plot_curve | 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 and
adding the argument type = "curve"
.
This function plots the a ranked specification curve. Confidence intervals can be included. Significant results are highlighted (negative = red, positive = blue, grey = nonsignificant). This functions creates the upper panel in plot_specs()
.
plot_curve( df, var = .data$estimate, group = NULL, desc = FALSE, ci = TRUE, ribbon = FALSE, legend = FALSE, null = 0 )
df |
a data frame resulting from |
var |
which variable should be evaluated? Defaults to estimate (the effect sizes computed by |
group |
Should the arrangement of the curve be grouped by a particular choice? Defaults to NULL, but can be any of the present choices (e.g., x, y, controls...) |
desc |
logical value indicating whether the curve should the arranged in a descending order. Defaults to FALSE. |
ci |
logical value indicating whether confidence intervals should be plotted. |
ribbon |
logical value indicating whether a ribbon instead should be plotted. |
legend |
logical value indicating whether the legend should be plotted Defaults to FALSE. |
null |
Indicate what value represents the null hypothesis (Defaults to zero) |
a ggplot object.
# load additional library library(ggplot2) # for further customization of the plots # Run specification curve analysis results <- run_specs(df = example_data, y = c("y1", "y2"), x = c("x1", "x2"), model = c("lm"), controls = c("c1", "c2"), subsets = list(group1 = unique(example_data$group1), group2 = unique(example_data$group2))) # Plot simple specification curve plot_curve(results) # Ribbon instead of CIs and customize further plot_curve(results, ci = FALSE, ribbon = TRUE) + geom_hline(yintercept = 0) + geom_hline(yintercept = median(results$estimate), linetype = "dashed") + theme_linedraw()
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