View source: R/summarise_specs.r
summarise_specs | R Documentation |
This function is deprecated because the new version of specr uses a new analytic framework.
In this framework, you can plot a similar figure simply by using the generic
plot()
function.
This function allows to inspect results of the specification curves by returning a comparatively simple summary of the results. This summary can be produced for various specific analytical choices and customized summary functions.
summarise_specs( df, ..., var = .data$estimate, stats = list(median = median, mad = mad, min = min, max = max, q25 = function(x) quantile(x, prob = 0.25), q75 = function(x) quantile(x, prob = 0.75)) )
df |
a data frame resulting from |
... |
one or more grouping variables (e.g., subsets, controls,...) that denote the available analytical choices. |
var |
which variable should be evaluated? Defaults to estimate (the effect sizes computed by |
stats |
named vector or named list of summary functions (individually defined summary functions can included). If it is not named, placeholders (e.g., "fn1") will be used as column names. |
a tibble.
plot_summary()
to visually investigate the affect of analytical choices.
# Run specification curve analysis results <- run_specs(df = example_data, y = c("y1", "y2"), x = c("x1", "x2"), model = c("lm"), controls = c("c1", "c2"), subsets = list(group1 = unique(example_data$group1), group2 = unique(example_data$group2))) # overall summary summarise_specs(results) # Summary of specific analytical choices summarise_specs(results, # data frame x, y) # analytical choices # Summary of other parameters across several analytical choices summarise_specs(results, subsets, controls, var = p.value, stats = list(median = median, min = min, max = max)) # Unnamed vector instead of named list passed to `stats` summarise_specs(results, controls, stats = c(mean = mean, median = median))
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