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TJ Mahr tjmahr Madison, WI tjmahr.com Language and data scientist

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issue commenttjmahr/lookr

weird timing thing

Ooops I think I used RTTime, but I read in both. My current code gazedata files is here https://github.com/tjmahr/littlelisteners/blob/master/R/gazedata.R

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issue closedtjmahr/lookr

weird timing thing

Today, I wondered if it would be possible to identify probable trial boundaries (Inter-Trial Intervals) from the time columns in gazedata files emitted by Eprime.

My first thought was to diff(gazedata$RTTime) to calculate the difference in time between Frame N and Frame N-1. The biggest jumps in time would be the ITIs. Which works perfectly:

image

But on the plot, I see some weird diffs:

image

The first one shows up in the data as:

image

And the second as:

image

Clocks

The column RTTime is what we might call Eprime Local Time. I have used this column because stimulus presentation times in the Eprime administration log are measured with the same clock and relative time, making it easy to sync up Eprime's stimulus presentation with gaze location. RTTime is measured in milliseconds, possibly the running milliseconds from the start of the experiment:

fivenum(gazedata$RTTime)
#> [1]  27871.0  80509.0 140730.0 194358.5 248730.0
fivenum(gazedata$RTTime) / 1000
#> [1]  27.8710  80.5090 140.7300 194.3585 248.7300
fivenum(gazedata$RTTime - min(gazedata$RTTime)) / 1000
#> [1]   0.0000  52.6380 112.8590 166.4875 220.8590

The gazedata file also provides a column for TETTime for Tobii Eye Tracker Time, which has none of these hiccups:

image

But it's apparently measured as milliseconds since whenever computer time 0 was in the 70's

head(gazedata$TETTime)
#> [1] 1.439215e+12 1.439215e+12 1.439215e+12 1.439215e+12 1.439215e+12
#> [6] 1.439215e+12
fivenum(gazedata$TETTime - min(gazedata$TETTime))
#> [1]      0.00  52411.89 112858.95 166487.96 220861.22
fivenum(gazedata$TETTime - min(gazedata$TETTime)) / 1000
#> [1]   0.00000  52.41189 112.85895 166.48796 220.86122

Questions

Some questions I need to figure out:

  • Does this clock stutter happen often? Or did I just choose the one file where it happened twice?
  • What does this indicate?
  • Does this mean I need to synchronize time using the Tobii?

When I synchronize time using the Tobii clock, it kinda straightens those kinks. Plots below focus on the first set of weird points...

fixed_time <- (gazedata$TETTime - min(gazedata$TETTime)) + min(gazedata$RTTime)
# Time since experiment start: Corrected vs Eprime
plot(fixed_time[2450:2700], gazedata$RTTime[2450:2700])  

image

# Interframe intervals: Eprime (red) vs Tobii (black)
plot(diff(gazedata$RTTime[2450:2700]), col = "red")
points(diff(fixed_time[2450:2700]))

image

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issue commenttjmahr/lookr

weird timing thing

I used TETTime.

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[PRE REVIEW]: bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework

I can review it. (I have done one review in the past for ROpenSci.)

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issue commentstan-dev/bayesplot

plots for model predictions without y variable

Hmm, I take it back.

My first hunch would be something like this,

f <- function(x, group, ...) {
  g(x, ...)
}

g <- function(x, ...) {
  print(x)
}

f(1, "a", "things") 

But that requires passing along all the arguments (except group) to g() which is tedious and gives you something else to update if the arguments to f() or g() change. Your way seems like the smarter, more automatic way to go about the problem.

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issue commentstan-dev/bayesplot

plots for model predictions without y variable

ungroup_call <- function(fn, call) {
  args <- rlang::call_args(call)
  args$called_from_internal <- TRUE
  args$... <- NULL
  rlang::call2(.fn = fn, !!!args, .ns = "bayesplot")
}

This is pretty tricky stuff. Not a judgment, just an observation.

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issue commentstan-dev/bayesplot

plots for model predictions without y variable

That seems fine at the data level. It's problem of creating six-different plots from an internal plotting function (like .mcmc_trace()) that I worry about. All of those plots are really tightly coupled and it's hard to work through. In comparison, it's really clear what does what in the ppc_dens_overlay() code. I think using helper functions, scales and geoms can help achieve consistency without over-coupling.

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issue commentstan-dev/bayesplot

plots for model predictions without y variable

So, here's the implementation of ppc_dens_overlay()

  check_ignored_arguments(...)
  data <- ppc_data(y, yrep)

  ggplot(data) +
    aes_(x = ~ value) +
    stat_density(
      aes_(group = ~ rep_id, color = "yrep"),
      data = function(x) dplyr::filter(x, !.data$is_y),
      geom = "line",
      position = "identity",
      size = size,
      alpha = alpha,
      trim = trim,
      bw = bw,
      adjust = adjust,
      kernel = kernel,
      n = n_dens
    ) +
    stat_density(
      aes_(color = "y"),
      data = function(x) dplyr::filter(x, .data$is_y),
      geom = "line",
      position = "identity",
      lineend = "round",
      size = 1,
      trim = trim,
      bw = bw,
      adjust = adjust,
      kernel = kernel,
      n = n_dens
    ) +
    scale_color_ppc_dist() +
    bayesplot_theme_get() +
    xlab(y_label()) +
    dont_expand_axes() +
    yaxis_title(FALSE) +
    xaxis_title(FALSE) +
    yaxis_text(FALSE) +
    yaxis_ticks(FALSE)

To implement this, we want to

  • have a ppd_data() function create the tidy dataframe
  • remove the second stat_density() layer
  • change the colors and labels to use something like y_pred instead of y_rep.

Is that basically what you had in mind? Following the format of the other functions?

I don't want us to be too clever and have functions that switch between ppd_ and ppc_ styles based on whether y is NULL. I know the temptation is there but I worry it make things harder to change down the road.

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Release v1.7.0?

Nope.

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pull request commentstan-dev/bayesplot

Feature tidyselect

I expanded the documentation to tidy-params to include all the select() gotchas I could think of. Everything else seems fine.

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PR opened stan-dev/bayesplot

Rank hotfix

The rank-normalization paper says:

Rank normalization proceeds as follows. First, replace each value θ^(nm) by its rank r^(nm) within the pooled draws from all chains. Average rank for ties are used to conserve the number of unique values of discrete quantities.

I was breaking ties by using dplyr::row_number() which works like rank(.., ties.method = "first"). This update changes the method to use averages.

It also fixes a warning from the unit tests.

+18 -15

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two small fixes - ranks should break ties by averaging, according to manuscript - reserve "rank-normalized" for when ranks are normalized (converted to a z or similar scale)

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pull request commentstan-dev/bayesplot

Rank histogram

I think we can drop window and the note there too. I wasn't sure at the time when I wrote the note.

But we never do filter() the data to the window (we just set the coordinate limits to the window) so we don't need to make it part of the data preparation.

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pull request commentstan-dev/bayesplot

Rank histogram

@tjmahr this looks great. Quick question: the size, np, and np_style arguments do not appear to be used inside mcmc_trace_data(). Can I remove these arguments?

Yes. Good catch. Then be sure to update the call to mcmc_trace_data() in .mcmc_trace().

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issue commentstan-dev/bayesplot

Warning message when working with dataframes with "Chain" column

Yeah, tibbles complain when we look up a name that isn't there. Normal dataframes don't.

df <- tibble::tibble(parameter=rnorm(n=1000), Chain=rep(1:10, each=100))
x <- bayesplot:::prepare_mcmc_array(df, "parameter")
#> Warning: Unknown or uninitialised column: 'chain'.
y <- bayesplot:::prepare_mcmc_array(as.data.frame(df), "parameter")

<sup>Created on 2019-05-21 by the reprex package (v0.3.0)</sup>

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pull request commentstan-dev/bayesplot

Rank histogram

Ready for review.

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pull request commentstan-dev/bayesplot

[WIP] Rank histogram

I've added mcmc_rank_hist().

Great! I would remove y axis

I have removed the y-axis and created an option to show a reference line.

library(bayesplot)
#> This is bayesplot version 1.6.0.9000
#> - Online documentation and vignettes at mc-stan.org/bayesplot
#> - bayesplot theme set to bayesplot::theme_default()
#>    * Does _not_ affect other ggplot2 plots
#>    * See ?bayesplot_theme_set for details on theme setting
x <- example_mcmc_draws()
color_scheme_set("viridisE")
mcmc_rank_hist(x, c("beta[1]"))

mcmc_rank_hist(x, c("beta[1]"), ref_line = TRUE)


mcmc_rank_hist(x, c("alpha", "beta[1]"), n_bins = 10)

mcmc_rank_hist(x, c("alpha", "beta[1]"), ref_line = TRUE)

<sup>Created on 2019-05-21 by the reprex package (v0.3.0)</sup>

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issue commentstan-dev/bayesplot

Release v1.7.0?

I'm putting last touches on the two rank plots

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ColorBrewer schemes with color_scheme_set() See #177

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pull request commentstan-dev/bayesplot

Feature tidyselect

I was worried about mixed models where you get a lot going on the brackets, so I tried to make a general one. This would require glue, but glue is dependency-less and it is required by most of the tidyverse packages that we already depend on.

library(tidyverse)
#> Registered S3 methods overwritten by 'ggplot2':
#>   method         from 
#>   [.quosures     rlang
#>   c.quosures     rlang
#>   print.quosures rlang
d <- structure(list(b_Intercept = c(0.25, 0.13, 0.77, 0.67, 0.44, 
0.33), sd_condition__Intercept = c(1.01, 1.01, 0.78, 0.77, 0.89, 
0.76), sigma = c(0.63, 0.54, 0.62, 0.59, 0.47, 0.44), `r_condition[A,Intercept]` = c(-0.2, 
0.16, -0.82, -0.28, -0.53, -0.25), `r_condition[B,Intercept]` = c(0.98, 
0.53, 0.44, 0.15, 0.74, 0.41), `r_condition[C,Intercept]` = c(1.33, 
1.79, 0.83, 1.5, 1.15, 1.54), `r_condition[A,Slope]` = c(0.86, 
0.78, 0.19, -0.14, 0.87, 0.49), `r_condition[B,Slope]` = c(-1.18, 
-0.96, -1.66, -1.62, -1.53, -1.1), lp__ = c(-51.66, -51.15, -53.32, 
-56.79, -56.48, -54.96)), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -6L))
  
param_glue <- function(pattern, ...) {
  dots <- as.list(expand.grid(...))
  nms <- as.character(glue::glue_data(dots, pattern))

  param_matches <- match(nms, tidyselect::peek_vars())
  param_matches[!is.na(param_matches)]
}

d
#> # A tibble: 6 x 9
#>   b_Intercept sd_condition__I~ sigma `r_condition[A,~ `r_condition[B,~
#>         <dbl>            <dbl> <dbl>            <dbl>            <dbl>
#> 1        0.25             1.01  0.63            -0.2              0.98
#> 2        0.13             1.01  0.54             0.16             0.53
#> 3        0.77             0.78  0.62            -0.82             0.44
#> 4        0.67             0.77  0.59            -0.28             0.15
#> 5        0.44             0.89  0.47            -0.53             0.74
#> 6        0.33             0.76  0.44            -0.25             0.41
#> # ... with 4 more variables: `r_condition[C,Intercept]` <dbl>,
#> #   `r_condition[A,Slope]` <dbl>, `r_condition[B,Slope]` <dbl>, lp__ <dbl>

d %>% 
  select(
    param_glue(
      "r_condition[{level},Intercept]", 
      level = c("A", "B"))
  )
#> # A tibble: 6 x 2
#>   `r_condition[A,Intercept]` `r_condition[B,Intercept]`
#>                        <dbl>                      <dbl>
#> 1                      -0.2                        0.98
#> 2                       0.16                       0.53
#> 3                      -0.82                       0.44
#> 4                      -0.28                       0.15
#> 5                      -0.53                       0.74
#> 6                      -0.25                       0.41

d %>% 
  select(
    param_glue(
      "r_condition[{level},{type}]", 
      level = c("A", "B"), 
      type = c("Intercept", "Slope"))
    )
#> # A tibble: 6 x 4
#>   `r_condition[A,In~ `r_condition[B,In~ `r_condition[A,S~ `r_condition[B,S~
#>                <dbl>              <dbl>             <dbl>             <dbl>
#> 1              -0.2                0.98              0.86             -1.18
#> 2               0.16               0.53              0.78             -0.96
#> 3              -0.82               0.44              0.19             -1.66
#> 4              -0.28               0.15             -0.14             -1.62
#> 5              -0.53               0.74              0.87             -1.53
#> 6              -0.25               0.41              0.49             -1.1

<sup>Created on 2019-05-20 by the reprex package (v0.3.0)</sup>

jgabry

comment created time in 2 months

pull request commentstan-dev/bayesplot

Add sbc_hist

Aah shoot, that last figure has 33 bins because of how 129 was being rounded.

General problem still stands.

jpritikin

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pull request commentstan-dev/bayesplot

Add sbc_hist

What's the point of the butterfly shape, as opposed to a ribbon?

I don't think the bin boundaries are working correctly. First one straddles 0 and last one do not appear to evenly line up with 128, at least on my device. Histograms work on continuous data, so they don't respect the discrete boundaries of the rankings by default.

The use of the ribbon expands the x-axis so that the nonsensical values are part of the binning, so I think we are getting 128 ranks pushed into 29 bins which is causing uneven binning.

sbc_hist(ranks) + 
  geom_text(aes(label = stat(count)), stat = "bin", bins = 32)

image

This is probably a better sense of what 32 bins should be...

sbc_hist(ranks) + 
  stat_count(aes(x = plyr::round_any(u, 4, floor)))

image

jpritikin

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pull request commentstan-dev/bayesplot

Feature tidyselect

num_range() doesn't work if the parameters have brackets in their names like "beta[1]", "beta[2]", ..., "beta[10]", which is far more common. But we could conceivably export a helper function like this:

This is great.

jgabry

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issue commentstan-dev/bayesplot

Facilitating DAGs

greta does something like this. https://greta-stats.org/articles/get_started.html#plotting

image

But I don't know how its DAG building is implemented, but the plotting uses DiagrammeR. I've used to DiagrammeR convert lavaan SEM models into graphs. DiagrammeR builds graphs from dataframes, so the main task would be to have a language that describe interrelations among model parameters and then use that build a dataframe for plotting.

bgoodri

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Pull request review commentstan-dev/bayesplot

Use markdown in roxygen

 % Please edit documentation in R/bayesplot-ggplot-themes.R \name{theme_default} \alias{theme_default}-\title{Default bayesplot plotting theme}+\title{Default \strong{bayesplot} plotting theme} \usage{ theme_default(base_size = getOption("bayesplot.base_size", 12),   base_family = getOption("bayesplot.base_family", "serif")) } \arguments{ \item{base_size, base_family}{Base font size and family (passed to-\code{\link[ggplot2]{theme_bw}}). It is possible to set-\code{"bayesplot.base_size"} and \code{"bayesplot.base_family"} via-\code{\link{options}} to change the defaults, which are \code{12} and-\code{"serif"}, respectively.}+\code{\link[ggplot2:theme_bw]{ggplot2::theme_bw()}}). It is possible to set \code{"bayesplot.base_size"} and+\code{"bayesplot.base_family"} via \code{\link[=options]{options()}} to change the defaults, w

i fixed it

jgabry

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push eventstan-dev/bayesplot

TJ Mahr

commit sha 4a94f368e10361ce4463f35b94e98e205a82d76b

fix typos

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Pull request review commentstan-dev/bayesplot

Use markdown in roxygen

 \name{bayesplot-package} \alias{bayesplot-package} \alias{bayesplot}-\title{Plots for Bayesian Models}+\title{\strong{bayesplot}: Plotting for Bayesian Models} \description{ \if{html}{    \figure{stanlogo.png}{options: width="50px" alt="mc-stan.org"}-   \emph{Stan Development Team} }+\emph{Stan Development Team} -The \pkg{bayesplot} package provides a variety of \pkg{ggplot2}-based+The \strong{bayesplot} package provides a variety of \strong{ggplot2}-based plotting functions for use after fitting Bayesian models (typically, though not exclusively, via Markov chain Monte Carlo). The package is designed not only to provide convenient functionality for users, but also a common set of functions that can be easily used by developers working on a variety of packages for Bayesian modeling, particularly (but not necessarily) packages-powered by \pkg{\link[rstan]{rstan}}. Examples of packages that will soon (or-already are) using \pkg{bayesplot} are \pkg{rstan} itself, as well as the-\pkg{rstan}-dependent \pkg{rstanarm} and \pkg{brms} packages for applied-regression modeling.+powered by \link[rstan:rstan-package]{rstan} (the \R interface to Stan).+Examples of packages that will soon (or already are) using \strong{bayesplot} are+\strong{rstan} itself, as well as the \strong{rstan}-dependent \strong{rstanarm} and+\strong{brms} packages for applied regression modeling. }

This is just an observation... I'm noticing that all the \pkg tags turn into \strong codes. I guess that will cause them to turn bold on the pkgdown site too, which is fine.

jgabry

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Pull request review commentstan-dev/bayesplot

Use markdown in roxygen

 % Please edit documentation in R/bayesplot-ggplot-themes.R \name{theme_default} \alias{theme_default}-\title{Default bayesplot plotting theme}+\title{Default \strong{bayesplot} plotting theme} \usage{ theme_default(base_size = getOption("bayesplot.base_size", 12),   base_family = getOption("bayesplot.base_family", "serif")) } \arguments{ \item{base_size, base_family}{Base font size and family (passed to-\code{\link[ggplot2]{theme_bw}}). It is possible to set-\code{"bayesplot.base_size"} and \code{"bayesplot.base_family"} via-\code{\link{options}} to change the defaults, which are \code{12} and-\code{"serif"}, respectively.}+\code{\link[ggplot2:theme_bw]{ggplot2::theme_bw()}}). It is possible to set \code{"bayesplot.base_size"} and+\code{"bayesplot.base_family"} via \code{\link[=options]{options()}} to change the defaults, w

which split across lines

jgabry

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