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ght)
\end{aligned}$$
(a Beta function, arising e.g. from normalizing a Beta prime distribution (https://en.wikipedia.org/wiki/Beta_prime_distribution#Generalization)).
Applying this in the case $\alpha = \nu + n$ and $k = k_n$ says
$$\begin{aligned}
2\int_ | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://en.wikipedia.org/wiki/Beta_prime_distribution#Generalization | external_url | 676,851 | https://stats.stackexchange.com/a/676851 | citations | Scientific-Citation-Graph:56ca72be4ec598dcbd102e90 | train | {
"accepted_answer_id": null,
"answers": [
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"answer_html": "<p><strong>I offer an elementary solution.</strong> It requires one substitution in an ordinary integral which, when repeated for each variable, yields a product of Beta function values.</p>\n<p>There are other solutions that might provide simp... |
ntercept in your case) instead. More general random-effects models (e.g., with the R coxme package (https://cran.r-project.org/package=coxme)) would allow modeling of nested correlation structures (e.g., individuals within working groups).
| CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://cran.r-project.org/package=coxme | external_url | 676,835 | https://stats.stackexchange.com/a/676835 | citations | Scientific-Citation-Graph:65433d2cde6d96c4609de978 | train | {
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"answer_html": "<blockquote>\n<p>I am considering using Kaplan–Meier curves and the log-rank test to compare the time-to-next-PR distributions between abandoned and non-abandoned focal PRs.</p>\n</blockquote>\n<p>That could be a start, but it won't allow you to... |
ested correlation structures (e.g., individuals within working groups).
The R survival package (https://cran.r-project.org/package=survival) has many vignettes illustrating aspects of survival modeling.
*The Kaplan-Meier survival es | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://cran.r-project.org/package=survival | external_url | 676,835 | https://stats.stackexchange.com/a/676835 | citations | Scientific-Citation-Graph:6cbbe4d28c395b9cb450cf53 | train | {
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"answer_html": "<blockquote>\n<p>I am considering using Kaplan–Meier curves and the log-rank test to compare the time-to-next-PR distributions between abandoned and non-abandoned focal PRs.</p>\n</blockquote>\n<p>That could be a start, but it won't allow you to... |
urvival model be more appropriate?
Yes.
The title of Chapter 8 of Therneau and Grambsch (https://www.springer.com/us/book/9780387987842) is "Multiple Events per Subject." Quoting:
A major issue in extending proportional hazards | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://www.springer.com/us/book/9780387987842 | external_url | 676,835 | https://stats.stackexchange.com/a/676835 | citations | Scientific-Citation-Graph:453b5ea997c363b1fcfe624b | train | {
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{
"answer_html": "<blockquote>\n<p>I am considering using Kaplan–Meier curves and the log-rank test to compare the time-to-next-PR distributions between abandoned and non-abandoned focal PRs.</p>\n</blockquote>\n<p>That could be a start, but it won't allow you to... |
, does $-2\log\lambda$ has a general asymptotic distribution in such case?
[image: ; source: https://i.sstatic.net/cWv54DMg.png] (https://i.sstatic.net/cWv54DMg.png)
# R code for simulation
nsim=1000 # number of simulation | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 676,842 | https://stats.stackexchange.com/questions/676842/the-asymptotic-distribution-of-likelihood-ratio-statistic-when-the-null-hypothes | citations | Scientific-Citation-Graph:78d9c973fd18190708db1051 | train | {
"accepted_answer_id": 676849,
"answers": [
{
"answer_html": "<p>It is still true that the asymptotic null distribution of the LRT is the null distribution of a local Gaussian shift alternative. In your <span class=\"math-container\">$N(\\mu,\\sigma^2)$</span> example that means the asymptotic null dist... | |
^2_1$s. The red line shows this CDF on your plot.
[image: enter image description here; source: https://i.sstatic.net/Qsmelzjn.png] (https://i.sstatic.net/Qsmelzjn.png)
For the more general case you have a limiting bivariate N | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 676,849 | https://stats.stackexchange.com/a/676849 | citations | Scientific-Citation-Graph:f5fd0d7ebd2f6fa40e5746dd | train | {
"accepted_answer_id": 676849,
"answers": [
{
"answer_html": "<p>It is still true that the asymptotic null distribution of the LRT is the null distribution of a local Gaussian shift alternative. In your <span class=\"math-container\">$N(\\mu,\\sigma^2)$</span> example that means the asymptotic null dist... | |
bull shape) as a function of covariates, but it's possible for example with the R flexsurv package (https://cran.r-project.org/package=flexsurv).
To summarize:
There can be interest in whether (and in what direction) the Weibull shape | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://cran.r-project.org/package=flexsurv | external_url | 676,861 | https://stats.stackexchange.com/a/676861 | citations | Scientific-Citation-Graph:932e5001b4696abd6c4ba91e | train | {
"accepted_answer_id": 676856,
"answers": [
{
"answer_html": "<p>The scale parameter is routinely analysed in survival models, because the scale parameter is a location parameter for <span class=\"math-container\">$\\log T$</span>. Accelerated-failure models are of the form\n<span class=\"math-container... |
ion (https://stats.stackexchange.com/a/468333/28500). Yours is the first optional parameterization (https://en.wikipedia.org/wiki/Weibull_distribution#First_option) in Wikipedia. The Rodríguez notes linked above use a form similar to Wikipedia's "Standard paramet | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://en.wikipedia.org/wiki/Weibull_distribution#First_option | external_url | 676,861 | https://stats.stackexchange.com/a/676861 | citations | Scientific-Citation-Graph:8be87220e13ecc497a4d7785 | train | {
"accepted_answer_id": 676856,
"answers": [
{
"answer_html": "<p>The scale parameter is routinely analysed in survival models, because the scale parameter is a location parameter for <span class=\"math-container\">$\\log T$</span>. Accelerated-failure models are of the form\n<span class=\"math-container... |
a. The Rodríguez notes linked above use a form similar to Wikipedia's "Standard parameterization," (https://en.wikipedia.org/wiki/Weibull_distribution#Standard_parameterization) but with a "$\lambda$" the inverse of Wikipedia's choice of "scale parameter" $\lambda$. The "$\la | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://en.wikipedia.org/wiki/Weibull_distribution#Standard_parameterization | external_url | 676,861 | https://stats.stackexchange.com/a/676861 | citations | Scientific-Citation-Graph:ce1746f1e9497680afa3d5b3 | train | {
"accepted_answer_id": 676856,
"answers": [
{
"answer_html": "<p>The scale parameter is routinely analysed in survival models, because the scale parameter is a location parameter for <span class=\"math-container\">$\\log T$</span>. Accelerated-failure models are of the form\n<span class=\"math-container... |
To see more about the basis for Thomas Lumley's answer, look at these course notes (https://grodri.github.io/survival/ParametricSurvival.pdf) by Germán Rodríguez. They start with the general form for accelerated-failure-time (AFT) models* i | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://grodri.github.io/survival/ParametricSurvival.pdf | external_url | 676,861 | https://stats.stackexchange.com/a/676861 | citations | Scientific-Citation-Graph:b186a17c60b081391e3d9a7f | train | {
"accepted_answer_id": 676856,
"answers": [
{
"answer_html": "<p>The scale parameter is routinely analysed in survival models, because the scale parameter is a location parameter for <span class=\"math-container\">$\\log T$</span>. Accelerated-failure models are of the form\n<span class=\"math-container... |
el switching. This is the point we make in the 2000 JASA paper with Gilles Celeux and Merilee Hurn (https://www.jstor.org/stable/2669477?searchText=celeux%20hurn%20robert&searchUri=%2Faction%2FdoBasicSearch%3FQuery%3Dceleux%2Bhurn%2Brobert%26so%3Drel%26efqs%3DeyJjdHkiOlsiYW05MWNtNWhiQT09Il19&ab_segments=0%2Fbasic_searc... | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://www.jstor.org/stable/2669477?searchText=celeux%20hurn%20robert&searchUri=%2Faction%2FdoBasicSearch%3FQuery%3Dceleux%2Bhurn%2Brobert%26so%3Drel%26efqs%3DeyJjdHkiOlsiYW05MWNtNWhiQT09Il19&ab_segments=0%2Fbasic_search_gsv2%2Fcontrol&refreqid=fastly-default%3A0dc4861d75073d6e1d8fc9756aa699a8 | external_url | 676,884 | https://stats.stackexchange.com/a/676884 | citations | Scientific-Citation-Graph:c8ac22206bff5eba8a86760d | train | {
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{
"answer_html": "<p>If you run a plain Gibbs on a classic mixture model, you face the paradox that the Markov chain is uniformly ergodic (because the latent space is finite and hence compact) AND usually fails to switch between modes, i.e. does not exhibit label... |
à la Chopin
These perspectives are discussed in our edited book
Handbook of Mixture Analysis (https://www.routledge.com/Handbook-of-Mixture-Analysis/Fruhwirth-Schnatter-Celeux-Robert/p/book/9780367732066) | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://www.routledge.com/Handbook-of-Mixture-Analysis/Fruhwirth-Schnatter-Celeux-Robert/p/book/9780367732066 | external_url | 676,884 | https://stats.stackexchange.com/a/676884 | citations | Scientific-Citation-Graph:590e8d13c2d5623a96517330 | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>If you run a plain Gibbs on a classic mixture model, you face the paradox that the Markov chain is uniformly ergodic (because the latent space is finite and hence compact) AND usually fails to switch between modes, i.e. does not exhibit label... |
ction) function, which does not calculate an adjusted $\text{R}^2$, and using the Analysis ToolPak (https://support.microsoft.com/en-US/Excel/use-the-analysis-toolpak-to-perform-complex-data-analysis).
Documentation for the latter is extremely sparse and only mentions that it uses LINEST intern | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://support.microsoft.com/en-US/Excel/use-the-analysis-toolpak-to-perform-complex-data-analysis | external_url | 676,903 | https://stats.stackexchange.com/a/676903 | citations | Scientific-Citation-Graph:c7afcf9e6d2c34dba49e95fe | train | {
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"answer_html": "<p>There's an aphorism that "friends don't let friends use Excel for statistics". I know of two built-in ways to conduct linear regression in Excel: using the <a href=\"https://support.microsoft.com/en-us/excel/functions/linest-functio... |
r statistics". I know of two built-in ways to conduct linear regression in Excel: using the LINEST (https://support.microsoft.com/en-us/excel/functions/linest-function) function, which does not calculate an adjusted $\text{R}^2$, and using the Analysis ToolPak (https | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://support.microsoft.com/en-us/excel/functions/linest-function | external_url | 676,903 | https://stats.stackexchange.com/a/676903 | citations | Scientific-Citation-Graph:d758cf98567a27024e255c77 | train | {
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"answer_html": "<p>There's an aphorism that "friends don't let friends use Excel for statistics". I know of two built-in ways to conduct linear regression in Excel: using the <a href=\"https://support.microsoft.com/en-us/excel/functions/linest-functio... |
what formula is actually being used to adjust $\text{R}^2$.
Using some random examples from R (https://www.r-project.org/) -- which I would let friends use for statistics -- we can show that the Analysis ToolPak doesn't m | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://www.r-project.org/ | external_url | 676,903 | https://stats.stackexchange.com/a/676903 | citations | Scientific-Citation-Graph:2d54ce1556cc20b600f17089 | train | {
"accepted_answer_id": null,
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"answer_html": "<p>There's an aphorism that "friends don't let friends use Excel for statistics". I know of two built-in ways to conduct linear regression in Excel: using the <a href=\"https://support.microsoft.com/en-us/excel/functions/linest-functio... |
actually performed on the HeLa line were claimed to have been on other cell lines. See this paper (https://doi.org/10.1371/journal.pone.0186281) by Horback and Halffman. Even if all of statistical analyses in those studies were perfect, the in | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | 10.1371/journal.pone.0186281 | https://doi.org/10.1371/journal.pone.0186281 | doi_url | 676,926 | https://stats.stackexchange.com/a/676926 | citations | Scientific-Citation-Graph:630f104d5643afdf4bc89cd6 | train | {
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"answer_html": "<blockquote>\n<p>but that control has already been shown to have reduced growth compared to another genotype where that process is disrupted</p>\n</blockquote>\n<p>A control can have a different baseline than the treated group. But you need to m... |
reading the proof of the bias-variance decomposition formula on Wikipedia (Bias-variance tradeoff (https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff)), and it appears to me that the derivation there is uselessly complicated. I think I can derive it | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff | external_url | 676,933 | https://stats.stackexchange.com/questions/676933/a-possibly-short-proof-of-the-bias-variance-decomposition-formula | citations | Scientific-Citation-Graph:2a4c79ec8eccb6a4caeed0f0 | train | {
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"answer_html": "<h3>TL:DR - this has sort of been asked before, but this deserves a separate answer</h3>\n<p>I think that you'll find that there are several ways to derive the relationship, and yours certainly gels with others (for example <a href=\"https://s... |
tra credit, try following the in-sample / out-of-sample discussion in Chapter 12 of Efron & Hastie (https://hastie.su.domains/CASI/)... | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://hastie.su.domains/CASI/ | external_url | 676,944 | https://stats.stackexchange.com/a/676944 | citations | Scientific-Citation-Graph:c45314c206a9362b8a9c9ef8 | train | {
"accepted_answer_id": 676944,
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"answer_html": "<h3>TL:DR - this has sort of been asked before, but this deserves a separate answer</h3>\n<p>I think that you'll find that there are several ways to derive the relationship, and yours certainly gels with others (for example <a href=\"https://s... |
e and out of sample error. As Matthew's answer nicely points out, Hastie, Tibshirani, and Friedman (https://hastie.su.domains/ElemStatLearn/) make this point explicit in the beginning of Chapter 7 of ESL:
Discussions of error rate es | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://hastie.su.domains/ElemStatLearn/ | external_url | 676,944 | https://stats.stackexchange.com/a/676944 | citations | Scientific-Citation-Graph:e3bdfae73dc5566d5ce1f97b | train | {
"accepted_answer_id": 676944,
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{
"answer_html": "<h3>TL:DR - this has sort of been asked before, but this deserves a separate answer</h3>\n<p>I think that you'll find that there are several ways to derive the relationship, and yours certainly gels with others (for example <a href=\"https://s... |
UPDATE (2/2):
Per @EdM 's comment, I used the lavaan approach from section 1.3.2 of this book (https://stefvanbuuren.name/fimd/sec-simplesolutions.html) and the bias does go away, but compared to just doing a standard multiple regression with only the | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://stefvanbuuren.name/fimd/sec-simplesolutions.html | external_url | 676,935 | https://stats.stackexchange.com/questions/676935/missing-data-break-multiple-regression-into-stages-how | citations | Scientific-Citation-Graph:259d2749df94f5230ff0ffc0 | train | {
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"answer_html": "<blockquote>\n<p>is trying to use all the data a flawed concept to begin with?</p>\n</blockquote>\n<p>No. On the contrary, limiting analysis only to complete-data cases is typically flawed. An exception is if the data are "missing completel... |
d describes the associated functions in his rms (https://cran.r-project.org/package=rms) and Hmisc (https://cran.r-project.org/package=Hmisc) packages.
Does the validity depend on the pattern of missing data?
Yes, in two ways. | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://cran.r-project.org/package=Hmisc | external_url | 676,936 | https://stats.stackexchange.com/a/676936 | citations | Scientific-Citation-Graph:f837844d292b8e5fe9a58488 | train | {
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"answer_html": "<blockquote>\n<p>is trying to use all the data a flawed concept to begin with?</p>\n</blockquote>\n<p>No. On the contrary, limiting analysis only to complete-data cases is typically flawed. An exception is if the data are "missing completel... |
en.name/fimd/) is a comprehensive guide to the process, which is implemented in his R mice package (https://cran.r-project.org/package=mice). Chapter 3 of Regression Modeling Strategies (https://hbiostat.org/rmsc/missing) by Frank Harrell | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://cran.r-project.org/package=mice | external_url | 676,936 | https://stats.stackexchange.com/a/676936 | citations | Scientific-Citation-Graph:bb23352b3020b9b97f90d63b | train | {
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"answer_html": "<blockquote>\n<p>is trying to use all the data a flawed concept to begin with?</p>\n</blockquote>\n<p>No. On the contrary, limiting analysis only to complete-data cases is typically flawed. An exception is if the data are "missing completel... |
k Harrell also covers how to handle missing data and describes the associated functions in his rms (https://cran.r-project.org/package=rms) and Hmisc (https://cran.r-project.org/package=Hmisc) packages.
Does the validity depend on | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://cran.r-project.org/package=rms | external_url | 676,936 | https://stats.stackexchange.com/a/676936 | citations | Scientific-Citation-Graph:1037e15629a3754577b830c1 | train | {
"accepted_answer_id": null,
"answers": [
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"answer_html": "<blockquote>\n<p>is trying to use all the data a flawed concept to begin with?</p>\n</blockquote>\n<p>No. On the contrary, limiting analysis only to complete-data cases is typically flawed. An exception is if the data are "missing completel... |
ice package (https://cran.r-project.org/package=mice). Chapter 3 of Regression Modeling Strategies (https://hbiostat.org/rmsc/missing) by Frank Harrell also covers how to handle missing data and describes the associated functions in | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://hbiostat.org/rmsc/missing | external_url | 676,936 | https://stats.stackexchange.com/a/676936 | citations | Scientific-Citation-Graph:03b4d73082e810ef8ebee131 | train | {
"accepted_answer_id": null,
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"answer_html": "<blockquote>\n<p>is trying to use all the data a flawed concept to begin with?</p>\n</blockquote>\n<p>No. On the contrary, limiting analysis only to complete-data cases is typically flawed. An exception is if the data are "missing completel... |
lity of the data estimates into account.
Stef van Buuren's Flexible Imputation of Missing Data (https://stefvanbuuren.name/fimd/) is a comprehensive guide to the process, which is implemented in his R mice package (https://cran. | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://stefvanbuuren.name/fimd/ | external_url | 676,936 | https://stats.stackexchange.com/a/676936 | citations | Scientific-Citation-Graph:fafd43c5fdcdda1f8fdc8795 | train | {
"accepted_answer_id": null,
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"answer_html": "<blockquote>\n<p>is trying to use all the data a flawed concept to begin with?</p>\n</blockquote>\n<p>No. On the contrary, limiting analysis only to complete-data cases is typically flawed. An exception is if the data are "missing completel... |
ted outdoor activity to decrease after laying begins.
[image: enter image description here; source: https://i.sstatic.net/65O8XMLB.png] (https://i.sstatic.net/65O8XMLB.png)
In contrast, the temperature smooth changed much more: ED | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 676,947 | https://stats.stackexchange.com/questions/676947/how-should-i-handle-a-date-defined-egg-laying-phase-in-a-gam | citations | Scientific-Citation-Graph:0099c90adecd1808924e5306 | train | {
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"answer_html": "<p>With these data, I don't see a way to separate the associations of date and temperature with time outside from that of egg-laying phase. That phase, defined by date with whichever cutoff you use, is inextricably linked to date and to the chan... | |
rather than identifying a distinct biological effect.
[image: enter image description here; source: https://i.sstatic.net/OlWsvas1.png] (https://i.sstatic.net/OlWsvas1.png)
Should I:
include this date-defined phase alongside | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 676,947 | https://stats.stackexchange.com/questions/676947/how-should-i-handle-a-date-defined-egg-laying-phase-in-a-gam | citations | Scientific-Citation-Graph:760819ed0bcdea7acbbe2d2f | train | {
"accepted_answer_id": null,
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"answer_html": "<p>With these data, I don't see a way to separate the associations of date and temperature with time outside from that of egg-laying phase. That phase, defined by date with whichever cutoff you use, is inextricably linked to date and to the chan... | |
dard deviation of cyl rather than per cylinder group.
Created on 2026-08-25 with reprex v2.1.1 (https://reprex.tidyverse.org) | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://reprex.tidyverse.org | external_url | 676,959 | https://stats.stackexchange.com/a/676959 | citations | Scientific-Citation-Graph:824a6b183fda44c295ab8493 | train | {
"accepted_answer_id": null,
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"answer_html": "<p>In stepwise regression, scaling the response variable changes coefficient magnitudes but has no other meaningful effect. Here is an example in R:</p>\n<pre class=\"lang-r prettyprint-override\"><code>data(mtcars)\n\n# Fit the same model with ... |
nts of 1 cat across the campus (~2.5 months of data):
[image: Plot points generated from R; source: https://i.sstatic.net/I6TIxhWk.png] (https://i.sstatic.net/I6TIxhWk.png)
The issue here is that the estimate of home range seem to | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 676,997 | https://stats.stackexchange.com/questions/676997/asking-for-advice-on-cleaning-gps-data-from-ble-trackers | citations | Scientific-Citation-Graph:122f7c2aacd356adf929396b | train | {
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"answer_html": "<p>Welcome to CV.<br />\nThis will not be an answer to your direct questions, nor will it be a particularly "statistical" answer, but a series of remarks, too long to fit in comments, which I hope can help, and a final remark, which un... | |
Here is an example of how the data is collected:
[image: enter image description here; source: https://i.sstatic.net/Woh1NewX.png] (https://i.sstatic.net/Woh1NewX.png)
For finding home range of each cat, I am using R package | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 676,997 | https://stats.stackexchange.com/questions/676997/asking-for-advice-on-cleaning-gps-data-from-ble-trackers | citations | Scientific-Citation-Graph:41a58e86b716950f78d405a7 | train | {
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"answers": [
{
"answer_html": "<p>Welcome to CV.<br />\nThis will not be an answer to your direct questions, nor will it be a particularly "statistical" answer, but a series of remarks, too long to fit in comments, which I hope can help, and a final remark, which un... | |
Qb8.png)
Here is the summary of wAKDE and AKDE respectively:
[image: L: wAKDE, R: AKDE; source: https://i.sstatic.net/bZFkYz3U.png] (https://i.sstatic.net/bZFkYz3U.png)
Here is the plot points of 1 cat across the campus (~2.5 | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 676,997 | https://stats.stackexchange.com/questions/676997/asking-for-advice-on-cleaning-gps-data-from-ble-trackers | citations | Scientific-Citation-Graph:921b224b66cc8dc4522e1bfb | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>Welcome to CV.<br />\nThis will not be an answer to your direct questions, nor will it be a particularly "statistical" answer, but a series of remarks, too long to fit in comments, which I hope can help, and a final remark, which un... | |
ting home range plots of 1 cat:
[image: L: Home range from wAKDE, R: Home range from AKDE ; source: https://i.sstatic.net/kXgSIQb8.png] (https://i.sstatic.net/kXgSIQb8.png)
Here is the summary of wAKDE and AKDE respectively:
[imag | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 676,997 | https://stats.stackexchange.com/questions/676997/asking-for-advice-on-cleaning-gps-data-from-ble-trackers | citations | Scientific-Citation-Graph:91b0f021cdfd373b2132de6c | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>Welcome to CV.<br />\nThis will not be an answer to your direct questions, nor will it be a particularly "statistical" answer, but a series of remarks, too long to fit in comments, which I hope can help, and a final remark, which un... | |
so I used Google map to get a satellite view of your campus. See below:
[image: Topography; source: https://i.sstatic.net/TjqmodJj.jpg] (https://i.sstatic.net/TjqmodJj.jpg)
The area of greatest interest (to me at least) was the easter | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 677,001 | https://stats.stackexchange.com/a/677001 | citations | Scientific-Citation-Graph:d3ae4ad1e3181bff4647b9fb | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>Welcome to CV.<br />\nThis will not be an answer to your direct questions, nor will it be a particularly "statistical" answer, but a series of remarks, too long to fit in comments, which I hope can help, and a final remark, which un... | |
when one or both parameters are less than $1.$ See the Wikipedia article on the Beta distribution (https://en.wikipedia.org/wiki/Beta_distribution#Two_unknown_parameters_2) for suggestions on improving this.
#
# Negative log likelihood of the Beta distribution.
# `lg | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://en.wikipedia.org/wiki/Beta_distribution#Two_unknown_parameters_2 | external_url | 677,036 | https://stats.stackexchange.com/a/677036 | citations | Scientific-Citation-Graph:c63f07bf8f232fd76b1b45f0 | train | {
"accepted_answer_id": 677036,
"answers": [
{
"answer_html": "<p><strong>Because it can be difficult or impossible to analyze numerical solutions mathematically, one would naturally resort to simulation</strong>. This amounts to selecting parameter values of interest and repeatedly creating a random sam... |
es of size $30$ from a Beta$(2, 3)$ distribution.
[image: enter image description here; source: https://i.sstatic.net/tCnpe77y.png] (https://i.sstatic.net/tCnpe77y.png)
The leftmost panel plots all one thousand $(\hat\alpha,\h | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 677,036 | https://stats.stackexchange.com/a/677036 | citations | Scientific-Citation-Graph:651e4d0689b2671dd255f57b | train | {
"accepted_answer_id": 677036,
"answers": [
{
"answer_html": "<p><strong>Because it can be difficult or impossible to analyze numerical solutions mathematically, one would naturally resort to simulation</strong>. This amounts to selecting parameter values of interest and repeatedly creating a random sam... | |
revamped the example
from Whuber: F. Mosteller and J. Tukey, Data Analysis and Regression (https://archive.org/details/dataanalysisregr0000most)
from M--: P. Roback and J. Legler: Beyond Multiple Linear Regression (https://bookdown.org/rob | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://archive.org/details/dataanalysisregr0000most | external_url | 677,039 | https://stats.stackexchange.com/a/677039 | citations | Scientific-Citation-Graph:e97ee30cdcabefba2885f2d0 | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>The regression coefficients for variables not in the interaction are main effects, conditional on all the other variables, including the interaction.</p>\n<p>I don't know of a book that states this, but you can either a) Do the math and show ... |
dataanalysisregr0000most)
from M--: P. Roback and J. Legler: Beyond Multiple Linear Regression (https://bookdown.org/roback/bookdown-BeyondMLR/)
My picks:
Ordinary regression, and general issues with GLMS: Weisberg: Applied Linear Re | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://bookdown.org/roback/bookdown-BeyondMLR/ | external_url | 677,039 | https://stats.stackexchange.com/a/677039 | citations | Scientific-Citation-Graph:3ce934c85a21737827b2d395 | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>The regression coefficients for variables not in the interaction are main effects, conditional on all the other variables, including the interaction.</p>\n<p>I don't know of a book that states this, but you can either a) Do the math and show ... |
ssion-4th-edition-p-9781118386088); Fox: Applied Regression Analysis and Generalized Linear Models (https://collegepublishing.sagepub.com/products/applied-regression-analysis-and-generalized-linear-models-3-237254)
Specific to logistic regression: Hosmer and Lemeshow: Applied Logistic Regression (https://onl | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://collegepublishing.sagepub.com/products/applied-regression-analysis-and-generalized-linear-models-3-237254 | external_url | 677,039 | https://stats.stackexchange.com/a/677039 | citations | Scientific-Citation-Graph:aaa6d8775088b596041055da | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>The regression coefficients for variables not in the interaction are main effects, conditional on all the other variables, including the interaction.</p>\n<p>I don't know of a book that states this, but you can either a) Do the math and show ... |
ls-3-237254)
Specific to logistic regression: Hosmer and Lemeshow: Applied Logistic Regression (https://onlinelibrary.wiley.com/doi/book/10.1002/9781118548387)
In Fox's Chapter 7, he discusses the interpretation of coefficients in the presence of intera | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | 10.1002/9781118548387 | https://onlinelibrary.wiley.com/doi/book/10.1002/9781118548387 | doi_url | 677,039 | https://stats.stackexchange.com/a/677039 | citations | Scientific-Citation-Graph:4352d404a61b34d550ba9772 | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>The regression coefficients for variables not in the interaction are main effects, conditional on all the other variables, including the interaction.</p>\n<p>I don't know of a book that states this, but you can either a) Do the math and show ... |
icks:
Ordinary regression, and general issues with GLMS: Weisberg: Applied Linear Regression (https://www.wiley.com/en-br/shop/general-introductory-statistics/applied-linear-regression-4th-edition-p-9781118386088); Fox: Applied Regression Analysis and Generalized Linear Models (https://collegepublishing.sagepub | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://www.wiley.com/en-br/shop/general-introductory-statistics/applied-linear-regression-4th-edition-p-9781118386088 | external_url | 677,039 | https://stats.stackexchange.com/a/677039 | citations | Scientific-Citation-Graph:c0c75a5957cb8c490b51dc7b | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>The regression coefficients for variables not in the interaction are main effects, conditional on all the other variables, including the interaction.</p>\n<p>I don't know of a book that states this, but you can either a) Do the math and show ... |
(true mean, true variance), resulting in 8 plots:
[image: enter image description here; source: https://i.sstatic.net/A2PFO7A8.png] (https://i.sstatic.net/A2PFO7A8.png)
[image: enter image description here; source: https://i.sstat | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 677,041 | https://stats.stackexchange.com/questions/677041/determining-which-areas-of-a-likelihood-surface-are-harder-to-estimate | citations | Scientific-Citation-Graph:6d12ca5032eb696af7e274fc | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>The thing to look up to analyse things like this is the Delta method.\nSimply put it states that functions of asymptotically normal estimators are themselves asymptotically normal and gives you a method for computing the limiting variance.</p... | |
et/GP2Hj45Q.png] (https://i.sstatic.net/GP2Hj45Q.png)
[image: enter image description here; source: https://i.sstatic.net/FyuH512V.png] (https://i.sstatic.net/FyuH512V.png)
[image: enter image description here; source: https://i.sstat | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 677,041 | https://stats.stackexchange.com/questions/677041/determining-which-areas-of-a-likelihood-surface-are-harder-to-estimate | citations | Scientific-Citation-Graph:8c6bb808196e03f87de3825c | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>The thing to look up to analyse things like this is the Delta method.\nSimply put it states that functions of asymptotically normal estimators are themselves asymptotically normal and gives you a method for computing the limiting variance.</p... | |
et/iy79u8j8.png] (https://i.sstatic.net/iy79u8j8.png)
[image: enter image description here; source: https://i.sstatic.net/GP2Hj45Q.png] (https://i.sstatic.net/GP2Hj45Q.png)
[image: enter image description here; source: https://i.sstat | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 677,041 | https://stats.stackexchange.com/questions/677041/determining-which-areas-of-a-likelihood-surface-are-harder-to-estimate | citations | Scientific-Citation-Graph:e8834a015dcdc117a3b241ac | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>The thing to look up to analyse things like this is the Delta method.\nSimply put it states that functions of asymptotically normal estimators are themselves asymptotically normal and gives you a method for computing the limiting variance.</p... | |
et/Oe6sxe18.png] (https://i.sstatic.net/Oe6sxe18.png)
[image: enter image description here; source: https://i.sstatic.net/JED0Ev2C.png] (https://i.sstatic.net/JED0Ev2C.png)
[image: enter image description here; source: https://i.sstat | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 677,041 | https://stats.stackexchange.com/questions/677041/determining-which-areas-of-a-likelihood-surface-are-harder-to-estimate | citations | Scientific-Citation-Graph:e5404ffc07d6a433d5e87808 | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>The thing to look up to analyse things like this is the Delta method.\nSimply put it states that functions of asymptotically normal estimators are themselves asymptotically normal and gives you a method for computing the limiting variance.</p... | |
et/t7tu9Uyf.png] (https://i.sstatic.net/t7tu9Uyf.png)
[image: enter image description here; source: https://i.sstatic.net/Oe6sxe18.png] (https://i.sstatic.net/Oe6sxe18.png)
[image: enter image description here; source: https://i.sstat | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 677,041 | https://stats.stackexchange.com/questions/677041/determining-which-areas-of-a-likelihood-surface-are-harder-to-estimate | citations | Scientific-Citation-Graph:74fccbb19d97320b01888457 | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>The thing to look up to analyse things like this is the Delta method.\nSimply put it states that functions of asymptotically normal estimators are themselves asymptotically normal and gives you a method for computing the limiting variance.</p... | |
et/JED0Ev2C.png] (https://i.sstatic.net/JED0Ev2C.png)
[image: enter image description here; source: https://i.sstatic.net/fzJdboR6.png] (https://i.sstatic.net/fzJdboR6.png)
Some general findings:
In general, $n$ increase, est | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 677,041 | https://stats.stackexchange.com/questions/677041/determining-which-areas-of-a-likelihood-surface-are-harder-to-estimate | citations | Scientific-Citation-Graph:7a76230532fb2b4e11fc461c | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>The thing to look up to analyse things like this is the Delta method.\nSimply put it states that functions of asymptotically normal estimators are themselves asymptotically normal and gives you a method for computing the limiting variance.</p... | |
et/A2PFO7A8.png] (https://i.sstatic.net/A2PFO7A8.png)
[image: enter image description here; source: https://i.sstatic.net/iy79u8j8.png] (https://i.sstatic.net/iy79u8j8.png)
[image: enter image description here; source: https://i.sstat | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 677,041 | https://stats.stackexchange.com/questions/677041/determining-which-areas-of-a-likelihood-surface-are-harder-to-estimate | citations | Scientific-Citation-Graph:c83ffab88c276162d4e81e51 | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>The thing to look up to analyse things like this is the Delta method.\nSimply put it states that functions of asymptotically normal estimators are themselves asymptotically normal and gives you a method for computing the limiting variance.</p... | |
et/FyuH512V.png] (https://i.sstatic.net/FyuH512V.png)
[image: enter image description here; source: https://i.sstatic.net/t7tu9Uyf.png] (https://i.sstatic.net/t7tu9Uyf.png)
[image: enter image description here; source: https://i.sstat | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 677,041 | https://stats.stackexchange.com/questions/677041/determining-which-areas-of-a-likelihood-surface-are-harder-to-estimate | citations | Scientific-Citation-Graph:e10b506c1c5a99454b297e6b | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>The thing to look up to analyse things like this is the Delta method.\nSimply put it states that functions of asymptotically normal estimators are themselves asymptotically normal and gives you a method for computing the limiting variance.</p... | |
onstant):
[image: Heatmap of limiting mean absolute percentage error of Beta mean estimate; source: https://i.sstatic.net/IvoRdhWk.png] (https://i.sstatic.net/IvoRdhWk.png)
The analysis of the plug-in variance estimator is similar | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 677,063 | https://stats.stackexchange.com/a/677063 | citations | Scientific-Citation-Graph:ce81ff8a29cad37075f540f1 | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>The thing to look up to analyse things like this is the Delta method.\nSimply put it states that functions of asymptotically normal estimators are themselves asymptotically normal and gives you a method for computing the limiting variance.</p... | |
ant temporal auto-correlation in the wet season. Any general advice is also welcome!
Data here (https://github.com/NateLaS/Toadfish/blob/main/share_version%20-%20toadfish.csv).
library(mgcv)
library(gratia)
library(DHARMa)
system.time(
by <- gam(total_count ~
| CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://github.com/NateLaS/Toadfish/blob/main/share_version%20-%20toadfish.csv | external_url | 677,062 | https://stats.stackexchange.com/questions/677062/how-to-check-for-temporal-auto-correlation-for-each-time-series-separately | citations | Scientific-Citation-Graph:93ea7a823d2863f52e865a4c | train | {
"accepted_answer_id": 677064,
"answers": [
{
"answer_html": "<p>The AI-generated code manually slices <code>{DHARMa}</code>'s internal arrays, which is fragile. The cleaner approach would be to use <code>recalculateResiduals()</code> with <code>NA</code> for the other season's rows, letting DHARMa's NA ... |
ld water and give time between sample processesing of new data.
[image: ChatGPT result; source: https://i.sstatic.net/XIgn1m2c.png] | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 677,062 | https://stats.stackexchange.com/questions/677062/how-to-check-for-temporal-auto-correlation-for-each-time-series-separately | citations | Scientific-Citation-Graph:6db78892ed1a4906f9c74e72 | train | {
"accepted_answer_id": 677064,
"answers": [
{
"answer_html": "<p>The AI-generated code manually slices <code>{DHARMa}</code>'s internal arrays, which is fragile. The cleaner approach would be to use <code>recalculateResiduals()</code> with <code>NA</code> for the other season's rows, letting DHARMa's NA ... | |
ason:", season))
}
#> --- Temporal Autocorrelation Test for Season: DRY ---
[image: ; source: https://i.sstatic.net/6r3XH8BM.png]
#> --- Temporal Autocorrelation Test for Season: WET ---
[image: ; source: https://i.ssta | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 677,064 | https://stats.stackexchange.com/a/677064 | citations | Scientific-Citation-Graph:e5ae79e197c35e25f1d6b7b4 | train | {
"accepted_answer_id": 677064,
"answers": [
{
"answer_html": "<p>The AI-generated code manually slices <code>{DHARMa}</code>'s internal arrays, which is fragile. The cleaner approach would be to use <code>recalculateResiduals()</code> with <code>NA</code> for the other season's rows, letting DHARMa's NA ... | |
t/6r3XH8BM.png]
#> --- Temporal Autocorrelation Test for Season: WET ---
[image: ; source: https://i.sstatic.net/LfmXWxdr.png]
This is essentially the same as what you got from your code, but without manual slicing and wh | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 677,064 | https://stats.stackexchange.com/a/677064 | citations | Scientific-Citation-Graph:9f896a4e2b2bbf58f0139db3 | train | {
"accepted_answer_id": 677064,
"answers": [
{
"answer_html": "<p>The AI-generated code manually slices <code>{DHARMa}</code>'s internal arrays, which is fragile. The cleaner approach would be to use <code>recalculateResiduals()</code> with <code>NA</code> for the other season's rows, letting DHARMa's NA ... | |
rop them during grouping:
library(mgcv)
library(DHARMa)
library(dplyr)
shiny_toad <- read.csv("https://raw.githubusercontent.com/NateLaS/Toadfish/main/share_version%20-%20toadfish.csv") |>
mutate(
fSeason = factor(Season),
fSite = factor(Site),
Date = as.Date(Date | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://raw.githubusercontent.com/NateLaS/Toadfish/main/share_version%20-%20toadfish.csv | external_url | 677,064 | https://stats.stackexchange.com/a/677064 | citations | Scientific-Citation-Graph:13176172fbb6a837c87fbbbf | train | {
"accepted_answer_id": 677064,
"answers": [
{
"answer_html": "<p>The AI-generated code manually slices <code>{DHARMa}</code>'s internal arrays, which is fragile. The cleaner approach would be to use <code>recalculateResiduals()</code> with <code>NA</code> for the other season's rows, letting DHARMa's NA ... |
data? (https://stats.stackexchange.com/q/664160)
Created on 2026-09-03 with reprex v2.1.1 (https://reprex.tidyverse.org) | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://reprex.tidyverse.org | external_url | 677,064 | https://stats.stackexchange.com/a/677064 | citations | Scientific-Citation-Graph:d6a7db3e700c73c93ba39801 | train | {
"accepted_answer_id": 677064,
"answers": [
{
"answer_html": "<p>The AI-generated code manually slices <code>{DHARMa}</code>'s internal arrays, which is fragile. The cleaner approach would be to use <code>recalculateResiduals()</code> with <code>NA</code> for the other season's rows, letting DHARMa's NA ... |
I think that my colleague was right. However, even Brunner's book (https://doi.org/10.1007/978-3-030-02914-2) does not address this issue, so I wrote the following proof:
Theorem: Suppose that $X_1\sim X_ | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | 10.1007/978-3-030-02914-2 | https://doi.org/10.1007/978-3-030-02914-2 | doi_url | 677,072 | https://stats.stackexchange.com/a/677072 | citations | Scientific-Citation-Graph:494dd0d0675caeb3463c0cc6 | train | {
"accepted_answer_id": 677074,
"answers": [
{
"answer_html": "<p>I think that my colleague was right. However, even <a href=\"https://doi.org/10.1007/978-3-030-02914-2\" rel=\"nofollow noreferrer\">Brunner's book</a> does not address this issue, so I wrote the following proof:</p>\n<p><strong>Theorem</st... |
.e. “the outcome of the procedure converges to the correct outcome as sample size goes to infinity (https://en.wikipedia.org/wiki/Consistency_(statistics))”) only under the following null hypothesis
$$p=P(X_1<X_2)+\frac{1}{2}P(X_1=X_2) = 0.5$$
Which can | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://en.wikipedia.org/wiki/Consistency_(statistics | external_url | 677,073 | https://stats.stackexchange.com/a/677073 | citations | Scientific-Citation-Graph:8cad665c15b6e4692fe0d663 | train | {
"accepted_answer_id": 677074,
"answers": [
{
"answer_html": "<p>I think that my colleague was right. However, even <a href=\"https://doi.org/10.1007/978-3-030-02914-2\" rel=\"nofollow noreferrer\">Brunner's book</a> does not address this issue, so I wrote the following proof:</p>\n<p><strong>Theorem</st... |
than expected: outliers at both margin(s) = 1, observations = 1145, p-value = 0.36.
Data here (https://github.com/NateLaS/Toadfish/blob/main/share_version%20-%20toadfish.csv)
shiny_toad$fSeason <- as.factor(shiny_toad$fSeason)
shiny_toad$fSite <- as.factor(shiny_toad$f | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://github.com/NateLaS/Toadfish/blob/main/share_version%20-%20toadfish.csv | external_url | 677,079 | https://stats.stackexchange.com/questions/677079/dharma-are-long-tails-in-dispersion-plot-a-problem | citations | Scientific-Citation-Graph:aee299b7017a4949434ead24 | train | {
"accepted_answer_id": 677089,
"answers": [
{
"answer_html": "<p>I see what's going on here. When <code>refit = F</code> DHARMa uses raw residuals and your raw response values are kind of crazy. 90% are zero, the second biggest number is 7 and then it maxes out at <em><strong>18</strong></em>:</p>\n<pre>... |
p-value = 0.44
alternative hypothesis: two.sided
[image: enter image description here; source: https://i.sstatic.net/JpiINT52.png] (https://i.sstatic.net/JpiINT52.png) | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 677,079 | https://stats.stackexchange.com/questions/677079/dharma-are-long-tails-in-dispersion-plot-a-problem | citations | Scientific-Citation-Graph:5ec1110b137951056e3c9378 | train | {
"accepted_answer_id": 677089,
"answers": [
{
"answer_html": "<p>I see what's going on here. When <code>refit = F</code> DHARMa uses raw residuals and your raw response values are kind of crazy. 90% are zero, the second biggest number is 7 and then it maxes out at <em><strong>18</strong></em>:</p>\n<pre>... | |
on underlayed with the red simulations from DHARMa. Hard to describe what we see exactly. ; source: https://i.sstatic.net/MLdk1fpB.png] (https://i.sstatic.net/MLdk1fpB.png) | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 677,089 | https://stats.stackexchange.com/a/677089 | citations | Scientific-Citation-Graph:0bd07f3ff47177dee81aa4b1 | train | {
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"answers": [
{
"answer_html": "<p>I see what's going on here. When <code>refit = F</code> DHARMa uses raw residuals and your raw response values are kind of crazy. 90% are zero, the second biggest number is 7 and then it maxes out at <em><strong>18</strong></em>:</p>\n<pre>... | |
common!) I think I should use a continuity correction (Correcting for continuity – corp.ling.stats (https://corplingstats.wordpress.com/2019/04/27/correcting-for-continuity/)), is that right?
Any pointers on how to adjust for multiple comparisons, given that I’ll be ca | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://corplingstats.wordpress.com/2019/04/27/correcting-for-continuity/ | external_url | 677,095 | https://stats.stackexchange.com/questions/677095/testing-differences-in-proportions-wilson-cis-continuity-corrections-and-mult | citations | Scientific-Citation-Graph:538fa128af4f2db4eb0b5a30 | train | {
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"answer_html": "<p>Welcome to CV!</p>\n<p>So you want to compare 2 proportions; e.g. “<em>Is the proportion of students who receive an A grade in MA101 the same as the proportion of students who receive an A grade in all other MA courses?</em>”.</p>\n<p>Well, t... |
10/15/bootstrapping-a-proportion/) Bootstrap intervals for the single proportion – corp.ling.stats (https://corplingstats.wordpress.com/2024/12/12/bootstrap-intervals/)) it seems that bootstrapping probably wasn’t the way, and I should have instead calculated Wilson | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://corplingstats.wordpress.com/2024/12/12/bootstrap-intervals/ | external_url | 677,095 | https://stats.stackexchange.com/questions/677095/testing-differences-in-proportions-wilson-cis-continuity-corrections-and-mult | citations | Scientific-Citation-Graph:d0065854d7c09822bf111028 | train | {
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"answer_html": "<p>Welcome to CV!</p>\n<p>So you want to compare 2 proportions; e.g. “<em>Is the proportion of students who receive an A grade in MA101 the same as the proportion of students who receive an A grade in all other MA courses?</em>”.</p>\n<p>Well, t... |
and from Chapter 8 Bootstrapping and Confidence Intervals | Statistical Inference via Data Science (https://moderndive.com/8-confidence-intervals.html) started out by calculating bootstrap confidence intervals around the proportion of A/F grades awar | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://moderndive.com/8-confidence-intervals.html | external_url | 677,095 | https://stats.stackexchange.com/questions/677095/testing-differences-in-proportions-wilson-cis-continuity-corrections-and-mult | citations | Scientific-Citation-Graph:f05f82e76eacf8ea39a2feee | train | {
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"answer_html": "<p>Welcome to CV!</p>\n<p>So you want to compare 2 proportions; e.g. “<em>Is the proportion of students who receive an A grade in MA101 the same as the proportion of students who receive an A grade in all other MA courses?</em>”.</p>\n<p>Well, t... |
However, having dug a bit further (e.g. Bootstrapping a Proportion - Probably Overthinking It, (https://www.allendowney.com/blog/2024/10/15/bootstrapping-a-proportion/) Bootstrap intervals for the single proportion – corp.ling.stats (https://corplingstats.wordpress.c | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://www.allendowney.com/blog/2024/10/15/bootstrapping-a-proportion/ | external_url | 677,095 | https://stats.stackexchange.com/questions/677095/testing-differences-in-proportions-wilson-cis-continuity-corrections-and-mult | citations | Scientific-Citation-Graph:730169cbdd65db17ecc1042d | train | {
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"answer_html": "<p>Welcome to CV!</p>\n<p>So you want to compare 2 proportions; e.g. “<em>Is the proportion of students who receive an A grade in MA101 the same as the proportion of students who receive an A grade in all other MA courses?</em>”.</p>\n<p>Well, t... |
le remaining “exact”, that is based on exactly the binomial distribution), such as the Blaker test (https://cran.r-universe.dev/exact2x2/doc/exact2x2.pdf).
But there is a subtlety in what you are trying to compare. On one hand, you have the proporti | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://cran.r-universe.dev/exact2x2/doc/exact2x2.pdf | external_url | 677,108 | https://stats.stackexchange.com/a/677108 | citations | Scientific-Citation-Graph:c885c1e78461a38c151e5e35 | train | {
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"answer_html": "<p>Welcome to CV!</p>\n<p>So you want to compare 2 proportions; e.g. “<em>Is the proportion of students who receive an A grade in MA101 the same as the proportion of students who receive an A grade in all other MA courses?</em>”.</p>\n<p>Well, t... |
edia.org/wiki/Contingency_table) (aka crosstab). And the “canonical” tests are either a chi-square (https://en.wikipedia.org/wiki/Chi-squared_test) test, or a Fisher-exact (https://en.wikipedia.org/wiki/Fisher%27s_exact_test) test (aka Fisher-0Ir | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://en.wikipedia.org/wiki/Chi-squared_test | external_url | 677,108 | https://stats.stackexchange.com/a/677108 | citations | Scientific-Citation-Graph:0202940b809f6108dd894b7d | train | {
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"answer_html": "<p>Welcome to CV!</p>\n<p>So you want to compare 2 proportions; e.g. “<em>Is the proportion of students who receive an A grade in MA101 the same as the proportion of students who receive an A grade in all other MA courses?</em>”.</p>\n<p>Well, t... |
A courses?”.
Well, the “canonical” way to compare 2 proportions is via a 2x2 contingency table (https://en.wikipedia.org/wiki/Contingency_table) (aka crosstab). And the “canonical” tests are either a chi-square (https://en.wikipedia.org/wiki/C | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://en.wikipedia.org/wiki/Contingency_table | external_url | 677,108 | https://stats.stackexchange.com/a/677108 | citations | Scientific-Citation-Graph:72341489ef3a423c5d438aec | train | {
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"answer_html": "<p>Welcome to CV!</p>\n<p>So you want to compare 2 proportions; e.g. “<em>Is the proportion of students who receive an A grade in MA101 the same as the proportion of students who receive an A grade in all other MA courses?</em>”.</p>\n<p>Well, t... |
s are either a chi-square (https://en.wikipedia.org/wiki/Chi-squared_test) test, or a Fisher-exact (https://en.wikipedia.org/wiki/Fisher%27s_exact_test) test (aka Fisher-0Irwin test).
Now, using binomial CI’s will not really answer your 2 questions, | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://en.wikipedia.org/wiki/Fisher%27s_exact_test | external_url | 677,108 | https://stats.stackexchange.com/a/677108 | citations | Scientific-Citation-Graph:db5d9b417694730556725407 | train | {
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"answer_html": "<p>Welcome to CV!</p>\n<p>So you want to compare 2 proportions; e.g. “<em>Is the proportion of students who receive an A grade in MA101 the same as the proportion of students who receive an A grade in all other MA courses?</em>”.</p>\n<p>Well, t... |
ate increases. Frank Harrell provides a compromise with the %ia% transformation in his rms package (https://cran.r-project.org/package=rms); it "represents restricted interactions in which products involving nonlinear effects on both vari | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://cran.r-project.org/package=rms | external_url | 677,119 | https://stats.stackexchange.com/a/677119 | citations | Scientific-Citation-Graph:1317481e0827de896aa65829 | train | {
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"answer_html": "<p>I'll start by addressing the questions, then finish with some thoughts based on the added study details.</p>\n<blockquote>\n<p>what is the minimum sample size I would need ...?</p>\n</blockquote>\n<p>Rules of thumb about the event/coefficient... |
different values in the factorial covariates?
Yes, that could lead to omitted-variable bias (https://en.wikipedia.org/wiki/Omitted-variable_bias), the technical term for what you fear. That's a particular problem in survival analysis. Unlike in | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://en.wikipedia.org/wiki/Omitted-variable_bias | external_url | 677,119 | https://stats.stackexchange.com/a/677119 | citations | Scientific-Citation-Graph:eb6cb06f67b9aec885050d4b | train | {
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"answer_html": "<p>I'll start by addressing the questions, then finish with some thoughts based on the added study details.</p>\n<blockquote>\n<p>what is the minimum sample size I would need ...?</p>\n</blockquote>\n<p>Rules of thumb about the event/coefficient... |
com/q/113766/28500).
Thoughts on your study
Frank Harrell's Regression Modeling Strategies (https://hbiostat.org/rmsc/) (RMS) is a freely available reference, written by an expert in clinical data analysis. For example | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://hbiostat.org/rmsc/ | external_url | 677,119 | https://stats.stackexchange.com/a/677119 | citations | Scientific-Citation-Graph:e8a549a8726dd8956f15713c | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>I'll start by addressing the questions, then finish with some thoughts based on the added study details.</p>\n<blockquote>\n<p>what is the minimum sample size I would need ...?</p>\n</blockquote>\n<p>Rules of thumb about the event/coefficient... |
much narrower concept than the customary meaning. Looking up independence statistics in Wikipedia (https://en.wikipedia.org/wiki/Independence_(probability_theory)), you obtain the following definition.
Two events are independent, statistically independen | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://en.wikipedia.org/wiki/Independence_(probability_theory | external_url | 677,118 | https://stats.stackexchange.com/a/677118 | citations | Scientific-Citation-Graph:592ea2f476ab1f07bc60e07e | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>Everything is dependent on your second C. Even random things like rolling dice (someone has to roll the dice, if there are no humans, no dice) so that seems a bit overdone.</p>\n<p>I guess you could argue that every pair of variables is only ... |
models isn't as simple as for standard linear regression. As @RickHass noted, the R DHARMa package (https://cran.r-project.org/package=DHARMa) provides useful tools for that, based on simulations from the model and the observed data.
| CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://cran.r-project.org/package=DHARMa | external_url | 677,144 | https://stats.stackexchange.com/a/677144 | citations | Scientific-Citation-Graph:d6c94d24538562d2fdaed7b2 | train | {
"accepted_answer_id": null,
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"answer_html": "<p>Putting together the comments:</p>\n<blockquote>\n<p>Is that p-value enough to fit the Linear mixed model anyway?</p>\n</blockquote>\n<p>Don't rely on such <em>p</em>-values for making decisions about the model to use. As @mastropi notes, wit... |
utions, but your number of samples per (sub)plot might not be adequate for a normal approximation (https://en.wikipedia.org/wiki/Chi-squared_distribution#Asymptotic_properties) to the distribution of variance estimates. Furthermore, your measurements of soil depth might not | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://en.wikipedia.org/wiki/Chi-squared_distribution#Asymptotic_properties | external_url | 677,144 | https://stats.stackexchange.com/a/677144 | citations | Scientific-Citation-Graph:15a08d513606639294693b05 | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>Putting together the comments:</p>\n<blockquote>\n<p>Is that p-value enough to fit the Linear mixed model anyway?</p>\n</blockquote>\n<p>Don't rely on such <em>p</em>-values for making decisions about the model to use. As @mastropi notes, wit... |
e. If you are sampling from normal distributions, variance estimates have chi-square distributions (https://en.wikipedia.org/wiki/Chi-squared_distribution#Occurrence_and_applications), while the standard deviation estimates have chi distributions (https://en.wikipedia.org/wiki/Chi_ | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://en.wikipedia.org/wiki/Chi-squared_distribution#Occurrence_and_applications | external_url | 677,144 | https://stats.stackexchange.com/a/677144 | citations | Scientific-Citation-Graph:71261ee65fbdf065b78795ef | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>Putting together the comments:</p>\n<blockquote>\n<p>Is that p-value enough to fit the Linear mixed model anyway?</p>\n</blockquote>\n<p>Don't rely on such <em>p</em>-values for making decisions about the model to use. As @mastropi notes, wit... |
bution#Occurrence_and_applications), while the standard deviation estimates have chi distributions (https://en.wikipedia.org/wiki/Chi_distribution#). Chi-square distributions may approach normal distributions, but your number of samples per (sub) | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://en.wikipedia.org/wiki/Chi_distribution# | external_url | 677,144 | https://stats.stackexchange.com/a/677144 | citations | Scientific-Citation-Graph:834ecd7d0805fa748fc11b11 | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>Putting together the comments:</p>\n<blockquote>\n<p>Is that p-value enough to fit the Linear mixed model anyway?</p>\n</blockquote>\n<p>Don't rely on such <em>p</em>-values for making decisions about the model to use. As @mastropi notes, wit... |
rectly relevant in your case, as the chi-square distribution is a special case of the gamma family (https://en.wikipedia.org/wiki/Gamma_distribution#General).
And do I need to check for any other assumptions if I go for GLMM (or any other mixed effe | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://en.wikipedia.org/wiki/Gamma_distribution#General | external_url | 677,144 | https://stats.stackexchange.com/a/677144 | citations | Scientific-Citation-Graph:4e69d7e2eabadb78be31ad36 | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>Putting together the comments:</p>\n<blockquote>\n<p>Is that p-value enough to fit the Linear mixed model anyway?</p>\n</blockquote>\n<p>Don't rely on such <em>p</em>-values for making decisions about the model to use. As @mastropi notes, wit... |
Can you provide a {reprex} (https://reprex.tidyverse.org/)? I'm getting a warning when I run your code
df <- structure(list(PreScore = c(16, 23, 17, 24 | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://reprex.tidyverse.org | external_url | 677,136 | https://stats.stackexchange.com/a/677136 | citations | Scientific-Citation-Graph:d1cce5c8a92a8407d8e7bb26 | train | {
"accepted_answer_id": 677139,
"answers": [
{
"answer_html": "<p>Can you provide a <a href=\"https://reprex.tidyverse.org/\" rel=\"nofollow noreferrer\"><code>{reprex}</code></a>? I'm getting a warning when I run your code</p>\n<pre class=\"lang-r prettyprint-override\"><code>df <- structure(list(Pre... |
Can you provide a {reprex} (https://reprex.tidyverse.org/)? I'm getting a warning when I run your code
df <- structure(list(PreScore = c(16, 23, 17, 24, | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://reprex.tidyverse.org/ | external_url | 677,136 | https://stats.stackexchange.com/a/677136 | citations | Scientific-Citation-Graph:021850fff9cd0b75fd1e6796 | train | {
"accepted_answer_id": 677139,
"answers": [
{
"answer_html": "<p>Can you provide a <a href=\"https://reprex.tidyverse.org/\" rel=\"nofollow noreferrer\"><code>{reprex}</code></a>? I'm getting a warning when I run your code</p>\n<pre class=\"lang-r prettyprint-override\"><code>df <- structure(list(Pre... |
You are using R version 4.6.1. Well, since R 4.6.0, from R News (https://cran.r-project.org/doc/manuals/r-release/NEWS.html):
wilcox.test() can now perform exact (conditional) inference in case
of ties. Based on cont | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://cran.r-project.org/doc/manuals/r-release/NEWS.html | external_url | 677,139 | https://stats.stackexchange.com/a/677139 | citations | Scientific-Citation-Graph:69d7781f76f13c9064a3b77e | train | {
"accepted_answer_id": 677139,
"answers": [
{
"answer_html": "<p>Can you provide a <a href=\"https://reprex.tidyverse.org/\" rel=\"nofollow noreferrer\"><code>{reprex}</code></a>? I'm getting a warning when I run your code</p>\n<pre class=\"lang-r prettyprint-override\"><code>df <- structure(list(Pre... |
tests im allgemeinen c-Stichprobenfall.” EDV in Medizin
und Biologie, 18(1), 12–19. ISSN 0300-8282.
https://ul.qucosa.de/api/qucosa%3A12624/attachment/ATT-0/ (https://ul.qucosa.de/api/qucosa%3A12624/attachment/ATT-0/). | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://ul.qucosa.de/api/qucosa%3A12624/attachment/ATT-0/ | external_url | 677,139 | https://stats.stackexchange.com/a/677139 | citations | Scientific-Citation-Graph:a2254404748d33f379f61416 | train | {
"accepted_answer_id": 677139,
"answers": [
{
"answer_html": "<p>Can you provide a <a href=\"https://reprex.tidyverse.org/\" rel=\"nofollow noreferrer\"><code>{reprex}</code></a>? I'm getting a warning when I run your code</p>\n<pre class=\"lang-r prettyprint-override\"><code>df <- structure(list(Pre... |
shown by text 2 rather than text 1 on the graph.
[image: enter image description here; source: https://i.sstatic.net/7o9W87he.png] (https://i.sstatic.net/7o9W87he.png)
Here we range from evident to speculative:
Integer | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 677,141 | https://stats.stackexchange.com/a/677141 | citations | Scientific-Citation-Graph:6d3c01c1b806ac0245a93d3b | train | {
"accepted_answer_id": 677139,
"answers": [
{
"answer_html": "<p>Can you provide a <a href=\"https://reprex.tidyverse.org/\" rel=\"nofollow noreferrer\"><code>{reprex}</code></a>? I'm getting a warning when I run your code</p>\n<pre class=\"lang-r prettyprint-override\"><code>df <- structure(list(Pre... | |
e:
According to the calculation by Ken Koon Wong,
[image: enter image description here; source: https://i.sstatic.net/v4LeyMo7.png] (https://i.sstatic.net/v4LeyMo7.png)
The position of knots should be c(-3,-3,-3,-3,-2, -1, 0, | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 677,137 | https://stats.stackexchange.com/questions/677137/why-does-a-cubic-b-spline-basis-matrix-contain-so-many-zero-columns | citations | Scientific-Citation-Graph:d1a778a68aae88adbb109fb1 | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>B-splines are designed to be local* (only nearby x-values affect the relevant predictor, and hence the fitted response).</p>\n<p>Consequently the predictor columns will be zero out of the local interval (described in terms of the nearby few k... | |
of a spline, with the elements that are non-zero at x=1.5 highlighted (solid, thick lines); source: https://i.sstatic.net/7o5ja3We.png] (https://i.sstatic.net/7o5ja3We.png)
library(splines)
x_i <- 1.5
x_all <- seq(-3, 6, length.ou | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 677,164 | https://stats.stackexchange.com/a/677164 | citations | Scientific-Citation-Graph:591d466932364c7543f738e6 | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>B-splines are designed to be local* (only nearby x-values affect the relevant predictor, and hence the fitted response).</p>\n<p>Consequently the predictor columns will be zero out of the local interval (described in terms of the nearby few k... | |
18013/varying-dispersion-parameter-dispformula-in-glmmtmb-in-r-to-account-for-heter)
Blog Post: https://lgraz.com/posts/lmm-heteroskedastic/ (https://lgraz.com/posts/lmm-heteroskedastic/)
Current Model output:
mod_disp_constant: hs | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://lgraz.com/posts/lmm-heteroskedastic/ | external_url | 677,147 | https://stats.stackexchange.com/questions/677147/glmmtmb-with-beta-distribution-for-repeated-measures-with-varying-dispersion-a | citations | Scientific-Citation-Graph:51d88b81c354160d019602ac | train | {
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"answer_html": "<p>Except for the comparison between <code>mod_disp_constant</code> and <code>mod_disp_background</code>, the <em>p</em>-values you show are based on Wald tests, which hold asymptotically in the limit of large numbers of cases. If you have only ... |
.stackexchange.com/a/104746/28500).
I couldn't find that the post about elevated Type I errors (https://lgraz.com/posts/lmm-heteroskedastic/) specified the glmmTMB version used. I also haven't taken the time to see exactly which types of p- | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://lgraz.com/posts/lmm-heteroskedastic/ | external_url | 677,163 | https://stats.stackexchange.com/a/677163 | citations | Scientific-Citation-Graph:f707b55972f0b9759a181f9e | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>Except for the comparison between <code>mod_disp_constant</code> and <code>mod_disp_background</code>, the <em>p</em>-values you show are based on Wald tests, which hold asymptotically in the limit of large numbers of cases. If you have only ... |
t the post seems to indicate elevated Type I error even with homoskedastic data and no dispformula (https://lgraz.com/posts/lmm-heteroskedastic/#glmmtmby-trt-1id-1).
In terms of changes with software versions, that post (https://lgraz.com/posts/lmm-heterosked | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://lgraz.com/posts/lmm-heteroskedastic/#glmmtmby-trt-1id-1 | external_url | 677,163 | https://stats.stackexchange.com/a/677163 | citations | Scientific-Citation-Graph:35df5680fc3f9d95c359988b | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>Except for the comparison between <code>mod_disp_constant</code> and <code>mod_disp_background</code>, the <em>p</em>-values you show are based on Wald tests, which hold asymptotically in the limit of large numbers of cases. If you have only ... |
p$-values based on Student-$t$ distributions. I redid the analysis with ddf corrections (code here (https://github.com/glmmTMB/glmmTMB/blob/master/misc/hetero_ddf_sim.R), here (https://github.com/glmmTMB/glmmTMB/blob/master/misc/hetero_ddf_sim_analysis.R)), with the f | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://github.com/glmmTMB/glmmTMB/blob/master/misc/hetero_ddf_sim.R | external_url | 677,165 | https://stats.stackexchange.com/a/677165 | citations | Scientific-Citation-Graph:50777dbff19c167fd1c06def | train | {
"accepted_answer_id": null,
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{
"answer_html": "<p>Except for the comparison between <code>mod_disp_constant</code> and <code>mod_disp_background</code>, the <em>p</em>-values you show are based on Wald tests, which hold asymptotically in the limit of large numbers of cases. If you have only ... |
orrections (code here (https://github.com/glmmTMB/glmmTMB/blob/master/misc/hetero_ddf_sim.R), here (https://github.com/glmmTMB/glmmTMB/blob/master/misc/hetero_ddf_sim_analysis.R)), with the following results (the binom.test p column tests whether the type-I error is significan | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://github.com/glmmTMB/glmmTMB/blob/master/misc/hetero_ddf_sim_analysis.R | external_url | 677,165 | https://stats.stackexchange.com/a/677165 | citations | Scientific-Citation-Graph:4fcb7882346c97b16e20ff2e | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>Except for the comparison between <code>mod_disp_constant</code> and <code>mod_disp_background</code>, the <em>p</em>-values you show are based on Wald tests, which hold asymptotically in the limit of large numbers of cases. If you have only ... |
by the test by having a p-value bigger than 0.05.
[image: enter image description here; source: https://i.sstatic.net/AqvWf28J.png] (https://i.sstatic.net/AqvWf28J.png) | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 677,151 | https://stats.stackexchange.com/questions/677151/wilcoxon-test-assumptions | citations | Scientific-Citation-Graph:1561e983ebc6d5f29b3838b2 | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>I presume that you are interested in comparing medians of paired data, and that somehow you felt that your data was not “normal enough” to warrant a paired t-test? Hence your choice to use a Wilcoxon-signed-Rank test (WSRt).</p>\n<p>The first... | |
compensation. So I think we do have independence.
[image: enter image description here; source: https://i.sstatic.net/wMwItQY8.png] (https://i.sstatic.net/wMwItQY8.png)
For the symmetry assumption, the data points were ordered | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 677,151 | https://stats.stackexchange.com/questions/677151/wilcoxon-test-assumptions | citations | Scientific-Citation-Graph:b0001bfdad0736581e972410 | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>I presume that you are interested in comparing medians of paired data, and that somehow you felt that your data was not “normal enough” to warrant a paired t-test? Hence your choice to use a Wilcoxon-signed-Rank test (WSRt).</p>\n<p>The first... | |
test of the pseudomedian (https://en.wikipedia.org/wiki/Pseudomedian), aka Hodges-Lehman estimator (https://en.wikipedia.org/wiki/Hodges%E2%80%93Lehmann_estimator).
Take your 2 samples, compute the paired differences, then compute all possible paired averages $ | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://en.wikipedia.org/wiki/Hodges%E2%80%93Lehmann_estimator | external_url | 677,153 | https://stats.stackexchange.com/a/677153 | citations | Scientific-Citation-Graph:b33a34437539bf6984f1738b | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>I presume that you are interested in comparing medians of paired data, and that somehow you felt that your data was not “normal enough” to warrant a paired t-test? Hence your choice to use a Wilcoxon-signed-Rank test (WSRt).</p>\n<p>The first... |
is a sad commentary on the state of statistical education. The WSRt is a test of the pseudomedian (https://en.wikipedia.org/wiki/Pseudomedian), aka Hodges-Lehman estimator (https://en.wikipedia.org/wiki/Hodges%E2%80%93Lehmann_estimator).
Ta | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://en.wikipedia.org/wiki/Pseudomedian | external_url | 677,153 | https://stats.stackexchange.com/a/677153 | citations | Scientific-Citation-Graph:87fb12d63231b875f518bd9d | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>I presume that you are interested in comparing medians of paired data, and that somehow you felt that your data was not “normal enough” to warrant a paired t-test? Hence your choice to use a Wilcoxon-signed-Rank test (WSRt).</p>\n<p>The first... |
to test medians, w/o any consideration to lack (or not) of normality, then you can use a Sign test (https://en.wikipedia.org/wiki/Sign_test) (available for paired data, and which is basically a binomial test).
So in the end, I can not | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://en.wikipedia.org/wiki/Sign_test | external_url | 677,153 | https://stats.stackexchange.com/a/677153 | citations | Scientific-Citation-Graph:57607fe91a490b9939a53dee | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>I presume that you are interested in comparing medians of paired data, and that somehow you felt that your data was not “normal enough” to warrant a paired t-test? Hence your choice to use a Wilcoxon-signed-Rank test (WSRt).</p>\n<p>The first... |
he sampling distribution of the mean, with very reasonable sample sizes (e.g. ~ 20). See e.g. here (https://statistical-engineering.com/clt-summary/clt-bimodal-distribution/) for a nice simulation, starting with a very non-normal, but symmetric, bimodal distribution. So un | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://statistical-engineering.com/clt-summary/clt-bimodal-distribution/ | external_url | 677,153 | https://stats.stackexchange.com/a/677153 | citations | Scientific-Citation-Graph:da2519d535dfcc4cba53a950 | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>I presume that you are interested in comparing medians of paired data, and that somehow you felt that your data was not “normal enough” to warrant a paired t-test? Hence your choice to use a Wilcoxon-signed-Rank test (WSRt).</p>\n<p>The first... |
tter outcomes. But for Wilcoxon to have optimum power, the proportional odds assumption must hold (https://fharrell.com/post/powilcoxon). | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://fharrell.com/post/powilcoxon | external_url | 677,160 | https://stats.stackexchange.com/a/677160 | citations | Scientific-Citation-Graph:ce06145db8c3ba7078be3bc5 | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>I presume that you are interested in comparing medians of paired data, and that somehow you felt that your data was not “normal enough” to warrant a paired t-test? Hence your choice to use a Wilcoxon-signed-Rank test (WSRt).</p>\n<p>The first... |
he signed-rank test as obsolete and always use Kornbrot's rank difference test as exemplified here (https://hbiostat.org/bbr/nonpar).
Regarding assumptions of the Wilcoxon test (original and rank difference version) it doesn't | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://hbiostat.org/bbr/nonpar | external_url | 677,160 | https://stats.stackexchange.com/a/677160 | citations | Scientific-Citation-Graph:b7e341ad7b8b99edd908e4c1 | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>I presume that you are interested in comparing medians of paired data, and that somehow you felt that your data was not “normal enough” to warrant a paired t-test? Hence your choice to use a Wilcoxon-signed-Rank test (WSRt).</p>\n<p>The first... |
Nonetheless, big boss likes seeing a p value :/ )
[image: enter image description here; source: https://i.sstatic.net/opGUh8A4.png] (https://i.sstatic.net/opGUh8A4.png) | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 677,168 | https://stats.stackexchange.com/questions/677168/performing-correct-statistical-analysis-on-fluorescent-intensity-data | citations | Scientific-Citation-Graph:88d893f4a9fed3a7c13e3f73 | train | {
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"answers": [
{
"answer_html": "<p>(i) Yes, I think that you are over-thinking it. If you plot the data with a logarithmic scale for the fluorescence density you will very likely find that the does-response relationship is sufficiently clear that no statistical analysis is goi... | |
ons?
iii) Is trying to account for the distribution actually required?
[image: ; source: https://i.sstatic.net/zOGT7c65.png] (https://i.sstatic.net/zOGT7c65.png)
EDIT 22/09/26
I've looked at log of the data, a | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 677,168 | https://stats.stackexchange.com/questions/677168/performing-correct-statistical-analysis-on-fluorescent-intensity-data | citations | Scientific-Citation-Graph:ec735ef07297705aaa983a59 | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>(i) Yes, I think that you are over-thinking it. If you plot the data with a logarithmic scale for the fluorescence density you will very likely find that the does-response relationship is sufficiently clear that no statistical analysis is goi... | |
ttps://journals.sagepub.com/doi/full/10.1177/2515245921999602)).
You could also use a median test (https://en.wikipedia.org/wiki/Median_test): it supports comparing multiple samples, and makes no distributional (or other such) assumption (i | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://en.wikipedia.org/wiki/Median_test | external_url | 677,197 | https://stats.stackexchange.com/a/677197 | citations | Scientific-Citation-Graph:3e8d53698ca87ff629177815 | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p>(i) Yes, I think that you are over-thinking it. If you plot the data with a logarithmic scale for the fluorescence density you will very likely find that the does-response relationship is sufficiently clear that no statistical analysis is goi... |
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