Id stringlengths 1 6 | PostTypeId stringclasses 7
values | AcceptedAnswerId stringlengths 1 6 ⌀ | ParentId stringlengths 1 6 ⌀ | Score stringlengths 1 4 | ViewCount stringlengths 1 7 ⌀ | Body stringlengths 0 38.7k | Title stringlengths 15 150 ⌀ | ContentLicense stringclasses 3
values | FavoriteCount stringclasses 3
values | CreationDate stringlengths 23 23 | LastActivityDate stringlengths 23 23 | LastEditDate stringlengths 23 23 ⌀ | LastEditorUserId stringlengths 1 6 ⌀ | OwnerUserId stringlengths 1 6 ⌀ | Tags list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7457 | 1 | 7458 | null | 13 | 250810 | I'm trying to normalize a set of columns of data in an excel spreadsheet.
I need to get the values so that the highest value in a column is = 1 and lowest is = to 0, so I've come up with the formula:
`=(A1-MIN(A1:A30))/(MAX(A1:A30)-MIN(A1:A30))`
This seems to work fine, but when I drag down the formula to populate the ... | How to stop excel from changing a range when you drag a formula down? | CC BY-SA 2.5 | null | 2011-02-21T16:54:06.813 | 2013-01-30T20:54:57.593 | 2011-02-21T19:04:33.937 | 919 | 3348 | [
"excel"
] |
7458 | 2 | null | 7457 | 44 | null | A '$' will lock down the reference to an absolute one versus a relative one. You can lock down the column, row or both. Here is a locked down absolute reference for your example.
```
(A1-MIN($A$1:$A$30))/(MAX($A$1:$A$30)-MIN($A$1:$A$30))
```
| null | CC BY-SA 2.5 | null | 2011-02-21T17:02:21.187 | 2011-02-21T17:02:21.187 | null | null | 2040 | null |
7459 | 2 | null | 7450 | 15 | null | Update: 7 Apr 2011
This answer is getting quite long and covers multiple aspects of the problem at hand. However, I've resisted, so far, breaking it into separate answers.
I've added at the very bottom a discussion of the performance of Pearson's $\chi^2$ for this example.
---
Bruce M. Hill authored, perhaps, the "s... | null | CC BY-SA 3.0 | null | 2011-02-21T17:31:02.337 | 2011-04-08T01:51:15.640 | 2011-04-08T01:51:15.640 | 2970 | 2970 | null |
7460 | 2 | null | 7224 | 5 | null | If you want to do it yourself, I would recommend using Intel's free and open source OpenCV (CV for computer vision) project.
[http://opencv.willowgarage.com/](http://opencv.willowgarage.com/)
[http://oreilly.com/catalog/9780596516130](http://oreilly.com/catalog/9780596516130)
| null | CC BY-SA 2.5 | null | 2011-02-21T18:23:19.980 | 2011-02-21T18:23:19.980 | null | null | 74 | null |
7461 | 2 | null | 6870 | 3 | null | Thanks for the tips David. I posted this question on the JAGS support forum and got a useful answer. The key was to use a two dimensional array for the 'true' values.
```
for (j in 1:n){
x_obs[j] ~ dnorm(xy_true[j,1], prec_x)T(xy_true[j,1],)
y_obs[j] ~ dnorm(xy_true[j,2], prec_y)
xy_true[j, ] ~ dmnorm(mu[ z [... | null | CC BY-SA 2.5 | null | 2011-02-21T18:53:41.467 | 2011-02-21T18:53:41.467 | null | null | 2310 | null |
7462 | 2 | null | 6653 | 1 | null | Check out the stats nerds at [Football Outsiders](http://www.footballoutsiders.com/info/methods) as well as the book [Mathletics](http://rads.stackoverflow.com/amzn/click/069113913X) for some inspiration.
The Football Outsiders guys make game predictions based on every play in a football game.
Winston in Mathletics us... | null | CC BY-SA 2.5 | null | 2011-02-21T19:22:09.767 | 2011-02-21T19:22:09.767 | null | null | 74 | null |
7465 | 2 | null | 6538 | 5 | null | I would go to the curriculum websites of the top stats schools, write down the books they use in their undergrad courses, see which ones are highly rated on Amazon, and order them at your public/university library.
Some schools to consider:
- MIT - technically, cross-taught with Harvard.
- Caltech
- Carnegie Mellon... | null | CC BY-SA 2.5 | null | 2011-02-21T19:51:03.823 | 2011-02-22T00:26:09.117 | 2011-02-22T00:26:09.117 | 74 | 74 | null |
7466 | 1 | 7468 | null | 6 | 2286 | I want to cluster elements in array. The crucial difference from a normal clustering algorithm is that the order of elements is significant. For instance if we look at a simple sequence of numbers like this:
```
1.1, 1.2, 1.0, 3.3, 3.3, 2.9, 1.0, 1.1, 3.0, 2.8, 3.2
```
It is obvious that there are two clusters in ther... | Sequential clustering algorithm | CC BY-SA 2.5 | null | 2011-02-21T20:09:05.493 | 2011-02-21T20:28:50.977 | null | null | 255 | [
"clustering"
] |
7467 | 1 | 7472 | null | 18 | 13329 | Background
I am overseeing the input of data from primary literature into a [database](http://ebi-forecast.igb.illinois.edu/). The data entry process is error prone, particularly because users must interpret experimental design, extract data from graphics and tables, and transform results to standardized units.
Data ar... | Quality assurance and quality control (QA/QC) guidelines for a database | CC BY-SA 3.0 | null | 2011-02-21T20:24:52.310 | 2016-08-18T18:36:49.497 | 2016-08-18T18:36:49.497 | 22468 | 1381 | [
"dataset",
"meta-analysis",
"quality-control",
"database"
] |
7468 | 2 | null | 7466 | 0 | null | Constrained clustering maintains data order. There is a package in R called 'rioja' that implements this in the function 'chclust'.
The procedure isn't too complex though:
- Calculate inter-point distance
- Find the smallest distance between adjacent points
- Average the value of the two points to generate a single... | null | CC BY-SA 2.5 | null | 2011-02-21T20:28:50.977 | 2011-02-21T20:28:50.977 | null | null | null | null |
7469 | 2 | null | 4805 | 4 | null | The classical transformations include the log, sqrt, and inverse (1/Y) transformations. More sophisticated transformations include the power transformation, from which the Box-Cox optimization chooses a particular transformation which optimized a log-likelihood. Which transformation to use is almost becoming a lost art... | null | CC BY-SA 2.5 | null | 2011-02-21T20:55:54.027 | 2011-02-21T20:55:54.027 | null | null | 2773 | null |
7470 | 2 | null | 7152 | 11 | null | A very nice discussion of structural zeros in contingency tables is provided by
West, L. and Hankin, R. (2008), “Exact Tests for Two-Way Contingency Tables with Structural Zeros,” Journal of Statistical Software, 28(11), 1–19.
URL [http://www.jstatsoft.org/v28/i11](http://www.jstatsoft.org/v28/i11)
As the title implie... | null | CC BY-SA 2.5 | null | 2011-02-21T21:01:10.717 | 2011-02-21T21:01:10.717 | null | null | 2773 | null |
7471 | 1 | null | null | 14 | 9958 | Can the standard deviation be calculated for the harmonic mean? I understand that the standard deviation can be calculated for arithmetic mean, but if you have harmonic mean, how do you calculate the standard deviation or CV?
| Can the standard deviation be calculated for harmonic mean? | CC BY-SA 2.5 | null | 2011-02-21T22:39:49.407 | 2021-09-26T06:03:51.507 | 2017-02-28T13:35:29.330 | 11887 | null | [
"standard-deviation",
"harmonic-mean"
] |
7472 | 2 | null | 7467 | 25 | null | This response focuses on the second question, but in the process a partial answer to the first question (guidelines for a QA/QC procedure) will emerge.
By far the best thing you can do is check data quality at the time entry is attempted. The user checks and reports are labor-intensive and so should be reserved for la... | null | CC BY-SA 2.5 | null | 2011-02-21T23:27:05.723 | 2011-02-21T23:27:05.723 | null | null | 919 | null |
7473 | 2 | null | 7455 | 15 | null | In machine learning a full probability model p(x,y) is called generative because it can be used to generate the data whereas a conditional model p(y|x) is called discriminative because it does not specify a probability model for p(x) and can only generate y given x. Both can be estimated in Bayesian fashion.
Bayesian... | null | CC BY-SA 2.5 | null | 2011-02-21T23:50:01.567 | 2011-02-22T00:06:57.613 | 2011-02-22T00:06:57.613 | 493 | 493 | null |
7474 | 2 | null | 7471 | 2 | null | Here is an example for Exponential r.v's.
The harmonic mean for $n$ data points is defined as
$$S = \frac{1}{\frac{1}{n} \sum_{i=1}^n X_i}$$
Suppose you have $n$ iid samples of an Exponential random variable, $X_i \sim {\rm Exp}(\lambda)$. The sum of $n$ Exponential variables follows a Gamma distribution
$$\sum_{i=1}... | null | CC BY-SA 3.0 | null | 2011-02-21T23:51:06.993 | 2017-02-28T16:55:14.717 | 2017-02-28T16:55:14.717 | 919 | 530 | null |
7475 | 1 | 7479 | null | 1 | 1044 | I am trying to do a multiple logistic regression for 2 similar groups. I have a few questions:
- In doing a univariate analysis, do I enter each independent variable, one at a time, first into the binary regression, before going on to do the multivariate analysis? Or is the significance values from Chi-square or t-tes... | Entering variables in multivariate logistic regression and running regression across two groups | CC BY-SA 2.5 | null | 2011-02-22T01:09:31.170 | 2011-02-22T06:57:06.070 | 2011-02-22T06:57:06.070 | 2116 | null | [
"logistic"
] |
7476 | 1 | 7493 | null | 11 | 1662 | Before submission of my meta-analysis I want to make a funnel plot to test for heterogeneity and publication bias. I have the pooled effect size and the effect sizes from each study, that take values from -1 to +1. I have the sample sizes n1, n2 for patients and controls from each study. As I cannot calculate the stand... | Alternative funnel plot, without using standard error (SE) | CC BY-SA 2.5 | null | 2011-02-22T01:12:00.607 | 2011-02-22T12:08:20.327 | 2011-02-22T12:02:14.783 | 8 | 3333 | [
"meta-analysis",
"sample-size",
"standard-error",
"funnel-plot",
"publication-bias"
] |
7477 | 2 | null | 6538 | 86 | null | (Very) short story
Long story short, in some sense, statistics is like any other technical field: [There is no fast track](http://norvig.com/21-days.html).
Long story
Bachelor's degree programs in statistics are relatively rare in the U.S. One reason I believe this is true is that it is quite hard to pack all that is n... | null | CC BY-SA 3.0 | null | 2011-02-22T02:08:30.283 | 2016-12-20T19:57:24.177 | 2016-12-20T19:57:24.177 | 22047 | 2970 | null |
7478 | 1 | null | null | 3 | 251 | I've performed a study which yielded (?) the following results:
```
- no bike box bike box % change
correct procedure 173 55 -27%
incorrect procedure 68 50 69%
```
Since a result could only be one of the two - correct and incorrect proce... | How to compare outcomes from single variable experiment? | CC BY-SA 2.5 | null | 2011-02-22T02:32:12.893 | 2015-12-20T00:19:15.963 | 2015-12-20T00:19:15.963 | 28666 | 3357 | [
"contingency-tables",
"fishers-exact-test",
"relative-risk"
] |
7479 | 2 | null | 7475 | 4 | null | I would start with estimating a (simple) bivariate correlation matrix which includes your outcome variable as well as all predictors. This will give you very first insights into the dependency structure of all your variables. Especially correlation coefficients of $|r| > 0.4$ (between your predictor variables) can indi... | null | CC BY-SA 2.5 | null | 2011-02-22T02:34:30.143 | 2011-02-22T02:34:30.143 | null | null | 307 | null |
7480 | 2 | null | 7478 | 2 | null | You can report percent correct along with sample size $n$, and reporting percent correct instead would be sufficient in most cases, even if you focus more on the percent incorrect in your interpretation.
| null | CC BY-SA 2.5 | null | 2011-02-22T03:03:53.333 | 2011-02-22T03:03:53.333 | null | null | 1381 | null |
7481 | 1 | null | null | 9 | 25781 |
### Context
I have a survey that asks 11 questions about self-efficacy.
Each question has 3 response options (disagree, agree, strongly agree).
Nine questions ask about self-esteem.
I have used a factor analysis of the 11 self-efficacy items and extracted two factors.
$x_1$ to $x_{11}$ denote the 11 self-efficacy ... | How to use variables derived from factor analysis as predictors in logistic regression? | CC BY-SA 2.5 | null | 2011-02-22T03:24:55.477 | 2014-11-07T07:55:53.000 | 2011-02-22T07:46:48.870 | 2116 | null | [
"logistic",
"factor-analysis"
] |
7482 | 1 | null | null | 4 | 356 | If kNN doesn't perform well for classification on a dataset, is there any hope for parametric methods to perform better? Kernel-based methods, SVM, random forests, and neural networks. Could any of these outperform kNN method?
| Accuracy of advanced parametric methods compared to kNN method | CC BY-SA 2.5 | null | 2011-02-22T06:56:36.897 | 2011-02-23T21:39:34.423 | 2011-02-23T21:39:34.423 | null | null | [
"machine-learning",
"k-nearest-neighbour"
] |
7483 | 2 | null | 7482 | 4 | null | Hastie et al give a nice overview in [their book](http://www-stat.stanford.edu/~tibs/ElemStatLearn/), look into 2nd chapter. The short answer is yes. Otherwise why do you think these methods were developed and are still widely used?
| null | CC BY-SA 2.5 | null | 2011-02-22T07:02:04.770 | 2011-02-22T07:02:04.770 | null | null | 2116 | null |
7484 | 2 | null | 7471 | 15 | null | The harmonic mean $H$ of random variables $X_1,...,X_n$ is defined as
$$H=\frac{1}{\frac{1}{n}\sum_{i=1}^n\frac{1}{X_i}}$$
Taking moments of fractions is a messy business, so instead I would prefer working with the $1/H$. Now
$$\frac{1}{H}=\frac{1}{n}\sum_{i=1}^n\frac{1}{X_i}$$.
Usin central limit theorem we immediatel... | null | CC BY-SA 4.0 | null | 2011-02-22T07:43:52.837 | 2019-05-05T21:57:24.093 | 2019-05-05T21:57:24.093 | 22452 | 2116 | null |
7487 | 1 | null | null | 2 | 1550 | How can I test the statistical significance of regression coefficients in multivariate multiple regression?
| How to test for statistical significance of regression coefficients in multivariate multiple regression? | CC BY-SA 2.5 | null | 2011-02-22T09:12:10.840 | 2011-02-22T14:37:56.237 | 2011-02-22T09:23:48.610 | 2116 | null | [
"regression"
] |
7488 | 2 | null | 7478 | 2 | null | If I understand correctly, your IV is "bike box" vs. "no bike box", and your DV is "correct" vs. "incorrrect". The resulting $2 \times 2$ classification table can be summarized with the Odds Ratio: "given the bike-box condition, what are the odds of getting a correct response?" compared to "given the no-bike-box condit... | null | CC BY-SA 2.5 | null | 2011-02-22T10:35:29.137 | 2011-02-22T23:27:11.683 | 2011-02-22T23:27:11.683 | 1909 | 1909 | null |
7489 | 2 | null | 7487 | 6 | null | For a start, have a look at this pdf: [multivariate multiple regression](http://www.psych.yorku.ca/lab/psy6140/lectures/MultivariateRegression2x2.pdf). An example in R:
```
N <- 50 # number of participants
X1 <- rnorm(N, 175, 7) # predictor 1
X2 <- rnorm(N, 30, 8) # predictor 2
X3 <- rnorm(N... | null | CC BY-SA 2.5 | null | 2011-02-22T11:15:15.733 | 2011-02-22T11:15:15.733 | null | null | 1909 | null |
7490 | 2 | null | 7481 | 11 | null | If I understand you correctly, you are using FA to extract two subscales from your 11-item questionnaire. They are supposed to reflect some specific dimensions of self-efficacy (for example, self-regulatory vs. self-assertive efficacy).
Then, you are free to use individual mean (or sum) scores computed on the two subsc... | null | CC BY-SA 2.5 | null | 2011-02-22T11:23:45.177 | 2011-02-22T11:23:45.177 | null | null | 930 | null |
7491 | 2 | null | 7481 | 10 | null |
### Using factor scores as predictors
Yes, you can use variables derived from a factor analysis as predictors in subsequent analyses.
Other options include running some form of structural equation model where you posit a latent variable with the items or bundles of items as observed variables.
### Mean as scale sc... | null | CC BY-SA 2.5 | null | 2011-02-22T11:24:18.743 | 2011-02-22T11:24:18.743 | null | null | 183 | null |
7492 | 2 | null | 7481 | 1 | null | Everything have be said by chl and Jeromy for the theorical part... If you don't have use sum/mean of variables you identify with FA you can use scores of FA.
Regarding the syntax you use you're probably using SAS. So to do a correct use of factor analysis you must use the score of observations and not the mean of vari... | null | CC BY-SA 2.5 | null | 2011-02-22T11:37:13.330 | 2011-02-22T12:55:24.417 | 2011-02-22T12:55:24.417 | 1154 | 1154 | null |
7493 | 2 | null | 7476 | 13 | null | Q: Can I still make a funnel plot with effect size on the horizontal axon and total sample size n (n=n1+n2) on the vertical axis?
A: Yes
Q: How should such a funnel plot be interpreted?
A: It is still a funnel plot. However, funnel plots should be interpreted with caution. For example, if you have only 5-10 effect siz... | null | CC BY-SA 2.5 | null | 2011-02-22T12:08:20.327 | 2011-02-22T12:08:20.327 | null | null | 307 | null |
7494 | 1 | 7507 | null | 4 | 1254 | There is a catalog of noninformative priors over here:
[http://www.stats.org.uk/priors/noninformative/YangBerger1998.pdf](http://www.stats.org.uk/priors/noninformative/YangBerger1998.pdf)
in page 11, they give the noninformative Jeffreys prior for the Dirichlet distribution. They give the Fisher information matrix for ... | Fisher information matrix for the Dirichlet distribution | CC BY-SA 2.5 | null | 2011-02-22T14:24:42.300 | 2011-02-22T16:26:30.153 | 2011-02-22T14:48:49.360 | 2116 | 3347 | [
"distributions"
] |
7495 | 2 | null | 7482 | 7 | null | The "no free lunch" theorems (Wolpert) suggest there are no a-priori distinctions between classifiers; essentially whether one classifier performs better than another depends on the nature of the dataset. Note also for kNN a lot depends on what distance metric you use and how you choose a good value for k. It is not ... | null | CC BY-SA 2.5 | null | 2011-02-22T14:35:49.593 | 2011-02-22T14:35:49.593 | null | null | 887 | null |
7496 | 2 | null | 7487 | 1 | null | The standard approach is to use the partial F test, explained on fine websites all over town.
| null | CC BY-SA 2.5 | null | 2011-02-22T14:37:56.237 | 2011-02-22T14:37:56.237 | null | null | 5792 | null |
7497 | 1 | 7506 | null | 29 | 11688 | This is a question about terminology. Is a "vague prior" the same as a non-informative prior, or is there some difference between the two?
My impression is that they are same (from looking up vague and non-informative together), but I can't be certain.
| Is a vague prior the same as a non-informative prior? | CC BY-SA 2.5 | null | 2011-02-22T14:49:46.453 | 2013-11-01T11:21:02.647 | 2011-02-22T16:13:36.303 | 8 | 3347 | [
"bayesian",
"prior",
"terminology"
] |
7498 | 2 | null | 7497 | 3 | null | I suspect "vague prior" is used to mean a prior that is known to encode some small, but non-zero amount of knowledge regarding the true value of a parameter, whereas a "non-informative prior" would be used to mean complete ignorance regarding the value of that parameter. It would perhaps be used to show that the analy... | null | CC BY-SA 2.5 | null | 2011-02-22T15:01:50.813 | 2011-02-22T15:01:50.813 | null | null | 887 | null |
7499 | 1 | 7550 | null | 4 | 3039 | When presenting statistical information using bar charts, when will you need to use Error Bar charts?
| Which type of statistical information will need Error Bar charts for presentation? | CC BY-SA 2.5 | null | 2011-02-22T15:14:04.833 | 2011-02-24T14:56:55.430 | 2011-02-24T11:54:32.427 | null | 546 | [
"data-visualization",
"error"
] |
7500 | 2 | null | 7497 | 10 | null | Definitely not, although they are frequently used interchangeably. A vague prior (relatively uninformed, not really favoring some values over others) on a parameter $\theta$ can actually induce a very informative prior on some other transformation $f(\theta)$. This is at least part of the motivation for Jeffreys' prior... | null | CC BY-SA 3.0 | null | 2011-02-22T15:21:27.733 | 2013-11-01T11:21:02.647 | 2013-11-01T11:21:02.647 | 17230 | 26 | null |
7501 | 2 | null | 7251 | 3 | null | Short answer.
The problem you mention is well studied by Granger C.W.J. with co-authors, and known as the forecasts combination (or pooling) problem. The general idea is to choose the loss function criterion and the parameters (may be time dependent) that minimize the latter. Below I put some references that may be use... | null | CC BY-SA 2.5 | null | 2011-02-22T15:26:12.237 | 2011-02-22T15:26:12.237 | null | null | 2645 | null |
7503 | 2 | null | 7497 | 6 | null | Lambert et al (2005) raise the question ["How Vague is Vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS](http://onlinelibrary.wiley.com/doi/10.1002/sim.2112/abstract)". They write: "We do not advocate the use of the term non-informative prior distribution as we consi... | null | CC BY-SA 2.5 | null | 2011-02-22T15:37:55.493 | 2011-02-22T15:37:55.493 | null | null | 307 | null |
7504 | 2 | null | 7494 | 2 | null | I think it's meant to have constant off-diagonal entries, i.e. it could also be written
$$I(\alpha_1, \alpha_2, \ldots, \alpha_k) = \operatorname{diag}\left[ PG(1,\alpha_1) , PG(1, \alpha_2), \ldots, PG(1,\alpha_k) \right] - PG(1,\alpha_0) J_k $$
where $J_k$ is a $k \times k$ [matrix of ones](http://en.wikipedia.org/wi... | null | CC BY-SA 2.5 | null | 2011-02-22T15:45:19.270 | 2011-02-22T15:45:19.270 | null | null | 449 | null |
7505 | 1 | null | null | 3 | 710 | there is a short introduction to AB Tests in [this question](https://stats.stackexchange.com/questions/4884/aggregation-level-in-ab-tests) or [here at 20bits](http://20bits.com/articles/statistical-analysis-and-ab-testing/).
We are currently testing different versions of landing pages and are using the conversion rate ... | Moving from conversion rates to sales volume in A/B tests | CC BY-SA 2.5 | null | 2011-02-22T15:52:50.807 | 2011-04-21T15:05:07.710 | 2017-04-13T12:44:46.680 | -1 | 3367 | [
"confidence-interval",
"hypothesis-testing",
"ab-test"
] |
7506 | 2 | null | 7497 | 18 | null | Gelman et al. (2003) say:
>
there has long been a desire for prior distributions that can be guaranteed to play a minimal role in the posterior distribution. Such distributions are sometimes called 'reference prior distributions' and the prior density is described as vague, flat, or noninformative.[emphasis from orig... | null | CC BY-SA 3.0 | null | 2011-02-22T16:13:39.453 | 2013-06-08T19:37:30.417 | 2013-06-08T19:37:30.417 | 22047 | 1381 | null |
7507 | 2 | null | 7494 | 8 | null | Let's work it out.
The logarithm of the Dirichlet density function is
$$\lambda(\mathbf{x}|\mathbf{\alpha}) = \log(\Gamma(\alpha_0)) - \sum_{i=1}^{k}{\log(\Gamma(\alpha_i)))} + \sum_{i=1}^{k}{(\alpha_i - 1)\log(x_i)},$$
where $\alpha_0 = \alpha_1 + \alpha_2 + \cdots + \alpha_k$.
Taking second partial derivatives with r... | null | CC BY-SA 2.5 | null | 2011-02-22T16:26:30.153 | 2011-02-22T16:26:30.153 | null | null | 919 | null |
7508 | 1 | null | null | 11 | 2513 | If I hypothesize that a gene signature will identify subjects at a lower risk of recurrence, that is decrease by 0.5 (hazard ratio of 0.5) the event rate in 20% of the population and I intend to use samples from a retrospective cohort study does the sample size need to be adjusted for unequal numbers in the two hypothe... | Power analysis for survival analysis | CC BY-SA 2.5 | null | 2011-02-22T16:39:14.313 | 2011-11-13T20:52:42.547 | 2011-11-13T20:52:42.547 | 930 | null | [
"survival",
"statistical-power",
"genetics"
] |
7509 | 2 | null | 2787 | 1 | null | Jakob Nielsen [recommends testing with five users](http://www.useit.com/alertbox/20000319.html) for optimal results. This assertion has been challenged a few times, both empirically and theoretically, but generally seems to hold quite well.
| null | CC BY-SA 2.5 | null | 2011-02-22T17:40:39.037 | 2011-02-22T17:40:39.037 | null | null | 3367 | null |
7510 | 2 | null | 7481 | 1 | null | Continuous latent variables with discrete (polytomous in your case) manifest variables is part of item response analysis. Package 'ltm' in R covers a variety of such models. I refer you to [this](http://goo.gl/lJh5s) paper, which deals with exactly same problem.
| null | CC BY-SA 2.5 | null | 2011-02-22T18:06:08.293 | 2011-02-22T18:06:08.293 | null | null | 609 | null |
7511 | 1 | null | null | 5 | 137 | We are dealing with a measurement apparatus that is cursed by noise and are trying to find out if a measurement was noise or an actual measurement.
Assume we have a beam of light incident on an square array of photo detectors. The "counts" measured in the individual tubes follow Poisson statistics. A typical beam distr... | Construct probability that measurement belongs to one of two sets | CC BY-SA 2.5 | null | 2011-02-22T18:27:41.433 | 2011-02-23T16:02:13.747 | 2011-02-22T18:58:19.827 | 56 | 56 | [
"hypothesis-testing",
"correlation"
] |
7512 | 1 | null | null | 3 | 3312 | What is the best way to correlate zero-inflated count variables with a small sample size (n=~50 and N=99)?
| Zero inflated correlation | CC BY-SA 2.5 | null | 2011-02-22T18:43:11.643 | 2012-02-13T16:43:21.527 | null | null | null | [
"correlation"
] |
7513 | 1 | 7530 | null | 21 | 116981 | Could anyone offer some pointers on how to use the `weights` argument in R's `lm` function? Say, for instance you were trying to fit a model on traffic data, and you had several hundred rows, each of which represented a city (with a different population). If you wanted the model to adjust the relative influence of ea... | How to use weights in function lm in R? | CC BY-SA 2.5 | null | 2011-02-22T19:38:50.313 | 2016-11-03T15:17:41.363 | 2011-02-23T08:20:59.090 | 2116 | 3320 | [
"r",
"regression"
] |
7514 | 2 | null | 7478 | 0 | null | my brother posted the original data.
what is happening here is that i am observing two different conditions. the control is no bike box. this is the baseline. a normal intersection with bike lanes. the experimental condition is an intersection that has bike boxes added. i am attempting to interpret the correct/incorre... | null | CC BY-SA 2.5 | null | 2011-02-22T19:58:23.480 | 2011-02-22T19:58:23.480 | null | null | null | null |
7515 | 1 | 7516 | null | 22 | 7984 | What are some techniques for sampling two correlated random variables:
- if their probability
distributions are parameterized
(e.g., log-normal)
- if they have non-parametric
distributions.
The data are two time series for which we can compute non-zero correlation coefficients. We wish to simulate these data in th... | What are some techniques for sampling two correlated random variables? | CC BY-SA 2.5 | null | 2011-02-22T20:43:25.633 | 2018-01-03T16:14:47.500 | 2011-02-22T21:35:51.057 | 919 | 2260 | [
"correlation",
"sampling",
"monte-carlo",
"stochastic-processes",
"copula"
] |
7516 | 2 | null | 7515 | 26 | null | I think what you're looking for is a copula. You've got two marginal distributions (specified by either parametric or empirical cdfs) and now you want to specify the dependence between the two. For the bivariate case there are all kinds of choices, but the basic recipe is the same. I'll use a Gaussian copula for ease o... | null | CC BY-SA 3.0 | null | 2011-02-22T21:09:42.563 | 2018-01-03T16:14:47.500 | 2018-01-03T16:14:47.500 | 7290 | 26 | null |
7517 | 2 | null | 7511 | 3 | null | According to the comments after the question, this is a hypothesis testing situation. You have stipulated that you can accurately assess the null distribution of the individual cell counts. We need a test statistic. The nature of the problem suggests running a small kernel over the array (essentially to deconvolve t... | null | CC BY-SA 2.5 | null | 2011-02-22T21:18:31.240 | 2011-02-23T16:02:13.747 | 2011-02-23T16:02:13.747 | 919 | 919 | null |
7518 | 1 | null | null | 3 | 139 | If I define a function in IML:
```
start func(a);
submit a / R;
print(&a);
endsubmit;
finish;
```
and run it:
```
run func("character string");
```
I get the error message: object 'character string' does not exist.
So R or IML is evaluating the character string into an object. I want R to output "character st... | How to pass character strings to R from IML Studio | CC BY-SA 2.5 | null | 2011-02-22T21:20:34.683 | 2011-02-23T00:49:33.997 | 2011-02-22T21:22:59.367 | null | null | [
"r",
"sas"
] |
7519 | 1 | 7526 | null | 33 | 6151 | Consider a Jeffreys prior where $p(\theta) \propto \sqrt{|i(\theta)|}$, where $i$ is the Fisher information.
I keep seeing this prior being mentioned as a uninformative prior, but I never saw an argument why it is uninformative. After all, it is not a constant prior, so there has to be some other argument.
I understand... | Why are Jeffreys priors considered noninformative? | CC BY-SA 2.5 | null | 2011-02-22T23:01:36.607 | 2020-10-21T18:31:53.380 | 2011-02-22T23:49:45.727 | null | 3347 | [
"bayesian",
"prior"
] |
7520 | 2 | null | 7513 | 3 | null | What you suggest should work. See if this makes sense:
```
lm(c(8000, 50000, 116000) ~ c(6, 7, 8))
lm(c(8000, 50000, 116000) ~ c(6, 7, 8), weight = c(123, 123, 246))
lm(c(8000, 50000, 116000, 116000) ~ c(6, 7, 8, 8))
```
The second line produces the same intercept and slope as the third line (distinct from the first ... | null | CC BY-SA 2.5 | null | 2011-02-22T23:05:22.627 | 2011-02-22T23:05:22.627 | null | null | 2958 | null |
7521 | 1 | 7566 | null | 3 | 353 | I have quarterly sales data for a variety of stores and would like to estimate the effect of a regulation on sales. A panel type model would appear to be appropriate in this case, with the regulation as a dummy variable.
However, the quantity of sales varies by two-orders of magnitude between stores. How should I acc... | Subjects with different order of magnitude values in panel data | CC BY-SA 2.5 | null | 2011-02-23T00:30:42.133 | 2017-11-12T17:21:31.740 | 2017-11-12T17:21:31.740 | 11887 | 179 | [
"time-series",
"panel-data"
] |
7522 | 2 | null | 7518 | 2 | null | I haven't used the new R/IML interface functionality, but from reading the help it looks like `submit foo` does text substitution. That is, if the IML variable `foo` contains the value `x`, then any occurrence of `&foo` in the submit block is replaced with `x`. When R is involved, IML generates R code to treat x as a s... | null | CC BY-SA 2.5 | null | 2011-02-23T00:33:24.057 | 2011-02-23T00:49:33.997 | 2011-02-23T00:49:33.997 | 1569 | 1569 | null |
7523 | 1 | null | null | 3 | 199 | I would appreciate some advice on how best to weight or give more importance to a percentage with a larger denominator.
Eg
- A. 1 out of 2 = 50%
- B. 5 out of 10 = 50%
- C. 500 out of 1000 = 50%
Some of the data is sparse and continually emerging so
- A) could just be a blip and could change,
- B) is emerging ... | How do I introduce features and their confidence values into classifiers | CC BY-SA 2.5 | null | 2011-02-23T01:01:33.197 | 2011-03-01T11:57:42.837 | 2011-03-01T11:57:42.837 | null | null | [
"proportion",
"weighted-mean"
] |
7525 | 2 | null | 7523 | 4 | null | It might be a good idea to rename the question to something like "how do I introduce features and their confidence values into classifiers". I can think of two ways to do it, but if you phrase the question differently, more people will look at it and will maybe have additional suggestions.
The first option is to add tw... | null | CC BY-SA 2.5 | null | 2011-02-23T02:02:42.370 | 2011-02-24T20:36:45.130 | 2011-02-24T20:36:45.130 | 3369 | 3369 | null |
7526 | 2 | null | 7519 | 15 | null | It's considered noninformative because of the parameterization invariance. You seem to have the impression that a uniform (constant) prior is noninformative. Sometimes it is, sometimes it isn't.
What happens with Jeffreys' prior under a transformation is that the Jacobian from the transformation gets sucked into the or... | null | CC BY-SA 3.0 | null | 2011-02-23T02:27:01.233 | 2013-10-23T15:43:12.733 | 2013-10-23T15:43:12.733 | 17230 | 26 | null |
7527 | 1 | 7529 | null | 17 | 3147 | I'm trying to implement a "change point" analysis, or a multiphase regression using `nls()` in R.
[Here's some fake data I've made](https://i.stack.imgur.com/27f1S.png). The formula I want to use to fit the data is:
$y = \beta_0 + \beta_1x + \beta_2\max(0,x-\delta)$
What this is supposed to do is fit the data up to a ... | Change point analysis using R's nls() | CC BY-SA 3.0 | null | 2011-02-23T03:27:05.930 | 2020-01-10T11:13:01.667 | 2015-05-12T08:04:38.237 | 35989 | 287 | [
"r",
"regression",
"change-point",
"nls"
] |
7528 | 1 | 7557 | null | 3 | 644 | A simplified regression equation $ES=\frac{a+b}{n_1+n_2}$ has been suggested as an alternative to Egger's regression equation $\frac{ES}{SE}=\frac{a+b}{SE}$, where ES=Effect Size, $n_1$=sample size of the patients, $n_2$=sample size of the controls, SE=Standard Error.
This alternative test, that was presented by Peters... | Alternative Egger's test, without using standard error | CC BY-SA 2.5 | null | 2011-02-23T03:42:14.227 | 2011-02-24T00:34:36.170 | 2011-02-23T22:19:05.607 | null | 3333 | [
"meta-analysis",
"standard-error",
"effect-size",
"funnel-plot",
"publication-bias"
] |
7529 | 2 | null | 7527 | 13 | null | (At first I thought it could be a problem resulting from the fact that `max` is not vectorized, but that's not true. It does make it a pain to work with changePoint, wherefore the following modification:
```
changePoint <- function(x, b0, slope1, slope2, delta) {
b0 + (x*slope1) + (sapply(x-delta, function (t) max... | null | CC BY-SA 3.0 | null | 2011-02-23T06:27:52.350 | 2015-05-12T07:52:01.270 | 2015-05-12T07:52:01.270 | 35989 | 2975 | null |
7530 | 2 | null | 7513 | 17 | null | I think R help page of `lm` answers your question pretty well. The only requirement for weights is that the vector supplied must be the same length as the data. You can even supply only the name of the variable in the data set, R will take care of the rest, NA management, etc. You can also use formulas in the `weight` ... | null | CC BY-SA 3.0 | null | 2011-02-23T08:15:22.403 | 2016-11-03T15:17:41.363 | 2016-11-03T15:17:41.363 | 25138 | 2116 | null |
7531 | 1 | 7545 | null | 4 | 936 | I have the data about process duration (in minutes) and components (procedures) done during it like this (CSV):
---
```
id,time,p1,p2,p3,p4
1,30,1,0,0,0
2,32,1,0,0,0
3,56,1,1,0,0
4,78,1,1,0,1
5,78,1,1,0,1
6,100,1,1,1,1
7,98,0,1,1,1
```
I need to estimate the duration of each component(procedure)
I want to get some... | Multiple regression with binary predictors. Component value analysis | CC BY-SA 2.5 | null | 2011-02-23T08:23:08.650 | 2011-02-25T13:23:21.070 | 2011-02-24T10:48:25.453 | 3376 | 3376 | [
"methodology",
"multiple-regression"
] |
7532 | 1 | 7540 | null | 9 | 24799 | I would like to ask you, what is the correct number of Lags in ARCH LM Test? I am referring to ArchTest in FinTS package, but other ArchTest (such as the one in Eviews) provide same results. In many time series, when I choose Lags between 1:5 the p.value is usually higher than 0.05, but with increasing of Lags, p.valu... | How to choose number of lags in ARCH models using ARCH LM test? | CC BY-SA 2.5 | null | 2011-02-23T09:50:38.280 | 2017-10-25T09:51:40.533 | 2011-02-23T11:39:21.630 | 2116 | 3378 | [
"time-series",
"garch"
] |
7533 | 2 | null | 7531 | 1 | null | Software:
I advice to use R because it is free and designed for data analysis.
[http://cran.r-project.org/](http://cran.r-project.org/)
About your first question, the R code to answer it, is the following (I suppose that your data are in the file "data.csv")
```
# load the file "data.csv"
d <- read.table("data.csv",hea... | null | CC BY-SA 2.5 | null | 2011-02-23T09:52:39.107 | 2011-02-23T13:46:38.320 | 2011-02-23T13:46:38.320 | 2116 | 3377 | null |
7534 | 1 | 7646 | null | 3 | 410 | I have a contingency table which is not fixed, it varies depending on on the data.
I want to know how significant a particular cell is compared with the other cells. Is there a method of doing this?
| How do you determine if a particular cell of an i x j contingency table is statistically significant among all other cells? | CC BY-SA 3.0 | null | 2011-02-23T10:12:20.310 | 2012-09-02T01:46:33.087 | 2012-09-02T01:46:33.087 | 3826 | 3379 | [
"contingency-tables"
] |
7535 | 1 | 7537 | null | 17 | 2076 | What is the appropriate strategy for deciding which model to use with count data?
I have count data that i need to model as a multilevel model and it was recommended to me (on this site) that the best way to do so this is through bugs or MCMCglmm. However i am still trying to learn about bayesian statistics, and i tho... | Strategy for deciding appropriate model for count data | CC BY-SA 4.0 | null | 2011-02-23T11:05:14.260 | 2020-01-05T13:43:33.923 | 2020-01-05T13:43:33.923 | 11887 | 1871 | [
"generalized-linear-model",
"poisson-distribution",
"count-data",
"negative-binomial-distribution",
"overdispersion"
] |
7536 | 2 | null | 6330 | 5 | null | By Their Fruits Ye Shall Know Them
The assumption (to be tested) is that the errors from the model have constant variance. Note this does not mean the errors from an assumed model. When you use a simple graphical analysis you are essentially assuming a linear model in time.
Thus if you have an inadequate model such as ... | null | CC BY-SA 4.0 | null | 2011-02-23T13:29:56.240 | 2018-06-19T11:57:38.263 | 2018-06-19T11:57:38.263 | 3382 | 3382 | null |
7537 | 2 | null | 7535 | 10 | null | You can always compare count models by looking at their predictions (preferrably on a hold out set). J. Scott Long discusses this graphically (plotting the predicted values against actuals). His text book [here](http://rads.stackoverflow.com/amzn/click/0803973748) describes in details but you can also look at [6.4 on t... | null | CC BY-SA 2.5 | null | 2011-02-23T14:24:38.070 | 2011-02-24T07:37:31.597 | 2011-02-24T07:37:31.597 | 2116 | 2040 | null |
7539 | 2 | null | 7535 | 5 | null | A couple things to add to what B_Miner said:
1) You wrote that the models varied from "everything significant" to "nothing significant" but this is not a good way to compare models. Look, instead, at predicted values (as B_miner suggested) and effect sizes.
2) If 70% of the data are 0, I can't imagine that a model w... | null | CC BY-SA 2.5 | null | 2011-02-23T15:20:31.053 | 2011-02-23T15:20:31.053 | null | null | 686 | null |
7540 | 2 | null | 7532 | 9 | null | Arch LM tests whether coefficients in the regression:
$$a_t^2=\alpha_0+\alpha_1 a_{t-1}^2+...+\alpha_p a_{t-p}^2+e_t$$
are zero, where $a_t$ is either observed series which we want to test for ARCH effects. So the null hypothesis is
$$\alpha_1=...=\alpha_p=0$$
If hypothesis is accepted then we can say that series have ... | null | CC BY-SA 3.0 | null | 2011-02-23T15:21:49.200 | 2017-10-25T09:51:40.533 | 2017-10-25T09:51:40.533 | 2116 | 2116 | null |
7541 | 2 | null | 7534 | 0 | null | I am not sure if I understand the first part of your question - everything varies depending on the data, does it not?
There are ways to partition chi-square; I am not near my references, but this article seems apropos [http://www.jstor.org/pss/2283933](http://www.jstor.org/pss/2283933)
If you know in advance which cell... | null | CC BY-SA 2.5 | null | 2011-02-23T15:24:51.763 | 2011-02-23T15:24:51.763 | null | null | 686 | null |
7542 | 1 | null | null | 5 | 2482 | I have to demonstrate that a generator of VoIP calls generates calls uniformly distributed between callers.
In particular the distribution is the uniform (min, max) one where the volume per caller distribution is uniformly distributed between a minimum and maximum. So by running a test with 10000 users and a min value ... | How to perform goodness of fit test and how to assign probability with uniform distribution? | CC BY-SA 3.0 | null | 2011-02-23T16:03:54.663 | 2013-06-28T19:10:21.147 | 2012-06-02T22:17:39.543 | null | 3342 | [
"distributions",
"chi-squared-test",
"goodness-of-fit",
"uniform-distribution"
] |
7543 | 2 | null | 7084 | 7 | null |
### The question:
- How can normality be validated without using visual cues such as QQ plots? (the validation will be a part of larger software)
- Can a "goodness of fit" score be calculated?
Although enumerated separately, these parts are (appropriately) one question: you compute an appropriate goodness of f... | null | CC BY-SA 2.5 | null | 2011-02-23T16:21:47.927 | 2011-02-23T16:21:47.927 | 2020-06-11T14:32:37.003 | -1 | 919 | null |
7544 | 2 | null | 7084 | 0 | null | I would maybe (I don't know if it is feasible in your context) suggest another approach.
You could force your experimental data to follow a standard normal distribution by applying a normal quantile tranformation on it. The principle is to
1) rank your values from high to low
2) assign the value of r-th rank the (r-0.... | null | CC BY-SA 2.5 | null | 2011-02-23T18:11:56.453 | 2011-02-23T18:11:56.453 | null | null | null | null |
7545 | 2 | null | 7531 | 5 | null | I don't think the problem, as is, is well-defined. You mention the possibility that the duration of each operation may vary if done in combination with others. If that's the case, the "duration of operation" is not defined. E.g. in your example, op1 time is 30-32 min, and you say "therefore op2 time is 24-26 min", but ... | null | CC BY-SA 2.5 | null | 2011-02-23T18:21:58.147 | 2011-02-25T13:23:21.070 | 2011-02-25T13:23:21.070 | 3376 | 3369 | null |
7546 | 1 | null | null | 7 | 1324 | For R, I understand that the package lme4 and the function glmer roughly corresponds to glimmix in SAS. What is the default covariance structure when fit and can it be changed? If so how?
| What is the default covariance structure in glmer and can I change it? | CC BY-SA 2.5 | null | 2011-02-23T18:29:52.990 | 2011-02-24T07:51:30.473 | null | null | 1364 | [
"r",
"mixed-model",
"covariance-matrix"
] |
7547 | 2 | null | 7542 | -2 | null | What you are describing resembles a "continuous uniform distribution",
[http://mathworld.wolfram.com/UniformDistribution.html](http://mathworld.wolfram.com/UniformDistribution.html)
-Ralph Winters
| null | CC BY-SA 2.5 | null | 2011-02-23T19:43:14.900 | 2011-02-23T19:43:14.900 | null | null | 3489 | null |
7548 | 2 | null | 4200 | 0 | null | You should consider the Cross Correlation Function as that is meant to identify the lead/lag relationship. Dirk had mentioned the Autocorrelation Function, but that is meant for just one single time series and not for multivariate. You should consider looking at the Box-Jenkins textbook Chapter 10 where they introduce... | null | CC BY-SA 2.5 | null | 2011-02-23T19:56:53.833 | 2011-02-23T20:19:48.650 | 2011-02-23T20:19:48.650 | 8 | 3382 | null |
7549 | 1 | null | null | 6 | 1171 | As the title suggests, I'm pretty well befuddled about which approach makes the most sense for my data. Let me try to succinctly explain the problem.
I have binary choice data representing whether a specific person for a specific event took the train or bus. I have event level predictors (location of event, duration of... | Inverse logistic regression vs. repeated-measures vs. latent class? | CC BY-SA 2.5 | null | 2011-02-23T20:03:53.400 | 2011-02-24T16:39:05.287 | 2011-02-23T21:24:22.357 | null | 3387 | [
"regression",
"latent-class"
] |
7550 | 2 | null | 7499 | 6 | null | Adding error bars to a bar graph is a choice you make as a presenter to communicate more information to your audience. They are useful because they communicate visually how certain you can be, based on your data, of the specific values you are presenting.
In some cases, there is no uncertainty. Imagine you are grap... | null | CC BY-SA 2.5 | null | 2011-02-23T20:06:57.793 | 2011-02-23T20:06:57.793 | null | null | 3388 | null |
7551 | 1 | 7553 | null | 9 | 10355 | I am running an ordinal logistic regression in R and running into trouble when I include dummy variables. My model works great with my first set of predictors. Next I want to add dummy variables for each of the years represented in my dataset.
I created the dummy variables with `car:recode` in this manner (one stat... | Singular information matrix error in lrm.fit in R | CC BY-SA 2.5 | null | 2011-02-23T20:22:00.287 | 2011-02-24T16:55:33.830 | 2011-02-24T16:55:33.830 | 3388 | 3388 | [
"r",
"logistic"
] |
7553 | 2 | null | 7551 | 7 | null | Creating dummy variables should not be necessary. You should just use factors when modeling in R.
```
admityear <- factor(admityear)
m4 <- lrm(Outcome ~ relGPA + mcAvgGPA + Interview_Z + WorkHistory_years +
GMAT + UGI_Gourman + admityear, data=fsd)
```
If the singular condition still persists... | null | CC BY-SA 2.5 | null | 2011-02-23T22:45:27.753 | 2011-02-23T22:45:27.753 | null | null | 2129 | null |
7554 | 1 | null | null | 7 | 21032 | I have data about how many unique users do a certain thing for each day of a month. I can average it, and i would like to display the variation in a intuitive format (such as % of something).
Is there a standard way of doing this?
I've found standard error, which is $\frac{\sigma}{\sqrt{n}}$, which is not particularl... | How to express error as a percentage? | CC BY-SA 3.0 | null | 2011-02-24T00:05:24.610 | 2015-11-12T14:24:03.723 | 2012-07-19T11:20:52.490 | 12540 | 3392 | [
"variance"
] |
7555 | 1 | 7573 | null | 6 | 9100 | I am trying to manage a meta-regression in SPSS17 using the effect size as the dependent variable. I want to explore if my independent variables affects the effect size. Some small practical questions:
- What is the minimum number of studies necessary for a meta-regression?
Some people suggest at least 10 studies are... | How to do meta-regression in SPSS? | CC BY-SA 2.5 | null | 2011-02-24T00:21:51.900 | 2016-06-17T13:09:29.513 | 2011-02-24T07:22:39.853 | 2116 | 3333 | [
"spss",
"meta-analysis",
"sample-size",
"meta-regression"
] |
7556 | 2 | null | 7554 | 4 | null | It may be meaningful to just divide by the average. E.g. the average number is 1000 and the std is 200, so in a sense this means the actual number can vary by 20% from the baseline.
Also, if you could say who the user of the data are and what they are doing with it, it might be useful.
| null | CC BY-SA 2.5 | null | 2011-02-24T00:28:50.507 | 2011-02-24T00:28:50.507 | null | null | 3369 | null |
7557 | 2 | null | 7528 | 2 | null | Yes, I think that this approach can be use with other types of effect sizes as long as they are (approximately) normally distributed (that's why you use $log(OR)$; to be more precise, the errors of the linear regression model need to be $N(0,1)$).
Your regression equation is wrong. It is $\overline{ES} = a + b \cdot \f... | null | CC BY-SA 2.5 | null | 2011-02-24T00:34:36.170 | 2011-02-24T00:34:36.170 | 2017-04-13T12:44:44.530 | -1 | 307 | null |
7558 | 2 | null | 7554 | 2 | null | The standard error of the mean tells you how precise your estimate of the mean is; that doesn't seem to capture what you're trying to do. I would use either a) a histogram, if you care mostly about showing variation, or b) a line chart or area chart, if you want to say something about variation while also showing prog... | null | CC BY-SA 2.5 | null | 2011-02-24T01:02:48.370 | 2011-02-24T01:02:48.370 | null | null | 2669 | null |
7559 | 1 | 7560 | null | 7 | 3397 | The linear SVM in textbook takes form of maximizing
$L_D = \sum_i{a_i} - \frac{1}{2}\sum_{i,j}{a_ia_jy_iy_jx_i^Tx_j}$
over $a_i$ where $a_i \geq 0$ and $\sum_i{a_iy_i} = 0$
Since $w = \sum_i{a_iy_ix_i}$, the classifier will take the form $\text{Sgn}(wx - b)$.
Thus, it seems to solve linear SVM, I need to figure out $a_... | The difference between linear SVM and other linear classifiers? | CC BY-SA 2.5 | null | 2011-02-24T03:01:15.167 | 2011-02-24T07:24:09.663 | 2011-02-24T07:24:09.663 | 2116 | 3395 | [
"svm",
"linear-model"
] |
7560 | 2 | null | 7559 | 8 | null | There are two things going on here.
- Difference between primal and dual problem. The "original" objective function of SVM is to minimize $1/2 ||w||^2$. This is called "primal form". Turns out that the objective function you wrote (the one involving $L_D$) is the dual form of this problem. So the two lead to equivalen... | null | CC BY-SA 2.5 | null | 2011-02-24T03:11:36.903 | 2011-02-24T03:11:36.903 | null | null | 3369 | null |
7561 | 2 | null | 7546 | 1 | null | I do not know about SAS, but variance in `glmer` is controlled by `family` argument. If you want to change correlation structure then I suspect you will have to use `nlme` from nlme package.
| null | CC BY-SA 2.5 | null | 2011-02-24T07:51:30.473 | 2011-02-24T07:51:30.473 | null | null | 2116 | null |
7562 | 1 | 8390 | null | 9 | 5114 | I just got my hands on the [ANES (American National Election Studies)](http://www.electionstudies.org/) 2008 data set, and would like to do some simple analysis in R. However, I've never worked with this complex of a data set before and I've run into an issue.
The survey uses oversampling and has a variable for post st... | Simple post-stratification weights in R | CC BY-SA 2.5 | null | 2011-02-24T10:15:16.720 | 2017-09-15T03:49:24.013 | 2011-02-24T11:57:29.023 | null | 726 | [
"r",
"survey",
"post-hoc",
"stratification"
] |
7563 | 1 | 7568 | null | 11 | 322 | What techniques/approaches are useful in testing statistical software? I'm particularly interested in programs that do parametric estimation using maximum likelihood.
Comparing results to those from other programs or published sources is not always possible since most of the time when I write a program of my own it is ... | Testing statistical software | CC BY-SA 2.5 | null | 2011-02-24T10:16:34.377 | 2012-11-21T23:16:47.400 | null | null | 1393 | [
"software",
"computational-statistics"
] |
7564 | 1 | null | null | 8 | 389 | I would like to know if there are SOM implementations (preferably R) available that accept fuzzy input. That is, I have data in which some nominal features are spread out between a number of categories. For example: feature 1 has 5 categories and an observation might have the values (which are actually probabilities) [... | Self-organizing maps: fuzzy input? | CC BY-SA 3.0 | null | 2011-02-24T10:53:47.720 | 2018-08-12T15:46:33.880 | 2017-12-15T20:34:36.997 | 128677 | 3401 | [
"machine-learning",
"neural-networks",
"self-organizing-maps"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.