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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
4490 | 2 | null | 4489 | 21 | null | Add `bty="n"` in both plot commands.
For time series, add `frame.plot=FALSE` for the same effect.
For fancier Tufte axes, see [http://www.cl.cam.ac.uk/~sjm217/projects/graphics/](http://www.cl.cam.ac.uk/~sjm217/projects/graphics/)
| null | CC BY-SA 3.0 | null | 2010-11-13T02:04:40.637 | 2013-07-24T01:23:45.453 | 2013-07-24T01:23:45.453 | 159 | 159 | null |
4491 | 1 | 4494 | null | 2 | 1570 | I'm using the following code to insert vertical lines in a timeseries plot:
```
abline(v=as.Date("2007-09-19"),col="red",lty=2)
abline(v=as.Date("2008-03-10"),col="red",lty=2)
abline(v=as.Date("2008-04-28"),col="red",lty=2)
abline(v=as.Date("2009-08-07"),col="red",lty=2)
```
But I would rather prefer a small triangle ... | Inserting small arrows (triangles) in bottom axis pointing up with R | CC BY-SA 2.5 | null | 2010-11-13T03:08:04.377 | 2010-11-13T13:32:38.697 | 2010-11-13T09:21:23.217 | 930 | 990 | [
"r",
"data-visualization"
] |
4492 | 2 | null | 4469 | 3 | null | 1) You can always find out exactly how an algorithm in SPSS is defined mathematically from the Algorithms link on the Help menu.
2) SPSS also has a Weighted Least Square procedure that allows you to model the error variance and correct for heteroscedasticity.
2) In order to run R within SPSS (version 16 or later), you ... | null | CC BY-SA 2.5 | null | 2010-11-13T03:18:29.467 | 2010-11-13T03:18:29.467 | null | null | 1971 | null |
4493 | 2 | null | 4489 | 4 | null | If you use
```
par(bty = 'n')
```
Before calling plot that will fix it for zoo. It might also fix it for a variety of situations where it isn't passable to the plotting command.
(Check out bty option in the par() help for other kinds of frames for the plot)
| null | CC BY-SA 2.5 | null | 2010-11-13T03:19:10.860 | 2010-11-13T03:19:10.860 | null | null | 601 | null |
4494 | 2 | null | 4491 | 3 | null | There's a variety of ways. You could use a pch = '^' and put that near the bottom or look up an arrow symbol character. Also you could just do what you're doing with the arrows() command and draw short arrows instead of abline(). With arrows you would need to specify both ends instead of just the horizontal position... | null | CC BY-SA 2.5 | null | 2010-11-13T03:28:41.470 | 2010-11-13T13:32:38.697 | 2010-11-13T13:32:38.697 | 601 | 601 | null |
4496 | 1 | 4509 | null | 3 | 2370 | Consider the following code:
```
require(zoo)
data <- read.csv(file="summary.csv",sep=",",head=TRUE)
data = zoo(data$compressed, as.Date(data$date))
data <- aggregate(data, identity, tail, 1)
days = seq(start(data), end(data), "day")
data2 = na.locf(merge(data, zoo(,days)))
par(bty = 'n')
plot(data2,xlab='',ylab='entr... | Adjusting axis properties in a time series graph | CC BY-SA 3.0 | null | 2010-11-13T04:08:55.587 | 2016-12-30T08:28:47.977 | 2016-12-30T08:28:47.977 | 22047 | 990 | [
"r",
"data-visualization"
] |
4497 | 2 | null | 3542 | 12 | null | If you are interested in table design, I would definitely recommend two papers on the subject by Andrew Gelman:
A necessary preface to the paper on table design is Gelman et al, 2002 [Let's practice what we preach: Turning Tables Into Graphs](http://www.stat.columbia.edu/~gelman/research/published/dodhia.pdf)
Gelman a... | null | CC BY-SA 4.0 | null | 2010-11-13T05:24:16.083 | 2019-09-11T05:16:40.350 | 2019-09-11T05:16:40.350 | 1381 | 1381 | null |
4498 | 1 | 4502 | null | 39 | 9495 | They all seem to represent random variables by the nodes and (in)dependence via the (possibly directed) edges. I'm esp interested in a bayesian's point-of-view.
| What's the relation between hierarchical models, neural networks, graphical models, bayesian networks? | CC BY-SA 2.5 | null | 2010-11-13T05:43:15.703 | 2015-12-08T14:19:58.223 | 2010-11-13T20:33:21.760 | 930 | 1795 | [
"causality",
"neural-networks",
"multilevel-analysis",
"graphical-model"
] |
4499 | 2 | null | 4491 | 0 | null | You could manually construct the triangles using the 'polygon' command. There might be a nice way to automate the construction of these but depending on how many you want to make it probably wouldn't be too bad to figure out the coordinates by hand.
| null | CC BY-SA 2.5 | null | 2010-11-13T06:03:11.923 | 2010-11-13T06:03:11.923 | null | null | 1028 | null |
4500 | 1 | 4506 | null | 7 | 790 | Context:
I've recently adopted version control as part of my data analysis work (finally I may hear you saying: see my [earlier question on SO](https://stackoverflow.com/questions/2712421/r-and-version-control-for-the-solo-data-analyst)). This prompted me to think more about repositories and the directory structure I u... | Repositories and data analysis projects | CC BY-SA 2.5 | null | 2010-11-13T06:26:43.493 | 2010-11-14T23:06:00.820 | 2017-05-23T12:39:26.593 | -1 | 183 | [
"reproducible-research",
"project-management"
] |
4501 | 1 | 4503 | null | 3 | 503 | I've been thinking recently about conventions for figure width in data analysis reports that lead to PDFs (e.g., in A4 or Letter size). My usual context is R, Sweave, and LaTeX.
The default figure width in Sweave is 80% of the text-width (i.e., the width of a paragraph of text).
```
\setkeys{Gin}{width=0.80\textwidth}
... | Typographic conventions for width of figures in LaTeX data analysis reports | CC BY-SA 2.5 | null | 2010-11-13T07:02:26.113 | 2017-05-18T21:19:05.927 | 2017-05-18T21:19:05.927 | 28666 | 183 | [
"presentation"
] |
4502 | 2 | null | 4498 | 34 | null | A Bayesian network is a type of graphical model. The other "big" type of graphical model is a Markov Random Field (MRF). Graphical models are used for inference, estimation and in general, to model the world.
The term hierarchical model is used to mean many things in different areas.
While neural networks come with "g... | null | CC BY-SA 2.5 | null | 2010-11-13T08:37:26.060 | 2010-11-13T08:37:26.060 | null | null | 1540 | null |
4503 | 2 | null | 4501 | 7 | null | I'll second @onestop comment about the fact that this question seems marginally related to statistical analysis or reporting.
That being said, I can't refrain from thinking of Ed. Tufte's work on the display of quantitative information, especially the design of his books which mixes different graphics layouts: some fi... | null | CC BY-SA 2.5 | null | 2010-11-13T09:06:44.280 | 2010-11-13T10:18:47.823 | 2010-11-13T10:18:47.823 | 930 | 930 | null |
4504 | 2 | null | 4491 | 4 | null | I prefer to use dedicated symbol for that purpose. For example, use `points()` with `pch=17` (filled triangle) like in the example below:
```
dd <- dotplot(m~i|p,data=res.EQ.long,subset=i %in% eq5d.items.names[1:5],
ylab="Score",ylim=c(0.5,3.5),
scales=list(x=list(rot=45,at=1:5,
... | null | CC BY-SA 2.5 | null | 2010-11-13T09:19:10.647 | 2010-11-13T09:19:10.647 | null | null | 930 | null |
4505 | 2 | null | 4498 | 10 | null | As [@carlosdc](https://stats.stackexchange.com/questions/4498/whats-the-relation-between-hierarchical-models-neural-networks-graphical-model/4502#4502) said, a bayesian network is a type of Graphical Model (i.e., a directed acyclic graph (DAG) whose structure defines a set of conditional independence properties). [Hier... | null | CC BY-SA 2.5 | null | 2010-11-13T10:05:45.920 | 2010-11-13T10:05:45.920 | 2017-04-13T12:44:36.923 | -1 | 930 | null |
4506 | 2 | null | 4500 | 5 | null | Regarding your first question:
>
What strategies do people use to map studies, publications, and analyses onto repositories?
One year ago or so, I decided to have one repository for each publication, presentation or semester/class. My typical directory looks like this:
.git
.gitignore
README.org
ana
dat
doc
org
The... | null | CC BY-SA 2.5 | null | 2010-11-13T11:37:51.957 | 2010-11-13T11:37:51.957 | 2017-05-23T12:39:26.203 | -1 | 307 | null |
4507 | 1 | 4514 | null | 5 | 956 | Let $G = (V,E)$ be a directed acyclic graph.
Let $i \rightarrow j$ be an edge such that the parents of $j$ are exactly:
- the parents of $i$,
- and $i$.
Let $L\left(G \right)$ be the set defined by {a vertex and its parents, for all vertices in $G$}
Let $G' = (V,F)$ where $F$ is $E$ with $j \rightarrow i$ in... | Factorization of a joint density over a directed graph | CC BY-SA 2.5 | null | 2010-11-13T13:41:20.420 | 2019-04-29T20:08:37.633 | 2010-11-13T21:21:50.660 | 1351 | 1351 | [
"distributions",
"data-visualization",
"self-study",
"graphical-model"
] |
4508 | 2 | null | 4496 | 2 | null | For the first question try:
```
plot(data2,xlab='',ylab='entropy (bytes)', xaxs = 'i', yaxs = 'i')
```
| null | CC BY-SA 2.5 | null | 2010-11-13T16:04:13.600 | 2010-11-13T16:54:51.440 | 2010-11-13T16:54:51.440 | 1050 | 1050 | null |
4509 | 2 | null | 4496 | 2 | null | For the second Q, you can use `axis.Date()` with argument `labels = FALSE` to add minor tick marks at locations defined by argument `at`.
| null | CC BY-SA 2.5 | null | 2010-11-13T17:28:45.037 | 2010-11-13T17:28:45.037 | null | null | 1390 | null |
4510 | 1 | 4511 | null | 2 | 18651 | I have fit a linear model using the `lm` function in R...
```
model <- lm(trans.baseline.CD4 ~ hiv$Julian.Date)
```
... and I would like to assess the quality of the model's fit. Is there a function in R that will do this? Alternatively, I found a formula for goodness-of-fit involving the sum of squared residuals give... | How to compute goodness of fit for a linear model in R | CC BY-SA 2.5 | null | 2010-11-13T17:47:12.277 | 2010-11-13T18:17:03.820 | 2010-11-13T18:17:03.820 | 930 | 1973 | [
"r",
"regression",
"goodness-of-fit"
] |
4511 | 2 | null | 4510 | 7 | null | It all starts with
```
summary(model)
```
after your fit. There are numerous commands to assess the fit, test commands, compare alternative models, ... in base R as well as in add-on packages on CRAN.
But you may want to do some reading, for example with Dalgaard's book or another introduction to statistics with R... | null | CC BY-SA 2.5 | null | 2010-11-13T17:56:39.150 | 2010-11-13T17:56:39.150 | null | null | 334 | null |
4512 | 2 | null | 4510 | 1 | null | You should probably take a look at this:
[https://stackoverflow.com/questions/1181025/goodness-of-fit-functions-in-r](https://stackoverflow.com/questions/1181025/goodness-of-fit-functions-in-r)
| null | CC BY-SA 2.5 | null | 2010-11-13T17:59:01.260 | 2010-11-13T17:59:01.260 | 2017-05-23T12:39:26.150 | -1 | 1540 | null |
4513 | 1 | null | null | 5 | 327 | Can you give me examples of machine learning algorithms which learn from the statistical properties of the dataset not the individual observations itself i.e. employ the statistical query model?
| Statistical query model algorithms? | CC BY-SA 2.5 | null | 2010-11-13T21:43:52.613 | 2017-01-04T13:16:06.610 | null | null | 1961 | [
"machine-learning",
"algorithms"
] |
4514 | 2 | null | 4507 | 5 | null | What you are looking for is termed a covered edge reversal, which preserves Markov equivalence of directed acyclic graphs. A proof of equivalence is given in Lemma 1 of [A Transformational Characterization of Equivalent Bayesian Network Structures](http://research.microsoft.com/pubs/65664/uai95.pdf), by Max Chickering.... | null | CC BY-SA 2.5 | null | 2010-11-13T22:15:43.690 | 2010-11-13T22:15:43.690 | null | null | 495 | null |
4515 | 1 | null | null | 4 | 534 | Lets say we have $T$ tags and $N$ articles and lets say that for each tag $t_{i}$ we know that it has tagged $n_{i}$ articles. Meaning that, frequency($t_{i}$)=$n_{i}$.
Given the above information how could I compute the probability P($t_{i}$ to tag a new article) or more generally how could I go about scoring the "imp... | How to measure/weight the importance of tags? | CC BY-SA 3.0 | null | 2010-11-13T22:59:12.890 | 2017-11-09T14:10:58.733 | 2017-11-09T14:10:58.733 | 101426 | null | [
"probability",
"predictive-models",
"data-mining"
] |
4516 | 2 | null | 4396 | 2 | null | Actually, if you want ranks, try the RANK command. Or use the menus: Transform>Rank Cases. It can generate ranks within groups.
If you prefer the AGGREGATE approach, note that it does NOT require sorting first.
HTH,
Jon Peck
| null | CC BY-SA 2.5 | null | 2010-11-13T23:21:12.727 | 2010-11-13T23:21:12.727 | null | null | 1971 | null |
4517 | 1 | 4518 | null | 70 | 201451 | Is it possible to have a (multiple) regression equation with two or more dependent variables? Sure, you could run two separate regression equations, one for each DV, but that doesn't seem like it would capture any relationship between the two DVs?
| Regression with multiple dependent variables? | CC BY-SA 2.5 | null | 2010-11-14T02:50:03.993 | 2022-02-24T14:36:28.833 | null | null | 1977 | [
"regression"
] |
4518 | 2 | null | 4517 | 40 | null | Yes, it is possible. What you're interested is is called "Multivariate Multiple Regression" or just "Multivariate Regression". I don't know what software you are using, but you can do this in R.
[Here's a link that provides examples](https://web.archive.org/web/20181024155136/http://www.public.iastate.edu/%7Emaitra/s... | null | CC BY-SA 4.0 | null | 2010-11-14T03:32:24.503 | 2022-02-24T14:28:42.420 | 2022-02-24T14:28:42.420 | 11887 | 485 | null |
4519 | 1 | 5931 | null | 3 | 1545 | I plotted a set of about 200,000 points and got a triangular shaped region. The shape is roughly like the triangle made by the points $(1,0)$, $(0,1)$ and $(0,0)$. My points have the property that if $(a,b)$ is a member of the set, then $(b,a)$ is also a member. I wanted to do a regression on the variable associated... | Regression on a triangular shaped region of points representing a symmetric relation | CC BY-SA 2.5 | null | 2010-11-14T05:28:34.397 | 2011-01-03T19:53:37.627 | 2010-11-14T20:50:03.263 | 847 | 847 | [
"regression"
] |
4520 | 2 | null | 4515 | 1 | null | First for calculating the importance of each tag in a new article I would use the bag of words format to store the corpus information plus the new article added to the last row. I would calculate the tf-idf of the sparse matrix containing the rows with articles and the columns with words. Then I would just Keep the col... | null | CC BY-SA 2.5 | null | 2010-11-14T09:40:39.110 | 2010-11-14T09:40:39.110 | null | null | 1808 | null |
4521 | 2 | null | 4517 | 2 | null | I would do this by first transforming the regression variables to PCA calculated variables, and then I would to the regression with the PCA calculated variables. Of course I would store the eigenvectors to be able to calculate the corresponding pca values when I have a new instance I wanna classify.
| null | CC BY-SA 2.5 | null | 2010-11-14T09:43:19.713 | 2010-11-14T09:43:19.713 | null | null | 1808 | null |
4522 | 2 | null | 4517 | -4 | null | It's called structural equation model or simultaneous equation model.
| null | CC BY-SA 2.5 | null | 2010-11-14T10:44:06.097 | 2010-11-14T10:44:06.097 | null | null | 1966 | null |
4523 | 1 | 4524 | null | 1 | 742 | The following code:
```
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), end(data), "day")
data2 = na.... | Tweaking axis properties in a timeseries graph | CC BY-SA 2.5 | null | 2010-11-14T16:21:47.483 | 2010-11-14T17:28:03.050 | null | null | 990 | [
"r",
"data-visualization"
] |
4524 | 2 | null | 4523 | 3 | null | Two quick suggestions:
- axis.Date() has a format field, set it to `"%Y/%m" to get '2007/07' (but then for all labels)
- See help(axTicks) and help(rug) which should help you.
Also try the [xts](http://packages.r-project.org/package=xts) package which extends the [zoo](http://packages.r-project.org/package=zoo) pac... | null | CC BY-SA 2.5 | null | 2010-11-14T17:28:03.050 | 2010-11-14T17:28:03.050 | null | null | 334 | null |
4525 | 2 | null | 4500 | 2 | null | I keep a separate repository for each project, with a project being centered around a particular data set or question being addressed. The repo contains the data, code, and Sweave documents/plots that explain and express the results.
I maintain a separate repo for each discrete publication or presentation because
- A ... | null | CC BY-SA 2.5 | null | 2010-11-14T17:50:08.343 | 2010-11-14T17:50:08.343 | null | null | 1916 | null |
4526 | 2 | null | 4453 | 5 | null | To avoid the ambiguity of a terminology that refers to everything as an "item," let's suppose the set $S$ consists of $n$ distinct "letters." And, because this is a pretty generalization of the Birthday Paradox, let's generalize it a little further and set $P$ to be the set of all $r$-letter words having all distinct ... | null | CC BY-SA 2.5 | null | 2010-11-14T19:02:45.520 | 2010-11-30T18:21:49.360 | 2010-11-30T18:21:49.360 | 919 | 919 | null |
4527 | 1 | 4588 | null | 0 | 1535 | I am exploring the use of the `survreg` function in R to analyze my current experiment.
Does anyone know what the "Value" column in the output of the function stands for?
| What is the "Value" output of Survreg in R? | CC BY-SA 2.5 | null | 2010-11-14T19:55:14.707 | 2010-11-16T11:37:33.347 | 2010-11-14T19:59:53.407 | 930 | 1862 | [
"r",
"survival",
"interpretation"
] |
4528 | 1 | 4534 | null | 18 | 30326 | I have been trying to discern what exactly the "coef" and "(exp)coef" output of coxph signify. It seems that the "(exp)coef" are comparisons of the first variable in the model according to the group assigned in the command.
How does the coxph function arrive at the values for "coef" and "(exp)coef"?
Additionally, how ... | What is the difference between the "coef" and "(exp)coef" output of coxph in R? | CC BY-SA 2.5 | null | 2010-11-14T20:39:07.227 | 2010-11-15T22:25:52.023 | null | null | 1862 | [
"r",
"survival",
"interpretation"
] |
4529 | 2 | null | 4528 | 8 | null | To quote the documentation for the print method for a coxph object, obtained in R by typing `?survival::print.coxph`:
>
coefficients the coefficients of the linear predictor, which multiply the columns of the model matrix.
That's all the documentation the author of the package provides. The package contains no us... | null | CC BY-SA 2.5 | null | 2010-11-14T21:10:23.387 | 2010-11-14T21:10:23.387 | null | null | 449 | null |
4530 | 1 | null | null | 4 | 12457 | Yesterday, I ran a repeated-measures ANCOVA. The purpose was to determine the usability of two computer systems, call them 1 and 2. Each subject completed three (conceptually different) tasks, call them A B and C, on each system. (The covariate was whether the subject had prior experience with the current system, sy... | When to interpret multivariate tests when performing repeated-measures ANCOVA? | CC BY-SA 3.0 | null | 2010-11-14T21:14:33.743 | 2011-10-03T05:24:24.107 | 2011-10-03T05:24:24.107 | 183 | 1977 | [
"anova",
"spss",
"multivariate-analysis",
"manova"
] |
4531 | 2 | null | 4530 | 8 | null | This is not a complete answer as ANOVA with repeated-measures is a complex topic. You can look at your design from a multivariate point of view if you regard your data not as representing realisations of one DV in different conditions, but of (ultimately) different DVs which are to be analysed simultaneously. (Note tha... | null | CC BY-SA 2.5 | null | 2010-11-14T22:29:08.370 | 2010-11-14T22:54:11.630 | 2010-11-14T22:54:11.630 | 1909 | 1909 | null |
4534 | 2 | null | 4528 | 25 | null | If you have a single explanatory variable, say treatment group, a Cox's regression model is fitted with `coxph()`; the coefficient (`coef`) reads as a regression coefficient (in the context of the Cox model, described hereafter) and its exponential gives you the hazard in the treatment group (compared to the control or... | null | CC BY-SA 2.5 | null | 2010-11-15T00:02:10.313 | 2010-11-15T22:25:52.023 | 2017-04-13T12:44:45.640 | -1 | 930 | null |
4535 | 2 | null | 4530 | 5 | null | Your question brings up a substantial problem with SPSS... too much automatic output. You are absolutely correct in your implication that the automatic generation of multiple tests is problematic. Some people take the fact that their computer program does this as evidence that it is good procedure. It isn't. Trust ... | null | CC BY-SA 2.5 | null | 2010-11-15T00:28:09.360 | 2010-11-15T00:28:09.360 | null | null | 601 | null |
4536 | 2 | null | 4517 | 10 | null | @Brett's response is fine.
If you are interested in describing your two-block structure, you could also use [PLS regression](http://en.wikipedia.org/wiki/Partial_least_squares_regression). Basically, it is a regression framework which relies on the idea of building successive (orthogonal) linear combinations of the va... | null | CC BY-SA 2.5 | null | 2010-11-15T00:34:37.603 | 2010-11-15T00:34:37.603 | null | null | 930 | null |
4537 | 2 | null | 3242 | 3 | null | If you're interested in bivariate power-law functions (as opposed to univariate power-law distributions), then
Warton et al. "[Bivariate line-fitting methods for allometry](http://www.maths.unsw.edu.au/statistics/files/preprint-2005-02.pdf)." Biol. Rev. 81, 259-201 (2006)
is an excellent reference. In this case, regres... | null | CC BY-SA 2.5 | null | 2010-11-15T06:44:49.957 | 2010-11-15T06:44:49.957 | null | null | null | null |
4538 | 2 | null | 4364 | 9 | null | Let $\eta$ and $\xi$ be two independent random variables with a common symmetric distribution such that
$$ P\left ( \left |\frac{\xi+\eta}{\sqrt{2}}\right | \geq t \right )\leq P(|\xi|\geq t).$$
Then these random variables are gaussian. (Obviously, if the $\xi$ and $\eta$ are centered gaussian, it is true.)
This is ... | null | CC BY-SA 2.5 | null | 2010-11-15T07:06:37.643 | 2010-11-15T15:07:17.930 | 2010-11-15T15:07:17.930 | 919 | 223 | null |
4539 | 1 | 4541 | null | 5 | 10987 | I have kinda puzzled when I heard from the other about the calculation of incidence rate
From Kenneth Rothman's Modern Epidemiology, the incidence rate is calculated as `number of cases in a fix period of time` divided by `person-time at risk at that period of time`, which mean if the patient has the disease in the mid... | Calculation of incidence rate for epidemiological study in hospital | CC BY-SA 2.5 | null | 2010-11-15T10:23:55.267 | 2011-08-15T13:31:32.683 | 2010-11-15T11:57:43.427 | 449 | 588 | [
"epidemiology",
"definition"
] |
4540 | 2 | null | 4539 | 1 | null | I think Rothman's definition should be used. In the second definition all patients seem to be given the same duration (is this correct?) so that the incidence will not be the incidence rate but will be proportional to the cumulative incidence (cases / total in given time frame)
| null | CC BY-SA 2.5 | null | 2010-11-15T11:16:20.133 | 2010-11-15T11:16:20.133 | null | null | 1573 | null |
4541 | 2 | null | 4539 | 4 | null | It is commonly admitted that the denominator for IRs is the "population at risk" (i.e., all individuals in which the studied event(s) may occur). Although your first formula is generally used, I found in The new public health, by Tulchinsky and Varavikova (Elsevier, 2009, 2nd. ed., [p. 84](http://books.google.fr/books?... | null | CC BY-SA 2.5 | null | 2010-11-15T11:33:03.200 | 2010-11-15T11:33:03.200 | null | null | 930 | null |
4542 | 1 | 4593 | null | 4 | 198 | After running a study based on interactive genetic algorithms, I have a univariate data file containing multiple participants each doing multiple generations (blocks) of multiple trials.
Is there an established approach used to analyse this?
I've looked at using a grand mean on each of the component variables. I've als... | Are there any recommended approaches for analysing data from genetic algorithms? | CC BY-SA 2.5 | null | 2010-11-15T16:23:13.670 | 2010-11-16T17:23:45.307 | null | null | 1950 | [
"genetic-algorithms"
] |
4543 | 1 | 4554 | null | 11 | 4636 | I am fitting a linear model in R using `glmnet`. The original (non-regularized) model was fitted using `lm` and did not have a constant term (i.e. it was in the form `lm(y~0+x1+x2,data)`).
`glmnet` takes a matrix of predictors and a vector of responses. I've been reading `glmnet` documentation, and can find no mention ... | In R, does "glmnet" fit an intercept? | CC BY-SA 2.5 | null | 2010-11-15T16:35:22.907 | 2010-11-15T20:54:46.520 | 2010-11-15T20:54:46.520 | 439 | 439 | [
"r",
"regression",
"lasso"
] |
4544 | 1 | 4549 | null | 29 | 47692 | Please provide R code which allows one to conduct a between-subjects ANOVA with -3, -1, 1, 3 contrasts. I understand there is a debate regarding the appropriate Sum of Squares (SS) type for such an analysis. However, as the default type of SS used in SAS and SPSS (Type III) is considered the standard in my area. Thu... | How does one do a Type-III SS ANOVA in R with contrast codes? | CC BY-SA 2.5 | null | 2010-11-15T17:01:36.680 | 2018-05-06T10:35:31.813 | 2011-09-13T20:14:59.197 | 442 | 196 | [
"r",
"anova",
"contrasts",
"sums-of-squares"
] |
4545 | 2 | null | 4544 | 10 | null | You may want to have a look at this blog post:
[Obtaining the same ANOVA results in R as in SPSS - the difficulties with Type II and Type III sums of squares](http://myowelt.blogspot.com/2008/05/obtaining-same-anova-results-in-r-as-in.html)
(Spoiler: add `options(contrasts=c("contr.sum", "contr.poly"))` at the beginnin... | null | CC BY-SA 2.5 | null | 2010-11-15T17:30:52.187 | 2010-11-15T17:30:52.187 | null | null | 582 | null |
4546 | 2 | null | 4288 | 1 | null | I'm not sure I understand your question, but you have no other answers, so I thought I'd give it a shot and revive this topic: If I understand you correctly for a number of different sampling units (say people) you have estimates that they have given attribute, e.g. there is a probability that they have eaten turkey t... | null | CC BY-SA 2.5 | null | 2010-11-15T17:36:43.753 | 2010-11-17T05:28:47.827 | 2010-11-17T05:28:47.827 | 196 | 196 | null |
4547 | 2 | null | 4462 | 11 | null | The median of medians is not the same as the median of the raw scores. A simple case of this is that when you have an odd number of sales, the median is the middle value; when you have an even number of sales, the median is commonly taken as the average between those two values. A more "real world" challenge to this i... | null | CC BY-SA 2.5 | null | 2010-11-15T17:50:20.730 | 2010-11-15T17:50:20.730 | null | null | 196 | null |
4548 | 2 | null | 4451 | 1 | null | A good resource for finding datasets is:
[/r/datasets](http://datasets.reddit.com) on Reddit.
A quick glance at that page reveals [this source](http://networkdata.ics.uci.edu/resources.php), which might contain something useful for you.
| null | CC BY-SA 2.5 | null | 2010-11-15T18:07:57.280 | 2010-12-10T05:46:27.450 | 2010-12-10T05:46:27.450 | 115 | 115 | null |
4549 | 2 | null | 4544 | 25 | null | Type III sum of squares for ANOVA are readily available through the `Anova()` function from the [car](http://cran.r-project.org/web/packages/car/index.html) package.
Contrast coding can be done in several ways, using `C()`, the `contr.*` family (as indicated by @nico), or directly the `contrasts()` function/argument. T... | null | CC BY-SA 2.5 | null | 2010-11-15T18:26:07.910 | 2010-11-15T18:26:07.910 | null | null | 930 | null |
4550 | 2 | null | 4544 | 3 | null | Try the Anova command in the car library. Use the type="III" argument, as it defaults to type II. For example:
```
library(car)
mod <- lm(conformity ~ fcategory*partner.status, data=Moore, contrasts=list(fcategory=contr.sum, partner.status=contr.sum))
Anova(mod, type="III")
```
| null | CC BY-SA 2.5 | null | 2010-11-15T18:31:59.447 | 2010-11-15T18:31:59.447 | null | null | 101 | null |
4551 | 1 | null | null | 254 | 27187 | I'm a grad student in psychology, and as I pursue more and more independent studies in statistics, I am increasingly amazed by the inadequacy of my formal training. Both personal and second hand experience suggests that the paucity of statistical rigor in undergraduate and graduate training is rather ubiquitous within ... | What are common statistical sins? | CC BY-SA 3.0 | null | 2010-11-15T18:46:37.113 | 2022-12-07T12:54:23.950 | 2016-08-11T17:17:48.537 | 22468 | 364 | [
"fallacy"
] |
4552 | 2 | null | 4551 | 25 | null | Correlation implies causation, which is not as bad as accepting the Null Hypothesis.
| null | CC BY-SA 2.5 | null | 2010-11-15T18:56:19.547 | 2010-11-15T18:56:19.547 | null | null | 1307 | null |
4553 | 2 | null | 4551 | 42 | null | Dichotomization of a continuous predictor variable to either "simplify" analysis or to solve for the "problem" of non-linearity in the effect of the continuous predictor.
| null | CC BY-SA 2.5 | null | 2010-11-15T18:57:56.170 | 2010-11-15T21:25:05.107 | 2010-11-15T21:25:05.107 | 364 | 364 | null |
4554 | 2 | null | 4543 | 13 | null | Yes, an intercept is included in a [glmnet](http://cran.r-project.org/web/packages/glmnet/index.html) model, but it is not regularized (cf. [Regularization Paths for Generalized Linear Models via Coordinate Descent](http://www-stat.stanford.edu/~jhf/ftp/glmnet.pdf), p. 13). More details about the implementation could c... | null | CC BY-SA 2.5 | null | 2010-11-15T19:02:36.613 | 2010-11-15T19:02:36.613 | null | null | 930 | null |
4555 | 2 | null | 4551 | 23 | null | Analysis of rate data (accuracy, etc) using ANOVA, thereby assuming that rate data has Gaussian distributed error when it's actually binomially distributed.
[Dixon (2008)](http://dx.doi.org/10.1016/j.jml.2007.11.004) provides a discussion of the consequences of this sin and exploration of more appropriate analysis appr... | null | CC BY-SA 2.5 | null | 2010-11-15T19:12:03.287 | 2010-11-16T15:47:31.527 | 2010-11-16T15:47:31.527 | 364 | 364 | null |
4556 | 1 | 6724 | null | 8 | 117711 | Having the Transfer Function of a discrete system as such:
$$H(z) = \frac{0.8}{z(z-0.8)}$$
I am asked to find the Steady State Gain of the system.
I have the solution and it simply states:
Steady State Gain:
$$H(1) = \frac{0.8}{1-0.8} = 4$$
I have no idea why this is the case, and the book, the slides, the solutio... | How to compute the steady state gain from the transfer function of a discrete time system? | CC BY-SA 2.5 | null | 2010-11-15T19:12:46.883 | 2018-01-12T15:20:02.297 | 2010-11-15T22:57:21.427 | 159 | 1994 | [
"time-series",
"self-study"
] |
4557 | 2 | null | 4551 | 4 | null | Application of least-squares minimization when maximum-likelihood procedures exist.
| null | CC BY-SA 2.5 | null | 2010-11-15T19:16:20.850 | 2010-11-15T19:16:20.850 | null | null | 364 | null |
4558 | 2 | null | 4551 | 18 | null | A current popular one is plotting 95% confidence intervals around the raw performance values in repeated measures designs when they only relate to the variance of an effect. For example, a plot of reaction times in a repeated measures design with confidence intervals where the error term is derived from the MSE of a r... | null | CC BY-SA 3.0 | null | 2010-11-15T19:26:09.143 | 2012-03-09T13:33:02.260 | 2012-03-09T13:33:02.260 | 601 | 601 | null |
4559 | 2 | null | 4517 | 0 | null | Did you already come across the term "canonical correlation"? There you have sets of variables on the independent as well as on the dependent side. But maybe there are more modern concepts available, the descriptions I have are all of the eighties/nineties...
| null | CC BY-SA 2.5 | null | 2010-11-15T19:59:37.720 | 2010-11-15T19:59:37.720 | null | null | 1818 | null |
4560 | 2 | null | 4551 | 14 | null | My intro psychometrics course in undergrad spent at least two weeks teaching how to perform a stepwise regression. Is there any situation where stepwise regression is a good idea?
| null | CC BY-SA 2.5 | null | 2010-11-15T20:01:39.920 | 2010-11-15T20:01:39.920 | null | null | 1118 | null |
4561 | 1 | null | null | 1 | 2927 | For a university paper, I have to compare whether two program versions are statistically significantly different. The comparison in terms of runtime performance is straightforward -- I determine whether the confidence intervals for the two sets of measurements overlap. However, in the case of memory footprint, I have n... | Comparison of two values in a statistically sound manner | CC BY-SA 2.5 | null | 2010-11-15T20:14:11.647 | 2010-11-16T00:13:45.617 | 2010-11-15T22:58:10.100 | 159 | 1992 | [
"hypothesis-testing",
"statistical-significance",
"self-study",
"inference"
] |
4562 | 2 | null | 4561 | 4 | null | It is quite simple; memory footprint is constant, this means it has no deviation and thus any difference is significant.
| null | CC BY-SA 2.5 | null | 2010-11-15T20:32:16.927 | 2010-11-15T20:32:16.927 | null | null | null | null |
4563 | 2 | null | 4551 | 14 | null | Failing to test the assumption that error is normally distributed and has constant variance between treatments. These assumptions aren't always tested, thus least-squares model fitting is probably often used when it is actually inappropriate.
| null | CC BY-SA 2.5 | null | 2010-11-15T21:39:05.163 | 2010-11-17T16:29:42.977 | 2010-11-17T16:29:42.977 | 319 | 101 | null |
4564 | 1 | 4763 | null | 7 | 446 | It is known that [belief propagation](http://en.wikipedia.org/wiki/Belief_propagation) gives exact result on trees, are there interesting examples when [Generalized Belief Propagation](http://www.merl.com/publications/TR2001-22/) is exact? (edit junction tree is not interesting because it is exactly solvable without GB... | When is Generalized Belief Propagation exact? | CC BY-SA 2.5 | null | 2010-11-15T21:47:29.903 | 2010-11-20T23:51:23.590 | 2010-11-20T22:30:54.403 | 511 | 511 | [
"graphical-model"
] |
4566 | 2 | null | 4561 | 3 | null | You're incorrect about the confidence intervals. Assuming they are 95% confidence intervals then they can overlap quite a bit and be statistically significantly different. What you need is for the confidence interval of the difference between the two to not overlap 0. The standard error of the difference is sqrt(2 *... | null | CC BY-SA 2.5 | null | 2010-11-16T00:13:45.617 | 2010-11-16T00:13:45.617 | null | null | 601 | null |
4567 | 2 | null | 4551 | 118 | null | Most interpretations of p-values are sinful! The conventional usage of p-values is badly flawed; a fact that, in my opinion, calls into question the standard approaches to the teaching of hypothesis tests and tests of significance.
Haller and Krause have found that statistical instructors are almost as likely as studen... | null | CC BY-SA 4.0 | null | 2010-11-16T00:15:32.840 | 2022-11-28T14:19:15.210 | 2022-11-28T14:19:15.210 | 362671 | 1679 | null |
4568 | 1 | null | null | 5 | 2880 | Thanks for all the answer for the question [Calculation of incidence rate for epidemiological study in hospital](https://stats.stackexchange.com/questions/4539/calculation-of-incidence-rate-for-epidemiological-study-in-hospital). And here come's the second part of the question:
What about the prevalence rate then? I ha... | Calculation of incidence rate for epidemiological study -- prevalence rate this time | CC BY-SA 3.0 | null | 2010-11-16T01:24:41.520 | 2012-12-28T12:39:49.547 | 2017-04-13T12:44:40.883 | -1 | 588 | [
"epidemiology",
"definition"
] |
4569 | 1 | 4577 | null | 9 | 11649 | I would like to gather input from people in the field about the Yates continuity correction for 2 x 2 contingency tables. [The Wikipedia article](http://en.wikipedia.org/wiki/Yates%27_correction_for_continuity) mentions it may adjust too far, and is thus only used in a limited sense. The [related post here](https://sta... | Yates continuity correction for 2 x 2 contingency tables | CC BY-SA 3.0 | null | 2010-11-16T01:27:37.710 | 2017-11-12T21:25:54.187 | 2017-11-12T21:25:54.187 | 11887 | 696 | [
"categorical-data",
"chi-squared-test",
"yates-correction"
] |
4570 | 2 | null | 4551 | 5 | null | I would say, doing tests and regressions on a small set of data.
Edit: Without looking at the confidence intervals, or when the confidence intervals/error bars are not easy to calculate.
| null | CC BY-SA 2.5 | null | 2010-11-16T02:30:58.087 | 2010-11-19T09:35:48.883 | 2010-11-19T09:35:48.883 | 1709 | 1709 | null |
4571 | 2 | null | 4569 | 3 | null | If you have counts low enough that the Yates Correction is a worry (as in your example), you probably should be using Fisher's exact test. Otherwise, I recommend that after you use the chi-square test on a 2x2 table, you confirm your test with a log odds-ratio z-test.
| null | CC BY-SA 2.5 | null | 2010-11-16T02:48:09.623 | 2010-11-16T02:48:09.623 | null | null | 5792 | null |
4572 | 2 | null | 4551 | 26 | null | The one that I see quite often and always grinds my gears is the assumption that a statistically significant main effect in one group and a non-statistically significant main effect in another group implies a significant effect x group interaction.
| null | CC BY-SA 3.0 | null | 2010-11-16T02:49:20.940 | 2014-07-01T16:47:32.577 | 2014-07-01T16:47:32.577 | 196 | 196 | null |
4573 | 2 | null | 4451 | 3 | null | visit the Max Planck institute. They have also collected several datasets for OSNs.
| null | CC BY-SA 2.5 | null | 2010-11-16T07:16:21.443 | 2010-11-16T07:16:21.443 | null | null | null | null |
4574 | 2 | null | 4551 | 8 | null | (With a bit of luck this will be controversial.)
Using a Neyman-Pearson approach to statistical analysis of scientific experiments. Or, worse, using an ill-defined hybrid of Neyman-Pearson and Fisher.
| null | CC BY-SA 2.5 | null | 2010-11-16T07:52:02.757 | 2010-11-16T07:52:02.757 | null | null | 1679 | null |
4575 | 1 | 4576 | null | 15 | 4291 | Are there good tutorials on object-oriented programming in R?
It would be great if it included the following:
- how to define a class;
- differences between S3 and S4 classes;
- operator overloading (I'd like to be able to write a+b where a and b are instances of the class I have in mind).
| Tutorials on object-oriented programming in R | CC BY-SA 2.5 | null | 2010-11-16T08:10:48.690 | 2015-08-02T21:11:59.127 | null | null | 439 | [
"r"
] |
4576 | 2 | null | 4575 | 10 | null | Hadley Wickham's wiki on [devtools](https://github.com/hadley/devtools/wiki/) is a great resource for the necessary information in a concise form. However, if you want an exhaustive resource, the R language manual's [OOP](http://cran.r-project.org/doc/manuals/R-lang.html#Object_002doriented-programming) section may be ... | null | CC BY-SA 3.0 | null | 2010-11-16T08:20:20.087 | 2015-08-02T21:11:59.127 | 2015-08-02T21:11:59.127 | 30351 | 1307 | null |
4577 | 2 | null | 4569 | 6 | null | Yates' correction results in tests that are more conservative as with Fisher's "exact" tests.
Here is an online tutorial on the use of [Yates’s continuity correction](http://faculty.london.edu/cstefanescu/Yates.pdf), by Stefanescu et al, which clearly points to various flaws of systematic correction for continuity (pp... | null | CC BY-SA 2.5 | null | 2010-11-16T08:22:44.013 | 2010-11-16T08:22:44.013 | null | null | 930 | null |
4578 | 2 | null | 4515 | 2 | null | I am not absolutely sure what you want to achieve but I will give it a try.
Measuring Importance
The concept of importance without any target is rather ambiguous. Suppose you have the tags "football","crops" and "funny". Now want to create a classification algorithm to determine whether a certain article is in the cate... | null | CC BY-SA 2.5 | null | 2010-11-16T08:25:35.340 | 2010-11-16T08:25:35.340 | null | null | 264 | null |
4579 | 1 | null | null | 2 | 9871 | My question is: plus or minus?
Is the Lagrange function:
- the objective PLUS lambda times constraints, or
- the objective function MINUS lambda times constraints?
Example:
want to maximize A=xy
subject to g(x,y)=2x+y-400=0
is F(x,y,lambda):
- xy + lambda (2x+y-400), or
- xy - lambda (2x+y-400)
I found both no... | Is the Lagrange function objective plus lambda times constraints or objective minus lambda times constraints? | CC BY-SA 3.0 | null | 2010-11-16T08:27:45.800 | 2013-01-04T06:11:03.597 | 2012-11-10T02:07:00.883 | 183 | null | [
"optimization"
] |
4580 | 1 | null | null | 16 | 3983 | Who of you in this forum uses ">R with the [multicore](http://cran.r-project.org/web/packages/multicore/index.html), [snow](http://cran.r-project.org/web/packages/snow/index.html) packages, or [CUDA](http://www.nvidia.com/object/cuda_home_new.html), so for advanced calculations that need more power than a workstation C... | Who uses R with multicore, SNOW or CUDA package for resource intense computing? | CC BY-SA 2.5 | null | 2010-11-16T08:28:42.500 | 2012-10-19T01:57:54.343 | 2010-11-16T21:05:51.823 | 930 | 2006 | [
"r",
"mathematical-statistics",
"computational-statistics",
"parallel-computing"
] |
4581 | 2 | null | 4575 | 7 | null | In addition to [@suncoolsu](https://stats.stackexchange.com/questions/4575/tutorials-on-object-oriented-programming-in-r/4576#4576) excellent response, there is [A (Not So) Short Introduction to S4](http://cran.r-project.org/doc/contrib/Genolini-S4tutorialV0-5en.pdf), by Christophe Genolini. It is available on CRAN web... | null | CC BY-SA 2.5 | null | 2010-11-16T08:30:14.563 | 2010-11-16T08:30:14.563 | 2017-04-13T12:44:55.360 | -1 | 930 | null |
4582 | 2 | null | 4579 | 1 | null | It is exactly the same!
You want the constraint to be respected, and you don't care about the sign of g(x,y)
| null | CC BY-SA 2.5 | null | 2010-11-16T09:27:03.307 | 2010-11-16T09:27:03.307 | null | null | 1709 | null |
4584 | 2 | null | 4580 | 4 | null | Since you ask, I am using the [foreach](http://cran.r-project.org/web/packages/foreach/index.html) package with the [multicore](http://cran.r-project.org/web/packages/multicore/index.html) backend. I use it to split an [embarrassingly parallel](http://en.wikipedia.org/wiki/Embarrassingly_parallel) workload across multi... | null | CC BY-SA 2.5 | null | 2010-11-16T09:49:15.153 | 2010-11-16T09:49:15.153 | null | null | 439 | null |
4585 | 1 | 4586 | null | 6 | 3053 | I was reading about [exponential smoothing](http://en.wikipedia.org/wiki/Exponential_smoothing) on Wikipedia. I saw a sentence saying that I could find the optimal parameter $\alpha$ by using the method of least squares. How do I go about doing that? Are there other ways I can find the right $\alpha$?
Edit: Also, if... | Optimal parameter $\alpha$ for exponential smoothing using least squares | CC BY-SA 2.5 | null | 2010-11-16T09:52:30.103 | 2010-11-16T10:28:32.583 | 2010-11-16T10:28:32.583 | 159 | 847 | [
"time-series",
"estimation",
"optimization"
] |
4586 | 2 | null | 4585 | 7 | null | Minimize the sum of squared one-step forecast errors. If $\hat{Y}_t$ is the prediction of $Y_t$ given $Y_1,\dots,Y_{t-1}$, then $e_t=Y_t-\hat{Y}_t$ is the one-step forecast error. So minimize $e_2^2+\cdots+e_n^2$.
You can also use maximum likelihood estimation as discussed in [my Springer book](http://www.exponentialsm... | null | CC BY-SA 2.5 | null | 2010-11-16T10:04:36.643 | 2010-11-16T10:18:31.483 | 2010-11-16T10:18:31.483 | 159 | 159 | null |
4587 | 2 | null | 4580 | 3 | null | I work in academy and I'm using multicore for some heavy benchmarks of machine learning algorithms, mostly on our Opteron based Sun Constellation and some smaller clusters; those are also rather embarrassingly parallel problems so multicore's main role is to spread computation over node without multiplication of memory... | null | CC BY-SA 2.5 | null | 2010-11-16T10:23:55.127 | 2010-11-16T10:23:55.127 | null | null | null | null |
4588 | 2 | null | 4527 | 1 | null | The value column simply gives the values for the coefficient estimates.
| null | CC BY-SA 2.5 | null | 2010-11-16T11:37:33.347 | 2010-11-16T11:37:33.347 | null | null | 1806 | null |
4589 | 2 | null | 4551 | 24 | null | Especially in epidemiology and public health - using arithmetic instead of logarithmic scale when reporting graphs of relative measures of association (hazard ratio, odds ratio or risk ratio).
More information [here](http://www.thelancet.com/journals/lancet/article/PIIS0140-6736%2810%2960541-7/fulltext).
| null | CC BY-SA 2.5 | null | 2010-11-16T12:19:21.080 | 2010-11-16T12:19:21.080 | null | null | 22 | null |
4590 | 2 | null | 4551 | 47 | null | Testing the hypotheses $H_0: \mu=0$ versus $H_1: \mu\neq 0$
(for example in a Gaussian setting)
to justify that $\mu=0$ in a model (i.e mix "$H_0$ is not rejected" and "$H_0$ is true").
A very good example of that type of (very bad) reasoning is when you test whether the variances of two Gaussians are equal (or not) ... | null | CC BY-SA 3.0 | null | 2010-11-16T13:30:48.163 | 2011-09-22T15:00:43.493 | 2011-09-22T15:00:43.493 | 6432 | 223 | null |
4591 | 2 | null | 4341 | 5 | null | Another option using Excel that gives you a fair amount of flexibility over the number & size of bins is the `=frequency(data_array, bins_array)` function. It is an array function that expects two arguments. The first argument is your data, the second is your bins that you define.
Let's assume your data is in cells A1 ... | null | CC BY-SA 2.5 | null | 2010-11-16T14:27:29.113 | 2010-11-16T14:27:29.113 | null | null | 696 | null |
4592 | 2 | null | 4551 | 43 | null | Not really answering the question, but there's an entire book on this subject:
Phillip I. Good, James William Hardin (2003). Common errors in statistics (and how to avoid them). Wiley. ISBN [9780471460688](http://en.wikipedia.org/w/index.php?title=Special%3ABookSources&isbn=9780471460688)
| null | CC BY-SA 2.5 | null | 2010-11-16T16:24:50.713 | 2010-11-16T16:24:50.713 | null | null | 449 | null |
4593 | 2 | null | 4542 | 3 | null | The following approaches might be of interest. In (1) [http://www.chemoton.org/ref31.html](http://www.chemoton.org/ref31.html) a GA was used on image analysis but data was first treated. In order to treat the data, the GA was hybridized with K-means clustering, a well-know data classification procedure. Among classific... | null | CC BY-SA 2.5 | null | 2010-11-16T17:23:45.307 | 2010-11-16T17:23:45.307 | null | null | null | null |
4595 | 1 | null | null | 5 | 342 | Genetic algorithms are avoided in econometry literature as often as possible, but still sometimes they are inevitable. The question is: Which well known models are the most difficult to estimate using conventional algorithms? (By Conventional algorithms I mean Gauss-Newton method, Levenberg–Marquardt algorithm, and so ... | Nonlinear models which are hard to estimate | CC BY-SA 2.5 | null | 2010-11-16T17:25:53.533 | 2021-07-22T13:45:46.400 | 2010-11-16T22:59:21.257 | 159 | 1643 | [
"estimation",
"genetic-algorithms",
"nonlinear-regression",
"optimization"
] |
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