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``` r
library(correlation)
```
Works:
``` r
mtcars |>
transform(gear = factor(gear)) |>
correlation(select = c("gear", "hp", "mpg"), multilevel = TRUE)
#> Parameter1 | Parameter2 | …
-
``` r
library(correlation)
#> Warning: package 'correlation' was built under R version 4.2.3
data("mtcars")
mtcars$gear Parameter1 | Parameter2 | r | CI | t(30) | p
#…
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I find the `multilevel` argument name confusing, and there have been several issues from users lately that have expressed similar confusion.
Based on the name, I would expect a decomposition of the…
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It would be useful to compute the multilevel correlation as a random intercept & random slope mixed-effects model, which allows ID slopes to vary. This option, along with the current approach (random…
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### Background of rma.mv function:
Standard meta-analytic models assume independence between the observed effect sizes or outcomes obtained from a set of studies. This assumption is often violated…
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As discussed in out meeting yesterday, the correlation package should be broken down (and built up!) into the following "bits":
### A simple 1:1 correlation function (currently done in `cor_test()`…
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Hi there! I have been using papaja for a while now and i find it terrific! Thanks for all the work, and saving hours of my time! (If you want some bavarian beer, just PM an address:)
And now to my …
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_From @Mrmoods on July 21, 2018 17:11_
We need a way to compare someone's set of responses to the questions with:
1. predicted responses from a selected set of MPs who are prominent in the Brexit …
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Currently, the Stan code of a multilevel models looks a little verbose due to first indexing columns and then looping over observations to select the right elements of the computed vectors. This has h…