# generate It generates a multivarite random series according to the model `x` ```r generate(x = NULL, ...) ## Default S3 method: generate( x, FUN = rnorm, n = 100, K = 3, names = NULL, cov = NULL, gap.filling = NULL, ... ) ## S3 method for class 'varest' generate( x, FUN = rnorm, n = 100, names = NULL, noise = NULL, exogen = NULL, xprev = NULL, gap.filling = NULL, ... ) ## S3 method for class 'varest2' generate( x, FUN = rnorm, n = 100, names = NULL, noise = NULL, exogen = NULL, xprev = NULL, gap.filling = NULL, ... ) ## S3 method for class 'GPCAvarest2' generate( x, FUN = rnorm, n = 100, names = NULL, noise = NULL, exogen = NULL, xprev = NULL, extremes = TRUE, type = 3, gap.filling = NULL, GPCA.row.gap.filling.option = TRUE, ... ) ## S3 method for class 'matrix' generate( x, FUN = rnorm, n = 100, noise = NULL, xprev = NULL, names = NULL, gap.filling = NULL, type = c("autoregression", "covariance"), ... ) ## S3 method for class 'list' generate(x, factor.series = names(x), n = NA, ...) ## S3 method for class 'MonthlyList' generate(x, origin, n, ...) ``` ## Arguments - `x`: null object or the model used for random generation , e.g. a VAR model as a `varest-class` or `varest2-class` object. Default is `NULL`. - `...`: further arguments for `FUN` - `FUN`: random function of the probability distribution used for noise random generation. Default is `rnorm`. See [ https://CRAN.R-project.org/view=Distributions]( https://CRAN.R-project.org/view=Distributions) - `n`: number of generations requested - `K`: number of the variables to be generated simultaneously, i.e. the K parameters of a VAR. It is automatically detected by `x`, `names` or `cov`, if one of these is not `NULL`. - `names`: null object or string vectors or names of the variables to be generated simultaneously. Default is `NULL`. - `cov`: null object or covariance matrix of the random variables to be generated simultaneously. Default is `NULL`, not used in case this information can be detected from `x`. - `gap.filling`: data frame with time series with gabs (`NA` values) to be filled. Default is `NULL` and not considered, otherwise the method returns this data frame with `NA` row replaced with generated (e.g auto-regressed) values. - `noise`: null object or a generic external noise for `x` model residuals, e.g. standard white noise, for random generation with the model `x`. Default is `NULL`. If `NULL` the noise is automatically calculated. - `exogen`: null object or amatrix or data frame with exogeneous variables (predictors) id requested by `x`. Default is `NULL` - `xprev`: null object or initial condition of the multivariate random process to be generated. Default is `NULL`. - `extremes`: see `inv_GPCA` - `type`: character string used in some method implementations. See `inv_GPCA`. In the matrix implementation, default is `"autoregression"`, i.e. the matrix is used as a vector auto-regression coefficient, if it is `"covariance"` the method genereted a sample with covariance matrix given by `x`. - `GPCA.row.gap.filling.option`: logical value. Default is `TRUE`. In case of `GPCAvarest2-class` objects, If `gap.filling` contains both `NA` and finite values in the same row, this row will contains all `NA` values after GPCA. In this case all row values are generated through auto-regression. If `GPCA.row.gap.filling.option` all insterted non-NA `gap.filling` values are repleced before returning the function value. Otherwise, in the rows with `NA`s all values are re-generated. The option `TRUE` is not safe in case the gaps are vary long becouse the genereted values is used for subsequent auto-regrossion. - `factor.series`: factor series used by 'factor.series' - `origin`: start date for generation. See `adddate` ## Returns a matrix or a data frame object ## Examples ```r library(RGENERATE) set.seed(122) NSTEP <- 1000 x <- rnorm(NSTEP) y <- x+rnorm(NSTEP) z <- c(rnorm(1),y[-1]+rnorm(NSTEP-1)) df <- data.frame(x=x,y=y,z=z) var <- VAR(df,type="none") gg <- generate(var,n=20) cov <- cov(gg) ggg <- generate(FUN=rnorm,n=NSTEP,cov=cov) library(RMAWGEN) exogen <- as.data.frame(x+5) gpcavar <- getVARmodel(data=df,suffix=NULL,p=3,n_GPCA_iteration=5, n_GPCA_iteration_residuals=5,exogen=exogen) gpcagg <- generate(gpcavar,n=20,exogen=exogen) ## Generate an auto-regrassive time-series with a generic matrix A <- diag(c(1,-1,1)) mgg <- generate(A,n=100) ### Gap Filling Examples dfobs <- df dfobs[20:30,] <- NA n <- nrow(df) dffill <- generate(gpcavar,n=n,exogen=exogen,gap.filling=dfobs,names=names(dfobs)) qqplot(dfobs$y,dffill$y) abline(0,1) ### Gap filling with matrix mgg_n <- mgg mgg_n[20:30,2] <- NA mgg_nfill <- generate(A,gap.filling=mgg_n) print(mgg_n[1:31,]) print(mgg_nfill[1:31,]) dfobs2 <- df dfobs2[20:30,2] <- NA n <- nrow(df) dffill2 <- generate(gpcavar,n=n,exogen=exogen,gap.filling=dfobs2,names=names(dfobs2)) qqplot(dfobs$y,dffill$y) abline(0,1) ### generation with 'generetion.matrix' ### and matrix 'x' is a covariance matrix covariance <- array(0.5,c(3,3)) diag(covariance) <- 1 set.seed(127) ngns <- 1000 gg1 <- generate(FUN=rnorm,n=ngns,cov=covariance) set.seed(127) gg2 <- generate(covariance,type="covariance",n=ngns) # with a list of covariance matrix ndim <- 5 dim <- c(ndim,ndim) CS1 <- array(0.3,dim) CS2 <- array(0.5,dim) CS3 <- array(0.7,dim) CS4 <- array(0.1,dim) diag(CS1) <- 1 diag(CS2) <- 1 diag(CS3) <- 1 diag(CS4) <- 1 list <- list(CS1=CS1,CS2=CS2,CS3=CS3,CS4=CS4) series <- rep(1:4,times=4,each=100) series <- sprintf("CS%d",series) names_A <- sprintf("A%d",1:ndim) ggs <- generate(list,factor.series=series,FUN=rnorm,type="covariance",names=names_A) ggs_CS1 <- ggs[series=="CS1",] cov(ggs_CS1) ggs_CS3 <- ggs[series=="CS3",] cov(ggs_CS3) ``` ## See Also `getVARmodel`

generate function