cvedgenet-methods: Find the optimal shrinkage parameters for edgenet
In netReg: Network-Regularized Regression Models

Description Usage Arguments Value Examples

Finds the optimal regulariztion parameters using cross-validation for edgenet. We use the BOBYQA algorithm to find the optimial regularization parameters in a cross-validation framework.

cv.edgenet(
  X,
  Y,
  G.X = NULL,
  G.Y = NULL,
  lambda = NA_real_,
  psigx = NA_real_,
  psigy = NA_real_,
  thresh = 1e-05,
  maxit = 1e+05,
  learning.rate = 0.01,
  family = gaussian,
  optim.maxit = 100,
  optim.thresh = 0.01,
  nfolds = 10
)

## S4 method for signature 'matrix,numeric'
cv.edgenet(
  X,
  Y,
  G.X = NULL,
  G.Y = NULL,
  lambda = NA_real_,
  psigx = NA_real_,
  psigy = NA_real_,
  thresh = 1e-05,
  maxit = 1e+05,
  learning.rate = 0.01,
  family = gaussian,
  optim.maxit = 100,
  optim.thresh = 0.01,
  nfolds = 10
)

## S4 method for signature 'matrix,matrix'
cv.edgenet(
  X,
  Y,
  G.X = NULL,
  G.Y = NULL,
  lambda = NA_real_,
  psigx = NA_real_,
  psigy = NA_real_,
  thresh = 1e-05,
  maxit = 1e+05,
  learning.rate = 0.01,
  family = gaussian,
  optim.maxit = 100,
  optim.thresh = 0.01,
  nfolds = 10
)

`X`	input matrix, of dimension (`n` x `p`) where `n` is the number of observations and `p` is the number of covariables. Each row is an observation vector.
`Y`	output matrix, of dimension (`n` x `q`) where `n` is the number of observations and `q` is the number of response variables Each row is an observation vector.
`G.X`	non-negativ affinity matrix for `X`, of dimensions (`p` x `p`) where `p` is the number of covariables. Providing a graph `G.X` will optimize the regularization parameter `psi.gx`. If this is not desired just set `G.X` to `NULL`.
`G.Y`	non-negativ affinity matrix for `Y`, of dimensions (`q` x `q`) where `q` is the number of responses `Y`. Providing a graph `G.Y` will optimize the regularization parameter `psi.gy`. If this is not desired just set `G.Y` to `NULL`.
`lambda`	`numerical` shrinkage parameter for LASSO. Per default this parameter is set to `NA_real_` which means that `lambda` is going to be estimated using cross-validation. If any `numerical` value for `lambda` is set, estimation of the optimal parameter will not be conducted.
`psigx`	`numerical` shrinkage parameter for graph-regularization of `G.X`. Per default this parameter is set to `NA_real_` which means that `psigx` is going to be estimated in the cross-validation. If any `numerical` value for `psigx` is set, estimation of the optimal parameter will not be conducted.
`psigy`	`numerical` shrinkage parameter for graph-regularization of `G.Y`. Per default this parameter is set to `NA_real_` which means that `psigy` is going to be estimated in the cross-validation. If any `numerical` value for `psigy` is set, estimation of the optimal parameter will not be conducted.
`thresh`	`numerical` threshold for the optimizer
`maxit`	maximum number of iterations for the optimizer (`integer`)
`learning.rate`	step size for Adam optimizer (`numerical`)
`family`	family of response, e.g. gaussian or binomial
`optim.maxit`	the maximum number of iterations for the optimization (`integer`). Usually 1e4 is a good choice.
`optim.thresh`	`numerical` threshold criterion for the optimization to stop. Usually 1e-3 is a good choice.
`nfolds`	the number of folds to be used - default is 10

An object of class cv.edgenet

`parameters`	the estimated, optimal regularization parameters
`lambda`	optimal estimated value for regularization parameter lambda (or, if provided as argument, the value of the parameter)
`psigx`	optimal estimated value for regularization parameter psigx (or, if provided as argument, the value of the parameter)
`psigy`	optimal estimated value for regularization parameter psigy (or, if provided as argument, the value of the parameter)
`estimated.parameters`	names of parameters that were estimated
`family`	family used for estimated
`fit`	an `edgenet` object fitted with the optimal, estimated paramters
`call`	the call that produced the object

X <- matrix(rnorm(100 * 10), 100, 10)
b <- matrix(rnorm(100), 10)
G.X <- abs(rWishart(1, 10, diag(10))[, , 1])
G.Y <- abs(rWishart(1, 10, diag(10))[, , 1])
diag(G.X) <- diag(G.Y) <- 0

# estimate the parameters of a Gaussian model
Y <- X %*% b + matrix(rnorm(100 * 10), 100)

## dont use affinity matrices and estimate lambda
fit <- cv.edgenet(
  X = X, Y = Y, family = gaussian,
  maxit = 1, optim.maxit = 1
)
## only provide one matrix and estimate lambda
fit <- cv.edgenet(
  X = X, Y = Y, G.X = G.X, psigx = 1, family = gaussian,
  maxit = 1, optim.maxit = 1
)
## estimate only lambda with two matrices
fit <- cv.edgenet(
  X = X, Y = Y, G.X = G.X, G.Y, psigx = 1, psigy = 1,
  family = gaussian, maxit = 1, optim.maxit = 1
)
## estimate only psigx
fit <- cv.edgenet(
  X = X, Y = Y, G.X = G.X, G.Y, lambda = 1, psigy = 1,
  family = gaussian, maxit = 1, optim.maxit = 1
)
## estimate all parameters
fit <- cv.edgenet(
  X = X, Y = Y, G.X = G.X, G.Y,
  family = gaussian, maxit = 1, optim.maxit = 1
)
## if Y is vectorial, we cannot use an affinity matrix for Y
fit <- cv.edgenet(
  X = X, Y = Y[, 1], G.X = G.X,
  family = gaussian, maxit = 1, optim.maxit = 1
)