s2dverification (version 2.10.3)

Eno: Computes Effective Sample Size With Classical Method

Description

Computes the effective number of independent values along the posdim dimension of a matrix. This effective number of independent observations can be used in statistical/inference tests. Based on eno function from Caio Coelho from rclim.txt.

Usage

Eno(obs, posdim)

Arguments

obs

Matrix of any number of dimensions up to 10.

posdim

Dimension along which to compute the effective sample size.

Value

Same dimensions as var but without the posdim dimension.

Examples

# NOT RUN {
# See examples on Load() to understand the first lines in this example
 
# }
# NOT RUN {
data_path <- system.file('sample_data', package = 's2dverification')
exp <- list(
        name = 'experiment',
        path = file.path(data_path, 'model/$EXP_NAME$/monthly_mean',
                         '$VAR_NAME$_3hourly/$VAR_NAME$_$START_DATES$.nc')
      )
obs <- list(
        name = 'observation',
        path = file.path(data_path, 'observation/$OBS_NAME$/monthly_mean',
                         '$VAR_NAME$/$VAR_NAME$_$YEAR$$MONTH$.nc')
      )
# Now we are ready to use Load().
startDates <- c('19851101', '19901101', '19951101', '20001101', '20051101')
sampleData <- Load('tos', list(exp), list(obs), startDates,
                  leadtimemin = 1, leadtimemax = 4, output = 'lonlat',
                  latmin = 27, latmax = 48, lonmin = -12, lonmax = 40)
 
# }
# NOT RUN {
 
# }
# NOT RUN {
sampleData$mod <- Season(sampleData$mod, 4, 11, 1, 12)
eno <- Eno(sampleData$mod[1, 1, , 1, , ], 1)
PlotEquiMap(eno, sampleData$lon, sampleData$lat)

# }