s2dverification: Regression – R documentation

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Regression

Computes The Regression Of An Array 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)

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@ic3.cat) - Original code
1.0 - 2013-09 (N. Manubens, nicolau.manubens@ic3.cat) - Formatting to CRAN

Examples

# 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)

s2dverification

Set of Common Tools for Forecast Verification

v2.10.0

Apache License 2.0

Authors

BSC-CNS [aut, cph], Virginie Guemas [aut], Nicolau Manubens [aut], An-Chi Ho [ctb, cre], Nuria Perez-Zanon [ctb], Javier Garcia-Serrano [aut], Neven Fuckar [aut], Louis-Philippe Caron [aut], Omar Bellprat [aut], Luis Rodrigues [aut], Veronica Torralba [aut], Alasdair Hunter [aut], Chloe Prodhomme [aut], Martin Menegoz [aut], Domingo Manubens [ctb], Constantin Ardilouze [ctb], Lauriane Batte [ctb], Fabian Lienert [ctb], Julia Giner [ctb], Jean-Philippe Baudouin [ctb], Nube Gonzalez [ctb], Ludovic Auger [ctb], Nicola Cortesi [ctb], Eleftheria Exarchou [ctb], Ruben Cruz [ctb], Isabel Andreu-Burillo [ctb], Ramiro Saurral [ctb]

Initial release