# model,report_ge_weight-method

##### model method for report_ge_weight' this method uses samples collected over the season to model the variation in weight of glass eel or yellow eels.

model method for report_ge_weight' this method uses samples collected over the season to model the variation in weight of glass eel or yellow eels.

##### Usage

```
# S4 method for report_ge_weight
model(object, model.type = "seasonal",
silent = FALSE)
```

##### Arguments

- object
An object of class report_ge_weight-class

- model.type
default "seasonal", "seasonal1","seasonal2","manual".

- silent
Default FALSE, if TRUE the program should no display messages

##### Details

Depending on model.type several models are produced

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*(d'-T)/365)+b with a period T. The modified day d' used is this model is set at 1 the 1st of august doy = d' + d0; d0 = 212, doy=julian days

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.

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 \(sin(\omega vt) + cos(\omega vt)\), where vt is the time index variable \(\omega\) is a constant that describes how the index variable relates to the full period (here, \(2\pi/365=0.0172\)). The model is written as following \(w~cos(0.0172*doy)+sin(0.0172*doy)+s(time).\)

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.

*Documentation reproduced from package stacomiR, version 0.5.4.2, License: GPL (>= 2)*