A character vector of arguments (character strings) of the form "<name>=<value>".
Values will be converted to logical or numeric when necessary.
Accepted <names> are below. Defaults in parenthesis:
- decomp
Either 'CP' or 'Tucker'. (Tucker)
- row.share
Logical. Should the variance be shared across rows of the projection matrices?
This will cause predictors to be or excluded for the whole model, instead of just for particular
latent factors. (T)
- seed
Numeric. Seed used for random initialization. (NA)
- scale
Logical. Should the input data columns should be scaled to have mean 0 and
standard deviation 1. (TRUE)
- m1.rows
Numeric. Number of rows (samples) for mode 1. (20)
- m2.rows
Numeric. Number of rows (samples) for mode 2. (25)
- m3.rows
Numeric. Number of rows (samples) for mode 3. (10)
- m1.cols
Numeric. Number of columns (predictors) for mode 1. (100)
- m2.cols
Numeric. Number of columns (predictors) for mode 2. (150)
- m3.cols
Numeric. Number of columns (predictors) for mode 3. (0)
- R
Numeric. If decomp=='CP'
the dimension of the latent space for all modes. (4)
- R1
Numeric. If decomp=='Tucker'
the dimension of the core (latent space) for
mode 1. (3)
- R2
Numeric. If decomp=='Tucker'
the dimension of the core (latent space) for
mode 2. (3)
- R3
Numeric. If decomp=='Tucker'
the dimension of the core (latent space) for
mode 3. (3)
- A1.intercept
Logical. Should a column of 1s be added to the input data for mode 1. (TRUE)
- A2.intercept
Logical. Should a column of 1s be added to the input data for mode 2. (TRUE)
- A3.intercept
Logical. Should a column of 1s be added to the input data for mode 3. (TRUE)
- H1.intercept
Logical. Should a column of 1s be added to the latent (H) matrix for mode 1. (TRUE)
- H2.intercept
Logical. Should a column of 1s be added to the latent (H) matrix for mode 2. (TRUE)
- H3.intercept
Logical. Should a column of 1s be added to the latent (H) matrix for mode 3. (TRUE)
- m1.true
Numeric. Number of predictors for mode 1 (not counting the constant)
contributing to the response. (15)
- m2.true
Numeric. Number of predictors for mode 2 (not counting the constant)
contributing to the response. (20)
- m3.true
Numeric. Number of predictors for mode 3 (not counting the constant)
contributing to the response. (0)
- A1.const.prob
Numeric. Probability (0-1) of the constant term for mode 1 contributing
to the response for mode 1. (1)
- A2.const.prob
Numeric. Probability (0-1) of the constant term for mode 2 contributing
to the response. (1)
- A3.const.prob
Numeric. Probability (0-1) of the constant term for mode 3 contributing
to the response. (1)
- A.samp.sd
Numeric. Standard deviation for sampling values for the projection (A) matrices. (1)
- H.samp.sd
Numeric. Standard deviation for sampling values for the latent (H) matrices. (1)
- R.samp.sd
Numeric. Standard deviation for sampling values for the core tensor. (1)
- true.0D
Numeric. 0 or 1, should a global intercept (0 dimensional intercept) be added
to all responses? Only possible if H1.intercept==H2.intercept==H3.intercept==TRUE
.
core.spar
is used if equal to NA
. (NA)
- true.1D.m[1-3]
Numeric. Number of interactions of 1 dimension in the core tensor (non-zero elements
on the edges of the core tensor if H#.intercept==TRUE
). core.spar
is used if
equal to NA
. (NA)
- true.2D.m[1-3]m[1-3]
Numeric. Number of interactions of 2 dimensions in the core tensor (non-zero elements
of the faces of the core tensor if H#.intercept==TRUE
). core.spar
is used if
equal to NA
. (NA)
- true.3D
Numeric. Number of interactions of 3 dimensions in the core tensor (non-zero elements
internal to the core tensor). core.spar
is used if equal to NA
. (NA)
- core.spar
Numeric. Fraction of core elements that are non-zero. (1)
- noise.sd
Numeric. Relative standard deviation of noise added to response tensor. (0.1)