X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=vignettes%2Freport.Rmd;h=4de2b4dad48217ab20dbe617ebab76a89318368b;hb=5859426b074bdb7084627f8eeba806f479f04f05;hp=cd6f4cf6d19fad59b7e64df408d5d7893e3ca1ae;hpb=dad25cd2d4973d76a5b79a7041b7f66ac4a9bfe2;p=morpheus.git

diff --git a/vignettes/report.Rmd b/vignettes/report.Rmd
index cd6f4cf..4de2b4d 100644
--- a/vignettes/report.Rmd
+++ b/vignettes/report.Rmd
@@ -68,14 +68,45 @@ For more informations on that subject, however, please refer to our article [XX]
 In this vignette let's rather focus on package usage.
 
 ## Usage
-<!--We assume that the random variable $X$ has a Gaussian distribution. We now focus on the situation where $X\sim \mathcal{N}(0,I_d)$, $I_d$ being the identity $d\times d$ matrix. All results may be easily extended to the situation 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? -->
+<!--We assume that the random variable $X$ has a Gaussian distribution.
+We now focus on the situation where $X\sim \mathcal{N}(0,I_d)$, $I_d$ being the
+identity $d\times d$ matrix. All results may be easily extended to the situation
+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:
+ * 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.
 
+### Estimation of directions
 
-3) Experiments: show package usage
+In a real situation you would have (maybe after some pre-processing) the matrices
+X and Y which contain vector inputs and binary output.
+However, a function is provided in the package to generate such data following a
+pre-defined law:
 
-\subsection{Experiments}
-In this section, we evaluate our algorithm in a first step using mean squared error (MSE). In a second step, we compare experimentally our moments method (morpheus package \cite{Loum_Auder}) and the likelihood method (with felxmix package \cite{bg-papers:Gruen+Leisch:2007a}). 
+io <- generateSampleIO(n=10000, p=1/2, beta=matrix(c(1,0,0,1),ncol=2), b=c(0,0), link="probit")
 
-TODO.........
+n is the total number of samples (lines in X, number of elements in Y)
+p is a vector of proportions, of size d-1 (because the last proportion is deduced from
+  the others: p elements sums to 1) [TODO: omega or p?]
+beta is the matrix of linear coefficients, as written above in the model.
+b is the vector of intercepts (as in linear regression, and as in the model above)
+link can be either "logit" or "probit", as mentioned earlier.
+
+This function outputs a list containing in particular the matrices X and Y, allowing to
+use the other functions (which all require either these, or the moments).
+
+TODO: computeMu(), explain input/output
+
+### Estimation of other parameters
+
+TODO: just run optimParams$run(...)
+
+### Monte-Carlo and bootstrap
+
+TODO: show example comparison with flexmix, show plots.