library(marginaleffects)
fit <- lm(data = mtcars, mpg ~ disp)
predictions(fit,
newdata = datagrid(disp = c(100, 200, 300)),
hypothesis = difference ~ pairwise)
library(marginaleffects)
fit <- lm(data = mtcars, mpg ~ disp)
predictions(fit,
newdata = datagrid(disp = c(100, 200, 300)),
hypothesis = difference ~ pairwise)
avg_predictions(variables="D"): Marginal means w/ counterfactual values of D.
avg_comparisons(variables="D"): ATE of D, i.e. difference between "counterfactual" marginal means
avg_predictions(variables="D"): Marginal means w/ counterfactual values of D.
avg_comparisons(variables="D"): ATE of D, i.e. difference between "counterfactual" marginal means
(5/n)
(5/n)
#rstats
#rstats
Get fueled for your next research project by exploring the latest data releases. Your perfect dataset might be waiting! myumi.ch/ICPSR-new-releases
#ICPSR #NewData #DataDiscovery #Research
Get fueled for your next research project by exploring the latest data releases. Your perfect dataset might be waiting! myumi.ch/ICPSR-new-releases
#ICPSR #NewData #DataDiscovery #Research
predictors_x0 <- data.frame(supp = "OJ", dose = 1.25)
predictors_x1 <- data.frame(supp = "VC", dose = 1.25)
predictors_fd <- rbind(predictors_x0, predictors_x1)
predictions_fd <- rstanarm::posterior_predict(
best_model_bayesian,
newdata = predictors_fd
)
predictors_x0 <- data.frame(supp = "OJ", dose = 1.25)
predictors_x1 <- data.frame(supp = "VC", dose = 1.25)
predictors_fd <- rbind(predictors_x0, predictors_x1)
predictions_fd <- rstanarm::posterior_predict(
best_model_bayesian,
newdata = predictors_fd
)
Der Befragungsdatensatz zu TransFAR 2020 ist veröffentlicht!
Check: bamf.de/fdz
#MigrationResearch #newdata
Der Befragungsdatensatz zu TransFAR 2020 ist veröffentlicht!
Check: bamf.de/fdz
#MigrationResearch #newdata
Wir haben unsere Daten zu Berufssprachkursen aktualisiert und erweitert.
Wer zu Spracherwerb, Lernen in Gruppen und/oder Migranten forscht, sollte hier mal reinschauen!
Wir freuen uns auf eure Forschungsideen!
#newdata #migrationresearch
Mehr dazu finden Sie unter www.bamf.de/fdz
Wir haben unsere Daten zu Berufssprachkursen aktualisiert und erweitert.
Wer zu Spracherwerb, Lernen in Gruppen und/oder Migranten forscht, sollte hier mal reinschauen!
Wir freuen uns auf eure Forschungsideen!
#newdata #migrationresearch
A little bit more complicated today. It would be very tedious in C or similar. Necessary to create many lists similar to existing list with one element removed.
A little bit more complicated today. It would be very tedious in C or similar. Necessary to create many lists similar to existing list with one element removed.
\expanded{
\noexpand\pgfoonew\expandafter\noexpand
\csname #2\endcsname=
new Value(\newdata,{\selfid,\otherid},*,0)
}
\expanded{
\noexpand\pgfoonew\expandafter\noexpand
\csname #2\endcsname=
new Value(\newdata,{\selfid,\otherid},*,0)
}
🔗 Read the article: www.sciencedirect.com/science/arti...
#WastewaterSurveillance #NewData
🔗 Read the article: www.sciencedirect.com/science/arti...
#WastewaterSurveillance #NewData
Over 60% with MetALD/ALD and 38% with MASLD change subclass.
This highlights that alcohol use and metabolic risk need regular reassessment, incl. for clinical trial eligibility 🧐
Link: www.cghjournal.org/article/S154...
Over 60% with MetALD/ALD and 38% with MASLD change subclass.
This highlights that alcohol use and metabolic risk need regular reassessment, incl. for clinical trial eligibility 🧐
Link: www.cghjournal.org/article/S154...
yay...
yay...
#Data #NewData #DataUpdates #Research #DiscoverData #ICPSR
#Data #NewData #DataUpdates #Research #DiscoverData #ICPSR
avg_slopes(mod, newdata = d_freechoice).
Sometimes the R environment can lead weird errors if you don't.
avg_slopes(mod, newdata = d_freechoice).
Sometimes the R environment can lead weird errors if you don't.
No, the difference in numeric values is due to the fact that when using `model=`, `insight::get_data()` calls `na.omit()`, whereas we take the `mean()` of the whole dataset with `newdata=dat`
No, the difference in numeric values is due to the fact that when using `model=`, `insight::get_data()` calls `na.omit()`, whereas we take the `mean()` of the whole dataset with `newdata=dat`
WATCH ON YOUTUBE: https://www.youtube.com/watch?v=lrAXSkYMyV4
WATCH ON YOUTUBE: https://www.youtube.com/watch?v=lrAXSkYMyV4
newdata-dcnr.opendata.arcgis.com/apps/DCNR::p...
newdata-dcnr.opendata.arcgis.com/apps/DCNR::p...
👉 Explore the data: myumi.ch/n167A
#ICPSR #NewData @umichmedicalschool.bsky.social
👉 Explore the data: myumi.ch/n167A
#ICPSR #NewData @umichmedicalschool.bsky.social
```
<script>
let { data } = $props();
let newData = $derived.by(()=>{
let _ = $state(data);
return _;
});
</script>
```
And just modify newData.stuff
```
<script>
let { data } = $props();
let newData = $derived.by(()=>{
let _ = $state(data);
return _;
});
</script>
```
And just modify newData.stuff
- feols and fepois have new split and fsplit arguments (just as in fixest!)
- you can check for separation in Poisson models via the „iterative rectifier“
- we’ve fixed a 🐞 in the predict method for WLS with newdata
- feols and fepois have new split and fsplit arguments (just as in fixest!)
- you can check for separation in Poisson models via the „iterative rectifier“
- we’ve fixed a 🐞 in the predict method for WLS with newdata