Perform stepwise regression fit to select for significant terms.

Performs stepwise regression and selects the significant polynomial terms from the full polynomial model. This function is succeeded by scMaSigPro::sc.p.vector().

sc.t.fit(
  scmpObj,
  selection_method = "backward",
  p_value = scmpObj@Parameters@p_value,
  nvar_correction = FALSE,
  family = scmpObj@Parameters@distribution,
  epsilon = scmpObj@Parameters@epsilon,
  offset = scmpObj@Parameters@offset,
  verbose = TRUE,
  parallel = FALSE,
  n_cores = availableCores() - 2,
  log_offset = scmpObj@Parameters@log_offset,
  max_it = scmpObj@Parameters@max_it,
  link = scmpObj@Parameters@link
)

Arguments

scmpObj

An object of class ScMaSigPro.

selection_method

Method for step-wise regression.

p_value

Significance level used for variable selection in the stepwise regression.

nvar_correction

Argument for correcting significance level. See details.

family

Distribution of the error term.

epsilon

Model convergence tolerance.

offset

A logical value specifying whether to use offset during fitting.

verbose

Print detailed output in the console. (Default is TRUE)

parallel

Use forking process to run parallelly. (Default is FALSE) (Currently, Windows is not supported)

n_cores

Explicitly specify the number of cores to use for parallel model fitting. (Default is inferred from the system using availableCores()-2)

log_offset

A logical value specifying whether to take the logarithm of the offsets.

max_it

Maximum number of iterations to fit the model.

link

Type of link function to use in the model. Default is "log".

Value

An object of class ScMaSigPro, with updated `Estimate` slot.

References

Conesa, A., Nueda M.J., Alberto Ferrer, A., Talon, T. 2006. maSigPro: a Method to Identify Significant Differential Expression Profiles in Time-Course Microarray Experiments. Bioinformatics 22, 1096-1102

See also

Estimates Class.

Author

Priyansh Srivastava spriyansh29@gmail.com, Ana Conesa and Maria Jose Nueda, mj.nueda@ua.es