Regression: Computes The Regression Of An Array On Another Along A...

Description Usage Arguments Value Author(s) Examples

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

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Regression(vary, varx, posREG = 2)

Arguments

vary

Array of any number of dimensions up to 10.

varx

Array of any number of dimensions up to 10. Same dimensions as vary.

posREG

Position along which to compute the regression.

Value

$regression

Array 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.

$filtered

Same dimensions as vary filtered out from the regression onto varx along the posREG dimension.

Author(s)

History:
0.1 - 2013-05 (V. Guemas, virginie.guemas at ic3.cat) - Original code
1.0 - 2013-09 (N. Manubens, nicolau.manubens at ic3.cat) - Formatting to CRAN

Examples

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# See examples on Load() to understand the first lines in this example
  ## Not run: 
data_path <- system.file('sample_data', package = 's2dverification')
expA <- list(name = 'experiment', path = file.path(data_path,
             'model/$EXP_NAME$/$STORE_FREQ$_mean/$VAR_NAME$_3hourly',
             '$VAR_NAME$_$START_DATE$.nc'))
obsX <- list(name = 'observation', path = file.path(data_path,
             '$OBS_NAME$/$STORE_FREQ$_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(expA), list(obsX), startDates,
                   output = 'lonlat', latmin = 27, latmax = 48, 
                   lonmin = -12, lonmax = 40)
  
## End(Not run)
  
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)


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