info.stats.lsd | R Documentation |
This function reads a 3 or 4-dimensional array produced by read.3d.lsd
or read.4d.lsd
and produces a list with 2D data frames containing the (Monte Carlo) mean, the standard deviation, the maximum, the minimum, and other optional statistics for each variable, at each time step.
info.stats.lsd( array, rows = 1, cols = 2, median = FALSE, ci = c( "none", "mean", "median", "auto" ), ci.conf = 0.95, ci.boot = NULL, boot.R = 999, seed = 1, na.rm = TRUE, inf.rm = TRUE )
array |
a 3D or 4D array as produced by |
rows |
an integer array dimension to be used as the rows for the statistics matrices, default is to use first array dimension. |
cols |
an integer array dimension to be used as the columns for the statistics matrices, default is to use second array dimension. |
median |
a logical value indicating if ( |
ci |
a character string specifying the type of confidence interval to compute, must be one of |
ci.conf |
confidence level of the confidence interval. |
ci.boot |
a character string specifying the type of bootstrap confidence interval to compute, must be one of |
boot.R |
number of bootstrap replicates. |
seed |
a single value, interpreted as an integer to define the pseudo-random number generator state used for the bootstrap process, or |
na.rm |
a logical value indicating whether |
inf.rm |
a logical value indicating whether non-finite values should be stripped before the computation proceeds. |
Returns a list containing four to seven matrices, with the original size and naming of the selected 2 dimensions of the argument.
avg |
a matrix with the mean of the MC experiments |
sd |
a matrix with the standard deviation of the MC experiments |
max |
a matrix with the maximum value of the MC experiments |
min |
a matrix with the minimum value of the MC experiments |
med |
a matrix with the median of the MC experiments (only present if argument |
mad |
a matrix with the median absolute deviation of the MC experiments (only present if argument |
ci.hi |
a matrix with the maximum value of the MC experiments (only present if argument |
ci.lo |
a matrix with the minimum value of the MC experiments (only present if argument |
n |
a matrix with the number of observations available for computation of statistics |
Marcelo C. Pereira
list.files.lsd()
read.3d.lsd()
,
read.4d.lsd()
,
info.dimensions.lsd()
# get the list of file names of example LSD results files <- list.files.lsd( system.file( "extdata", package = "LSDinterface" ) ) # read first instance of all variables from MC files (3D array) inst1Array <- read.3d.lsd( files ) # create statistics data frames for the variables inst1Stats <- info.stats.lsd( inst1Array ) print( inst1Stats$avg[ 10 : 20, ] ) print( inst1Stats$sd[ 10 : 20, ] ) # organize the stats, including medians, by variable (dim=2) and file (dim=3) inst1Stats2 <- info.stats.lsd( inst1Array, rows = 2, cols = 3, median = TRUE ) print( inst1Stats2$med[ , 1 : 2 ] ) # the same but for all instance of all variables (from a 4D array) # and a normal (non-boostrap) confidence intervals for the means allArray <- read.4d.lsd( files ) allStats <- info.stats.lsd( allArray, ci = "auto" ) print( allStats$ci.lo[ 3, 1 : 7 ] ) print( allStats$avg[ 3, 1 : 7 ] ) print( allStats$ci.hi[ 3, 1 : 7 ] ) # organize the stats by file (dim=4) and variable (dim=2) # plus boostrat confidence intervals for the median allStats2 <- info.stats.lsd( allArray, rows = 4, cols = 2, median = TRUE, ci = "auto", ci.boot = "bca" ) print( allStats2$ci.lo[ , 1 : 3 ] ) print( allStats2$med[ , 1 : 3 ] ) print( allStats2$ci.hi[ , 1 : 3 ] )
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