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An R script for preprocessing frequency list data from the Norwegian Web as Corpus (NoWaC), including instructions for downloading and preparing the corpus data.

The OpenSesame user base is skyrocketing but—of course—remains small in comparison to many other user bases that we are used to. Therefore, when developing an experiment in OpenSesame, there are still many opportunities to break the mould. When you need to do something beyond the standard operating procedure, it may take longer to find suitable resources than it takes when a more widespread tool is used. So, why would you still want to use OpenSesame?

I'm sending the triggers in a binary format because Python requires this. For instance, to send the trigger 1, I run the code serialport.write(b'1'). I have succeeded in sending triggers in this way. However, I encounter two problems. First, the triggers are converted in a way I cannot entirely decipher. For instance, when I run the code serialport.write(b'1'), the trigger displayed in BrainVision Recorder is S 49, not S 1 as I would hope (please see Appendix below). Second, I cannot send two triggers with the same code one after the other. For instance, if I run serialport.write(b'1'), a trigger appears in BrainVision Recorder, but if I run the same afterwards (no matter how many times), no trigger appears. I tried to solve these problems by opening the parallel port in addition to the serial port, but the problems persist.

A custom R function to create ggplot2 visualizations of power curves generated by the simr package's powerCurve function for mixed-effects models.

Whereas the direction of main effects can be interpreted from the sign of the estimate, the interpretation of interaction effects often requires plots. This task is facilitated by the R package sjPlot. For instance, using the plot_model function, I plotted the interaction between two continuous variables. library(lme4) #> Loading required package: Matrix library(sjPlot) #> Learn more about sjPlot with 'browseVignettes("sjPlot")'. library(ggplot2) theme_set(theme_sjplot()) # Create data partially based on code by Ben Bolker # from https://stackoverflow.

Whereas the direction of main effects can be interpreted from the sign of the estimate, the interpretation of interaction effects often requires plots. This task is facilitated by the R package sjPlot. For instance, using the plot_model function, I plotted the interaction between a continuous variable and a categorical variable. The categorical variable was passed to the fill argument of plot_model. library(lme4) #> Loading required package: Matrix library(sjPlot) #> Install package "strengejacke" from GitHub (`devtools::install_github("strengejacke/strengejacke")`) to load all sj-packages at once!

A custom R function to create ggplot2 visualizations of fixed effects from models refitted with multiple optimizers using lme4's allFit function, enabling visual assessment of convergence validity in mixed-effects models.

I’m developing a slightly tricky design in OpenSesame (a Python-based experiment builder). My stimuli comprise two kinds of sentences that contain different elements, and different numbers of elements. These sentences must be presented word by word. Furthermore, I need to attach triggers to some words in the first kind of sentences but not in the second kind. Last, these kinds of sentences must be intermixed within a block (or a sequence) of trials, because the first kind are targets and the second kind are fillers.

I am using jsPsych to create an experiment and I am struggling to sample from two variables simultaneously. Specifically, in each trial, I would like to present a primeWord and a targetWord by randomly sampling each of them from its own variable. I have looked into several resources—such as sampling without replacement, custom sampling and position indices—but to no avail. I’m a beginner at this, so it’s possible that one of these resources was relevant (especially the last one, I think).

Here’s an example of fuzzy-matching strings in R that I shared on StackOverflow. In stringdist_join, the max_dist argument is used to constrain the degree of fuzziness. library(fuzzyjoin) library(dplyr) #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union library(knitr) small_tab = data.frame(Food.Name = c('Corn', 'Squash', 'Peppers'), Food.Code = c(NA, NA, NA)) large_tab = data.