Data_Fn: Data inputs for TMB

Description Usage Arguments Details Value

Description

Generates a tagged list representing data inputs for use by TMB, and tries to check validity of inputs

Usage

1
Data_Fn(Aniso, FieldConfig, ObsModel, Options, b_i, a_i, v_i, s_i, t_i, a_xl, X_xj, Q_ik, MeshList)

Arguments

Aniso

Use geometric anisotropy for spatial variation (0=no, 1=yes)

FieldConfig

Vector describing what types of spatial variation to estimate

ObsModel

Integer for distribution of residual 'measurement' error (0=normal (log-link); 1=lognormal; 2=gamma; 4=ZANB; 5=ZINB; 11=lognormal-mixture; 12=gamma-mixture)

Options

Essentially deprecated

b_i

biomass for each observation i

a_i

Area swept (or other offset) for each observation i

v_i

vessel for each observation

s_i

Polygon associated with each observation

t_i

Year for each observation

a_xl

Matrix with dimension n_x by n_l, where a_xl[x,l] gives the total area in polygon x that should be included when calculating index l. Indices do not need to be mutually exclusive (i.e., rows of a_xl can sum to more than one)

X_xj

Matrix with dimension n_x by n_j, where X_xj[x,j] gives the value of the j-th covariate for polygon x

Q_ik

Matrix with dimension n_i by n_k, where Q_ik[i,k] gives the value of 'catchability' covariate k for sample i

MeshList

named list generated by Calc_Anisotropic_Mesh() function

Details

This function generates the named list that is then used by MakeADFun() to generate the TMB object.

Value

There are three matrix inputs (a_xl, X_xj, and Q_ik) that require specification:

a_xl

a matrix used when post-processing results to calculate indices that are informed by subsets of the data. Using a conventional area-weighting approach, the l-th index is calculated as the sum of a_xl[x,l]*D_x[x], where D_x[x] is the density in each polygon x. For example, if a_xl is a vector of 1s, then all polygons are calculated as having equal weight when calculating an index.

X_xj

a matrix of j measured covariates that contribute to spatial variation in density. These covariates are then used when calculating density D_x for each polygon x.

Q_ik

a matrix of k covariates that represent variation in catch rates but not variation in density, e.g., gear efficient and catchability. This could include vessel horsepower, bottom temperature, etc. These variables are NOT used when calculating D_x for each polygon x. Instead, D_x is calculated for a reference value of Q_ik = 0 (i.e., when controling for variables in Q_ik).

This function also calculates five dimensions that are needed in TMB:

n_t

Number of time periods (typically years) in data set

n_v

Number of vessels in data set

n_i

the number of observations in the data set

n_x

the number of polygons used to summarize spatial variation

n_s

the number of vertices ('knots') in the triangulated mesh calculated by Calc_Anisotropic_Mesh()

n_j

the number of covariates for spatial variation

n_k

the number of covariates describing variation in catchability among samples

n_l

the number of indices to be calculated via post-processing results


aaronmberger/Geo_dGLMM_habitat documentation built on May 10, 2019, 3:20 a.m.