[Stacomir-commits] r330 - pkg/stacomir/man

[noreply at r-forge.r-project.org]

Author: briand
Date: 2017-03-30 20:28:22 +0200 (Thu, 30 Mar 2017)
New Revision: 330

Modified:
   pkg/stacomir/man/model-Bilan_poids_moyen-method.Rd
Log:
correcting error in item list

Modified: pkg/stacomir/man/model-Bilan_poids_moyen-method.Rd
===================================================================
--- pkg/stacomir/man/model-Bilan_poids_moyen-method.Rd	2017-03-30 10:17:39 UTC (rev 329)
+++ pkg/stacomir/man/model-Bilan_poids_moyen-method.Rd	2017-03-30 18:28:22 UTC (rev 330)
@@ -13,12 +13,38 @@
 }
 \arguments{
 \item{object}{An object of class \link{Bilan_pois_moyen-class}}
+
+\item{model.type}{default "seasonal", "seasonal1","seasonal2","manual".}
 }
 \description{
 model method for Bilan_poids_moyen' 
 this method uses samples collected over the season to model the variation in weight of
 glass eel or yellow eels.
 }
+\details{
+Depending on model.type several models are produced
+\itemize{
+		\item{model.type="seasonal".}{ The simplest model uses a seasonal variation, it is
+				fitted with a sine wave curve allowing a cyclic variation 
+				w ~ a*cos(2*pi*(doy-T)/365)+b with a period T. The julian time d0 used is this model is set
+				at zero 1st of November d = d + d0; d0 = 305.}
+	\item{model.type="seasonal1".}{ A time component is introduced in the model, which allows
+			for a long term variation along with the seasonal variation. This long term variation is
+			is fitted with a gam, the time variable is set at zero at the beginning of the first day of observed values.
+			The seasonal variation is modeled on the same modified julian time as model.type="seasonal"
+			but here we use a cyclic cubic spline cc, which allows to return at the value of d0=0 at d=365.
+			This model was considered as the best to model size variations by Diaz & Briand in prep. but using a large set of values
+			over years.}
+\item{model.type="seasonal2"}{The seasonal trend in the previous model is now modelled with a sine
+			curve similar to the sine curve used in seasonal.  The formula for this is \eqn{sin(\omega vt) + cos(\omega vt)}{sin(omega vt) + cos(omega vt)}, 
+		where vt is the time index variable \eqn{\omega}{omega} is a constant that describes how the index variable relates to the full period
+			(here, \eqn{2\pi/365=0.0172}{2pi/365=0.0172}. The model is written as following \eqn{w~cos(0.0172*doy)+sin(0.0172*doy)+s(time).}}
+	\item{model.type="manual"}{ The dataset don (the raw data), coe (the coefficients already present in the
+			database, and newcoe the dataset to make the predictions from, are written to the environment envir_stacomi. 
+			please see example for further description on how to fit your own model, build the table of coefficients,
+			and write it to the database.}	
+}
+}
 \author{
 Cedric Briand \email{cedric.briand"at"eptb-vilaine.fr}
 }