# Stochastic Generation of a `PrecipitationOccurrenceModel` or `PrecipitationOccurrenceMultiSiteModel` model object It is an implentation of `generate` method ```r ## S3 method for class 'PrecipitationOccurrenceModel' generate( x, newdata = NULL, previous = NULL, n = 30, random = runif(n, min = 0, max = 1), exogen = NULL, monthly.factor = NULL, ... ) ## S3 method for class 'CCGammaObjectListPerEachMonth' generate(x, ...) ## S3 method for class 'PrecipitationOccurrenceMultiSiteModel' generate( x, exogen, n = 10, origin = "1961-1-1", end = "1990-1-1", previous = NULL, monthly.factor = NULL, ... ) ## S3 method for class 'PrecipitationAmountModel' generate(x, ...) ``` ## Arguments - `x`: model returned by `PrecipitationOccurrenceModel` or `PrecipitationOccurrenceMultiSiteModel` - `newdata`: predictor or exogenous variables. See `predict.PrecipitationOccurrenceModel` - `previous`: logical vector containing previously occurred states - `n`: number of generations. See `generate`. Here it is ignored and the number of generations is given by `origin`,`end` or `monthly.factor`. - `random`: vector of random or calculated numbers ranging between 0 and 1 - `exogen`: predictor or exogenous variables - `monthly.factor`: vector of factors indicating the month of the days - `...`: further arguments - `origin, end`: character strings (yyyy-dd-mm) indicating the start and/or end date of the daily weather generation. ## Returns A vector or a data frame reporting generated time series for each station. ## Examples ```r library(RGENERATEPREC) ## A function example can be found in the following script file: scriptfile <- system.file("example.generate.R",package="RGENERATEPREC") ## The corrent file path is given by 'scriptfile' variable: print(scriptfile) ## To run the example file, launch the file with 'source' command (uncomment the following line) #source(scriptfile) ## ALTERNATIVELY you can run the following lines: data(trentino) year_min <- 1961 year_max <- 1990 origin <- paste(year_min,1,1,sep="-") end <- paste(year_max,12,31,sep="-") period <- PRECIPITATION$year>=year_min & PRECIPITATION$year<=year_max period_temp <- TEMPERATURE_MAX$year>=year_min & TEMPERATURE_MAX$year<=year_max prec_mes <- PRECIPITATION[period,] Tx_mes <- TEMPERATURE_MAX[period_temp,] Tn_mes <- TEMPERATURE_MIN[period_temp,] accepted <- array(TRUE,length(names(prec_mes))) names(accepted) <- names(prec_mes) for (it in names(prec_mes)) { acc <- TRUE acc <- (length(which(!is.na(Tx_mes[,it])))==length(Tx_mes[,it])) acc <- (length(which(!is.na(Tn_mes[,it])))==length(Tn_mes[,it])) & acc accepted[it] <- (length(which(!is.na(prec_mes[,it])))==length(prec_mes[,it])) & acc } valmin <- 1.0 prec_mes <- prec_mes[,accepted] Tx_mes <- Tx_mes[,accepted] Tn_mes <- Tn_mes[,accepted] prec_occurrence_mes <- prec_mes>=valmin station <- names(prec_mes)[!(names(prec_mes) %in% c("day","month","year"))] it <- station[2] vect <- Tx_mes[,it]-Tn_mes[,it] months <- factor(prec_mes$month) model <- PrecipitationOccurrenceModel(x=prec_mes[,it],exogen=vect, monthly.factor=months,valmin=valmin) obs <- prec_mes[,it]>=valmin gen <- generate(model,exogen=vect,monthly.factor=months,n=length(months)) ### MultiSite Generation station <- station[1:2] exogen <- Tx_mes[,station]-Tn_mes[,station] months <- factor(prec_mes$month) model_multisite <- PrecipitationOccurrenceMultiSiteModel(x=prec_mes[,station], exogen=exogen,origin=origin,multisite_type="wilks") ## LOGIT-type Model model_multisite_logit <- PrecipitationOccurrenceMultiSiteModel(x=prec_mes,exogen=exogen, origin=origin,multisite_type="logit",station=station) obs_multisite <- prec_mes[,station]>=valmin gen_multisite <- generate(model_multisite,exogen=exogen,origin=origin,end=end) gen_multisite_logit <- generate(model_multisite_logit,exogen=exogen,origin=origin,end=end) ``` ## References D.S. Wilks (1998), Multisite Generalization of a Daily Stochastic Precipitation Generation Model, Journal of Hydrology, Volume 210, Issues 1-4, September 1998, Pages 178-191, [https://www.sciencedirect.com/science/article/pii/S0022169498001863](https://www.sciencedirect.com/science/article/pii/S0022169498001863) Muamaraldin Mhanna and Willy Bauwens (2011) A Stochastic Space-Time Model for the Generation of Daily Rainfall in the Gaza Strip, International Journal of Climatology, Volume 32, Issue 7, pages 1098-1112, tools:::Rd_expr_doi("10.1002/joc.2305") , [https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.2305](https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.2305) ## See Also `generate`,`predict.glm`,`PrecipitationOccurrenceModel`,`PrecipitationOccurrenceMultiSiteModel`

generate function