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  0.9584175 0.9437467 1.988361      0.03244409    0.04111276  5.589055
#> 2  1.0605426 1.0478110 1.877061      0.03587863    0.04338551  4.855778
#> 3  1.0497680 1.1237562 2.337244      0.03912182    0.05669211  8.178308
#> 4  1.0283020 0.9890142 1.622844      0.03344602    0.03432092  2.814957
#> 5  1.0017529 1.0169678 2.183948      0.03525910    0.04901147  7.288539
#> 6  0.9512145 0.9192355 1.595441      0.03105789    0.03145069  2.680380
#> 7  0.9963307 0.9981031 2.022379      0.03442999    0.04533432  6.250077
#> 8  1.0123914 0.9654978 1.701680      0.03281316    0.03607263  3.581580
#> 9  0.9760245 0.9621343 1.673310      0.03258573    0.03415843  3.039783
#> 10 1.0042888 1.0577224 2.308991      0.03676171    0.05239242  7.812165