distribution_estimate {bandit} R Documentation

## summarize_metrics

### Description

A convenience function to perform overall metric analysis: mean, median, CI.

### Usage

distribution_estimate(v, successes=NULL, num_quantiles=101, observed=FALSE)


### Arguments

 v a vector of values to be analyzed (for nonbinary data), or number of trials (for binary data) successes number of successes (for binary data) num_quantiles number of quantiles to split into observed whether to generate the observed distribution (rather than the estimated distribution of the mean); default FALSE

### Value

a data frame with the following columns:

 quantiles the estimated quantiles (0,0.01,0.02,...,1) for the mean, using a Beta-binomial estimate of p for binomial data, a bootstrapped quantile distribution for real-valued numbers x x values for plotting a lineplot of the estimated distribution y y values for plotting a lineplot of the estimated distribution mids mid values for plotting a barplot of the estimated distribution lefts left values for plotting a barplot of the estimated distribution rights right values for plotting a barplot of the estimated distribution widths width values for plotting a barplot of the estimated distribution heights height values for plotting a barplot of the estimated distribution probabilities probabilities indicating how much probability is contained in each barplot

### Author(s)

Thomas Lotze <thomaslotze@thomaslotze.com>

### Examples

metric_list = list(rbinom(n=100,size=1,prob=0.5),
rbinom(n=100,size=1,prob=0.7),
rpois(n=100, lambda=5))
distribution_estimate(length(metric_list[[1]]), sum(metric_list[[1]]))
distribution_estimate(length(metric_list[[2]]), sum(metric_list[[2]]))
de = distribution_estimate(metric_list[[3]])
plot(de$x, de$y, type="l")
barplot(de$heights, de$widths)
distribution_estimate(metric_list[[3]], observed=TRUE)


[Package bandit version 0.5.1 Index]