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[]), sum(metric_list[]))
distribution_estimate(length(metric_list[]), sum(metric_list[]))
de = distribution_estimate(metric_list[])
plot(de\$x, de\$y, type="l")
barplot(de\$heights, de\$widths)
distribution_estimate(metric_list[], observed=TRUE)
```

[Package bandit version 0.5.0 Index]