Display a specification curve across the multiverse.

Returns a ggplot object that displays the specification curve as proposed by (Simonsohn et al. 2020) . Note that the order of universes may not correspond to the order in the summary table.

spec_curve(
  .spec_summary,
  label = "name",
  order_by = c("estimate", "is_significant"),
  colour_by = "is_significant",
  palette_common = NULL,
  pointsize = 2,
  linewidth = 0.5,
  spec_matrix_spacing = 10,
  theme_common = ggplot2::theme_minimal(),
  sep = "---"
)

Arguments

.spec_summary

A specification table created using spec_summary().

label

If "name", uses the branch option names. If "code", display the codes used to define the branch options.

order_by

A character vector by which the curve is sorted.

colour_by

The name of the variable to colour the curve.

palette_common

A character vector of colours to match the values of the variable colour_by in the specification curve and the specification matrix. The palette must contain more colours than the number of unique values of colour_by variable.

pointsize

Size of the points in the specification curve and the specification matrix.

linewidth

Width of confidence interval lines.

spec_matrix_spacing

A numeric for adjusting the specification matrix spacing passed to combmatrix.label.extra_spacing in ggupset::theme_combmatrix().

theme_common

A ggplot theme to be used for both the specification curve and the specification matrix.

sep

A string used internally to create the specification matrix. The string must be distinct from all branch names, option names, and option codes. Use a different value if any of them contains the default value.

Value

a ggplot object with the specification curve plot for the estimates passed in the spec_summary().

References

Simonsohn U, Simmons JP, Nelson LD (2020). “Specification curve analysis.” Nature Human Behaviour, 4(11), 1208–1214. doi:10.1038/s41562-020-0912-z .

See also

Other specification curve analysis: spec_summary()

Examples

femininity <- mutate_branch(
  1 * (MasFem > 6), 1 * (MasFem > mean(MasFem))
)
y <- mutate_branch(log(alldeaths + 1), alldeaths)
intensity <- mutate_branch(
  Minpressure_Updated_2014,
  Category,
  NDAM,
  HighestWindSpeed
)
model <- formula_branch(
  y ~ femininity,
  y ~ femininity * intensity
)
family <- family_branch(
  gaussian, poisson
)
match_poisson <- branch_condition(alldeaths, poisson)
match_gaussian <- branch_condition(log(alldeaths + 1), gaussian)
stable <- mverse(hurricane) %>%
  add_mutate_branch(y, femininity, intensity) %>%
  add_formula_branch(model) %>%
  add_family_branch(family) %>%
  add_branch_condition(match_poisson, match_gaussian) %>%
  glm_mverse() %>%
  spec_summary("femininity")
# default behaviour
spec_curve(stable)

# coloring and sorting based on other variable
stable %>%
  dplyr::mutate(colour_by = y_branch) %>%
  spec_curve(order_by = c("estimate", "colour_by"), colour_by = "colour_by")

# Because the output is a \code{ggplot} object, you can
# further modify the asethetics of the specification curve
# using \code{ggplot2::theme()} and the specication matrix
# using \code{ggupset::theme_combmatrix()}
spec_curve(stable) +
  ggplot2::labs(y = "Estimates", colour = "Significant at 0.05 level",
                title = "Specification curve of femininity") +
  ggplot2::theme(legend.position = "bottom") +
  ggupset::theme_combmatrix(
    combmatrix.label.width = ggplot2::unit(c(25, 100, 0, 0), "pt")
  )