View source: R/distribution_estimate.R
distribution_estimate | R Documentation |
A convenience function to perform overall metric analysis: mean, median, CI.
distribution_estimate(v, successes=NULL, num_quantiles=101, observed=FALSE)
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 |
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 |
Thomas Lotze <thomaslotze@thomaslotze.com>
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)
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