Results #1
Extracting the results from the multiverse object
Next, we need to extract the results from the multiverse. The results
for each unique analysis combination (a universe in our multiverse), is
stored in an environment in the .results column. We can
extract data frames from this column using the function. This creates a
new column in our data frame, summary which itself consists
of data frames.
## # A tibble: 6 × 9
## .universe ci_method data_transform conf_level .parameter_assignment
## <int> <chr> <chr> <chr> <list>
## 1 1 t based log-transformed 50% <named list [3]>
## 2 2 t based log-transformed 67 <named list [3]>
## 3 3 t based log-transformed 95% <named list [3]>
## 4 4 t based log-transformed 99% <named list [3]>
## 5 5 t based untransformed 50% <named list [3]>
## 6 6 t based untransformed 67 <named list [3]>
## # ℹ 4 more variables: .code <list>, .results <list>, .errors <list>,
## # summary <list>As we can see above, each row in the summary column
consists of a \(4 \times 4\), data
frame which we will need to unpack. We will use the function to expand
the different columns of the data frame into their own columns. Finally,
we use the function to unnest the rows of the data frame into their own
row. Note that we have a .universe column which indexes
each universe in our multiverse i.e. each unique analysis
combination.
Below we can see the result of this transformation. You can see that
we have created four new columns (modality,
estimate, conf.low, conf.high).
In addition, we have four rows for each universe corresponding to the
results for each of the four conditions in our experiment.
df.mtbl <- unnest_wider(df.mtbl, c(summary))
df.mtbl <- unnest(df.mtbl, cols = c(modality, estimate, conf.low, conf.high))
head(df.mtbl)## # A tibble: 6 × 12
## .universe ci_method data_transform conf_level .parameter_assignment
## <int> <chr> <chr> <chr> <list>
## 1 1 t based log-transformed 50% <named list [3]>
## 2 1 t based log-transformed 50% <named list [3]>
## 3 1 t based log-transformed 50% <named list [3]>
## 4 1 t based log-transformed 50% <named list [3]>
## 5 2 t based log-transformed 67 <named list [3]>
## 6 2 t based log-transformed 67 <named list [3]>
## # ℹ 7 more variables: .code <list>, .results <list>, .errors <list>,
## # modality <chr>, estimate <dbl>, conf.low <dbl>, conf.high <dbl>Visualising the results
We will then sort the results, and transform the log transformed variables back on to the natural scale. We are then ready to visualise the result by animating over each universe.
df.mtbl <- arrange(df.mtbl, conf_level, desc(data_transform), desc(ci_method))
df.results <- df.mtbl
df.results$estimate[df.mtbl$data_transform == "log-transformed"] = exp(df.mtbl$estimate[df.mtbl$data_transform == "log-transformed"])
df.results$conf.low[df.mtbl$data_transform == "log-transformed"] = exp(df.mtbl$conf.low[df.mtbl$data_transform == "log-transformed"])
df.results$conf.high[df.mtbl$data_transform == "log-transformed"] = exp(df.mtbl$conf.high[df.mtbl$data_transform == "log-transformed"])
df.results |> head()## # A tibble: 6 × 12
## .universe ci_method data_transform conf_level .parameter_assignment
## <int> <chr> <chr> <chr> <list>
## 1 5 t based untransformed 50% <named list [3]>
## 2 5 t based untransformed 50% <named list [3]>
## 3 5 t based untransformed 50% <named list [3]>
## 4 5 t based untransformed 50% <named list [3]>
## 5 13 bootstrap untransformed 50% <named list [3]>
## 6 13 bootstrap untransformed 50% <named list [3]>
## # ℹ 7 more variables: .code <list>, .results <list>, .errors <list>,
## # modality <chr>, estimate <dbl>, conf.low <dbl>, conf.high <dbl>p <- df.results |>
ggplot() +
geom_vline( xintercept = 0, colour = '#979797' ) +
geom_point( aes(x = estimate, y = modality)) +
geom_errorbarh( aes(xmin = conf.low, xmax = conf.high, y = modality), height = 0) +
transition_manual( .universe ) +
theme_minimal()
animate(p, nframes = 28, fps = 2)## `nframes` and `fps` adjusted to match transition
The figure above shows the (geometric) mean completion time for each condition. At first sight, physical touch appears to be consistently faster than the other conditions, across all possible analysis combinations specified in the multiverse. However, since condition is a within-subject factor, it is preferable to examine within-subject differences, which we show in the next section.
