pdqr (version 0.2.0)

summ_center: Summarize distribution with center

Description

Functions to compute center of distribution. summ_center() is a wrapper for respective summ_*() functions (from this page) with default arguments.

Usage

summ_center(f, method = "mean")
summ_mean(f)
summ_median(f)
summ_mode(f, method = "global")

Arguments

f

A pdqr-function representing distribution.

method

Method of center computation. For summ_center() is one of "mean", "median", "mode". For summ_mode() is one of "global" or "local".

Value

summ_center(), summ_mean(), summ_median() and summ_mode(*, method = "global") always return a single number representing a center of distribution. summ_mode(*, method = "local") can return a numeric vector with multiple values representing local maxima.

Details

summ_mean() computes distribution's mean.

summ_median() computes a smallest x value for which cumulative probability is not less than 0.5. Essentially, it is a as_q(f)(0.5). This also means that for pdqr-functions with type "discrete" it always returns an entry of "x" column from f's "x_tbl" metadata.

summ_mode(*, method = "global") computes a smallest x (which is an entry of "x" column from f's x_tbl) with the highest probability/density. summ_mode(*, method = "local") computes all x values which represent non-strict local maxima of probability mass/density function.

See Also

summ_spread() for computing distribution's spread, summ_moment() for general moments.

Other summary functions: summ_classmetric, summ_distance, summ_entropy, summ_hdr, summ_interval, summ_moment, summ_order, summ_prob_true, summ_pval, summ_quantile, summ_roc, summ_separation, summ_spread

Examples

# NOT RUN {
# Type "continuous"
d_norm <- as_d(dnorm)
  # The same as `summ_center(d_norm, method = "mean")`
summ_mean(d_norm)
summ_median(d_norm)
summ_mode(d_norm)

# Type "discrete"
d_pois <- as_d(dpois, lambda = 10)
summ_mean(d_pois)
summ_median(d_pois)
  # Returns the smallest `x` with highest probability
summ_mode(d_pois)
  # Returns all values which are non-strict local maxima
summ_mode(d_pois, method = "local")

# Details of computing local modes
my_d <- new_d(data.frame(x = 11:15, y = c(0, 1, 0, 2, 0)/3), "continuous")
  # Several values, which are entries of `x_tbl`, are returned as local modes
summ_mode(my_d, method = "local")

# }