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 1.0010880 1.0156789 2.180656 7.320498 0.04903347 0.03521974
#> 2 0.9519697 0.9268446 1.624580 2.797863 0.03210639 0.03133999
#> 3 0.9970469 0.9970097 2.026603 6.280112 0.04536699 0.03439748
#> 4 1.0131649 0.9639471 1.702418 3.605621 0.03609214 0.03276535
#> 5 0.9747426 0.9622593 1.672917 3.040846 0.03416647 0.03259019
#> 6 1.0068025 1.0550962 2.316933 7.889020 0.05246657 0.03668335
#> 7 0.9582877 0.9409593 1.998839 5.666845 0.04120082 0.03236152
#> 8 1.0636858 1.0498080 1.865887 4.790975 0.04326234 0.03593399
#> 9 1.0529362 1.1242614 2.331727 8.141976 0.05661630 0.03913300
#> 10 1.0313465 0.9896316 1.618757 2.797992 0.03428182 0.03346307