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#imputation

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LIfBi_Bamberg<p>LIfBi + <span class="h-card" translate="no"><a href="https://social.bund.de/@destatis" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>destatis</span></a></span> = Intensiver Austausch unter Statistikerinnen und Statistikern 📊 💻 <br>Bei einem Workshop zusammen mit der Otto-Friedrich-Universität Bamberg ging es darum, wie mit fehlenden oder unplausiblen Werten in der amtlichen Statistik und in längsschnittlichen Erhebungen umgegangen werden kann.<br>Danke für den Besuch in Bamberg!<br><a href="https://eduresearch.social/tags/Imputation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Imputation</span></a> <a href="https://eduresearch.social/tags/Coding" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Coding</span></a> <a href="https://eduresearch.social/tags/Statistik" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Statistik</span></a> <a href="https://eduresearch.social/tags/Erhebungen" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Erhebungen</span></a> <a href="https://eduresearch.social/tags/Kooperation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Kooperation</span></a></p>
Statistics Globe<p>Final reminder: the Statistics Globe online workshop, "Missing Data Imputation in R," starts in just 24 hours!</p><p>It would be great if you also took part in the workshop. So if you are interested, please register now: <a href="https://statisticsglobe.com/online-workshop-missing-data-imputation-r" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">statisticsglobe.com/online-wor</span><span class="invisible">kshop-missing-data-imputation-r</span></a></p><p>Looking forward to seeing you there!</p><p>Joachim</p><p><a href="https://mastodon.social/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a> <a href="https://mastodon.social/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statistics</span></a> <a href="https://mastodon.social/tags/datascience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datascience</span></a> <a href="https://mastodon.social/tags/missingdata" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>missingdata</span></a> <a href="https://mastodon.social/tags/imputation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>imputation</span></a></p>

Only 3 days left until the start of the Statistics Globe online workshop, Missing Data Imputation in R.

Kicking off on February 20, this workshop includes eight weekly live sessions, beginning with the basics of handling missing data and advancing to sophisticated imputation techniques in R.

The workshop is limited to 15 participants, so enroll now to secure your spot.

Learn more and sign up here: statisticsglobe.com/online-wor

And it’s finally here 🥳 {resurface} my #rstats package for imputing missing genotype allele frequencies, such as those from pooled samples or populations. Stems from some scripts I wrote nearly 10 years ago, which I can now finally say is open. I hope to progressively add more imputation options.
#imputation #genomics #genotyping #bioinformatics
github.com/lpembleton/resurfac

GitHubGitHub - lpembleton/resurface: Imputation of Genotypic Allele FrequenciesImputation of Genotypic Allele Frequencies. Contribute to lpembleton/resurface development by creating an account on GitHub.

1/🧵I’ve been developing some genotype allele frequency (AF) #imputation #rstat scripts. While my algorithm returns a low raw bias (RB) of 0.05 to 0.1, simply imputing with the meanAF gives slightly worse but largely similar RB values. I’m wanting to quantifying accuracy better because despite the similar RB values, I find meanAF imputation inferior.
#genomics

😏 #imputation #statistics #math

... The student contacted Heshmati and eventually obtained spreadsheets of the data he had used in the paper. Heshmati acknowledged that, although he and his coauthor had not mentioned this fact in the paper, the data had gaps. He revealed in an email that these gaps had been filled by using Excel’s autofill function”....

retractionwatch.com/2024/02/21

Retraction Watch · How (not) to deal with missing data: An economist’s take on a controversial studyGary Smith Nearly 100 years ago, Muriel Bristol refused to drink a cup of tea that had been prepared by her colleague, the great British statistician Ronald Fisher, because Fisher had poured milk i…

retractionwatch.com/2024/02/05

"Heshmati told the student he had used Excel’s autofill function to mend the data. ... where there were no observations to use for the autofill operation, the professor had taken the values from an adjacent country in the spreadsheet."
#economics #imputation #heshmati #retraction #retractionwatch #Excel #AutoFill #datainvention #fudge #fake
"Journal of Cleaner Production":
sciencedirect.com/science/arti
"#Green #innovations and #patents in #OECD countries"

What model #statistics should one report after using multiple #imputation and #multilevel regressions, and how are they obtained? I'm using the #mice package in #rstats, and #lme4 on each imputed dataset. When pooling results, summary() yields what I need for each model term, but nothing for the whole model. If I didn't impute but deleted listwise, I would normally report AIC, BIC, Loglik. These are all in the mipo object, for each result for each imputed dataset, but they're not pooled. I'm sure I'm missing something here. Does anyone know an example article where such results are presented neatly?