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magp

magp is designed for experiments in which every component has both an amount and a position in a sequence. It fits an additive Gaussian process that uses both parts of the input, then returns predictions with optional uncertainty estimates. You can choose a compact two-dimensional mapping or a full mapping with q - 1 dimensions. The most intensive covariance and gradient calculations run in C++ through Rcpp.

Installation

Install Rcpp and nloptr, then install the source package:

install.packages(c("Rcpp", "nloptr"))
install.packages("magp_0.8.0.tar.gz", repos = NULL, type = "source")
library(magp)

A C++ toolchain is required when installing from source.

Data format

For q components, the input must contain 2*q columns:

1. the first q columns contain quantitative inputs;
2. the last q columns contain sequence positions.

Every row in the sequence columns must contain each value from 1 to q exactly once. A response column named y may be included in the same data frame; when it is present, the fitting functions can identify both y and q automatically.

Quantitative columns outside [0, 1] are transformed by min-max scaling during fitting. The fitted ranges are saved and used again for prediction. Inputs already in [0, 1] are not changed.

Example

train <- read.table(
  system.file("extdata", "example_train.txt", package = "magp"),
  header = TRUE
)
test <- read.table(
  system.file("extdata", "example_test.txt", package = "magp"),
  header = TRUE
)

fit_2d <- magp2d_fit(train, tau = 0.001, seed = 1)
prediction_2d <- predict(fit_2d, test)
magp2d_rmse(prediction_2d, test$y)

fit_full <- magpfull_fit(train, tau = 0.001, seed = 1)
prediction_full <- predict(fit_full, test)
magp2d_rmse(prediction_full, test$y)

uncertainty <- predict(
  fit_2d,
  test[1:5, ],
  se.fit = TRUE,
  type = "response"
)
data.frame(
  prediction = uncertainty$fit,
  standard_error = uncertainty$se.fit
)

tau is a fixed nugget variance added to the covariance diagonal. It is a variance, not a standard deviation.

Prediction types

• "script" reproduces the fitted training-row convention when a new row exactly matches a training row.
• "response" includes the nugget when calculating uncertainty for a future response.
• "latent" returns uncertainty for the noise-free surface.

The reported standard errors treat the fitted covariance parameters as fixed.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.