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 tostats::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 ifmax_moment > 3
, includes "ekurtosis" (excess kurtosis,E[(x-m)^4/s^4] - 3
). Default 2. Note that the Monte Carlo variability of theekurtois
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 ekurtosis sd.mc_std_err mean.mc_std_err
#> 1 0.9973735 0.9967847 2.027467 6.285380 0.04537118 0.03439063
#> 2 1.0132720 0.9639491 1.702073 3.604808 0.03608960 0.03276525
#> 3 0.9733775 0.9625180 1.674963 3.044439 0.03418782 0.03259972
#> 4 1.0068927 1.0551332 2.316476 7.886724 0.05246231 0.03668425
#> 5 0.9584722 0.9408649 1.999007 5.668644 0.04120152 0.03235857
#> 6 1.0625965 1.0501995 1.866235 4.787704 0.04326805 0.03594674
#> 7 1.0528953 1.1242920 2.331585 8.141165 0.05661557 0.03913392
#> 8 1.0294244 0.9895526 1.623136 2.812150 0.03432960 0.03346359
#> 9 1.0010880 1.0156789 2.180656 7.320498 0.04903347 0.03521974
#> 10 0.9519697 0.9268446 1.624580 2.797863 0.03210639 0.03133999