Display a specification curve across the multiverse.

spec_curve returns the specification curve as proposed by Simonsohn, Simmons, and Nelson (2020) <doi:10.1038/s41562-020-0912-z>. spec_curve are available for mverse objects fitted with lm_mverse(), glm_mverse(), and glm.nb_mverse(). Notice that the order of universes may not correspond to the order in the summary table.

spec_curve(.object, var, ...)

# S3 method for lm_mverse
spec_curve(
  .object,
  var,
  conf.int = TRUE,
  conf.level = 0.95,
  option = names(multiverse::parameters(.object)),
  universe_order = FALSE,
  color_order = FALSE,
  color = NULL,
  branch_order = NULL,
  point_size = 0.25,
  grid_size = 2,
  point_alpha = 1,
  brewer_palette = "Set2",
  yaxis_text_size = 8,
  ...
)

# S3 method for glm_mverse
spec_curve(
  .object,
  var,
  conf.int = TRUE,
  conf.level = 0.95,
  option = names(multiverse::parameters(.object)),
  universe_order = FALSE,
  color_order = FALSE,
  color = NULL,
  branch_order = NULL,
  point_size = 0.25,
  grid_size = 2,
  point_alpha = 1,
  brewer_palette = "Set2",
  yaxis_text_size = 8,
  ...
)

# S3 method for glm.nb_mverse
spec_curve(
  .object,
  var,
  conf.int = TRUE,
  conf.level = 0.95,
  option = names(multiverse::parameters(.object)),
  universe_order = FALSE,
  color_order = FALSE,
  color = NULL,
  branch_order = NULL,
  point_size = 0.25,
  grid_size = 2,
  point_alpha = 1,
  brewer_palette = "Set2",
  yaxis_text_size = 8,
  ...
)

Source

Uri Simonsohn, Joseph P. Simmons, and Leif D. Nelson. (2020). “Specification curve analysis” Nature Human Behaviour, 4, 1208–14. doi:10.1038/s41562-020-0912-z

Arguments

.object: a glm.nb_mverse object.
var: name for the variable to show.
...: ignored.
conf.int: when TRUE (default), the estimate output includes the confidence intervals.
conf.level: the confidence level of the confidence interval returned using conf.int = TRUE. Default value is 0.95.
option: a vector of branches to show the options included.
universe_order: when TRUE, order the universes according to the order in the summary table.
color_order: when TRUE, the estimated value will be ordered according to the color.
color: an expression to indicate how colors are assigned to markers. By default, colors are assigned based on 'p.value <= 0.05'.
branch_order: name for the branch to order.
point_size: size of points on the top plot.
grid_size: size of points on the bottom plot.
point_alpha: alpha level of points and point ranges.
brewer_palette: name of colorbrewer palette for the plot.
yaxis_text_size: text size of y-axis label

Value

a specification curve plot for the estimates

Examples

# \donttest{

# Display a specification curve for \code{lm} models
# fitted across the multiverse.
femininity <- mutate_branch(
  MasFem > 6, MasFem > mean(MasFem)
)
model <- formula_branch(
  alldeaths ~ femininity,
  alldeaths ~ femininity + HighestWindSpeed
)
mv <- mverse(hurricane) %>%
  add_mutate_branch(femininity) %>%
  add_formula_branch(model) %>%
  lm_mverse()
spec_curve(mv, var = "femininityTRUE")

# plot based on 90% confidence interval
spec_curve(mv, var = "femininityTRUE", color = p.value < .1)

# }
# \donttest{

# Display a specification curve for \code{glm} models
# fitted across the multiverse.
femininity <- mutate_branch(
  MasFem > 6, MasFem > mean(MasFem)
)
model <- formula_branch(
  alldeaths ~ femininity,
  alldeaths ~ femininity + HighestWindSpeed
)
fam <- family_branch(gaussian)
mv <- mverse(hurricane) %>%
  add_mutate_branch(femininity) %>%
  add_formula_branch(model) %>%
  add_family_branch(fam) %>%
  glm_mverse()
spec_curve(mv, var = "femininityTRUE")

# plot based on 90% confidence interval
spec_curve(mv, var = "femininityTRUE", color = p.value < .1)

# }
# \donttest{

# Display a specification curve for \code{glm.nb} models
# fitted across the multiverse.
femininity <- mutate_branch(
  MasFem > 6, MasFem > mean(MasFem)
)
model <- formula_branch(
  alldeaths ~ femininity,
  alldeaths ~ femininity + HighestWindSpeed
)
mv <- mverse(hurricane) %>%
  add_mutate_branch(femininity) %>%
  add_formula_branch(model) %>%
  glm.nb_mverse()
spec_curve(mv, var = "femininityTRUE")

# plot based on 90% confidence interval
spec_curve(mv, var = "femininityTRUE", color = p.value < .1)

# }