estNPresp: Estimate the taxon-wise response functions non-parametrically

Description Usage Arguments Value

View source: R/F_estNPresp.R

Description

Estimate the taxon-wise response functions non-parametrically

Usage

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estNPresp(
  sampleScore,
  muMarg,
  X,
  ncols,
  thetas,
  n,
  coefInit,
  coefInitOverall,
  dfSpline,
  vgamMaxit,
  degree,
  verbose,
  allowMissingness,
  naId,
  ...
)

Arguments

sampleScore

a vector of length n with environmental scores

muMarg

the offset matrix

X

the n-by-p data matrix

ncols

an integer, the number of columns of X

thetas

a vector of length p with dispersion parameters

n

an integer, the number of samples

coefInit

a 2-by-p matrix with current taxon-wise parameter estimates

coefInitOverall

a vector of length 2 with current overall parameters

dfSpline

a scalar, the degrees of freedom for the smoothing spline.

vgamMaxit

Maximal number of iterations in the fitting of the GAM model

degree

The degree if the parametric fit if the VGAM fit fails

verbose

a boolean, should number of failed fits be reported

allowMissingness

A boolean, are missing values present

naId

The numeric index of the missing values in X

...

further arguments, passed on to the VGAM:::vgam() function

The negative binomial likelihood is still maximized, but now the response function is a non-parametric one. To avoid a perfect fit and overly flexible functions, we enforce smoothness restrictions. In practice we use a generalized additive model (GAM), i.e. with splines. The same fitting procedure is carried out ignoring species labels. We do not normalize the parameters related to the splines: the psis can be calculated afterwards.

Value

A list with components

taxonCoef

The fitted coefficients of the sample-wise response curves

splinesList

A list of all the B-spline objects

rowMar

The row matrix

overall

The overall fit ignoring taxon labels, as a list of coefficients and a spline

rowVecOverall

The overall row vector, ignoring taxon labels


RCM documentation built on Nov. 8, 2020, 5:22 p.m.

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