| 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)