v1.0.14.0 NEW: add Vignette_Param datasets to reduce runtime of vignettes to pass CRAN check

DESCRIPTION

 Package: airGR
 Type: Package
 Title: Suite of GR Hydrological Models for Precipitation-Runoff Modelling
-Version: 1.0.13.19
-Date: 2018-09-27
+Version: 1.0.14.0
+Date: 2018-09-28
 Authors@R: c(
   person("Laurent", "Coron", role = c("aut", "trl"), comment = c(ORCID = "0000-0002-1503-6204")),
   person("Charles", "Perrin", role = c("aut", "ths"), comment = c(ORCID = "0000-0001-8552-1881")),

NEWS.rmd

@@ -14,7 +14,7 @@ output:
 
 
 
-### 1.0.13.19 Release Notes (2018-09-27) 
+### 1.0.14.0 Release Notes (2018-09-28) 
 
 
 #### Deprecated and defunct
@@ -57,6 +57,8 @@ output:
 
 - As recomanded by CRAN managers, the NEWS file is now at the text format and is no more just a link to the airGR Website
 
+- Added the <code>Vignette_Param.</code> datasets in order to reduce runtime during the re-building of vignettes. It contains different objects needed for param_optim and param_mcmc vignettes.
+
 ____________________________________________________________________________________
 
 

man/Vignette_Param.Rd

+\docType{data}
+\encoding{UTF-8}
+
+
+\name{Vignette_Param}
+\alias{IniParam}
+\alias{ListIniParam}
+\alias{list_opt}
+\alias{mcmcDRAM}
+\alias{optDE}
+\alias{optMALS}
+\alias{optPORT}
+\alias{optPORT_}
+\alias{optPSO}
+\alias{startGR4J}
+
+
+
+
+\title{Datasets needed to run param_optim or param_mcmc vignettes}
+
+
+
+
+\description{Datasets needed to run param_optim or param_mcmc vignettes.}
+
+
+
+

vignettes/V02.1_param_optim.Rmd

@@ -17,6 +17,7 @@ library(hydroPSO)
 library(Rmalschains)
 # source("airGR.R")
 set.seed(321)
+data("Vignette_Param")
 ```
 
 
@@ -80,9 +81,8 @@ upperGR4J <- rep(+9.99, times = 4)
 # Local optimization
 
 We start with a local optimization strategy by using the PORT routines (using the `nlminb()` of the `stats` package) and by setting a starting point in the transformed parameter space:
-```{r, warning=FALSE, results='hide'}
-startGR4J <- c(4.1, 3.9, -0.9, -8.7)
-optPORT <- stats::nlminb(start = startGR4J, 
+```{r, warning=FALSE, results='hide', eval=FALSE}
+optPORT <- stats::nlminb(start = c(4.1, 3.9, -0.9, -8.7), 
                          objective = OptimGR4J,
                          lower = lowerGR4J, upper = upperGR4J,
                          control = list(trace = 1))
@@ -92,7 +92,7 @@ The RMSE value reaches a local minimum value after 35 iterations.
 We can also try a multi-start approach to test the consistency of the local optimization.
 Here we use the same grid used for the filtering step of the Michel's calibration strategy (`Calibration_Michel()` function).
 For each starting point, a local optimization is performed.
-```{r, warning=FALSE, results='hide'}
+```{r, warning=FALSE, results='hide', eval=FALSE}
 startGR4J <- expand.grid(data.frame(CalibOptions$StartParamDistrib))
 optPORT_ <- function(x) {
   opt <- stats::nlminb(start = x, 
@@ -128,7 +128,7 @@ Here we use the following R implementation of some popular strategies:
 * [Rmalschains: memetic algorithms](https://cran.r-project.org/package=Rmalschains)
 
 ## Differential Evolution
-```{r, warning=FALSE, results='hide'}
+```{r, warning=FALSE, results='hide', eval=FALSE}
 optDE <- DEoptim::DEoptim(fn = OptimGR4J,
                           lower = lowerGR4J, upper = upperGR4J,
                           control = DEoptim::DEoptim.control(NP = 40, trace = 10))
@@ -136,14 +136,14 @@ optDE <- DEoptim::DEoptim(fn = OptimGR4J,
 
 
 ## Particle Swarm
-```{r, warning=FALSE, results='hide', message=FALSE}
+```{r, warning=FALSE, results='hide', message=FALSE, eval=FALSE}
 optPSO <- hydroPSO::hydroPSO(fn = OptimGR4J,
                              lower = lowerGR4J, upper = upperGR4J,
                              control = list(write2disk = FALSE, verbose = FALSE))
 ```
 
 ## MA-LS-Chains
-```{r, warning=FALSE, results='hide'}
+```{r, warning=FALSE, results='hide', eval=FALSE}
 optMALS <- Rmalschains::malschains(fn = OptimGR4J,
                                    lower = lowerGR4J, upper = upperGR4J, 
                                    maxEvals = 2000)

vignettes/V02.2_param_mcmc.Rmd

@@ -18,6 +18,7 @@ library(ggmcmc)
 library(dplyr)
 # source("airGR.R")
 set.seed(123)
+data("Vignette_Param")
 ```
 
 
@@ -49,9 +50,8 @@ First, we need to define a function that returns twice the opposite of the log-l
 
 Nota: in the `RunAirGR4J()` function, the computation of the log-likelihood is simplified in order to ensure a good computing performance. It corresponds to a translation  of the two following lines. 
 ```{r, echo=TRUE, eval=FALSE}
-  Likelihood <- sum((ObsY - ModY)^2, na.rm = TRUE)^(-sum(!is.na(ObsY)) / 2)
-  LL <- -2 * log(Likelihood)
-
+Likelihood <- sum((ObsY - ModY)^2, na.rm = TRUE)^(-sum(!is.na(ObsY)) / 2)
+LL <- -2 * log(Likelihood)
 ```
 
 In our simplified setting of Gaussian likelihood with measurement error integrated out, the log of the sum of squared error is related to the log-likelihood.
@@ -78,7 +78,7 @@ RunAirGR4J <- function(Param_Optim) {
 
 ## Estimation of the best-fit parameters as a starting point
 We start by using the PORT optimization routine to estimate the best-fit parameters.
-```{r, results='hide'}
+```{r, results='hide', eval=FALSE}
 optPORT <- stats::nlminb(start = c(4.1, 3.9, -0.9, -8.7), 
                          objective = RunAirGR4J,
                          lower = rep(-9.9, times = 4), upper = rep(9.9, times = 4),
@@ -95,7 +95,7 @@ Nota: in this example, there are relatively few iterations (2000), in order to l
 
 With the DRAM algorithm, the covariance of the proposal is updated every 100 runs and delayed rejection is applied.
 
-```{r, results='hide'}
+```{r, results='hide', eval=FALSE}
 ListIniParam <- data.frame(Chain1 = IniParam, Chain2 = IniParam, Chain3 = IniParam,
                            row.names = paste0("X", 1:4))
 ListIniParam <- sweep(ListIniParam, MARGIN = 2, STATS = c(1, 0.9, 1.1), FUN = "*")
@@ -143,7 +143,6 @@ First, the evolution of the Markov chains can be seen with a traceplot:
 ```{r, fig.width=6, fig.height=9, warning=FALSE}
 ParamDRAM <- ggmcmc::ggs(MultDRAM) ## to convert objet for using by all ggs_* graphical functions
 ggmcmc::ggs_traceplot(ParamDRAM)
-
 ```
 
 The posterior density for each parameter can then be visualised: