performance difference.
I have 20 runs with the same... | Comparing two genetic algorithms | CC BY-SA 2.5 | null | 2010-08-18T08:29:52.133 | 2010-08-18T15:25:32.860 | 2010-08-18T15:25:32.860 | 986 | 986 | [
"hypothesis-testing",
"genetic-algorithms"
] |
1814 | 2 | null | 1812 | 2 | null | Great Question. This is not really an answer, but just a few thoughts.
In most of the applications where I have used factor analysis, permitting correlated factors makes more theoretical sense. I tend to rely on the proxmax rotation method. I used to do this in SPSS and now I use the `factanal` function in R.
| null | CC BY-SA 2.5 | null | 2010-08-18T08:37:21.273 | 2010-08-18T08:37:21.273 | null | null | 183 | null |
1815 | 1 | null | null | 63 | 34457 | What are the panel's recommendations for books on design of experiments?
Ideally, books should be still in print or available electronically, although that may not always be feasible. If you feel moved to add a few words on what's so good about the book that would be great too.
Also, aim for one book per answer so that... | Recommended books on experiment design? | CC BY-SA 2.5 | null | 2010-08-18T08:54:23.650 | 2023-01-27T15:52:18.737 | null | null | 174 | [
"references",
"experiment-design"
] |
1816 | 2 | null | 1813 | 9 | null | Testing stochastic algorithms can be rather tricky!
I work in systems biology and there are many stochastic simulators available to use to simulate a model. Testing these simulators is tricky since any two realizations from a single model will be typically different.
In the [dsmts](http://code.google.com/p/dsmts/) we h... | null | CC BY-SA 2.5 | null | 2010-08-18T08:58:46.820 | 2010-08-18T12:23:43.077 | 2010-08-18T12:23:43.077 | 8 | 8 | null |
1817 | 2 | null | 1815 | 13 | null | [Montgomery's Design and Analysis of Experiments](http://rads.stackoverflow.com/amzn/click/0470128666) is a classic and highly regarded text:
If you are interested in experimental design in a particular field (eg. clinical trials) other more specialised texts may be appropriate.
| null | CC BY-SA 2.5 | null | 2010-08-18T10:40:25.813 | 2010-08-18T13:07:11.413 | 2010-08-18T13:07:11.413 | 8 | 521 | null |
1818 | 1 | 1823 | null | 14 | 35536 | I need some help about repeated measurement ANOVA.
We are investigating the effect of some intervention on reducing blood stream infection (BSI) rate in some wards. We plan to capture the BSI rate information at a monthly basis, 12 months without intervention first, then 12 months with intervention.
We are thinking of ... | How to determine the sample size needed for repeated measurement ANOVA? | CC BY-SA 2.5 | null | 2010-08-18T10:56:21.970 | 2012-05-10T12:06:11.957 | 2010-08-18T11:03:13.940 | 8 | 588 | [
"anova",
"repeated-measures",
"sample-size",
"statistical-power"
] |
1819 | 2 | null | 1815 | 3 | null | If you're interested in pharmaceutical trials, two books I recommend:
- Statistical Issues in Drug Development by Stephen Senn (Amazon link)
- Cross-over Trials in Clinical Research by Stephen Senn (Amazon link)
| null | CC BY-SA 2.5 | null | 2010-08-18T10:56:44.153 | 2010-08-18T10:56:44.153 | null | null | 561 | null |
1821 | 2 | null | 887 | 1 | null | 1) The standard deviation of the sample (stdev(S)) is an unbiased estimate of the standard deviation of the population.
2) Given we have estimated both the population mean and variance we need to take this into account when we evaluate whether a new observation x is a member of this population.
We don't use Z = (x - me... | null | CC BY-SA 2.5 | null | 2010-08-18T11:09:28.313 | 2010-08-18T11:09:28.313 | null | null | 521 | null |
1822 | 1 | 1827 | null | 3 | 2928 | How do I test/verify if I can analyze my panel like dataset by simply pooling the individual series?
I have a dataset structured as a panel. Now I am wondering if I can simply
pool the individual series and estimate it via OLS or if I have to use another estimation technique.
(Any R hints and references are highly welc... | Test for Poolability of Individual Data Series | CC BY-SA 4.0 | null | 2010-08-18T11:37:10.547 | 2021-07-20T13:59:20.793 | 2021-07-20T13:59:20.793 | 94889 | 283 | [
"panel-data",
"survey"
] |
1823 | 2 | null | 1818 | 12 | null | How to perform power analysis on repeated measures ANOVA?
[G*Power 3](http://www.psycho.uni-duesseldorf.de/abteilungen/aap/gpower3/) is free software that provides a user-friendly GUI interface for performing power calculations.
It supports power calculations for repeated measures ANOVA.
What is the appropriate analysi... | null | CC BY-SA 3.0 | null | 2010-08-18T11:40:44.177 | 2012-05-10T12:06:11.957 | 2012-05-10T12:06:11.957 | 183 | 183 | null |
1824 | 2 | null | 1813 | 4 | null | Maybe you could measure the average difference between two runs of the same algorithm to the average difference between two runs from different algorithms. Doesn't solve the problem of how to measure that difference, but might be a more tractable problem. And the individual values of the time series would feed into the... | null | CC BY-SA 2.5 | null | 2010-08-18T12:00:36.353 | 2010-08-18T14:09:05.340 | 2010-08-18T14:09:05.340 | 979 | 979 | null |
1825 | 2 | null | 1799 | 2 | null | If temperature levels X1...X5 are specific degree values, I'm not sure how temperature and stimulus (hot/cold) can be completely crossed. I presume then that "temperature" consists of 5 ordinal categories, ranging from "likely to cause minimal discomfort" to "likely to cause maximum discomfort permissible by my researc... | null | CC BY-SA 2.5 | null | 2010-08-18T12:51:41.617 | 2010-08-18T12:51:41.617 | null | null | 364 | null |
1826 | 1 | 1830 | null | 87 | 51875 | How would you describe [cross-validation](http://en.wikipedia.org/wiki/Cross-validation_%28statistics%29) to someone without a data analysis background?
| Cross-Validation in plain english? | CC BY-SA 2.5 | null | 2010-08-18T13:11:19.190 | 2020-12-18T13:43:22.253 | 2020-12-18T13:43:22.253 | 11887 | 5 | [
"cross-validation",
"intuition"
] |
1827 | 2 | null | 1822 | 4 | null | You can fit a hierarchical bayesian (HB) model without pooling and do an ordinary OLS by pooling the data and compare the models in terms of model fit, hold-out predictions etc to evaluate whether pooling outperforms the HB model. The model very briefly will look like so:
Model
$y_i \sim N(X\ \beta_i,\sigma^2\ I)$
$\be... | null | CC BY-SA 2.5 | null | 2010-08-18T13:15:34.937 | 2010-08-18T13:25:06.397 | 2010-08-18T13:25:06.397 | null | null | null |
1828 | 2 | null | 1826 | 44 | null | I think that this is best described with [the following picture](http://genome.tugraz.at/proclassify/help/pages/XV.html) (in this case showing k-fold cross-validation):
Cross-validation is a technique used to protect against overfitting in a predictive model, particularl... | null | CC BY-SA 2.5 | null | 2010-08-18T13:20:28.593 | 2010-08-18T13:20:28.593 | null | null | 5 | null |
1829 | 1 | 1845 | null | 51 | 32492 | I usually hear about "ordinary least squares". Is that the most widely used algorithm used for linear regression? Are there reasons to use a different one?
| What algorithm is used in linear regression? | CC BY-SA 2.5 | null | 2010-08-18T13:30:31.750 | 2016-06-13T18:50:53.980 | 2016-06-13T18:50:53.980 | 28666 | 988 | [
"regression",
"least-squares",
"algorithms",
"computational-statistics",
"numerics"
] |
1830 | 2 | null | 1826 | 60 | null | Consider the following situation:
>
I want to catch the subway to go to my office. My plan is to take my car, park at the subway and then take the train to go to my office. My goal is to catch the train at 8.15 am every day so that I can reach my office on time. I need to decide the following: (a) the time at which I... | null | CC BY-SA 3.0 | null | 2010-08-18T13:38:29.843 | 2016-04-21T01:33:54.363 | 2016-04-21T01:33:54.363 | 46761 | null | null |
1831 | 2 | null | 1822 | 3 | null | Srikant is right. The book you want is ["Data Analysis Using Regression and Multilevel/Hierarchical Models"](http://www.stat.columbia.edu/~gelman/arm/) by Gelman and Hill, all [the R code from the book](http://www.stat.columbia.edu/~gelman/arm/software/), and the associated [arm package in R](http://cran.r-project.org... | null | CC BY-SA 2.5 | null | 2010-08-18T13:42:13.063 | 2010-08-18T13:42:13.063 | null | null | 5 | null |
1832 | 2 | null | 1815 | 19 | null | for me, the best book around is by George Box:
[Statistics for Experimenters: Design, Innovation, and Discovery](http://rads.stackoverflow.com/amzn/click/0471718130)
of course the book by Maxwell and Delaney is also pretty good:
[Designing Experiments and Analyzing Data: A Model Comparison Perspective, Second Edition]... | null | CC BY-SA 2.5 | null | 2010-08-18T13:50:28.787 | 2010-08-18T13:50:28.787 | null | null | 447 | null |
1833 | 2 | null | 1829 | 6 | null | The wiki link: [Estimation Methods for Linear Regression](http://en.wikipedia.org/wiki/Linear_regression#Estimation_methods) gives a fairly comprehensive list of estimation methods including OLS and the contexts in which alternative estimation methods are used.
| null | CC BY-SA 3.0 | null | 2010-08-18T14:01:06.170 | 2013-03-26T15:47:07.683 | 2013-03-26T15:47:07.683 | 603 | null | null |
1834 | 2 | null | 1829 | 4 | null | It is easy to get confused between definitions and terminology. Both terms are used, sometimes interchangeably. A quick lookup on Wikipedia should help:
- ordinary least squares
- lnear regression
Ordinary Least Squares (OLS) is a method used to fit linear regression models. Because of the demonstrable consistenc... | null | CC BY-SA 2.5 | null | 2010-08-18T14:01:19.650 | 2010-08-18T14:01:19.650 | null | null | 334 | null |
1835 | 2 | null | 1822 | 3 | null | The only further comment I would make is that the approach need not be Bayesian and the model need not be a mixed or random effects model.
In the simplest case if you had two series in x the mean model may be:
y = b01 + I*b02 + b11*x + I.b12*x
Where I indicates a sample from the 2nd series. An omnibus F-test can be us... | null | CC BY-SA 2.5 | null | 2010-08-18T14:13:25.737 | 2010-08-18T14:13:25.737 | null | null | 521 | null |
1836 | 2 | null | 1807 | 32 | null | This may surprise many, but to solve this problem you don't necessarily need to estimate s. In fact, you don't need to know anything about the spread of the data (although that would be helpful, of course). For instance, Wall, Boen, and Tweedie in a 2001 article describe how to find a finite confidence interval for t... | null | CC BY-SA 3.0 | null | 2010-08-18T14:33:06.530 | 2015-05-16T19:36:06.987 | 2015-05-16T19:36:06.987 | 919 | 919 | null |
1837 | 2 | null | 1321 | 5 | null | A method that can be very effective--one I have found extremely useful--is to sort the data by time and draw a connected X,Y scatterplot. (That is, successive points are connected by line segments or a spline.) This much is straightforward in almost any statistical plotting package. If the result is too confusing, a... | null | CC BY-SA 3.0 | null | 2010-08-18T14:47:31.707 | 2011-08-18T20:30:22.123 | 2017-04-13T12:44:21.613 | -1 | 919 | null |
1838 | 1 | 1843 | null | 11 | 20448 | Greetings,
Currently I'm doing the following in R:
```
require(zoo)
data <- read.csv(file="summary.csv",sep=",",head=TRUE)
cum = zoo(data$dcomp, as.Date(data$date))
data = zoo(data$compressed, as.Date(data$date))
data <- aggregate(data, identity, tail, 1)
cum <- aggregate(cum, identity, sum, 1)
days = seq(start(data)... | How do I vertically stack two graphs with the same x scale, but a different y scale in R? | CC BY-SA 2.5 | null | 2010-08-18T14:55:55.133 | 2013-11-16T01:50:25.777 | 2010-08-18T15:39:27.513 | 990 | 990 | [
"r",
"time-series",
"data-visualization",
"entropy"
] |
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