Generates a list containing parameter settings for the ensemble algorithm.
plaid.grid(method = "BCPlaid", cluster = "b", fit.model = y ~ m + a + b,
background = TRUE, background.layer = NA, background.df = 1,
row.release = c(0.5, 0.6, 0.7), col.release = c(0.5, 0.6, 0.7),
shuffle = 3, back.fit = 0, max.layers = 20, iter.startup = 5,
iter.layer = 10, verbose = FALSE)
Here BCPlaid, to perform Plaid algorithm
'r', 'c' or 'b', to cluster rows, columns or both (default 'b')
Model (formula) to fit each layer. Usually, a linear model is used, that estimates three parameters: m (constant for all elements in the bicluster), a(contant for all rows in the bicluster) and b (constant for all columns). Thus, default is: y ~ m + a + b.
If 'TRUE' the method will consider that a background layer (constant for all rows and columns) is present in the data matrix.
If background='TRUE' a own background layer (Matrix with dimension of x) can be specified.
Degrees of Freedom of backround layer if background.layer is specified.
Before a layer is added, it's statistical significance is compared against a number of layers obtained by random defined by this parameter. Default is 3, higher numbers could affect time performance.
Number of iterations to find starting values
Number of iterations to find each layer
After a layer is added, additional iterations can be done to refine the fitting of the layer (default set to 0)
Scalar in [0,1](with interval recommended [0.5-0.7]) used as threshold to prune rows in the layers depending on row homogeneity
As above, with columns
Maximum number of layer to include in the model
If 'TRUE' prints extra information on progress.
A list containing parameter settings