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Fit a MaGP model with a full sequence map

Source: R/engine-rcpp.R
magpfull_fit.Rd

Fits the quantitative-sequence model with q - 1 latent mapping dimensions, giving the sequence positions a less constrained coordinate representation.

Usage

magpfull_fit(
  X,
  y = NULL,
  q = NULL,
  tau = 0.001,
  maxeval = 500,
  xtol_rel = 1e-05,
  lb_sigma = 10,
  ub_sigma = 1000,
  lb_theta = 0.5,
  ub_theta = 1000,
  lb_delta = -1,
  ub_delta = 1,
  seed = NULL,
  n_starts = 1,
  workers = 1
)

Arguments

X

A numeric matrix or data frame. The first q input columns contain quantitative levels and the next q columns contain sequence positions. Each sequence row must be a permutation of 1:q. A column named y may be included as the response.

y

An optional numeric response vector. It may be omitted when X contains a response column named y.

q

The number of components. It is inferred from the number of input columns when omitted. The two-dimensional model requires at least three components; the full model requires at least two.

tau

A fixed nonnegative nugget variance added to the covariance diagonal.

maxeval

Maximum number of objective evaluations used by nloptr.

xtol_rel

Relative parameter tolerance used by nloptr.

lb_sigma, ub_sigma

Lower and upper bounds for the additive variance parameters.

lb_theta, ub_theta

Lower and upper bounds for the quantitative correlation parameters.

lb_delta, ub_delta

Lower and upper bounds for the mapping parameters.

seed

An optional nonnegative integer used to generate the initial parameter vectors. The first start retains the result produced by this seed when n_starts = 1.

n_starts

Number of independent parameter starts. The fitted object contains the result with the lowest objective among the converged starts.

workers

Number of local worker processes. Values greater than one use a socket cluster and are capped at two or n_starts, whichever is lower.

Value

An object of class magpfull.

Details

The data layout, quantitative scaling, and covariance construction are the same as in magp2d_fit(). The difference is the number of latent coordinates used to represent the sequence positions. With more than one start, the function keeps the converged result with the lowest objective and records the outcome of every start in fit$multistart$starts.

Examples

# \donttest{
train <- read.table(
  system.file("extdata", "example_train.txt", package = "magp"),
  header = TRUE
)
fit <- magpfull_fit(train, seed = 1, n_starts = 2)
fit
#> <magpfull model>
#>   mapping dimension: 3 
#>   components: 4 
#>   training runs: 31 
#>   nugget: 0.001 
#>   fitted mean: 15.7929 
#>   objective: 237.5634 
#>   optimizer: converged (status 4 )
#>   starts: 2 (best 1 )
#>   execution: sequential with 1 worker(s)
# }