Fix vignette

diff --git a/.gitignore b/.gitignore
index e6a7351..eb90000 100644 (file)
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,5 @@
+/_unversioned/
+

 #ignore roxygen2 generated files
 *.Rd
 !*-package.Rd
 #ignore roxygen2 generated files
 *.Rd
 !*-package.Rd


@@ -13,9 +15,10 @@
 /reports/*
 !/reports/*.R
 !/reports/*.sh
 /reports/*
 !/reports/*.R
 !/reports/*.sh

+/vignettes/cache/
+/vignettes/figure/

 /vignettes/report.html
 /vignettes/report.html

-/vignettes/report_cache/
-/vignettes/report_files/
+/vignettes/report.md

 /vignettes/*.zip
 
 #ignore R session files
 /vignettes/*.zip
 
 #ignore R session files

diff --git a/pkg/DESCRIPTION b/pkg/DESCRIPTION
index 706925c..d3b5427 100644 (file)
--- a/pkg/DESCRIPTION
+++ b/pkg/DESCRIPTION
@@ -1,3 +1,4 @@
+Encoding: UTF-8

 Package: morpheus
 Title: Estimate Parameters of Mixtures of Logistic Regressions
 Description: Mixture of logistic regressions parameters (H)estimation with
 Package: morpheus
 Title: Estimate Parameters of Mixtures of Logistic Regressions
 Description: Mixture of logistic regressions parameters (H)estimation with


@@ -24,7 +25,7 @@ Suggests:
     testthat (>= 3.0.0),
     roxygen2
 License: MIT + file LICENSE
     testthat (>= 3.0.0),
     roxygen2
 License: MIT + file LICENSE

-RoxygenNote: 7.1.1
+RoxygenNote: 7.3.2

 URL: https://github.com/yagu0/morpheus
 Collate:
     'utils.R'
 URL: https://github.com/yagu0/morpheus
 Collate:
     'utils.R'

diff --git a/pkg/R/plot.R b/pkg/R/plot.R
index 328b18c..93e9e65 100644 (file)
--- a/pkg/R/plot.R
+++ b/pkg/R/plot.R
@@ -88,8 +88,7 @@ plotBox <- function(mr, x, y, ...)
   args <- list(...)
   for (i in 1:L)
   {
   args <- list(...)
   for (i in 1:L)
   {

-    boxplot(params[[i]],
-      ifelse("ylab" %in% names(args), args$ylab, "Parameter value"))
+    boxplot(params[[i]], ...)

   }
 }
 
   }
 }
 

diff --git a/vignettes/report.Rmd b/vignettes/report.Rmd
index de98b9f..41a0a09 100644 (file)
--- a/vignettes/report.Rmd
+++ b/vignettes/report.Rmd
@@ -44,12 +44,12 @@ $Y=1$ conditionally to $X=x$ is given by $g(\langle \beta, x \rangle +b)$, where
 $\beta\in \R^{d}$ is the vector of regression coefficients and $b\in\R$ is the intercept.
 Popular examples of link functions are the logit link function where for any real $z$,
 $g(z)=e^z/(1+e^z)$ and the probit link function where $g(z)=\Phi(z),$ with $\Phi$
 $\beta\in \R^{d}$ is the vector of regression coefficients and $b\in\R$ is the intercept.
 Popular examples of link functions are the logit link function where for any real $z$,
 $g(z)=e^z/(1+e^z)$ and the probit link function where $g(z)=\Phi(z),$ with $\Phi$

-the cumulative distribution function of the standard normal ${\cal N}(0,1)$.
+the cumulative distribution function of the standard normal ${\mathcal N}(0,1)$.

 Both are implemented in the package.
 
 If now we want to modelise heterogeneous populations, let $K$ be the number of
 populations and $\omega=(\omega_1,\cdots,\omega_K)$ their weights such that
 Both are implemented in the package.
 
 If now we want to modelise heterogeneous populations, let $K$ be the number of
 populations and $\omega=(\omega_1,\cdots,\omega_K)$ their weights such that

-$\omega_{j}\geq 0$, $j=1,\ldots,K$ and $\sum_{j=1}^{K}\omega{j}=1$.
+$\omega_{j}\geq 0$, $j=1,\ldots,K$ and $\sum_{j=1}^{K}\omega_{j}=1$.

 Define, for $j=1,\ldots,K$, the regression coefficients in the $j$-th population
 by $\beta_{j}\in\R^{d}$ and the intercept in the $j$-th population by
 $b_{j}\in\R$. Let $\omega =(\omega_{1},\ldots,\omega_{K})$,
 Define, for $j=1,\ldots,K$, the regression coefficients in the $j$-th population
 by $\beta_{j}\in\R^{d}$ and the intercept in the $j$-th population by
 $b_{j}\in\R$. Let $\omega =(\omega_{1},\ldots,\omega_{K})$,


@@ -78,10 +78,12 @@ where $X\sim \mathcal{N}(m,\Sigma)$, $m\in \R^{d}$, $\Sigma$ a positive and
 symetric $d\times d$ matrix. ***** TODO: take this into account? -->
 
 The two main functions are:
 symetric $d\times d$ matrix. ***** TODO: take this into account? -->
 
 The two main functions are:

+

  * computeMu(), which estimates the parameters directions, and
  * optimParams(), which builds an object \code{o} to estimate all other parameters
    when calling \code{o$run()}, starting from the directions obtained by the
    previous function.
  * computeMu(), which estimates the parameters directions, and
  * optimParams(), which builds an object \code{o} to estimate all other parameters
    when calling \code{o$run()}, starting from the directions obtained by the
    previous function.

+

 A third function is useful to run Monte-Carlo or bootstrap estimations using
 different models in various contexts: multiRun(). We'll show example for all of them.
 
 A third function is useful to run Monte-Carlo or bootstrap estimations using
 different models in various contexts: multiRun(). We'll show example for all of them.
 


@@ -127,9 +129,8 @@ The other parameters are estimated by solving an optimization problem.
 The following function builds and return an optimization algorithm object:
 
 ```{r, results="show", include=TRUE, echo=TRUE}
 The following function builds and return an optimization algorithm object:
 
 ```{r, results="show", include=TRUE, echo=TRUE}

-M <- computeMoments(io$X, io$Y)
-# X and Y must be provided if the moments matrix is not given
-algopt <- optimParams(K=2, link="probit", optargs=list(M=M))
+# M <- computeMoments(io$X, io$Y) #optional (if several runs)
+algopt <- optimParams(io$X, io$Y, K=2, link="probit") #, M=M)

 # Optimization starts at beta = mu, b = 0 and p = uniform distribution
 x0 <- list(beta = mu)
 theta <- algopt$run(x0)
 # Optimization starts at beta = mu, b = 0 and p = uniform distribution
 x0 <- list(beta = mu)
 theta <- algopt$run(x0)


@@ -194,7 +195,7 @@ see ?plotHist).
 
 ```{r, results="show", include=TRUE, echo=TRUE}
 # Second row, first column; morpheus on the left, flexmix on the right
 
 ```{r, results="show", include=TRUE, echo=TRUE}
 # Second row, first column; morpheus on the left, flexmix on the right

-plotBox(mr1, 2, 1, "Target value: -1")
+plotBox(mr1, 2, 1, main="Target value: -1")

 ```
 
 ```{r, results="show", include=TRUE, echo=TRUE}
 ```
 
 ```{r, results="show", include=TRUE, echo=TRUE}


@@ -204,7 +205,7 @@ mr2 <- multiRun(list(n=1000,p=1/2,beta=beta,b=c(0,0),optargs=list(link="logit"))
   function(fargs) {
     library(morpheus)
     mu <- computeMu(fargs$X, fargs$Y, fargs$optargs)
   function(fargs) {
     library(morpheus)
     mu <- computeMu(fargs$X, fargs$Y, fargs$optargs)

-    optimParams(fargs$K,fargs$link,fargs$optargs)$run(list(beta=mu))$beta
+    optimParams(fargs$X,fargs$Y,fargs$K,fargs$link,fargs$M)$run(list(beta=mu))$beta

   },
   # flexmix
   function(fargs) {
   },
   # flexmix
   function(fargs) {


@@ -222,7 +223,7 @@ mr2 <- multiRun(list(n=1000,p=1/2,beta=beta,b=c(0,0),optargs=list(link="logit"))
     fargs$Y = io$Y
     fargs$K = ncol(fargs$beta)
     fargs$link = fargs$optargs$link
     fargs$Y = io$Y
     fargs$K = ncol(fargs$beta)
     fargs$link = fargs$optargs$link

-    fargs$optargs$M = computeMoments(io$X,io$Y)
+    fargs$M = computeMoments(io$X,io$Y)

     fargs
   }, N=10, ncores=3)
 # As in example 1, align results:
     fargs
   }, N=10, ncores=3)
 # As in example 1, align results:


@@ -232,6 +233,7 @@ for (i in 1:2)
 
 ```{r, results="show", include=TRUE, echo=TRUE}
 # Second argument = true parameters matrix; third arg = index of method (here "morpheus")
 
 ```{r, results="show", include=TRUE, echo=TRUE}
 # Second argument = true parameters matrix; third arg = index of method (here "morpheus")

-plotCoefs(mr2, beta, 1)
+plotCoefs(mr2[[1]], beta, 1)
+plotCoefs(mr2[[2]], beta, 1)

 # Real params are on the continous line; estimations = dotted line
 ```
 # Real params are on the continous line; estimations = dotted line
 ```