| Title: | Experience Life Tables |
|---|---|
| Description: | Build experience life tables. |
| Authors: | Julien Tomas, Frederic Planchet, Wassim Youssef |
| Maintainer: | Wassim Youssef <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.7 |
| Built: | 2024-11-23 06:35:03 UTC |
| Source: | CRAN |
Collection of functions that can be used following a pre-established procedure to build and validate actuarial life tables.
| Package: | ELT |
| Type: | Package |
| Version: | 1.6 |
| Date: | 2016-04-10 |
| License: | GNU |
| Depends: | locfit,lattice,latticeExtra,xlsx |
The package is meant to be used following a pre-established procedure.
See the reference for more info.
Please notice that the package includes the following internal functions:
.BeforeAfterCompletion(); .ComparisonFitsMethods(); .ComparisonFitsMethodsLog(); .ComparisonResidualsMethods(); .ComparisonResidualsMethods(); .ComparisonTrendsMethods(); .CompletionDG2005(); .CompLevel1(); .CompLevel2(); .CompLevel3(); .DevFct(); .ExportHistoryInExcel(); .ExportPeriodicLifeExpinExcel(); .ExportSingleIndiciesinExcel(); .ExportValidationL1inExcel(); .ExportValidationL2inExcel(); .FctCohortLifeExp5(); .FctPerLifeExp(); .FctSingleIndices(); .FitPopsAfterCompletionLog(); .FittedDxtAndConfInt(); .GetCritLevel1(); .GetCritLevel2(); .GetCV(); .GetFitSim(); .GetHistory(); .GetQtiles(); .GetRelDisp(); .GetSimExp(); .PlotCrit(); .PlotCritChoice(); .PlotDIntConf(); .PlotExpQtle(); .PlotFittedYear(); .PlotFittedYearLog(); .PlotMethod(); .PlotParamCompletion(); .PlotPerExp(); .PlotRelDisp(); .PlotRes(); .ResFct(); .SimDxt(); .ValidationLevel3(); .WarningInvalidAge() .
These functions can be accessed with the prefix ELT::: using the following syntax: ELT:::[name of the function] . For example : ELT:::.GetHistory(). See technical note II1291-15 (http://www.ressources-actuarielles.net/gtmortalite) for the arguments and examples of the functions.
Tomas, J. , Planchet, F. , Prospective mortality tables and portfolio experience, Chapter 9 in Computational Actuarial Science, with R ; Arthur Charpentier Editor, Chapman, 2014
Tomas, J. , Planchet, F. , Constructing entity specific prospective mortality table : adjustment to a reference, Les cahiers de recherche de l'ISFA, 2013(13), pp.1-31, 2013.
Tomas, J. , Planchet, F. , Construction d'une table de mortalite par positionnement : Mode d'emploi, Institut des Actuaires, Rapport technique II1291-15, pp. 1-27, 2013
Tomas, J. , Planchet, F. , Criteres de Validation : Aspects Methodologiques, Institut des Actuaires, Rapport technique II1291-14, pp. 1-31, 2013
Tomas, J. , Planchet, F. , Methodes de positionnement : Aspects Methodologiques, Institut des Actuaires, Rapport technique II1291-12, pp. 1-12, 2013
Denuit, M. and Goderniaux, A. C. (2005). Closing and projecting life tables using log-linear models. Bulletin of the Swiss Association of Actuaries, (1), 29-48
http://www.ressources-actuarielles.net/gtmortalite for data and exemple codes.
## Not run: data(MyPortfolio) data(ReferenceMale) data(ReferenceFemale) ## ------------------------------------------------------------------------ ## ## Initialize Age variables ## ## ------------------------------------------------------------------------ ## AgeRange <- 30:90 AgeCrit <- 30:90 AgeRef <- 30:95 History <- ReadHistory(MyPortfolio = MyPortfolio, DateBegObs = "1996/01/01", DateEndObs = "2007/12/31", DateFormat = " MyData <- AddReference(History = History, ReferenceMale = ReferenceMale, ReferenceFemale = ReferenceFemale) ## ######################################################################## ## ## METHOD 1 ############################################################### ## ## ######################################################################## ## ## ------------------------------------------------------------------------ ## ## Execute method 1 ## ## ------------------------------------------------------------------------ ## OutputMethod1 <- Method1(MyData = MyData, AgeRange = AgeRange, Plot = T) ## ------------------------------------------------------------------------ ## ## Validate method 1 by the 1st level citeria ## ## ------------------------------------------------------------------------ ## ## ---------- Execute 1st level citeria. ValidationLevel1Method1 <- ValidationLevel1(OutputMethod = OutputMethod1, MyData = MyData, AgeCrit = AgeCrit, ValCrit = 0.05, Plot = T, Excel = T) ## ---------- If the criterions corresponding to the 1st level are not ## ---------- satisfied, we can modify the age range used to compute the SMR ## ---------- and reexecute ## ---------- OutputMethod1 <- Method1(...) ## ---------- and ## ---------- ValidationLevel1Method1 <- ValidationLevel1(...). ## ---------- If the criterions corresponding to the 1st level are still not ## ---------- satisfied, we turn to the method 2, and it is useless to ## ---------- pursue the completion of the table and the validation. ## ---------- If the criterions are satisfied, we continue the validation with ## ---------- the criterions corresponding to the 2nd level. ## ---------- We can also turn to method 3 or 4 to improve the fit at a cost ## ---------- of a somewhat greeter complexity. ## ------------------------------------------------------------------------ ## ## Validate method 1 by the 2nd level citeria ## ## ------------------------------------------------------------------------ ## ## ---------- Execute 2nd level criterions ValidationLevel2Method1 <- ValidationLevel2(OutputMethod = OutputMethod1, MyData = MyData, AgeCrit = AgeCrit, ValCrit = 0.05, Excel = T) ## ---------- If the criterions corresponding to the 2nd level are not satisfied ## ---------- we turn to the method 2 and it is useless to pursue the ## ---------- completion of the table and the validation. ## ---------- If the criterions are satisfied, we continue the validation with ## ---------- the completion of the table and the criterions corresponding to ## ---------- the 3rd level. ## ------------------------------------------------------------------------ ## ## Completion Method 1 ## ## ------------------------------------------------------------------------ ## ## ---------- Age range for the selection of the optimal starting age. AgeRangeOptMale <- AgeRangeOptFemale <- c(80, 80) ## ---------- In theory, we could select the optimal starting age, however ## ---------- the optimal starting age can vary a lot with the calendar years ## ---------- leading to a relatively irregular surface. In practice, we ## ---------- select then a fixed age for the whole years. ## ---------- Starting age for which the fitted probabilities of the death are ## ---------- replaced by the values obtained from the completion model. BegAgeCompMale <- BegAgeCompFemale <- 85 ## ---------- We check if the completion is smoothed with graphical ## ---------- diagnostics. CompletionMethod1 <- CompletionA(OutputMethod = OutputMethod1, MyData = MyData, AgeRangeOptMale = AgeRangeOptMale, AgeRangeOptFemale = AgeRangeOptFemale, BegAgeCompMale = BegAgeCompMale, BegAgeCompFemale = BegAgeCompFemale, ShowPlot = T) ## ---------- If the completion is not satisfying, we modify the values ## ---------- AgeRangeOpt and BegAgeComp, and we repeat the previous script ## ---------- CompletionA() ## ---------- If the completion is satisfying, we execute FinalMethod1 <- CompletionB(ModCompletion = CompletionMethod1, OutputMethod = OutputMethod1, MyData = MyData, Plot = T, Excel = T) ## ------------------------------------------------------------------------ ## ## Validate method 1 by the 3rd level citeria ## ## ------------------------------------------------------------------------ ## ## ---------- Execute 3rd level criterions ValidationLevel3Method1 <- ValidationLevel3(FinalMethod = FinalMethod1, MyData = MyData, Plot = T, Excel = T) ## ------------------------------------------------------------------------ ## ## Coef Varition, Conf int. and rel. disp. of fitted per. life exp. ## ## ------------------------------------------------------------------------ ## ## ---------- Compute the coefficient of variation, confidence intervals and ## ---------- relative dispersion of the fitted perdiodic life expectancies DispersionMethod1 <- Dispersion(FinalMethod = FinalMethod1, MyData = MyData, Plot = T,NbSim = 10) ## ######################################################################## ## ## METHOD 2 ############################################################### ## ## ######################################################################## ## ## ------------------------------------------------------------------------ ## ## Execute method 2 ## ## ------------------------------------------------------------------------ ## OutputMethod2 <- Method2(MyData = MyData, AgeRange = AgeRange, Plot = T) ## ------------------------------------------------------------------------ ## ## Validate method 2 by the 1st level citeria ## ## ------------------------------------------------------------------------ ## ## ---------- Execute 1st level citeria. ValidationLevel1Method2 <- ValidationLevel1(OutputMethod = OutputMethod2, MyData = MyData, AgeCrit = AgeCrit, ValCrit = 0.05, Plot = T, Excel = T) ## ---------- If the criterions corresponding to the 1st level are not ## ---------- satisfied, we turn to the method 3, and it is useless to ## ---------- pursue the completion of the table and the validation. ## ---------- If the criterions are satisfied, we continue the validation with ## ---------- the criterions corresponding to the 2nd level. ## ---------- We can also turn to method 4 to improve the fit at a cost ## ---------- of a somewhat greeter complexity. ## ------------------------------------------------------------------------ ## ## Validate method 2 by the 2nd level citeria ## ## ------------------------------------------------------------------------ ## ## ---------- Execute 2nd level criterions ValidationLevel2Method2 <- ValidationLevel2(OutputMethod = OutputMethod2, AgeCrit = AgeCrit, ValCrit = 0.05, MyData = MyData, Excel = T) ## ---------- If the criterions corresponding to the 2nd level are not satisfied ## ---------- we turn to the method 3 and it is useless to pursue the ## ---------- completion of the table and the validation. ## ---------- If the criterions are satisfied, we continue the validation with ## ---------- the completion of the table and the criterions corresponding to ## ---------- the 3rd level. ## ------------------------------------------------------------------------ ## ## Completion Method 2 ## ## ------------------------------------------------------------------------ ## ## ---------- We check if the completion is smoothed with graphical ## ---------- diqgnostics. CompletionMethod2 <- CompletionA(OutputMethod = OutputMethod2, MyData = MyData, AgeRangeOptMale = AgeRangeOptMale, AgeRangeOptFemale = AgeRangeOptFemale, BegAgeCompMale = BegAgeCompMale, BegAgeCompFemale = BegAgeCompFemale, ShowPlot = T) ## ---------- If the completion is not satisfying, we modify the values ## ---------- AgeRangeOpt and BegAgeComp, and we repeat the previous script ## ---------- CompletionA() ## ---------- If the completion is satisfying, we execute FinalMethod2 <- CompletionB(ModCompletion = CompletionMethod2, OutputMethod = OutputMethod2, MyData = MyData, Plot = T, Excel = T) ## ------------------------------------------------------------------------ ## ## Validate method 2 by the 3rd level citeria ## ## ------------------------------------------------------------------------ ## ## ---------- Execute 3rd level criterions ValidationLevel3Method2 <- ValidationLevel3(FinalMethod = FinalMethod2, MyData = MyData, Plot = T, Excel = T) ## ------------------------------------------------------------------------ ## ## Coef Varition, Conf int. and rel. disp. of fitted per. life exp. ## ## ------------------------------------------------------------------------ ## ## ---------- Compute the coefficient of variation, confidence intervals and ## ---------- relative dispersion of the fitted perdiodic life expectancies DispersionMethod2 <- Dispersion(FinalMethod = FinalMethod2, MyData = MyData, Plot = T, NbSim = 10) ## ######################################################################## ## ## METHOD 3 ############################################################### ## ## ######################################################################## ## ## ------------------------------------------------------------------------ ## ## Execute method 3 ## ## ------------------------------------------------------------------------ ## OutputMethod3 <- Method3(MyData = MyData, AgeRange = AgeRange, Plot = T) ## ------------------------------------------------------------------------ ## ## Validate method 3 by the 1st level citeria ## ## ------------------------------------------------------------------------ ## ## ---------- Execute 1st level citeria. ValidationLevel1Method3 <- ValidationLevel1(OutputMethod = OutputMethod3, MyData = MyData, AgeCrit = AgeCrit, ValCrit = 0.05, Plot = T, Excel = T) ## ---------- If the criterions corresponding to the 1st level are not ## ---------- satisfied, we turn to the method 4, and it is useless to ## ---------- pursue the completion of the table and the validation. ## ---------- If the criterions are satisfied, we continue the validation with ## ---------- the criterions corresponding to the 2nd level. ## ------------------------------------------------------------------------ ## ## Validate method 3 by the 2nd level citeria ## ## ------------------------------------------------------------------------ ## ## ---------- Execute 2nd level criterions ValidationLevel2Method3 <- ValidationLevel2(OutputMethod = OutputMethod3, MyData = MyData, AgeCrit = AgeCrit, ValCrit = 0.05, Excel = T) ## ---------- If the criterions corresponding to the 2nd level are not satisfied ## ---------- we turn to the method 4 and it is useless to pursue the ## ---------- completion of the table and the validation. ## ---------- If the criterions are satisfied, we continue the validation with ## ---------- the completion of the table and the criterions corresponding to ## ---------- the 3rd level. ## ------------------------------------------------------------------------ ## ## Completion Method 3 ## ## ------------------------------------------------------------------------ ## ## ---------- We check if the completion is smoothed with graphical ## ---------- diqgnostics. CompletionMethod3 <- CompletionA(OutputMethod = OutputMethod3, MyData = MyData, AgeRangeOptMale = AgeRangeOptMale, AgeRangeOptFemale = AgeRangeOptFemale, BegAgeCompMale = BegAgeCompMale, BegAgeCompFemale = BegAgeCompFemale, ShowPlot = T) ## ---------- If the completion is not satisfying, we modify the values ## ---------- AgeRangeOpt and BegAgeComp, and we repeat the previous script ## ---------- CompletionA() ## ---------- If the completion is satisfying, we execute FinalMethod3 <- CompletionB(ModCompletion = CompletionMethod3, OutputMethod = OutputMethod3, MyData = MyData, Plot = T, Excel = T) ## ------------------------------------------------------------------------ ## ## Validate method 3 by the 3rd level citeria ## ## ------------------------------------------------------------------------ ## ## ---------- Execute 3rd level criterions ValidationLevel3Method3 <- ValidationLevel3(FinalMethod = FinalMethod3, MyData = MyData, Plot = T, Excel = T) ## ------------------------------------------------------------------------ ## ## Coef Varition, Conf int. and rel. disp. of fitted per. life exp. ## ## ------------------------------------------------------------------------ ## ## ---------- Compute the coefficient of variation, confidence intervals and ## ---------- relative dispersion of the fitted perdiodic life expectancies DispersionMethod3 <- Dispersion(FinalMethod = FinalMethod3, MyData = MyData, Plot = T, NbSim = 10) ## ######################################################################## ## ## METHOD 4 ############################################################### ## ## ######################################################################## ## ## ------------------------------------------------------------------------ ## ## Execute method 4 ## ## ------------------------------------------------------------------------ ## ## ---------- Execute method 4 first part. OutputMethod4PartOne <- Method4A(MyData = MyData, AgeRange = AgeRange, AgeCrit = AgeCrit, ShowPlot = T) ## ---------- Select the optimal smoothing parameters. ## ---------- Execute method 4 second part. OutputMethod4 <- Method4B(PartOne, MyData = MyData, OptMale = c(1, 16), OptFemale = c(1, 14), Plot = T) ## ------------------------------------------------------------------------ ## ## Validate method 4 by the 1st level citeria ## ## ------------------------------------------------------------------------ ## ## ---------- Execute 1st level citeria. ValidationLevel1Method4 <- ValidationLevel1(OutputMethod = OutputMethod4, MyData = MyData, AgeCrit = AgeCrit, ValCrit = 0.05, Plot = T, Excel = T) ## ---------- If the criterions corresponding to the 1st level are not ## ---------- satisfied, we turn to the method 4, and it is useless to ## ---------- pursue the completion of the table and the validation. ## ---------- If the criterions are satisfied, we continue the validation with ## ---------- the criterions corresponding to the 2nd level. ## ------------------------------------------------------------------------ ## ## Validate method 4 by the 2nd level citeria ## ## ------------------------------------------------------------------------ ## ## ---------- Execute 2nd level criterions ValidationLevel2Method4 <- ValidationLevel2(OutputMethod = OutputMethod4, MyData = MyData, AgeCrit = AgeCrit, ValCrit = 0.05, Excel = T) ## ---------- If the criterions corresponding to the 2nd level are not satisfied ## ---------- we turn to the method 4 and it is useless to pursue the ## ---------- completion of the table and the validation. ## ---------- If the criterions are satisfied, we continue the validation with ## ---------- the completion of the table and the criterions corresponding to ## ---------- the 3rd level. ## ------------------------------------------------------------------------ ## ## Completion Method 4 ## ## ------------------------------------------------------------------------ ## ## ---------- We check if the completion is smoothed with graphical ## ---------- diqgnostics. CompletionMethod4 <- CompletionA(OutputMethod = OutputMethod4, MyData = MyData, AgeRangeOptMale = AgeRangeOptMale, AgeRangeOptFemale = AgeRangeOptFemale, BegAgeCompMale = BegAgeCompMale, BegAgeCompFemale = BegAgeCompFemale, ShowPlot = T) ## ---------- If the completion is not satisfying, we modify the values ## ---------- AgeRangeOpt and BegAgeComp, and we repeat the previous script ## ---------- CompletionA() ## ---------- If the completion is satisfying, we execute FinalMethod4 <- CompletionB(ModCompletion = CompletionMethod4, OutputMethod = OutputMethod4, MyData = MyData, Plot = T, Excel = T) ## ------------------------------------------------------------------------ ## ## Validate method 4 by the 3rd level citeria ## ## ------------------------------------------------------------------------ ## ## ---------- Execute 3rd level criterions ValidationLevel3Method4 <- ValidationLevel3(FinalMethod = FinalMethod4, MyData = MyData, Plot = T, Excel = T) ## ------------------------------------------------------------------------ ## ## Coef Varition, Conf int. and rel. disp. of fitted per. life exp. ## ## ------------------------------------------------------------------------ ## ## ---------- Set the number of simulations ## ---------- Compute the coefficient of variation, confidence intervals and ## ---------- relative dispersion of the fitted perdiodic life expectancies DispersionMethod4 <- Dispersion(FinalMethod = FinalMethod4, MyData = MyData, Plot = T, NbSim = 10) ## ######################################################################## ## ## COMPARISON OF THE METHODS ############################################## ## ## ######################################################################## ## ## ---------- Once we have fitted the data with a number of methods, we can ## ---------- compare them. In the following, we compare the fitted ## ---------- probabilities of death in original and log scale, the ## ---------- residuals, the fitted deaths as well as the coherence of the ## ---------- extrapolated mortality trends ## ---------- You can change the color vector for comparison, color need to ## ---------- be in html format ## ---------- Store the output into a list ListOutputs <- list(OutputMethod1, OutputMethod2, OutputMethod3, OutputMethod4) ListValidationLevel1 <- list(ValidationLevel1Method1, ValidationLevel1Method2, ValidationLevel1Method3, ValidationLevel1Method4) ListValidationLevel2 <- list(ValidationLevel2Method1, ValidationLevel2Method2, ValidationLevel2Method3, ValidationLevel2Method4) ListValidationLevel3 <- list(ValidationLevel3Method1, ValidationLevel3Method2, ValidationLevel3Method3, ValidationLevel3Method4) ComparisonsMethodsLevels123 <- ComparisonMethods(ListOutputs, ListValidationLevel1, ListValidationLevel2, ListValidationLevel3, MyData = MyData, Plot = T, AgeCrit = AgeCrit) ## End(Not run)## Not run: data(MyPortfolio) data(ReferenceMale) data(ReferenceFemale) ## ------------------------------------------------------------------------ ## ## Initialize Age variables ## ## ------------------------------------------------------------------------ ## AgeRange <- 30:90 AgeCrit <- 30:90 AgeRef <- 30:95 History <- ReadHistory(MyPortfolio = MyPortfolio, DateBegObs = "1996/01/01", DateEndObs = "2007/12/31", DateFormat = " MyData <- AddReference(History = History, ReferenceMale = ReferenceMale, ReferenceFemale = ReferenceFemale) ## ######################################################################## ## ## METHOD 1 ############################################################### ## ## ######################################################################## ## ## ------------------------------------------------------------------------ ## ## Execute method 1 ## ## ------------------------------------------------------------------------ ## OutputMethod1 <- Method1(MyData = MyData, AgeRange = AgeRange, Plot = T) ## ------------------------------------------------------------------------ ## ## Validate method 1 by the 1st level citeria ## ## ------------------------------------------------------------------------ ## ## ---------- Execute 1st level citeria. ValidationLevel1Method1 <- ValidationLevel1(OutputMethod = OutputMethod1, MyData = MyData, AgeCrit = AgeCrit, ValCrit = 0.05, Plot = T, Excel = T) ## ---------- If the criterions corresponding to the 1st level are not ## ---------- satisfied, we can modify the age range used to compute the SMR ## ---------- and reexecute ## ---------- OutputMethod1 <- Method1(...) ## ---------- and ## ---------- ValidationLevel1Method1 <- ValidationLevel1(...). ## ---------- If the criterions corresponding to the 1st level are still not ## ---------- satisfied, we turn to the method 2, and it is useless to ## ---------- pursue the completion of the table and the validation. ## ---------- If the criterions are satisfied, we continue the validation with ## ---------- the criterions corresponding to the 2nd level. ## ---------- We can also turn to method 3 or 4 to improve the fit at a cost ## ---------- of a somewhat greeter complexity. ## ------------------------------------------------------------------------ ## ## Validate method 1 by the 2nd level citeria ## ## ------------------------------------------------------------------------ ## ## ---------- Execute 2nd level criterions ValidationLevel2Method1 <- ValidationLevel2(OutputMethod = OutputMethod1, MyData = MyData, AgeCrit = AgeCrit, ValCrit = 0.05, Excel = T) ## ---------- If the criterions corresponding to the 2nd level are not satisfied ## ---------- we turn to the method 2 and it is useless to pursue the ## ---------- completion of the table and the validation. ## ---------- If the criterions are satisfied, we continue the validation with ## ---------- the completion of the table and the criterions corresponding to ## ---------- the 3rd level. ## ------------------------------------------------------------------------ ## ## Completion Method 1 ## ## ------------------------------------------------------------------------ ## ## ---------- Age range for the selection of the optimal starting age. AgeRangeOptMale <- AgeRangeOptFemale <- c(80, 80) ## ---------- In theory, we could select the optimal starting age, however ## ---------- the optimal starting age can vary a lot with the calendar years ## ---------- leading to a relatively irregular surface. In practice, we ## ---------- select then a fixed age for the whole years. ## ---------- Starting age for which the fitted probabilities of the death are ## ---------- replaced by the values obtained from the completion model. BegAgeCompMale <- BegAgeCompFemale <- 85 ## ---------- We check if the completion is smoothed with graphical ## ---------- diagnostics. CompletionMethod1 <- CompletionA(OutputMethod = OutputMethod1, MyData = MyData, AgeRangeOptMale = AgeRangeOptMale, AgeRangeOptFemale = AgeRangeOptFemale, BegAgeCompMale = BegAgeCompMale, BegAgeCompFemale = BegAgeCompFemale, ShowPlot = T) ## ---------- If the completion is not satisfying, we modify the values ## ---------- AgeRangeOpt and BegAgeComp, and we repeat the previous script ## ---------- CompletionA() ## ---------- If the completion is satisfying, we execute FinalMethod1 <- CompletionB(ModCompletion = CompletionMethod1, OutputMethod = OutputMethod1, MyData = MyData, Plot = T, Excel = T) ## ------------------------------------------------------------------------ ## ## Validate method 1 by the 3rd level citeria ## ## ------------------------------------------------------------------------ ## ## ---------- Execute 3rd level criterions ValidationLevel3Method1 <- ValidationLevel3(FinalMethod = FinalMethod1, MyData = MyData, Plot = T, Excel = T) ## ------------------------------------------------------------------------ ## ## Coef Varition, Conf int. and rel. disp. of fitted per. life exp. ## ## ------------------------------------------------------------------------ ## ## ---------- Compute the coefficient of variation, confidence intervals and ## ---------- relative dispersion of the fitted perdiodic life expectancies DispersionMethod1 <- Dispersion(FinalMethod = FinalMethod1, MyData = MyData, Plot = T,NbSim = 10) ## ######################################################################## ## ## METHOD 2 ############################################################### ## ## ######################################################################## ## ## ------------------------------------------------------------------------ ## ## Execute method 2 ## ## ------------------------------------------------------------------------ ## OutputMethod2 <- Method2(MyData = MyData, AgeRange = AgeRange, Plot = T) ## ------------------------------------------------------------------------ ## ## Validate method 2 by the 1st level citeria ## ## ------------------------------------------------------------------------ ## ## ---------- Execute 1st level citeria. ValidationLevel1Method2 <- ValidationLevel1(OutputMethod = OutputMethod2, MyData = MyData, AgeCrit = AgeCrit, ValCrit = 0.05, Plot = T, Excel = T) ## ---------- If the criterions corresponding to the 1st level are not ## ---------- satisfied, we turn to the method 3, and it is useless to ## ---------- pursue the completion of the table and the validation. ## ---------- If the criterions are satisfied, we continue the validation with ## ---------- the criterions corresponding to the 2nd level. ## ---------- We can also turn to method 4 to improve the fit at a cost ## ---------- of a somewhat greeter complexity. ## ------------------------------------------------------------------------ ## ## Validate method 2 by the 2nd level citeria ## ## ------------------------------------------------------------------------ ## ## ---------- Execute 2nd level criterions ValidationLevel2Method2 <- ValidationLevel2(OutputMethod = OutputMethod2, AgeCrit = AgeCrit, ValCrit = 0.05, MyData = MyData, Excel = T) ## ---------- If the criterions corresponding to the 2nd level are not satisfied ## ---------- we turn to the method 3 and it is useless to pursue the ## ---------- completion of the table and the validation. ## ---------- If the criterions are satisfied, we continue the validation with ## ---------- the completion of the table and the criterions corresponding to ## ---------- the 3rd level. ## ------------------------------------------------------------------------ ## ## Completion Method 2 ## ## ------------------------------------------------------------------------ ## ## ---------- We check if the completion is smoothed with graphical ## ---------- diqgnostics. CompletionMethod2 <- CompletionA(OutputMethod = OutputMethod2, MyData = MyData, AgeRangeOptMale = AgeRangeOptMale, AgeRangeOptFemale = AgeRangeOptFemale, BegAgeCompMale = BegAgeCompMale, BegAgeCompFemale = BegAgeCompFemale, ShowPlot = T) ## ---------- If the completion is not satisfying, we modify the values ## ---------- AgeRangeOpt and BegAgeComp, and we repeat the previous script ## ---------- CompletionA() ## ---------- If the completion is satisfying, we execute FinalMethod2 <- CompletionB(ModCompletion = CompletionMethod2, OutputMethod = OutputMethod2, MyData = MyData, Plot = T, Excel = T) ## ------------------------------------------------------------------------ ## ## Validate method 2 by the 3rd level citeria ## ## ------------------------------------------------------------------------ ## ## ---------- Execute 3rd level criterions ValidationLevel3Method2 <- ValidationLevel3(FinalMethod = FinalMethod2, MyData = MyData, Plot = T, Excel = T) ## ------------------------------------------------------------------------ ## ## Coef Varition, Conf int. and rel. disp. of fitted per. life exp. ## ## ------------------------------------------------------------------------ ## ## ---------- Compute the coefficient of variation, confidence intervals and ## ---------- relative dispersion of the fitted perdiodic life expectancies DispersionMethod2 <- Dispersion(FinalMethod = FinalMethod2, MyData = MyData, Plot = T, NbSim = 10) ## ######################################################################## ## ## METHOD 3 ############################################################### ## ## ######################################################################## ## ## ------------------------------------------------------------------------ ## ## Execute method 3 ## ## ------------------------------------------------------------------------ ## OutputMethod3 <- Method3(MyData = MyData, AgeRange = AgeRange, Plot = T) ## ------------------------------------------------------------------------ ## ## Validate method 3 by the 1st level citeria ## ## ------------------------------------------------------------------------ ## ## ---------- Execute 1st level citeria. ValidationLevel1Method3 <- ValidationLevel1(OutputMethod = OutputMethod3, MyData = MyData, AgeCrit = AgeCrit, ValCrit = 0.05, Plot = T, Excel = T) ## ---------- If the criterions corresponding to the 1st level are not ## ---------- satisfied, we turn to the method 4, and it is useless to ## ---------- pursue the completion of the table and the validation. ## ---------- If the criterions are satisfied, we continue the validation with ## ---------- the criterions corresponding to the 2nd level. ## ------------------------------------------------------------------------ ## ## Validate method 3 by the 2nd level citeria ## ## ------------------------------------------------------------------------ ## ## ---------- Execute 2nd level criterions ValidationLevel2Method3 <- ValidationLevel2(OutputMethod = OutputMethod3, MyData = MyData, AgeCrit = AgeCrit, ValCrit = 0.05, Excel = T) ## ---------- If the criterions corresponding to the 2nd level are not satisfied ## ---------- we turn to the method 4 and it is useless to pursue the ## ---------- completion of the table and the validation. ## ---------- If the criterions are satisfied, we continue the validation with ## ---------- the completion of the table and the criterions corresponding to ## ---------- the 3rd level. ## ------------------------------------------------------------------------ ## ## Completion Method 3 ## ## ------------------------------------------------------------------------ ## ## ---------- We check if the completion is smoothed with graphical ## ---------- diqgnostics. CompletionMethod3 <- CompletionA(OutputMethod = OutputMethod3, MyData = MyData, AgeRangeOptMale = AgeRangeOptMale, AgeRangeOptFemale = AgeRangeOptFemale, BegAgeCompMale = BegAgeCompMale, BegAgeCompFemale = BegAgeCompFemale, ShowPlot = T) ## ---------- If the completion is not satisfying, we modify the values ## ---------- AgeRangeOpt and BegAgeComp, and we repeat the previous script ## ---------- CompletionA() ## ---------- If the completion is satisfying, we execute FinalMethod3 <- CompletionB(ModCompletion = CompletionMethod3, OutputMethod = OutputMethod3, MyData = MyData, Plot = T, Excel = T) ## ------------------------------------------------------------------------ ## ## Validate method 3 by the 3rd level citeria ## ## ------------------------------------------------------------------------ ## ## ---------- Execute 3rd level criterions ValidationLevel3Method3 <- ValidationLevel3(FinalMethod = FinalMethod3, MyData = MyData, Plot = T, Excel = T) ## ------------------------------------------------------------------------ ## ## Coef Varition, Conf int. and rel. disp. of fitted per. life exp. ## ## ------------------------------------------------------------------------ ## ## ---------- Compute the coefficient of variation, confidence intervals and ## ---------- relative dispersion of the fitted perdiodic life expectancies DispersionMethod3 <- Dispersion(FinalMethod = FinalMethod3, MyData = MyData, Plot = T, NbSim = 10) ## ######################################################################## ## ## METHOD 4 ############################################################### ## ## ######################################################################## ## ## ------------------------------------------------------------------------ ## ## Execute method 4 ## ## ------------------------------------------------------------------------ ## ## ---------- Execute method 4 first part. OutputMethod4PartOne <- Method4A(MyData = MyData, AgeRange = AgeRange, AgeCrit = AgeCrit, ShowPlot = T) ## ---------- Select the optimal smoothing parameters. ## ---------- Execute method 4 second part. OutputMethod4 <- Method4B(PartOne, MyData = MyData, OptMale = c(1, 16), OptFemale = c(1, 14), Plot = T) ## ------------------------------------------------------------------------ ## ## Validate method 4 by the 1st level citeria ## ## ------------------------------------------------------------------------ ## ## ---------- Execute 1st level citeria. ValidationLevel1Method4 <- ValidationLevel1(OutputMethod = OutputMethod4, MyData = MyData, AgeCrit = AgeCrit, ValCrit = 0.05, Plot = T, Excel = T) ## ---------- If the criterions corresponding to the 1st level are not ## ---------- satisfied, we turn to the method 4, and it is useless to ## ---------- pursue the completion of the table and the validation. ## ---------- If the criterions are satisfied, we continue the validation with ## ---------- the criterions corresponding to the 2nd level. ## ------------------------------------------------------------------------ ## ## Validate method 4 by the 2nd level citeria ## ## ------------------------------------------------------------------------ ## ## ---------- Execute 2nd level criterions ValidationLevel2Method4 <- ValidationLevel2(OutputMethod = OutputMethod4, MyData = MyData, AgeCrit = AgeCrit, ValCrit = 0.05, Excel = T) ## ---------- If the criterions corresponding to the 2nd level are not satisfied ## ---------- we turn to the method 4 and it is useless to pursue the ## ---------- completion of the table and the validation. ## ---------- If the criterions are satisfied, we continue the validation with ## ---------- the completion of the table and the criterions corresponding to ## ---------- the 3rd level. ## ------------------------------------------------------------------------ ## ## Completion Method 4 ## ## ------------------------------------------------------------------------ ## ## ---------- We check if the completion is smoothed with graphical ## ---------- diqgnostics. CompletionMethod4 <- CompletionA(OutputMethod = OutputMethod4, MyData = MyData, AgeRangeOptMale = AgeRangeOptMale, AgeRangeOptFemale = AgeRangeOptFemale, BegAgeCompMale = BegAgeCompMale, BegAgeCompFemale = BegAgeCompFemale, ShowPlot = T) ## ---------- If the completion is not satisfying, we modify the values ## ---------- AgeRangeOpt and BegAgeComp, and we repeat the previous script ## ---------- CompletionA() ## ---------- If the completion is satisfying, we execute FinalMethod4 <- CompletionB(ModCompletion = CompletionMethod4, OutputMethod = OutputMethod4, MyData = MyData, Plot = T, Excel = T) ## ------------------------------------------------------------------------ ## ## Validate method 4 by the 3rd level citeria ## ## ------------------------------------------------------------------------ ## ## ---------- Execute 3rd level criterions ValidationLevel3Method4 <- ValidationLevel3(FinalMethod = FinalMethod4, MyData = MyData, Plot = T, Excel = T) ## ------------------------------------------------------------------------ ## ## Coef Varition, Conf int. and rel. disp. of fitted per. life exp. ## ## ------------------------------------------------------------------------ ## ## ---------- Set the number of simulations ## ---------- Compute the coefficient of variation, confidence intervals and ## ---------- relative dispersion of the fitted perdiodic life expectancies DispersionMethod4 <- Dispersion(FinalMethod = FinalMethod4, MyData = MyData, Plot = T, NbSim = 10) ## ######################################################################## ## ## COMPARISON OF THE METHODS ############################################## ## ## ######################################################################## ## ## ---------- Once we have fitted the data with a number of methods, we can ## ---------- compare them. In the following, we compare the fitted ## ---------- probabilities of death in original and log scale, the ## ---------- residuals, the fitted deaths as well as the coherence of the ## ---------- extrapolated mortality trends ## ---------- You can change the color vector for comparison, color need to ## ---------- be in html format ## ---------- Store the output into a list ListOutputs <- list(OutputMethod1, OutputMethod2, OutputMethod3, OutputMethod4) ListValidationLevel1 <- list(ValidationLevel1Method1, ValidationLevel1Method2, ValidationLevel1Method3, ValidationLevel1Method4) ListValidationLevel2 <- list(ValidationLevel2Method1, ValidationLevel2Method2, ValidationLevel2Method3, ValidationLevel2Method4) ListValidationLevel3 <- list(ValidationLevel3Method1, ValidationLevel3Method2, ValidationLevel3Method3, ValidationLevel3Method4) ComparisonsMethodsLevels123 <- ComparisonMethods(ListOutputs, ListValidationLevel1, ListValidationLevel2, ListValidationLevel3, MyData = MyData, Plot = T, AgeCrit = AgeCrit) ## End(Not run)
This function imports reference tables.
AddReference(History, ReferenceMale = NULL, ReferenceFemale = NULL)AddReference(History, ReferenceMale = NULL, ReferenceFemale = NULL)
History |
History as returned by the ReadHistory function. |
ReferenceMale |
data.frame representing the reference table. See data(ReferenceMale) for the format. |
ReferenceFemale |
data.frame representing the reference table. See data(ReferenceFemale) for the format. |
This function compares two or several methods using the three groups of criteria from the validation process.
ComparisonMethods(ListOutputs, ListValidationLevel1, ListValidationLevel2, ListValidationLevel3, MyData = MyData, Plot = F, ColorComp = c("#FF6590", "#309BFF", "#AD79FC", "#3CAB5F"), LtyComp = rep(1, 4), AgeCrit)ComparisonMethods(ListOutputs, ListValidationLevel1, ListValidationLevel2, ListValidationLevel3, MyData = MyData, Plot = F, ColorComp = c("#FF6590", "#309BFF", "#AD79FC", "#3CAB5F"), LtyComp = rep(1, 4), AgeCrit)
ListOutputs |
For the comparisons of n methods, a list of n elements containing the returned value of the functions Methodn(). |
ListValidationLevel1 |
For the comparisons of n methods, a list of n elements containing the returned value of the function ValidationLevel1() for each of the n methods. |
ListValidationLevel2 |
For the comparisons of n methods, a list of n elements containing the returned value of the function ValidationLevel2() for each of the n methods. |
ListValidationLevel3 |
For the comparisons of n methods, a list of n elements containing the returned value of the function ValidationLevel3() for each of the n methods. |
MyData |
The list returned by the AddReference() function. |
Plot |
If set to TRUE, a sub-directory will be created in the working directory. This sub-directory will contains png plots corresponding to the smoothed surface. |
ColorComp |
The color that will be used for the plots (HTML notation). For the comparisons of n methods, ColorComp is a vector of length n. |
LtyComp |
Vector of parameters (length n) for the lty plot parameter. |
AgeCrit |
Age range for the comparison of adjusted mortality and observed mortality. |
This function executes the first part of table closure using Denuit and Goderniaux (2005)
CompletionA(OutputMethod, MyData, AgeRangeOptMale, AgeRangeOptFemale, BegAgeCompMale, BegAgeCompFemale, Color = MyData$Param$Color, ShowPlot = T)CompletionA(OutputMethod, MyData, AgeRangeOptMale, AgeRangeOptFemale, BegAgeCompMale, BegAgeCompFemale, Color = MyData$Param$Color, ShowPlot = T)
OutputMethod |
The list returned by one of these functions : Method1(), Method2(), Method3() or Method4B(). |
MyData |
The list returned by the AddReference() function. |
AgeRangeOptMale |
Age range from which the optimal starting age is selected for males |
AgeRangeOptFemale |
Age range from which the optimal starting age is selected for females |
BegAgeCompMale |
For ages after BegAgeCompMale, observed death probabilitiy is replaced by the model output. |
BegAgeCompFemale |
For ages after BegAgeCompFemale, observed death probabilitiy is replaced by the model output. |
Color |
The color that will be used for the plots (HTML notation). |
ShowPlot |
If true, create graphics comparing Before/After the completion create graphics of the completed surfaces. |
This function executes the second part of table closure
CompletionB(ModCompletion, OutputMethod, MyData, Color = MyData$Param$Color, Plot = F, Excel = F)CompletionB(ModCompletion, OutputMethod, MyData, Color = MyData$Param$Color, Plot = F, Excel = F)
ModCompletion |
Output of the function CompletionA(). |
OutputMethod |
The list returned by one of these functions : Method1(), Method2(), Method3() or Method4B(). |
MyData |
The list returned by the AddReference() function. |
Color |
The color that will be used for the plots (HTML notation). |
Plot |
If true, create graphics. |
Excel |
If true, create Excel files. |
This function allows to calculate confidence intervals for period life expectancies.
Dispersion(FinalMethod, MyData, NbSim, CompletionTable = T, Plot = F, Color = MyData$Param$Color)Dispersion(FinalMethod, MyData, NbSim, CompletionTable = T, Plot = F, Color = MyData$Param$Color)
FinalMethod |
The list returned by the CompletionB() function. |
MyData |
The list returned by the AddReference() function. |
NbSim |
The number of simulations for the Dispersion. |
CompletionTable |
If TRUE, apply completion |
Plot |
If set to TRUE, a sub-directory will be created in the working directory. This sub-directory will contains png plots describing the validation analysis. |
Color |
The color that will be used for the plots (HTML notation). |
FctMethod1() is an alternative to Method1(). It allows to process the smoothing without using a "Data" object and by defining all the needed parameters independently.
FctMethod1(d, e, qref, x1, x2, t1, t2)FctMethod1(d, e, qref, x1, x2, t1, t2)
d |
Number of deaths. |
e |
Exposure to risk. |
qref |
Mortality rates in Reference Table. |
x1 |
Age range used for calculation. |
x2 |
Age range of reference table. |
t1 |
Calendar years used for the calculation. It corresponds to the common years among observations and the reference table. |
t2 |
Calendar years of the reference. |
FctMethod2() is an alternative to Method2(). It allows to process the smoothing without using a "Data" object and by defining all the needed parameters independently.
FctMethod2(d, e, qref, x1, x2, t1, t2)FctMethod2(d, e, qref, x1, x2, t1, t2)
d |
Number of deaths. |
e |
Exposure to risk. |
qref |
Mortality rates in Reference Table. |
x1 |
Age range used for calculation. |
x2 |
Age range of reference table. |
t1 |
Calendar years used for the calculation. It corresponds to the common years among observations and the reference table. |
t2 |
Calendar years of the reference. |
FctMethod3() is an alternative to Method3(). It allows to process the smoothing without using a "Data" object and by defining all the needed parameters independently.
FctMethod3(d, e, qref, x1, x2, t1, t2)FctMethod3(d, e, qref, x1, x2, t1, t2)
d |
Number of deaths. |
e |
Exposure to risk. |
qref |
Mortality rates in Reference Table. |
x1 |
Age range used for calculation. |
x2 |
Age range of reference table. |
t1 |
Calendar years used for the calculation. It corresponds to the common years among observations and the reference table. |
t2 |
Calendar years of the reference. |
FctMethod4_1stPart() is an alternative to Method4A(). It allows to process the smoothing without using a "Data" object and by defining all the needed parameters independently.
FctMethod4_1stPart(d, e, qref, x1, x2, t1)FctMethod4_1stPart(d, e, qref, x1, x2, t1)
d |
Number of deaths. |
e |
Exposure to risk. |
qref |
Mortality rates in Reference Table. |
x1 |
Age range used for calculation. |
x2 |
Age range of reference table. |
t1 |
Calendar years used for the calculation. It corresponds to the common years among observations and the reference table. |
FctMethod4_2ndPart() is an alternative to Method4B(). It allows to process the smoothing without using a "Data" object and by defining all the needed parameters independently.
FctMethod4_2ndPart(d, e, qref, x1, x2, t1, t2, P.Opt, h.Opt)FctMethod4_2ndPart(d, e, qref, x1, x2, t1, t2, P.Opt, h.Opt)
d |
Number of deaths. |
e |
Exposure to risk. |
qref |
Mortality rates in Reference Table. |
x1 |
Age range used for calculation. |
x2 |
Age range of reference table. |
t1 |
Calendar years used for the calculation. It corresponds to the common years among observations and the reference table. |
t2 |
Calendar years of the reference table. |
P.Opt |
Degree of approximation. |
h.Opt |
Window width. |
This function fits the Qxt using method 1 (SMR method, see reference).
Method1(MyData, AgeRange, Plot = F, Color = MyData$Param$Color)Method1(MyData, AgeRange, Plot = F, Color = MyData$Param$Color)
MyData |
The list returned by the AddReference() function. |
AgeRange |
Age range used for the calculation of the SMR. |
Plot |
If set to TRUE, a sub-directory will be created in the working directory. This sub-directory will contains png plots corresponding to the smoothed surface. |
Color |
The color that will be used for the plots (HTML notation). |
This function fits the Qxt using method 2 (two parameters relational method, see reference).
Method2(MyData, AgeRange, Plot = F, Color = MyData$Param$Color)Method2(MyData, AgeRange, Plot = F, Color = MyData$Param$Color)
MyData |
The list returned by the AddReference() function. |
AgeRange |
Age range used for the calculation of the parameters. |
Plot |
If set to TRUE, a sub-directory will be created in the working directory. This sub-directory will contains png plots corresponding to the smoothed surface. |
Color |
The color that will be used for the plots (HTML notation). |
This function fits the Qxt using method 3 (Poisson GLM, see reference).
Method3(MyData, AgeRange, Plot = F, Color = MyData$Param$Color)Method3(MyData, AgeRange, Plot = F, Color = MyData$Param$Color)
MyData |
The list returned by the AddReference() function. |
AgeRange |
Age range used for the calculation of the parameters of the Poisson model. |
Plot |
If set to TRUE, a sub-directory will be created in the working directory. This sub-directory will contains png plots corresponding to the smoothed surface. |
Color |
The color that will be used for the plots (HTML notation). |
This function fits the Qxt using method 4 (first step) (non-parametric smoothing, see reference).
Method4A(MyData, AgeRange, AgeCrit, ShowPlot = F)Method4A(MyData, AgeRange, AgeCrit, ShowPlot = F)
MyData |
The list returned by the AddReference() function. |
AgeRange |
Age range used for the construction of the life table. |
AgeCrit |
Age range for the comparison of adjusted mortality and observed mortality. |
ShowPlot |
AIC plots and plots allowing to judge about the fit. |
This function fits the Qxt using method 4 (second step) (non-parametric smoothing, see reference).
Method4B(PartOne, MyData, OptMale, OptFemale, Plot = F, ShowPlot = F, Color = MyData$Param$Color)Method4B(PartOne, MyData, OptMale, OptFemale, Plot = F, ShowPlot = F, Color = MyData$Param$Color)
PartOne |
The list returned by the Method4A() function. |
MyData |
The list returned by the AddReference() function. |
OptMale |
Optimal smoothing parameters, obtained from the graphics generated by Method4A() for the male population. |
OptFemale |
Optimal smoothing parameters, obtained from the graphics generated by Method4A() for the female population. |
Plot |
If set to TRUE, a sub-directory will be created in the working directory. This sub-directory will contains png plots corresponding to the smoothed surface. |
ShowPlot |
If true, show plots. |
Color |
The color that will be used for the plots (HTML notation). |
Artificial Portfolio data exemple.
data(MyPortfolio)data(MyPortfolio)
data(MyPortfolio)data(MyPortfolio)
This function allows to keep the adjustment used by the locating method for high ages (for methods 1, 2 or 3).
NoCompletion(OutputMethod, MyData, Color = MyData$Param$Color, Plot = F, Excel = F)NoCompletion(OutputMethod, MyData, Color = MyData$Param$Color, Plot = F, Excel = F)
OutputMethod |
The list returned by one of these functions : Method1(), Method2(), Method3() or Method4B(). |
MyData |
The list returned by the AddReference() function. |
Color |
The color that will be used for the plots (HTML notation). |
Plot |
If TRUE, final mortality surfaces will be saved in Results/Graphics/FinalTables |
Excel |
If TRUE, final tables will be saved in Results/Excel/FinalTables.xlsx |
This function reads a data.frame and calculates exposure and number of deaths. This is the first function the user must call to build a mortality table.
ReadHistory(MyPortfolio, DateBegObs, DateEndObs, DateFormat, Plot = F, Color = "#A4072E", Excel = F)ReadHistory(MyPortfolio, DateBegObs, DateEndObs, DateFormat, Plot = F, Color = "#A4072E", Excel = F)
MyPortfolio |
MyPortfolio is a data.frame of 6 columns as follows : -Id : Id for the line ; -Gender : Male or Female ; -DateOfBirth : aaaa/mm/jj ; -DateIn : aaaa/mm/jj ; -DateOut : aaaa/mm/jj ; -Status : "other" or "deceased". |
DateBegObs |
Date for the beginning of the observations. |
DateEndObs |
Date for the end of the observations. |
DateFormat |
Date format as expected by the as.Date R function. |
Plot |
If set to TRUE, a sub-directory will be created in the working directory. This sub-directory will contains png plots corresponding to the smoothed surface. |
Color |
The color that will be used for the plots (HTML notation). |
Excel |
If set to TRUE, a sub-directory will be created in the working directory. This sub-directory will contains excel files corresponding to the smoothed surface. |
This data corresponds to an adjusted version of the French national demographic projections INSEE 2060 for the female population.
data(ReferenceFemale)data(ReferenceFemale)
data(ReferenceFemale)data(ReferenceFemale)
This data corresponds to an adjusted version of the French national demographic projections INSEE 2060 for the male population.
data(ReferenceMale)data(ReferenceMale)
data(ReferenceMale)data(ReferenceMale)
Allows to plot a surface.
SurfacePlot(xx, zexpr, mainexpr, axis, cc)SurfacePlot(xx, zexpr, mainexpr, axis, cc)
xx |
data as matrix. |
zexpr |
Title of z axis. |
mainexpr |
Name for the graphic. |
axis |
c(min(abscissa), max(abscissa), min(ordinate), max(ordinate)). |
cc |
Color. |
This function performs the first level of validation on the returned value of one of these functions : Method1(), Method2(), Method3() or Method4B().
ValidationLevel1(OutputMethod, MyData, ValCrit, AgeCrit, Plot = F, Color = MyData$Param$Color, Excel = F)ValidationLevel1(OutputMethod, MyData, ValCrit, AgeCrit, Plot = F, Color = MyData$Param$Color, Excel = F)
OutputMethod |
The list returned by one of these functions : Method1(), Method2(), Method3() or Method4B(). |
MyData |
The list returned by the AddReference() function. |
ValCrit |
Critical value for the comparison of adjusted mortality and observed mortality. |
AgeCrit |
Age range for the comparison of adjusted mortality and observed mortality. |
Plot |
If set to TRUE, a sub-directory will be created in the working directory. This sub-directory will contains png plots describing the validation analysis. |
Color |
The color that will be used for the plots (HTML notation). |
Excel |
If set to TRUE, a sub-directory will be created in the working directory. This sub-directory will contains excel files describing the validation analysis. |
This function performs the second level of validation on the returned value of one of these functions : Method1(), Method2(), Method3() or Method4B() (see reference).
ValidationLevel2(OutputMethod, MyData, ValCrit, AgeCrit, Excel = F)ValidationLevel2(OutputMethod, MyData, ValCrit, AgeCrit, Excel = F)
OutputMethod |
The list returned by one of these functions : Method1(), Method2(), Method3() or Method4B(). |
MyData |
The list returned by the AddReference() function. |
ValCrit |
Critical value for the comparison of adjusted mortality and observed mortality. |
AgeCrit |
Age range for the comparison of adjusted mortality and observed mortality. |
Excel |
If set to TRUE, a sub-directory will be created in the working directory. This sub-directory will contains excel files describing the validation analysis. |
This function performs the third level of validation on the returned value of one of these functions : Method1(), Method2(), Method3() or Method4B().
ValidationLevel3(FinalMethod, MyData, Plot = F, Color = MyData$Param$Color, Excel = F)ValidationLevel3(FinalMethod, MyData, Plot = F, Color = MyData$Param$Color, Excel = F)
FinalMethod |
The list returned by the CompletionB() function. |
MyData |
The list returned by the AddReference() function. |
Plot |
If set to TRUE, a sub-directory will be created in the working directory. This sub-directory will contains png plots describing the validation analysis. |
Color |
The color that will be used for the plots (HTML notation). |
Excel |
If set to TRUE, a sub-directory will be created in the working directory. This sub-directory will contains excel files describing the validation analysis. |