Changes between Version 3 and Version 4 of udg/ecoms/RPackage/examples/visualization


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Timestamp:
May 15, 2016 1:05:11 PM (6 years ago)
Author:
dfrias
Comment:

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  • udg/ecoms/RPackage/examples/visualization

    v3 v4  
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    84 `visualizeR` uses its own particular classes for handling data. It is necessary to convert to the `visualizeR` classes before using the visualization functions:
    85 
    86 {{{#!text/R
    87 prd <- as.MrEnsemble(tx.forecast)
    88 class(prd)
    89 ## [1] "MrEnsemble"
    90 ## attr(,"package")
    91 ## [1] "visualizeR"
    92 obs <- as.MrGrid(obsintp)
    93 class(obs)
    94 ## [1] "MrGrid"
    95 ## attr(,"package")
    96 ## [1] "visualizeR"
    97 }}}
    98 
    99 
    10084We next show different forecast visualization plots:
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    120 For each member, the daily predictions are averaged to obtain a single seasonal forecast (this yields a first warning, as in this example). For rectangular spatial domains (i.e., for grids), the spatial average is first computed (with a warning) to obtain a unique series for the whole domain, as in this example. The corresponding terciles for each ensemble member are then computed for the analysis period. Thus, data is converted converted to a series of tercile categories by considering values above, between or below the terciles of the whole period. The probability of a member to fall into the observed tercile is represented by the colorbar (different color palettes are available through the argument `color.pal`). For instance, probabilities below 1/3 are very low, indicating that a minority of the members falls in the tercile. Conversely, probabilities above 2/3 indicate a high level of member agreement (more than 66% of members falling in the same tercile). The observed terciles (the events that actually occurred) are represented by the white circles.
     104For each member, the daily predictions are averaged to obtain a single seasonal forecast (this yields a first warning, as in this example). For rectangular spatial domains (i.e., for grids), the spatial average is first computed (with a warning) to obtain a unique series for the whole domain, as in this example. The corresponding terciles for each ensemble member are then computed for the analysis period. Thus, data is converted to a series of tercile categories by considering values above, between or below the terciles of the whole period. The probability of a member to fall into the observed tercile is represented by the colorbar (different color palettes are available through the argument `color.pal`). For instance, probabilities below 1/3 are very low, indicating that a minority of the members falls in the tercile. Conversely, probabilities above 2/3 indicate a high level of member agreement (more than 66% of members falling in the same tercile). The observed terciles (the events that actually occurred) are represented by the white circles.
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    122106Finally, the ROC Skill Score (ROCSS) is indicated in the secondary (right) Y axis. For each tercile, it provides a quantitative measure of the forecast skill, and it is commonly used to evaluate the performance of probabilistic systems. The value of this score ranges from 1 (perfect forecast system) to -1 (perfectly bad forecast system). A value zero indicates no skill compared with a random prediction.
     
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    150 Using the bubble plot allows for instance to test the effrect of data detrending, as we suggested in the previous example that may be the source of some -artificial- skill in the north-eastern sector of the study area. We control this through the logical argument `detrend`:
     134Using the bubble plot allows for instance to test the effect of data detrending, as we suggested in the previous example that may be the source of some -artificial- skill in the north-eastern sector of the study area. We control this through the logical argument `detrend`:
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