s.curve: Specification curve maker function
In jmobrien/SpecCurve: Constructing, Running, and Visualizing Specification Curves

Description Usage Arguments Value

View source: R/s.curve.R

Specification curve maker function

s.curve(dat, outcomes, treatment, cov.list, no.cov.exclude = NULL,
  extra.models = NULL, extra.treatment = NULL, mod.type = lm,
  mod.family = NULL, alpha = 0.05, tail = NULL, subsets = NULL,
  subsets.exclude = TRUE, weights = NULL, weights.exclude = TRUE,
  weights.ipw.vars = NULL, permutations = NULL, perm.pvalues = FALSE,
  perm.seed = NULL, cluster = FALSE, cluster.var = NULL,
  robust.se = NULL, cat.percent = TRUE, keep.tidy.models = TRUE,
  keep.full.models = FALSE, model.only = FALSE)

`dat`	a dataframe containing the variables
`outcomes`	vector of outcome variables
`treatment`	vector of treatment variables
`cov.list`	named list, sets of covariates / moderators - write moderators as "var1:var2"; eg: cov.list = list(gender = c("gender.roster", "gender.selfreport")
`no.cov.exclude`	Will add a model where each item of the list above is missing, unless specified here
`extra.models`	Models written literally; will be appended to the set (still crossed against subsets and weights)
`extra.treatment`	Treatment variable in extra models (length 1 or same as other, needed if doing inverse probability weighting
`mod.type`	takes lm, glm
`mod.family`	if family needed
`alpha`	nominal alpha value (if one tailed, will test against p <= .1 on that side)
`tail`	One tailed test? Takes "upper" and "lower"
`subsets`	runs subsets of data based on the specifications listed here (vector of conditions)
`subsets.exclude`	if subsets added, includes an un-subsetted version
`weights`	vector of weight variables names to add to runs, re-named if desired or, a weighting method (currenly accepts "ipw.calc" for inverse probability weighting)
`weights.exclude`	Includes an unweighted version where weights added
`weights.ipw.vars`	in addition to treatment variable, any other (factor or categorical) variables to consider when (re)weighting subsets
`permutations`	optional, number of permutations for p-curve
`perm.pvalues`	logical, calculates permutation test-based pvalues (if permutation test active)
`perm.seed`	optional, RNG seed to use for permutation test (integer, will create one if not present)
`cluster`	optional, Cluster robust standard errors
`cluster.var`	optional, Variable on which to cluster, string
`robust.se`	optional, heteroskedasticity-consistent standard error adjustment provided by package "sandwich". See details in ?vcovHC.
`cat.percent`	logical, displays summary output of end of the data for convenience in interactive mode. Set to false if using as a part of an Rmarkdown file.
`keep.tidy.models`	logical, set to false for large model samples
`keep.full.models`	logical, set to false for large model samples
`model.only`	logical, outputs the model for later use, rather than running (default is FALSE)