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 mean.mc_std_err sd.mc_std_err
#> 1 0.9949751 0.9899557 2.019045 7.130587 0.03130515 0.04730235
#> 2 1.0318434 1.0264235 1.923639 5.019422 0.03245836 0.04300403
#> 3 1.0061404 1.0089783 2.092968 6.925944 0.03190670 0.04766808
#> 4 0.9574418 0.9278591 2.023610 6.512844 0.02934148 0.04280953
#> 5 0.9995928 0.9756275 1.853696 4.541739 0.03085205 0.03946091
#> 6 0.9971444 1.0325341 1.983368 5.464365 0.03265160 0.04460968
#> 7 1.0193401 0.9656328 1.818538 4.702006 0.03053599 0.03953205
#> 8 1.0217978 0.9780189 1.658922 3.564306 0.03092767 0.03648387
#> 9 1.0543137 1.0398753 1.896702 4.803651 0.03288375 0.04289298
#> 10 0.9491824 0.9642585 1.982007 5.717452 0.03049253 0.04236008