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9697 | 2 | null | 9685 | 1 | null | Here is the code to do the chi square tests as well as generate a variety of test statistics. However, statistical tests of association of the table margins are useless here; the answer is obvious. No one does a statistical test to see if summer is hotter than winter.
```
Chompy<-matrix(c(30,10,1,31,20,10), 3, 2)
Chom... | null | CC BY-SA 3.0 | null | 2011-04-18T19:18:55.227 | 2011-04-18T19:18:55.227 | null | null | 1893 | null |
9698 | 2 | null | 9692 | 5 | null | You can use `lm()` instead of `aov()` in this case (the latter is a wrapper of the former).
Here is an illustration:
```
n <- 100
A <- gl(2, n/2, n, labels=paste("a", 1:2, sep=""))
B <- gl(2, n/4, n, labels=paste("b", 1:2, sep=""))
# generate fake data for a balanced two-way ANOVA
df <- data.frame(y=rnorm(n), A, B)
sum... | null | CC BY-SA 3.0 | null | 2011-04-18T19:19:11.087 | 2011-04-18T19:19:11.087 | null | null | 930 | null |
9699 | 1 | 9700 | null | 46 | 98800 | Is there a possibility to use R in a webinterface without the need to install it?
I have only one small script which I like to run but I just want to give it a shot without a long installation procedure.
Thank you.
| Using R online - without installing it | CC BY-SA 3.0 | null | 2011-04-18T19:26:12.323 | 2020-09-14T18:19:07.250 | null | null | 230 | [
"r"
] |
9700 | 2 | null | 9699 | 23 | null | Yes, there are some Rweb interface, like [this one](http://pbil.univ-lyon1.fr/Rweb/) (dead as of September 2020), RDDR [online REPL](https://rdrr.io/snippets/), or [Repl.it](https://repl.it/languages/).
Note: Installation of the R software is pretty straightforward and quick, on any platform.
| null | CC BY-SA 4.0 | null | 2011-04-18T19:35:38.070 | 2020-09-14T18:19:07.250 | 2020-09-14T18:19:07.250 | 930 | 930 | null |
9701 | 2 | null | 9666 | 2 | null | I saw you said you prefer Python, but there are a bunch of R libraries for this, see Highest Density Region function: [http://cran.r-project.org/web/packages/hdrcde/hdrcde.pdf](http://cran.r-project.org/web/packages/hdrcde/hdrcde.pdf)
The second iteration of your looking for the median wouldn't work, as your modes woul... | null | CC BY-SA 3.0 | null | 2011-04-18T19:42:11.797 | 2011-04-18T19:42:11.797 | null | null | 1893 | null |
9703 | 2 | null | 9662 | 1 | null | You can use logistic regression. In SPSS the categorical variable (group A, B, or C) can be entered as a single variable using the contrast command, in which case one of the three will be designated as the reference category, or if you prefer you can create 2 dummy variables to account for the 3 groups.
You would ru... | null | CC BY-SA 3.0 | null | 2011-04-18T19:55:39.293 | 2011-04-18T19:55:39.293 | null | null | 2669 | null |
9704 | 2 | null | 9666 | 2 | null | Had to change my answer because I had trouble with `strucchange`, which doesn't seem to like hard changes. Maybe this code will help a bit.
```
library (robfilter)
# Make phoney data...
clock <- ts (rnorm (1000, 1, 0.03) * approx (1:10, rgamma (10, 1, 1, 1), seq (0.01, 10, 0.01), method="constant")$y)
spike <- round (... | null | CC BY-SA 3.0 | null | 2011-04-18T20:21:42.377 | 2011-04-25T20:13:49.233 | 2011-04-25T20:13:49.233 | 1764 | 1764 | null |
9705 | 2 | null | 9626 | 0 | null | Balanced designs have really just one goal, orthogonal treatment effects. Orthogonal design lowers the risk of unobservables sneaking into your effect estimates in an uneven way. See: [http://www1.umn.edu/statsoft/doc/statnotes/stat06.txt](http://www1.umn.edu/statsoft/doc/statnotes/stat06.txt) for an excellent discussi... | null | CC BY-SA 3.0 | null | 2011-04-18T20:28:17.657 | 2011-04-18T20:28:17.657 | null | null | 1893 | null |
9706 | 2 | null | 9541 | 2 | null | The concept of numbers of parameters and hence df in the lmer model is kind of fuzzy. Don't bother with it, and use AICc; you stand on firmer theoretical ground: [http://warnercnr.colostate.edu/~anderson/PDF_files/TESTING.pdf](http://warnercnr.colostate.edu/~anderson/PDF_files/TESTING.pdf)
| null | CC BY-SA 3.0 | null | 2011-04-18T20:33:07.297 | 2011-04-18T20:33:07.297 | null | null | 1893 | null |
9707 | 2 | null | 9699 | 8 | null | [Sage](http://www.sagemath.org/) also has R included with a Python interface. The Sage system is available. Since a couple of years, the prefered way to run SageMath is via [CoCalc](https://www.cocalc.com/). It also allows you to run R directly, e.g. in a [Jupyter notebook using the R kernel](https://share.cocalc.com/s... | null | CC BY-SA 4.0 | null | 2011-04-18T20:38:17.917 | 2018-08-06T16:21:55.720 | 2018-08-06T16:21:55.720 | 13176 | 3911 | null |
9708 | 2 | null | 7775 | 1 | null | Well, r^2 is really just covariance squared over the product of the variances, so you could probably do something like cov(Yfull, Ytrue)/var(Ytrue)var(Yfull) - cov(YReduced, Ytrue)/var(Ytrue)var(YRed) regardless of model type; check to verify that gives you the same answer in the lm case though.
[http://www.stator-afm... | null | CC BY-SA 3.0 | null | 2011-04-18T20:45:12.783 | 2011-04-18T20:45:12.783 | null | null | 1893 | null |
9709 | 2 | null | 9699 | 8 | null | Also, if you want to provide a solution to other users, you can set up a webserver with [RApache](http://rapache.net/).
| null | CC BY-SA 3.0 | null | 2011-04-18T21:02:49.140 | 2011-04-18T21:02:49.140 | null | null | 582 | null |
9710 | 2 | null | 8147 | 1 | null | Sums of bernoullis are distributed exactly binomial, so one often would use logistic regression.
| null | CC BY-SA 3.0 | null | 2011-04-18T21:04:11.073 | 2011-04-18T21:04:11.073 | null | null | 1893 | null |
9711 | 2 | null | 9695 | 1 | null | Not sure it gives a final answer to the question, but I would give a look at [this](http://cscs.umich.edu/~crshalizi/weblog/491.html). Especially point 2. See also the discussion in appendix A2 of the [paper](http://arxiv.org/abs/0706.1062).
| null | CC BY-SA 3.0 | null | 2011-04-18T21:06:24.723 | 2011-04-18T21:06:24.723 | null | null | 4220 | null |
9712 | 1 | 9713 | null | 2 | 2204 | I'm in the process of learning R, in the hope of replacing everything I do in SPSS/Sigmplot with R. It's going well so far :) I've got to the point of running a repeated-measures ANOVA, but have come unstuck when trying to plot the results
I've worked out how to plot a set of means using ggplot2, but now I'm unsure of ... | How to add standard error to plots in ggplot2 with R? | CC BY-SA 3.0 | null | 2011-04-18T21:13:05.013 | 2011-04-18T21:50:00.160 | null | null | 4204 | [
"r",
"anova",
"ggplot2"
] |
9713 | 2 | null | 9712 | 2 | null | The reason you're running into multiple methods is because the target variability to visualize in a repeated measures design is not necessarily that straightforward to determine.
If you calculate the conventional SE then what you've done is give an estimate of how well you calculated the raw score. However, generally ... | null | CC BY-SA 3.0 | null | 2011-04-18T21:50:00.160 | 2011-04-18T21:50:00.160 | null | null | 601 | null |
9714 | 2 | null | 9693 | 3 | null | Hopefully, modelling the dynamics of tumor progression qualifies for this:
Anderson & Quaranta. [Integrative mathematical oncology](http://www.nature.com/nrc/journal/v8/n3/abs/nrc2329.html). Nature Reviews Cancer, 2008.
| null | CC BY-SA 3.0 | null | 2011-04-18T22:04:17.970 | 2011-04-18T22:04:17.970 | null | null | 3770 | null |
9715 | 1 | null | null | 24 | 32579 | I ran a multinomial logit model in JMP and got back results which included the AIC as well chi-squared p-values for each parameter estimate. The model has one categorical outcome and 7 categorical explanatory vars.
I then fit what I thought would build the same model in R, using the `multinom` function in the [nnet](ht... | How to set up and estimate a multinomial logit model in R? | CC BY-SA 4.0 | null | 2011-04-18T22:35:27.610 | 2022-12-07T13:20:28.120 | 2022-09-08T03:12:06.697 | 11887 | 3984 | [
"r",
"logistic",
"multinomial-distribution",
"jmp"
] |
9718 | 1 | null | null | 3 | 1673 | If the correlation between demographic dissimilarity and satisfaction is $r=.-14$ and the partial correlation, with career development partialled out, between demographic dissimilarity and satisfaction is $r=-.06$ across a very large sample of size $n$, what is the appropriate test to determine if these correlations ar... | How to test whether correlation measures differ when controlling or not for a third variable? | CC BY-SA 3.0 | null | 2011-04-19T01:49:14.960 | 2011-04-20T14:01:56.807 | 2011-04-19T14:52:12.420 | 930 | null | [
"correlation",
"statistical-significance",
"causality"
] |
9720 | 2 | null | 9627 | 5 | null | You cannot "systemically avoid this problem in the future", because it should not be called a "problem". If the reality of the material world features strong covariates, then we should accept it as fact and adjust our theories and models in consequence. I like the question very much, and hope that what follows will not... | null | CC BY-SA 3.0 | null | 2011-04-19T02:50:30.023 | 2011-04-19T02:50:30.023 | null | null | 3582 | null |
9721 | 2 | null | 9685 | 4 | null | I am going to assume that "100% survival" means that your sites only contained a single organism. so 30 means 30 organisms died, and 31 means 31 organisms didn't.
Based on this the chi-square should be fine, but it will only tell which hypothesis are not supported by the data - it won't tell you if two reasonable hypo... | null | CC BY-SA 3.0 | null | 2011-04-19T02:54:55.040 | 2011-04-19T02:54:55.040 | null | null | 2392 | null |
9722 | 2 | null | 9664 | 84 | null | If the quantity of interest, usually a functional of a distribution, is reasonably smooth and your data are i.i.d., you're usually in pretty safe territory. Of course, there are other circumstances when the bootstrap will work as well.
What it means for the bootstrap to "fail"
Broadly speaking, the purpose of the boots... | null | CC BY-SA 3.0 | null | 2011-04-19T03:32:57.203 | 2011-04-20T02:08:28.043 | 2011-04-20T02:08:28.043 | 2970 | 2970 | null |
9723 | 2 | null | 4111 | 3 | null | There are companies that specialize in counting people. For instance,
[www.lynce.es](http://www.lynce.es)$^\dagger$
(I am not affiliated nor have any interest whatsoever in such company).
They hung cameras over the groups they want to count, shoot pictures
and actually count heads. They only make small adjustments when... | null | CC BY-SA 4.0 | null | 2011-04-19T05:09:08.220 | 2022-12-08T14:15:11.347 | 2022-12-08T14:15:11.347 | 362671 | 892 | null |
9724 | 1 | 13369 | null | 3 | 505 | I'm hoping to hear from someone who has worked on mouse models or similar biological analyses where there is a tendency to run 'replicates' of an experiment. I know multiple testing is a sizeable kettle of fish which is definitely relevant to this discussion. I have some applications for projects where they talk about ... | Mouse models - 'replicates' and analysis | CC BY-SA 3.0 | null | 2011-04-19T05:19:00.737 | 2011-07-22T13:09:00.340 | 2011-06-20T19:19:31.947 | 82 | 4226 | [
"repeated-measures",
"multiple-comparisons",
"experiment-design",
"biostatistics"
] |
9727 | 2 | null | 9507 | 0 | null | To asnwer the first part of my question, does a flat initial guess lead to flat data, the answer would be "yes". Not only does having a flat guess flatten the result, it also makes it unchanging (a fact I missed thanks to a small error in my algorithm).
Here's a proof:
Assuming that $\langle R_{r\alpha} \rangle^{(t)} =... | null | CC BY-SA 3.0 | null | 2011-04-19T07:10:28.757 | 2011-04-20T06:52:07.723 | 2011-04-20T06:52:07.723 | 4141 | 4141 | null |
9728 | 1 | null | null | 1 | 295 | Under what circumstances would using regression with two given variables not increase accuracy of prediction?
| When is there no point in using regression? | CC BY-SA 3.0 | null | 2011-04-19T07:19:11.553 | 2011-04-29T05:14:44.927 | 2011-04-29T05:14:44.927 | 183 | null | [
"regression"
] |
9729 | 1 | null | null | 1 | 5321 | I was given the following question:
A survey found that 89% of a random sample of 1024 American adults approved of cloning endangered animals. Find the margin of error for this survey if we want 90% confidence in our estimate of the percent of American adults who approve of cloning endangered animals.
I know that for 9... | How to compute margin of error with a given confidence interval? | CC BY-SA 3.0 | null | 2011-04-19T08:27:19.990 | 2011-04-19T12:50:04.660 | 2011-04-19T12:50:04.660 | 930 | 4228 | [
"self-study",
"sampling",
"survey"
] |
9731 | 1 | null | null | 4 | 263 | >
Possible Duplicate:
Threshold for correlation coefficient to indicate statistical significance of a correlation in a correlation matrix
### Context
I am doing an exploratory study to investigate the relationship between a drug (actually measured in two ways - by direct and indirect methods) and 15 various para... | Adjust a large set of Spearman correlation analyses for multiple testing | CC BY-SA 3.0 | null | 2011-04-19T08:55:24.803 | 2011-06-07T04:21:27.120 | 2017-04-13T12:44:26.710 | -1 | 4229 | [
"correlation",
"multiple-comparisons",
"spearman-rho"
] |
9733 | 2 | null | 9728 | 5 | null | When the model assumptions are valid, but the data are not correlated.
When the model assumptions are invalid (e.g. the noise process is heteroskedastic) in which case a regression model may fit the data very well, but provide very poor out-of-sample predictions.
See also the excellent point about extrapolation made by... | null | CC BY-SA 3.0 | null | 2011-04-19T09:43:04.257 | 2011-04-19T14:18:17.637 | 2011-04-19T14:18:17.637 | 887 | 887 | null |
9734 | 1 | 31748 | null | 4 | 603 | Is it possible to use a continuous predictor in Bugs? The simplest way of doing this would be turning the size variable in alligators example from discrete to continuous.
Both Winbugs and JAGS examples use combination of values of covariates as indices as in
```
X[i,j,] ~ dmulti( p[i,j,] , n[i,j] );
```
where `i` i... | How to model logistic regression with continuous predictor in Bugs? | CC BY-SA 3.0 | null | 2011-04-19T09:47:27.687 | 2012-08-05T05:46:13.763 | 2011-04-19T12:53:18.977 | 930 | 3280 | [
"bayesian",
"logistic",
"bugs"
] |
9735 | 1 | 9866 | null | 8 | 1244 | ... (optional) within the context of Google Web Optimizer.
Suppose you have two groups and a binary response variable. Now you get the following outcome:
- Original: 401 trials, 125 successful trials
- Combination16: 441 trials, 141 successful trials
The difference is not statistically significant, however one can... | How does a frequentist calculate the chance that group A beats group B regarding binary response | CC BY-SA 3.0 | null | 2011-04-19T09:53:48.520 | 2011-05-06T11:17:56.070 | 2011-05-06T11:17:56.070 | 264 | 264 | [
"bayesian",
"ab-test"
] |
9736 | 1 | null | null | 5 | 219 | I have a time series (X) representing a natural phenomenon (wind speed, measured every 15 minutes) and I have to create similar time series (up to 20, Xdi, i=1,...,20) with the same structure (same average, same standard deviation, same percentiles distribution...) but with a predetermined correlation (about 0.7) betwe... | How to create n time series characterised by a defined average and correlation? | CC BY-SA 3.0 | null | 2011-04-19T11:00:41.787 | 2012-03-30T15:53:40.423 | 2011-04-21T13:57:37.540 | 4230 | 4230 | [
"time-series",
"correlation"
] |
9737 | 2 | null | 9729 | 3 | null | Because you are dealing with proportions, the variance is given by:
$$\frac{p(1-p)}{n}$$
And so the 90% CI ME is equal to $1.645\times \sqrt{\frac{p(1-p)}{n}}=1.645\times \sqrt{\frac{0.89(1-0.89)}{1024}}=0.016$
| null | CC BY-SA 3.0 | null | 2011-04-19T11:39:35.177 | 2011-04-19T11:39:35.177 | null | null | 2392 | null |
9738 | 1 | null | null | 5 | 2334 | I am attempting to build a Multinomial Logit model with dummy variables of the following form:
- The dependent variable represents 0-8 discrete choices.
- Dummy Variable 1: 965 dummy vars
- Dummy Variable 2: 805 dummy vars
The data set I am using has the dummy columns pre-created, so it's a table of 72,381 rows an... | Problem building multinomial logit model formula on huge data in R | CC BY-SA 3.0 | null | 2011-04-19T12:22:14.087 | 2014-05-18T00:20:31.843 | 2014-05-18T00:07:35.877 | 7291 | null | [
"r",
"logistic",
"multinomial-distribution"
] |
9739 | 1 | null | null | 13 | 5738 | I have a set of sea surface temperature (SST) monthly data and I want to apply some cluster methodology to detect regions with similar SST patterns. I have a set of monthly data files running from 1985 to 2009 and want to apply clustering to each month as a first step.
Each file contains gridded data for 358416 points ... | Clustering spatial data in R | CC BY-SA 3.0 | null | 2011-04-19T13:16:03.780 | 2016-09-19T00:56:11.053 | 2011-04-20T12:48:42.013 | null | 4147 | [
"r",
"clustering",
"spatial"
] |
9740 | 2 | null | 9728 | 5 | null | Considering the OLS case
$$Y_{i}=\alpha+\beta X_{i}$$
One case is when you try to predict using values of $X_{i}$ outside your sample range (extrapolation). Say if your data had $1<X_{i}<10$ in the sample, and you try to predict for when a new value is $X=100$.
In OLS you have a prediction interval for a new value $X_... | null | CC BY-SA 3.0 | null | 2011-04-19T13:23:26.303 | 2011-04-19T13:23:26.303 | null | null | 2392 | null |
9741 | 1 | 9782 | null | 5 | 1995 | I'm interested in assessing model performance on data with an ordinal categorical dependent variable. For my use case, the ideal metric would:
- Not assume equal intervals between classes or that recoding to a continuous scale is appropriate
- Be scale independent
- Give preference to models that rank the ou... | Model performance metrics for ordinal response | CC BY-SA 4.0 | null | 2011-04-19T13:57:46.837 | 2018-08-13T17:00:06.823 | 2018-08-13T17:00:06.823 | 7290 | 1611 | [
"r",
"model-selection",
"predictive-models",
"ordinal-data"
] |
9742 | 2 | null | 4884 | 7 | null | If the treatment is randomly assigned the aggregation won't matter in determining the effect of the treatment (or the average treatment effect). I use lowercase in the following examples to refer to disaggregated items and uppercase to refer to aggregated items. Lets a priori state a model of individual decision making... | null | CC BY-SA 3.0 | null | 2011-04-19T14:14:37.803 | 2011-06-23T17:24:00.630 | 2011-06-23T17:24:00.630 | 1036 | 1036 | null |
9743 | 2 | null | 9629 | 6 | null | Using the extra information you gave (being that quite some of the values are 0), it's pretty obvious why your solution returns nothing. For one, you have a probability that is 0, so :
- $e_i$ in the solution of Henry is 0 for at least one i
- $np_i$ in the solution of @probabilityislogic is 0 for at least one i
Wh... | null | CC BY-SA 4.0 | null | 2011-04-19T14:48:08.320 | 2022-01-02T13:36:16.320 | 2022-01-02T13:36:16.320 | 11887 | 1124 | null |
9744 | 1 | 9793 | null | 5 | 1587 | I'm trying to understand the following claim:
>
if the $t$-statistic is greater than
zero, it indicates that the variable
is explosive... but does that mean it
has unit root?
In the context of Dickey Fuller test.
| What is explosive variable? | CC BY-SA 3.0 | null | 2011-04-19T15:01:13.000 | 2011-04-20T15:56:42.650 | 2011-04-20T15:56:42.650 | 2645 | 333 | [
"hypothesis-testing",
"stationarity"
] |
9745 | 1 | 9746 | null | 6 | 4272 | How would you go about explaining "Stambaugh Bias" in simple relatively non-technical language?
| Stambaugh bias definition | CC BY-SA 4.0 | null | 2011-04-19T15:23:56.890 | 2021-02-15T07:23:27.413 | 2021-02-15T07:23:27.413 | 53690 | 333 | [
"time-series",
"autocorrelation",
"bias"
] |
9746 | 2 | null | 9745 | 7 | null | I'm not sure you can explain this term without using some technical terms, unfortunately. I'll give it my best shot.
Some definitions first:
- Bias: the difference between the expectation of an estimator and the true value of the parameter you're estimating.
- OLS: Ordinary Least Squares; a method for solving a regre... | null | CC BY-SA 3.0 | null | 2011-04-19T15:37:25.683 | 2011-04-19T15:37:25.683 | null | null | 781 | null |
9747 | 2 | null | 9667 | 14 | null | If you're coming from a mathematics background, and you want to learn time series, it's hard to go wrong with a combination of:
- The Analysis of Time Series (Chatfield): introduction at the undergraduate level
- Fourier Analysis of Time Series (Bloomfield): introduction to Fourier methods at the undergraduate level
... | null | CC BY-SA 3.0 | null | 2011-04-19T15:57:14.973 | 2011-04-19T15:57:14.973 | null | null | 781 | null |
9748 | 1 | 9750 | null | 3 | 445 | First of all, I’m new to statistics and this is the first time I am trying to apply it to a real world problem.
I am doing analysis of a series of observations of a variable over all weeks of a year. During certain weeks an event happened that I believe has impacted the variable and I want to check for this.
The value... | Can I split a series of observations of a variable over time into two groups instead of working with time series? | CC BY-SA 3.0 | null | 2011-04-19T15:59:49.677 | 2011-04-20T08:37:56.523 | 2011-04-20T08:37:56.523 | 4233 | 4233 | [
"time-series",
"statistical-significance",
"mean",
"t-test"
] |
9749 | 1 | null | null | 1 | 3859 | I have a set of data that are binomial, and am comparing them across 9 years. The first 5 years have low sample sizes ($~n=20$) and the last 4 have $n>100$. I've run a glm in R with the family set to "binomial", and the results look reasonable.
However, when I did the multiple comparisons afterwards using the [multcomp... | Binomial GLM post-hoc tests for unequal sample sizes | CC BY-SA 4.0 | null | 2011-04-19T16:14:33.530 | 2018-08-11T15:14:28.243 | 2018-08-11T15:14:28.243 | 11887 | 4238 | [
"r",
"binomial-distribution",
"generalized-linear-model",
"post-hoc"
] |
9750 | 2 | null | 9748 | 2 | null | Your reasoning sounds reasonable to me, although I have the feeling you are stretching the independence assumptions of t tests a little. Therefore, you should keep two things in mind.
First, the size of both groups (weeks with event versus weeks without event) should be comparable. E.g., 20 versus 30. would be fine I g... | null | CC BY-SA 3.0 | null | 2011-04-19T16:25:56.253 | 2011-04-19T16:25:56.253 | null | null | 442 | null |
9751 | 1 | null | null | 69 | 26610 | I often hear that post hoc tests after an ANOVA can only be used if the ANOVA itself was significant.
- However, post hoc tests adjust $p$-values to keep the global type I error rate at 5%, don't they?
- So why do we need the global test first?
- If we don't need a global test is the terminology "post hoc" correc... | Do we need a global test before post hoc tests? | CC BY-SA 3.0 | null | 2011-04-19T16:51:22.190 | 2016-09-06T20:40:41.843 | 2016-09-06T20:40:41.843 | 49647 | 4176 | [
"anova",
"statistical-significance",
"post-hoc"
] |
9752 | 1 | 9754 | null | 4 | 398 | I have a large set of customer data. For these customers, I have devised a customer loyalty score which is a measure of the loyalty of the customer. I want to find the features that are strongly associated/correlated with this score. Features could be number of purchases at various merchant types.
One obvious answer... | Suggestions for identifying key features | CC BY-SA 3.0 | null | 2011-04-19T17:18:10.903 | 2011-04-20T12:50:21.000 | 2011-04-20T12:50:21.000 | null | 4235 | [
"correlation",
"feature-selection"
] |
9753 | 2 | null | 9751 | 29 | null | (1) Post hoc tests might or might not achieve the nominal global Type I error rate, depending on (a) whether the analyst is adjusting for the number of tests and (b) to what extent the post-hoc tests are independent of one another. Applying a global test first is pretty solid protection against the risk of (even inadv... | null | CC BY-SA 3.0 | null | 2011-04-19T17:22:09.467 | 2011-04-19T17:22:09.467 | null | null | 919 | null |
9754 | 2 | null | 9752 | 2 | null | I understand that the loyalty score is calculated on the strength of some data. If your features include components that are used in calculating the loyalty score they will prove evidently influential.
Multivariate techniques are probably more useful than pairwise correlations:
- they can detect weaker features that m... | null | CC BY-SA 3.0 | null | 2011-04-19T17:58:00.073 | 2011-04-19T17:58:00.073 | null | null | 3911 | null |
9755 | 2 | null | 9752 | 1 | null | Sounds like Business Intelligence work (http://en.wikipedia.org/wiki/Business_intelligence). Could you confirm if it's a customer database or a survey that you ran? Both? Is it from a CRM database? Are customers segmented? Demographically/Physcographically? We need more detail as to what you have.
If it's a customer d... | null | CC BY-SA 3.0 | null | 2011-04-19T18:01:06.623 | 2011-04-19T18:01:06.623 | null | null | 776 | null |
9756 | 1 | null | null | 6 | 724 | I have trained an SVM Regression model using training data, $x_1,x_2,\dots,x_N$.
I want to perform active learning to improve the model; i.e., I want to add more samples to the training data and relearn a better model, and to choose these new samples in such a way as to maximize the resulting model performance.
For an ... | Active learning using SVM Regression | CC BY-SA 3.0 | null | 2011-04-19T18:10:25.063 | 2013-11-14T04:09:49.783 | 2011-04-20T13:00:49.357 | null | 4218 | [
"regression",
"cross-validation",
"svm"
] |
9757 | 2 | null | 9756 | 7 | null | Active learning requires a compromise between exploration and exploitation. If the model you have so far is bad, if you exploit this model to determine the best place to label mode data, it will probably suggest bad places to label the data as your current hypothesis is poor. It is a good idea to do some random explo... | null | CC BY-SA 3.0 | null | 2011-04-19T18:20:25.197 | 2011-04-19T18:20:25.197 | null | null | 887 | null |
9758 | 2 | null | 9752 | 1 | null | In addition to the suggestions from the previous answers, I would suggest the `catdes` function from the [FactoMineR](ftp://ftp.ccu.edu.tw/pub/languages/CRAN/web/packages/FactoMineR/FactoMineR.pdf) package in R. It gives a description of the categories of one factor by qualitative variables and/or by quantitative varia... | null | CC BY-SA 3.0 | null | 2011-04-19T18:24:28.563 | 2011-04-19T18:37:16.410 | 2011-04-19T18:37:16.410 | 3019 | 3019 | null |
9759 | 1 | 9798 | null | 16 | 13573 | I am about to dive into learning R and my learning project will entail applying mixed- or random-effects regression to a dataset in order to develop a predictive equation. I share the concern of the writer in this post
[How to choose nlme or lme4 R library for mixed effects models?](https://stats.stackexchange.com/que... | Can someone shed light on linear vs. nonlinear mixed-effects? | CC BY-SA 4.0 | null | 2011-04-19T18:46:18.587 | 2019-12-18T22:21:17.723 | 2019-12-18T22:21:17.723 | 92235 | 4237 | [
"r",
"regression",
"random-effects-model"
] |
9760 | 2 | null | 9759 | 1 | null | For the linear-nonlinear part, see: [CrossValidated article on the topic](https://stats.stackexchange.com/questions/8689/what-does-linear-stand-for-in-linear-regression), particularly the second-ranked answer by Charlie. I don't think there are any changes when dealing with mixed effects.
| null | CC BY-SA 3.0 | null | 2011-04-19T20:00:02.607 | 2011-04-19T20:00:02.607 | 2017-04-13T12:44:35.347 | -1 | 1764 | null |
9763 | 1 | null | null | 12 | 8263 | A typical image processing statistic is the use of [Haralick texture features](http://murphylab.web.cmu.edu/publications/boland/boland_node26.html), which are 14.
I am wondering about the 14th of these features: Given an adjacency map $P$ (which we can simply view an the empirical distribution of two integers $i,j < 25... | What is this "maximum correlation coefficient"? | CC BY-SA 3.0 | null | 2011-04-19T22:41:27.670 | 2013-01-02T15:30:52.380 | null | null | 2067 | [
"probability",
"computational-statistics"
] |
9764 | 2 | null | 9752 | 4 | null | One way to reformulate your problem is the following: you want to select a small set of features that predict well the loyalty score, using a linear model for example. This problem is called (best) subset selection.
Suppose that you want to pick k features. The first way to do it is to test all the subsets of k featur... | null | CC BY-SA 3.0 | null | 2011-04-19T23:17:10.557 | 2011-04-19T23:22:28.073 | 2011-04-19T23:22:28.073 | 4241 | 4241 | null |
9765 | 2 | null | 9718 | 2 | null | For this particular case there's not much of a difference to work with in practical terms, but for the general case, I'm going to go out on a limb and guess that there is no way to conduct a strict test of significance. The partial correlation will be a direct function of the correlations among the 3 variables. Depen... | null | CC BY-SA 3.0 | null | 2011-04-20T00:20:29.863 | 2011-04-20T00:20:29.863 | null | null | 2669 | null |
9766 | 1 | 9771 | null | 4 | 2460 | I have a question which asks:
>
Determine those values of the positive integer n for which a finite nth moment of X about zero exists.
How should I approach this question? Does it depend on the numbers of variables in X? I think that a first moment exists if the mean exists, and the second moment exists if the varia... | Determine whether a n-th finite moment of X exists | CC BY-SA 3.0 | null | 2011-04-20T02:46:09.323 | 2019-08-20T15:14:52.237 | 2011-04-20T13:00:27.937 | null | null | [
"self-study",
"moments"
] |
9767 | 1 | null | null | 6 | 1874 | Could anyone provide some suggestions on how to generate over-dispersed counts data with serial correlations? I am using R software to conduct a simulation study. Any references on this subject will be much appreciated.
Thanks for your help.
| Generating over-dispersed counts data with serial correlation | CC BY-SA 3.0 | null | 2011-04-20T03:13:26.073 | 2011-04-20T13:03:57.170 | null | null | 2742 | [
"r",
"time-series",
"distributions",
"poisson-distribution",
"simulation"
] |
9768 | 2 | null | 9724 | 1 | null | The first thing that comes to my mind when I read of the approach that you describe is that there is a miss-match between the idea of replicating an experiment and the use of "success" and "failure" as descriptors of the outcomes. Presumably a success would be a result that is significant in the Neyman-Pearson paradigm... | null | CC BY-SA 3.0 | null | 2011-04-20T03:18:45.590 | 2011-04-20T03:18:45.590 | null | null | 1679 | null |
9770 | 2 | null | 9766 | 3 | null | It seems for me that the question is ill-posted if there is no additional context about $X$ distribution or at least the family of distribution it belongs to (Student $t$, [Pareto](http://en.wikipedia.org/wiki/Pareto_distribution), [Cauchy](http://en.wikipedia.org/wiki/Cauchy_distribution)). For instance for normal dis... | null | CC BY-SA 3.0 | null | 2011-04-20T07:14:14.310 | 2011-04-20T07:14:14.310 | null | null | 2645 | null |
9771 | 2 | null | 9766 | 5 | null | If you have the probability density function $f$ of the random variable, then it is a matter of checking for which $n$ the integral
$$\int_{\mathbb{R}}x^nf(x)dx<\infty$$
This is then the standard exercise in real analysis. Alternatively if you know the [characteristic function](http://en.wikipedia.org/wiki/Characteris... | null | CC BY-SA 4.0 | null | 2011-04-20T08:13:14.423 | 2019-08-20T15:14:52.237 | 2019-08-20T15:14:52.237 | 95370 | 2116 | null |
9772 | 2 | null | 9767 | 2 | null | This is one way to do it:
```
v = rnorm(1, 30, 10)
for (i in 2:30) v = c(v, 0.5*v[i-1] + 0.5*rnorm(1, 30, 10))
round(v)
```
| null | CC BY-SA 3.0 | null | 2011-04-20T08:51:38.153 | 2011-04-20T08:51:38.153 | null | null | 3911 | null |
9774 | 1 | null | null | 6 | 258 | For many years I have been conducting t-tests on response to mailing activity. Recently I was challenged that we should infact be conducting tests on profit rather than response.
So, let me put this in context. If you have two groups of customers of sample size 10,000 each that you were mailing two different offers. On... | Given two responses for two groups, how to decide what to test on response or profit? | CC BY-SA 3.0 | null | 2011-04-20T10:01:59.467 | 2011-04-21T00:40:09.410 | 2011-04-20T16:40:37.660 | 919 | null | [
"t-test",
"decision-theory"
] |
9775 | 1 | 9780 | null | 1 | 3971 | Given a data-frame:
```
d1 <-c("A","B","C","A")
d2 <-c("A","V","C","F")
d3 <-c("B","V","E","F")
d4 <-c("A","B","C","A")
data.frame(d1,d2,d3,d4)
d1 d2 d3 d4
1 A A D A
2 B V B B
3 C C C C
4 A F A A
```
Also given that each row may have a unique pattern such that the occurrence of the values A,D,A (firs... | Manipulating and searching data-frames | CC BY-SA 3.0 | null | 2011-04-20T12:02:33.307 | 2011-04-20T12:55:50.920 | 2011-04-20T12:13:27.853 | 2116 | 18462 | [
"r"
] |
9777 | 2 | null | 9738 | 2 | null | Well, you are just exhausting RAM on your machine. Generally, you have four options:
- Fetch a bigger computer (rather a bad idea, since it is rather impossible to push more than few hundred GB in one node).
- Limit your problem.
- Look for HPC version of multinomial logit, probably outside R -- using sparse matrice... | null | CC BY-SA 3.0 | null | 2011-04-20T12:43:40.830 | 2011-04-20T12:43:40.830 | null | null | null | null |
9778 | 1 | null | null | 10 | 577 | Most clustering algorithms I've seen start with creating a each-to-each distances among all points, which becomes problematic on larger datasets. Is there one that doesn't do it? Or does it in some sort of partial/approximate/staggered approach?
Which clustering algorithm/implemention takes less than O(n^2) space?
Is ... | Space-efficient clustering | CC BY-SA 3.0 | null | 2011-04-20T12:44:27.060 | 2012-07-15T09:33:33.173 | null | null | 595 | [
"clustering",
"algorithms",
"large-data"
] |
9779 | 1 | 9819 | null | 1 | 6559 | I asked a question on StackOverflow for which I was suggested to use Kalman Filter. The question is as follows:
[https://stackoverflow.com/questions/5726358/what-class-of-algorithms-reduce-margin-of-error-in-continuous-stream-of-input/5728373#5728373](https://stackoverflow.com/questions/5726358/what-class-of-algorithms... | How to apply Kalman filter to one dimensional data? | CC BY-SA 3.0 | null | 2011-04-20T12:54:43.323 | 2011-04-21T09:39:02.513 | 2017-05-23T12:39:26.523 | -1 | 4251 | [
"kalman-filter"
] |
9780 | 2 | null | 9775 | 3 | null | Suppose the entries to data.frame contain single uppercase letters. Suppose that we have a vector containing the patterns and that only one pattern can be in one row.
```
d1 <-c("A","B","C","A")
d2 <-c("A","V","C","F")
d3 <-c("B","V","E","F")
d4 <-c("A","B","C","A")
dd <- data.frame(d1,d2,d3,d4)
> dd
d1 d2 d3 d4
1 ... | null | CC BY-SA 3.0 | null | 2011-04-20T12:55:50.920 | 2011-04-20T12:55:50.920 | null | null | 2116 | null |
9781 | 2 | null | 9573 | 60 | null | The central limit theorem is less useful than one might think in this context. First, as someone pointed out already, one does not know if the current sample size is "large enough". Secondly, the CLT is more about achieving the desired type I error than about type II error. In other words, the t-test can be uncompet... | null | CC BY-SA 3.0 | null | 2011-04-20T12:59:07.080 | 2011-04-20T12:59:07.080 | null | null | 4253 | null |
9782 | 2 | null | 9741 | 12 | null | A good measure is Somers' Dxy rank correlation, a generalization of ROC area for ordinal or continuous Y. It is computed for ordinal proportional odds regression in the lrm function in the rms package.
| null | CC BY-SA 3.0 | null | 2011-04-20T13:03:50.337 | 2011-04-20T13:03:50.337 | null | null | 4253 | null |
9783 | 2 | null | 9767 | 6 | null | A standard way of generating overdispersed count data is to generate data from a Poisson distribution with a random mean: $Y_i\sim Poisson(\lambda_i)$, $\lambda_i \sim F$. For example, if $\lambda_i$ has a Gamma distribution, you will get the negative binomial distribution for $Y$.
You can easily impose serial correla... | null | CC BY-SA 3.0 | null | 2011-04-20T13:03:57.170 | 2011-04-20T13:03:57.170 | null | null | 279 | null |
9784 | 2 | null | 9778 | 5 | null | K-Means and Mean-Shift use the raw sample descriptors (no need to pre-compute an affinity matrix).
Otherwise, for spectral clustering or power iteration clustering, you can use a sparse matrix representation (e.g. Compressed Sparse Rows) of the k-nearest-neighbours affinity matrix (for some distance or affinity metric)... | null | CC BY-SA 3.0 | null | 2011-04-20T13:38:13.817 | 2011-04-20T13:38:13.817 | null | null | 2150 | null |
9785 | 1 | null | null | 8 | 6497 | Rob Tibshirani propose to use lasso with Cox
regression for variable selection in his 1997 paper
"The lasso method for variable selection in the Cox
model" published in Statistics In Medicine 16:385.
Does anyone know of any R package/function or syntax in R that
does lasso with a Cox model?
| Cox model with LASSO | CC BY-SA 3.0 | null | 2011-04-20T13:46:29.630 | 2022-02-02T13:46:05.007 | 2022-02-02T13:46:05.007 | 53690 | null | [
"r",
"regression",
"survival",
"lasso",
"cox-model"
] |
9786 | 1 | null | null | 1 | 132 | I want to test the hypothesis of a decreased level of vitamin D in diabetic subjects. For this I have recorded blood glucose and vitamin D levels in 40 cases and 40 controls.
What kind of statistical test can I use to the above hypothesis?
| How to compare vitamin D and glucose levels between patients and controls? | CC BY-SA 3.0 | null | 2011-04-20T13:47:25.053 | 2011-04-20T15:28:56.520 | 2011-04-20T13:52:18.920 | 930 | null | [
"hypothesis-testing"
] |
9787 | 2 | null | 9785 | 9 | null | Here are two suggestions. First, you can take a look at the [glmnet](http://cran.r-project.org/web/packages/glmnet/index.html) package, from Friedman, Hastie and Tibshirani, but see their JSS 2010 (33) paper, [Regularization Paths for Generalized Linear Models via Coordinate Descent](http://www.jstatsoft.org/v33/i01/pa... | null | CC BY-SA 3.0 | null | 2011-04-20T14:01:49.567 | 2011-04-20T14:01:49.567 | null | null | 930 | null |
9788 | 2 | null | 9718 | 2 | null | I don't doubt a particular test statistic aiming to accomplish what your asking for exists, but I will offer some alternatives that you may be interested in that offer different answers (but probably still interesting) given the nature of the question.
Like Rolando already stated, the extent to which the partial correl... | null | CC BY-SA 3.0 | null | 2011-04-20T14:01:56.807 | 2011-04-20T14:01:56.807 | null | null | 1036 | null |
9789 | 2 | null | 9774 | 5 | null | The reason why you are conducting this test is to determine which policy is more valuable, and if value is measured in profitability, then it makes no sense to do statistical testing on any other variable. A properly conducted test on profitability gives you all the information needed for your companies' decision: onc... | null | CC BY-SA 3.0 | null | 2011-04-20T14:05:00.977 | 2011-04-21T00:40:09.410 | 2011-04-21T00:40:09.410 | 3567 | 3567 | null |
9790 | 2 | null | 7959 | 3 | null | I'd look at quantile regression. You can use it to determine a parametric estimate of whichever quantiles you want to look at. It make no assumption regarding normality, so it handles heteroskedasticity pretty well and can be used one a rolling window basis. It's basically an L1-Norm penalized regression, so it's not t... | null | CC BY-SA 3.0 | null | 2011-04-20T14:39:35.683 | 2011-04-20T19:09:57.173 | 2011-04-20T19:09:57.173 | 3737 | 3737 | null |
9791 | 2 | null | 9779 | 0 | null | I believe that for the Kalman Filter, you'll need to clarify your "can be off by 2 points" into something like, "error is normal with mean 0 and standard deviation of 0.8". Also, I believe that the usual statement of the Kalman Filter assumes you have a model that would predict how the actual value changes over time. (... | null | CC BY-SA 3.0 | null | 2011-04-20T15:00:21.363 | 2011-04-20T15:00:21.363 | null | null | 1764 | null |
9792 | 2 | null | 9786 | 2 | null | Generally this sound like a simple [t-test](http://en.wikipedia.org/wiki/T-test). That is, you have two groups (diabetics and controls) and you measured 1 variable (Vitamin D).
However, some more context/information about your data will lead to a lot better answers. For example, please answer chl's comment.
Second, wha... | null | CC BY-SA 3.0 | null | 2011-04-20T15:28:56.520 | 2011-04-20T15:28:56.520 | null | null | 442 | null |
9793 | 2 | null | 9744 | 5 | null | First of all it could be useful to read a bit about the [unit root](http://en.wikipedia.org/wiki/Unit_root) problem (you may start from the hypothesis section). So the nature of the explosiveness (exponential growth) is what matters. Roughly the growth could be explained either by deterministic part (for example linear... | null | CC BY-SA 3.0 | null | 2011-04-20T15:31:31.433 | 2011-04-20T15:31:31.433 | null | null | 2645 | null |
9794 | 1 | 9816 | null | 7 | 313 | Let's say I have two time series, one of which updates more frequently than the other:
$x_0,x_1,x_2,\dots,x_t,\dots$
$y_0,y_{10},y_{20},\dots,y_{10t},\dots$
I want to fit a model to this that predicts $y$ from $x$ (and possibly from previous values of $y$) at each of the values $1,2,3,\dots$, i.e. it gives a prediction... | Time series factor model with one series more frequent | CC BY-SA 3.0 | null | 2011-04-20T15:41:51.503 | 2019-07-23T19:18:54.480 | 2019-07-23T19:18:54.480 | 11887 | 2425 | [
"regression",
"time-series",
"predictive-models",
"unevenly-spaced-time-series"
] |
9795 | 1 | null | null | 1 | 3118 | >
Possible Duplicate:
Supervised learning with “rare” events, when rarity is due to the large number of counter-factual events
I am trying to predict diabetes using the [BRFSS dataset](http://www.cdc.gov/brfss/) by using a supervised learning classification model. But I see that the target variable which is having ... | How to handle skewed binary target variables? | CC BY-SA 3.0 | null | 2011-04-20T16:16:38.850 | 2012-01-20T01:38:40.197 | 2017-04-13T12:44:33.310 | -1 | 3897 | [
"machine-learning",
"sampling",
"unbalanced-classes"
] |
9796 | 2 | null | 9794 | 4 | null | I would cast the model in state-space form. Then there is no problem if one of the variables is observed more frequently than the other, or the observation times are irregular: the Kalman filter deals with missing and partially observed variables gracefully.
Without details on the exact kind of relationships you aim to... | null | CC BY-SA 3.0 | null | 2011-04-20T16:56:56.410 | 2011-04-20T16:56:56.410 | null | null | 892 | null |
9797 | 1 | 24654 | null | 8 | 521 | I have the following data, representing the binary state of four subjects at four times, note that it is only possible for each subject to transition $0\to 1$ but not $1\to 0$:
```
testdata <- data.frame(id = c(1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4),
day = c(1,1,1,1,8,8,8,8,16,16,16,16,24,24,24... | How can I estimate the time at which 50% of a binomial variable will have transitioned? | CC BY-SA 3.0 | null | 2011-04-20T17:20:17.850 | 2012-03-14T22:31:10.687 | 2011-11-15T15:28:22.663 | 1381 | 1381 | [
"logistic",
"censoring",
"interval-censoring"
] |
9798 | 2 | null | 9759 | 18 | null | There are several distinctions between linear and nonlinear regression models, but the primary mathematical one is that linear models are linear in the parameters, whereas nonlinear models are nonlinear in the parameters. Pinheiro and Bates (2000, pp. 284-285), authors of the `nlme` R package, elegantly described the m... | null | CC BY-SA 3.0 | null | 2011-04-20T17:21:42.293 | 2011-04-20T17:21:42.293 | null | null | 3964 | null |
9799 | 2 | null | 9779 | 0 | null | I found a nice simple introductory example of a Kalman filter (coded in matlab) [here](http://www.mathworks.com/matlabcentral/fileexchange/5377-learning-the-kalman-filter). The example the author provides in this code is on one dimensional data. Hopefully this will at least give you a starting point for figuring out h... | null | CC BY-SA 3.0 | null | 2011-04-20T18:21:57.773 | 2011-04-20T18:21:57.773 | null | null | 1913 | null |
9800 | 2 | null | 9751 | 74 | null | Since multiple comparison tests are often called 'post tests', you'd think they logically follow the one-way ANOVA. In fact, this isn't so.
>
"An unfortunate common practice is to pursue multiple comparisons only when the hull hypothesis of homogeneity is rejected." (Hsu, page 177)
Will the results of post tests be ... | null | CC BY-SA 3.0 | null | 2011-04-20T18:35:24.807 | 2011-04-20T18:35:24.807 | null | null | 25 | null |
9801 | 1 | 9802 | null | 10 | 11550 | I'm trying to understand matrix notation, and working with vectors and matrices.
Right now I'd like to understand how the vector of coefficient estimates $\hat{\beta}$ in multiple regression is computed.
The basic equation seems to be
$$
\frac{d}{d\boldsymbol{\beta}} (\boldsymbol{y}-\boldsymbol{X\beta})'(\boldsymbo... | Analytical solution to linear-regression coefficient estimates | CC BY-SA 3.0 | null | 2011-04-20T18:39:30.667 | 2021-11-22T04:52:33.890 | 2011-04-29T00:54:40.567 | 3911 | 2091 | [
"regression"
] |
9802 | 2 | null | 9801 | 13 | null | We have
$\frac{d}{d\beta} (y - X \beta)' (y - X\beta) = -2 X' (y - X \beta)$.
It can be shown by writing the equation explicitly with components. For example, write $(\beta_{1}, \ldots, \beta_{p})'$ instead of $\beta$. Then take derivatives with respect to $\beta_{1}$, $\beta_{2}$, ..., $\beta_{p}$ and stack everything... | null | CC BY-SA 3.0 | null | 2011-04-20T19:04:57.233 | 2011-04-21T14:16:35.683 | 2011-04-21T14:16:35.683 | 2129 | 3019 | null |
9803 | 2 | null | 9797 | 0 | null | We know that the $t_1$ transition time (from state 0 to state 1) of subject `id=1` was between two boundaries: $24<t_1<32$. An approximation is to assume that $t_1$ may have taken values within this range with uniform probability. Resampling the $t_i$ values we can get an approximate distribution of $\text{median}(t_i)... | null | CC BY-SA 3.0 | null | 2011-04-20T19:17:19.013 | 2011-04-20T23:40:21.120 | 2011-04-20T23:40:21.120 | 3911 | 3911 | null |
9805 | 2 | null | 8511 | 6 | null | if deviance were proportional to log likelihood, and one uses the definition (see for example McFadden's [here](http://www.ats.ucla.edu/stat/mult_pkg/faq/general/psuedo_rsquareds.htm))
```
pseudo R^2 = 1 - L(model) / L(intercept)
```
then the pseudo-$R^2$ above would be $1 - \frac{198.63}{958.66}$ = 0.7928
The questio... | null | CC BY-SA 3.0 | null | 2011-04-20T20:08:26.750 | 2017-12-02T22:32:21.847 | 2017-12-02T22:32:21.847 | 128677 | 2849 | null |
9806 | 2 | null | 9797 | -1 | null | Assuming that you will have more data of the same structure you will be able to use the [actuarial (life table) method](http://en.wikipedia.org/wiki/Life_table) to estimate median survival.
| null | CC BY-SA 3.0 | null | 2011-04-20T21:47:11.837 | 2011-04-20T21:56:31.867 | 2011-04-20T21:56:31.867 | 919 | 3911 | null |
9807 | 1 | null | null | 1 | 30439 | I performed a survey using a Likert 1 to 5 scale (totally agree/agree/neutral/ disagree/totally disagree) on 12 questions which are split into 3 statements which the respondent places a value of between 1 to 5 dependent on how much they agree or disagree - there are 36 statements in total.
Respondents:
```
Group 1 ... | Working with Likert scales in SPSS | CC BY-SA 3.0 | 0 | 2011-04-20T22:57:58.130 | 2016-10-25T01:51:39.020 | 2011-04-22T08:01:01.770 | 183 | 4262 | [
"spss",
"likert"
] |
9808 | 2 | null | 8511 | 61 | null | Don't forget the [rms](http://cran.r-project.org/web/packages/rms/index.html) package, by Frank Harrell. You'll find everything you need for fitting and validating GLMs.
Here is a toy example (with only one predictor):
```
set.seed(101)
n <- 200
x <- rnorm(n)
a <- 1
b <- -2
p <- exp(a+b*x)/(1+exp(a+b*x))
y <- factor(i... | null | CC BY-SA 3.0 | null | 2011-04-20T23:21:07.663 | 2011-04-20T23:27:35.910 | 2011-04-20T23:27:35.910 | 930 | 930 | null |
9809 | 1 | 9890 | null | 5 | 2002 | Does anyone know of a good resource listing known tricks (with examples?) for calculating closed form expressions from messy expectations? (e.g., moment generating function, law of iterated expectations, change of measure, etc.)
In a different setting, I've found [Summary of Rules for Identifying ARIMA Models](http://... | A list of tricks for calculating expectations? | CC BY-SA 3.0 | null | 2011-04-20T23:35:57.413 | 2018-10-28T13:26:39.690 | 2018-10-28T13:26:39.690 | 11887 | 3577 | [
"references",
"expected-value",
"moment-generating-function"
] |
9810 | 1 | null | null | 1 | 1156 | I want to compare if three groups are different by a non-parametric test. Now..., the problem is that two groups are paired (prior to treatment and one-year after treatment), and the (reference) group is other healthy individuals.
Is there a simple solution? I am using R,
| Which non-parametric test for difference between three groups, of which two are paired? | CC BY-SA 3.0 | null | 2011-04-20T23:49:40.123 | 2011-06-17T13:49:51.740 | null | null | 4229 | [
"multiple-comparisons",
"nonparametric"
] |
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