Matthew Kay

@mjskay

Assistant Professor . , uncertainty visualization, human-computer interaction, usable stats. Author of R pkg. he/him. 🏳️‍🌈

Vrijeme pridruživanja: svibanj 2009.

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  1. Prikvačeni tweet
    29. sij

    Pleased to announce that v2.0 (SLABS FOR DAYS edition) hit CRAN today. Lots of new stuff in this version: A THREAD

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  2. proslijedio/la je Tweet
    31. sij

    Love this story featuring tips from & on better visualizing uncertainty. One of their tips: instead of a bar chart, use an icon array or a “risk theater”

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  3. proslijedio/la je Tweet
    31. sij

    I am so very excited to announce that {ggeasy} is now a CRAN package! Having trouble remembering how to tweak your {ggplot2} components? We got you covered.

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  4. proslijedio/la je Tweet
    31. sij

    Could some phoneticians who use R try out my readtextgrid package on their textgrids? It does only one thing (reads textgrids into dataframes). I've been using it for months, but testing from other languages/settings would help.

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  5. proslijedio/la je Tweet
    29. sij

    i'm just going to fix this one little bug. oh first, i should set up a test case. oh wait, now something is wrong in docs. ah a dependency deprecated a function. ah ah ah

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  6. 29. sij

    Anyhue, read more about it in the changelog: Thanks to everyone who's given feedback on the package, and of course to the great software it builds upon, like , , , , the , etc.

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  7. 29. sij

    Finally and more esoterically, tidybayes now includes the marginal distribution for a single cell in an LKJ-distributed correlation matrix, to help with visualizing LKJ priors (because what even does an LKJ prior imply??)

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  8. 29. sij

    For more on these new geoms, check out the new slabinterval vignette:

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  9. 29. sij

    It's also easier to create Kruschke-style distribution-of-distribution plots for posterior predictions using the new stat_dist_slabh() applied to draws from the joint posterior for mu and sigma.

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  10. 29. sij

    That means you can now do halfeyes, gradient plots, lineribbons, dotplots, and everything else ON FREQUENTIST MODELS with broom + tidybayes by mapping estimates, standard errors, and (optionally) degrees of freedom onto distribution params. E.g. here's halfeyes and lineribbons

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  11. 29. sij

    The ability to visualize analytical distirbutions easily also makes it easy to do *frequentist* visualization in tidybayes (blasphemy!!!). See this vignette:

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  12. 29. sij

    ...or this visualization of priors from a model, also enabled by the new parse_dist() function which turns prior specs like "normal(0,1)" into columns that can be mapped onto aesthetics in a ggplot.

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  13. 29. sij

    The "stat_dist_..." subfamily also allows visualization of analytical distributions using all the same geoms above. E.g. This visualization showing a Beta(alpha, 10) distribution for different values of alpha...

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  14. 29. sij

    Variables can be mapped onto fill and color within-slabs, which both enables gradient plots (above) and allows custom plots, like this plot of a region of practical equivalence (ROPE)

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  15. 29. sij

    While a variety of "template" geoms and stats are provided for common combinations, numerous custom combinations are possible. E.g. we can remap the CCDF function onto alpha instead of slab thickness to make a CCDF gradient plot.

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  16. 29. sij

    The dotplots, btw, automatically choose bin size based on the viewport.

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  17. 29. sij

    ...and quantile dotplots, amongst other things. (dodge-able, just like everything else :) )

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  18. 29. sij

    ...gradient + interval plots...

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  19. 29. sij

    ...CCDF barplots... (also note dodging is supported by all slab+interval geoms)

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  20. 29. sij

    But also enables a variety of new geoms, like histogram+intervals...

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  21. 29. sij

    The slab+interval meta-geom now drives old standards like eyes and half-eyes...

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