diff --git a/DESCRIPTION b/DESCRIPTION
index e1681bf0609a533c7794122cc1b5e006d02898df..28b8bb6eaf36ee2df96fcc7dd9787dcb70ea20c8 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,8 +1,8 @@
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")),
diff --git a/NEWS.rmd b/NEWS.rmd
index a3a58a4c48375bb4026b856b4380d75ea6adaee9..ba0841e40a3a588cfce4b6ef81844d369782a16f 100644
--- a/NEWS.rmd
+++ b/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.
+
____________________________________________________________________________________
diff --git a/data/Vignette_Param.rda b/data/Vignette_Param.rda
new file mode 100644
index 0000000000000000000000000000000000000000..418e80c819cebee98a90aaf415bac316c8565fcc
Binary files /dev/null and b/data/Vignette_Param.rda differ
diff --git a/man/Vignette_Param.Rd b/man/Vignette_Param.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..4d39b5798e1d8dd40c2f97c940bbc75ac7f888e1
--- /dev/null
+++ b/man/Vignette_Param.Rd
@@ -0,0 +1,29 @@
+\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.}
+
+
+
+
diff --git a/vignettes/V02.1_param_optim.Rmd b/vignettes/V02.1_param_optim.Rmd
index 3ccc7b492a1000d3a8cdff13b76be2b04792c351..5c87fc9dd9270c20957589347e571ede2f84516e 100644
--- a/vignettes/V02.1_param_optim.Rmd
+++ b/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)
diff --git a/vignettes/V02.2_param_mcmc.Rmd b/vignettes/V02.2_param_mcmc.Rmd
index dc249b3e9245a80717b1dfe1acac1468f78e2de8..5192c6ea89a0258a9700c99be6d9f4dec60e5e20 100644
--- a/vignettes/V02.2_param_mcmc.Rmd
+++ b/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: