s2dverification (version 2.4.0)

Regression: Computes The Regression Of A Matrix On Another Along A Dimension

Description

Computes the regression of the input matrice vary on the input matrice varx along the posREG dimension by least square fitting. Provides the slope of the regression, the associated confidence interval, and the intercept. Provides also the vary data filtered out from the regression onto varx. The confidence interval relies on a student-T distribution.

Usage

Regression(vary, varx, posREG = 2)

Value

$regressionMatrix with same dimensions as varx and vary except along posREG dimension which is replaced by a length 4 dimension, corresponding to the lower limit of the 95% confidence interval, the slope, the upper limit of the 95% confidence interval and the intercept.$filteredSame dimensions as vary filtered out from the regression onto varx along the posREG dimension.

Examples

# See examples on Load() to understand the first lines in this example
  configfile <- paste0(tempdir(), '/sample.conf')
ConfigFileCreate(configfile, confirm = FALSE)
c <- ConfigFileOpen(configfile)
c <- ConfigEditDefinition(c, 'DEFAULT_GRID', 'r20x10', confirm = FALSE)
c <- ConfigEditDefinition(c, 'DEFAULT_VAR_MIN', '-1e19', confirm = FALSE)
c <- ConfigEditDefinition(c, 'DEFAULT_VAR_MAX', '1e19', confirm = FALSE)
c <- ConfigAddVar(c, '2d', 'tos')
data_path <- system.file('sample_data', package = 's2dverification')
exp_data_path <- paste0(data_path, '/model/$EXP_NAME$')
obs_data_path <- paste0(data_path, '/$OBS_NAME$')
c <- ConfigAddEntry(c, 'experiments', 'file-per-startdate',
     dataset_name = 'experiment', var_name = 'tos', main_path = exp_data_path,
     file_path = '$STORE_FREQ$_mean/$VAR_NAME$_3hourly/$VAR_NAME$_$START_DATE$.nc')
c <- ConfigAddEntry(c, 'observations', 'file-per-month',
     dataset_name = 'observation', var_name = 'tos', main_path = obs_data_path,
     file_path = '$STORE_FREQ$_mean/$VAR_NAME$/$VAR_NAME$_$YEAR$$MONTH$.nc')
ConfigFileSave(c, configfile, confirm = FALSE)

# Now we are ready to use Load().
startDates <- c('19851101', '19901101', '19951101', '20001101', '20051101')
sampleData <- Load('tos', c('experiment'), c('observation'), startDates, 
                   output = 'lonlat', latmin = 27, latmax = 48, lonmin = -12,
                   lonmax = 40, configfile = configfile)
  startDates <- c('19851101', '19901101', '19951101', '20001101', '20051101')
sampleData <- s2dverification:::.LoadSampleData('tos', c('experiment'), 
                                                c('observation'), startDates,
                                                output = 'lonlat', 
                                                latmin = 27, latmax = 48, 
                                                lonmin = -12, lonmax = 40)
sampleData$mod <- Season(sampleData$mod, 4, 11, 12, 2)
sampleData$obs <- Season(sampleData$obs, 4, 11, 12, 2)
reg <- Regression(Mean1Dim(sampleData$mod, 2),
                  Mean1Dim(sampleData$obs, 2), 2)
PlotEquiMap(reg$regression[1, 2, 1, , ], sampleData$lon, sampleData$lat, 
            toptitle='Regression of the prediction on the observations', 
            sizetit = 0.5)