Summarise and annotate data

Usage

bru_summarise(
  data,
  probs = c(0.025, 0.5, 0.975),
  x = NULL,
  cbind.only = FALSE,
  max_moment = 2
)

Arguments

data: A list of samples, each either numeric or a data.frame
probs: A numeric vector of probabilities with values in [0, 1], passed to stats::quantile
x: A data.frame of data columns that should be added to the summary data frame
cbind.only: If TRUE, only cbind the samples and return a matrix where each column is a sample
max_moment: integer, at least 2. Determines the largest moment order information to include in the output. If max_moment > 2, includes "skew" (skewness, E[(x-m)^3/s^3]), and if max_moment > 3, includes "ekurtosis" (excess kurtosis, E[(x-m)^4/s^4] - 3). Default 2. Note that the Monte Carlo variability of the ekurtois estimate may be large.

Value

A data.frame or Spatial[Points/Pixels]DataFrame with summary statistics, "mean", "sd", paste0("q", probs), "mean.mc_std_err", "sd.mc_std_err"

Examples

bru_summarise(matrix(rexp(10000), 10, 1000), max_moment = 4, probs = NULL)
#>         mean        sd     skew mean.mc_std_err sd.mc_std_err ekurtosis
#> 1  0.9558905 0.9389345 2.009806      0.03230460    0.04131330  5.742065
#> 2  1.0611409 1.0489573 1.873341      0.03591258    0.04334911  4.829320
#> 3  1.0536426 1.1239889 2.331875      0.03912434    0.05661570  8.146682
#> 4  1.0299244 0.9892172 1.623983      0.03345313    0.03433187  2.816050
#> 5  1.0037306 1.0177351 2.168777      0.03527648    0.04890251  7.233336
#> 6  0.9526178 0.9267548 1.623076      0.03133645    0.03209540  2.795507
#> 7  0.9973735 0.9967847 2.027467      0.03439063    0.04537118  6.285380
#> 8  1.0132720 0.9639491 1.702073      0.03276525    0.03608960  3.604808
#> 9  0.9733775 0.9625180 1.674963      0.03259972    0.03418782  3.044439
#> 10 1.0068927 1.0551332 2.316476      0.03668425    0.05246231  7.886724