R help - Aug 2019 - How to only include some covariates in a vglm model depending on the linear predictor?

Dear all, 





I am modelling a positive random variable with VGAM R-package with a truncated
normal distribution. I want to include some explanatory variables to model the
mean and other explanatory variables to model the standard deviation.




I try to do this with the "constraints" argument of vglm function. 




Here is an example with the "iris" dataset (it is not the actual data
I will use). I want to model the Sepal.Length's mean with the Sepal.Width,
and the Sepal.Length's standard deviation with the Petal.Length and the
Petal.Width.

Here is my code, with the list of constraints as I understood it should be
written.
library ( VGAM ) iris = iris
constraints = list ( "(Intercept)" = diag ( 2 ), Sepal.Width = rbind (
1 , 0 ), Petal.Length = rbind ( 0 , 1 ), Petal.Width = rbind ( 0 , 1 )) formula
= Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width
out = vglm ( formula , posnormal ( zero = NULL ), data = iris , constraints =
constraints )


Here are my constraint list: 
$ `(Intercept)` [, 1 ] [, 2 ] [ 1 ,] 1 0 [ 2 ,] 0 1 $ Sepal.Width [, 1 ] [ 1 ,]
1 [ 2 ,] 0 $ Petal.Length [, 1 ] [ 1 ,] 0 [ 2 ,] 1 $ Petal.Width [, 1 ] [ 1 ,] 0
[ 2 ,] 1


I get the following error message from function process.constraints: 
Error in process.constraints ( constraints , x = x , M = M , specialCM =
specialCM , : constraint matrix has too many columns "


This function takes as argument a list of constraint matrices, which appears to
be :
$ `(Intercept)` [, 1 ] [, 2 ] [, 3 ] [, 4 ] [ 1 ,] 1 1 0 0 [ 2 ,] 0 0 1 1 $
Sepal.Width [, 1 ] [, 2 ] [ 1 ,] 1 1 [ 2 ,] 0 0 $ Petal.Length [, 1 ] [, 2 ] [ 1
,] 0 0 [ 2 ,] 1 1 $ Petal.Width [, 1 ] [, 2 ] [ 1 ,] 0 0 [ 2 ,] 1 1


This is not what I entered, because there has been some postprocessing of my
initial constraint list, which adds some columns in the constraint matrix before
getting to the process.constraints function.

I don't get if this is a bug or if I did not initially write the right
constraints for what I want to do. Or maybe should I proceed differently?

I already posted this question on StackOverflow, but don't get any answer. 

Thanks for you help, 


Micha?l Zamo 
Ing?nieur d'?tudes et de d?veloppement 

M?t?o-France 
DirOP/COMPAS/DOP 
bureau B315 
42 avenue Coriolis 
31057 Toulouse cedex 05 
France 

T?l. : +33 5 61 07 86 36 
Courriel : michael.zamo at meteo.fr 


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