R/fitTsfm.control.R
In braverock/factorAnalytics: Factor Analytics for Asset Return Data
Defines functions
fitTsfm.control
Documented in
fitTsfm.control
#' @title List of control parameters for \code{fitTsfm}
#'
#' @description Creates a list of control parameters for \code{\link{fitTsfm}}.
#' All control parameters that are not passed to this function are set to
#' default values. This function is meant for internal use only!!
#'
#' @details This control function is used to process optional arguments passed
#' via \code{...} to \code{fitTsfm}. These arguments are validated and defaults
#' are set if necessary before being passed internally to one of the following
#' functions: \code{\link[stats]{lm}}, \code{\link[RobStatTM]{lmrobdetMM}},
#' \code{\link[stats]{step}}, \code{\link[leaps]{regsubsets}},
#' \code{\link[lars]{lars}} and \code{\link[lars]{cv.lars}}. See their
#' respective help files for more details. The arguments to each of these
#' functions are listed above in approximately the same order for user
#' convenience.
#'
#' The scalar \code{decay} is used by \code{\link{fitTsfm}} to compute
#' exponentially decaying weights for \code{fit.method="DLS"}. Alternately, one
#' can directly specify \code{weights}, a weights vector, to be used with
#' "LS" or "Robust". Especially when fitting multiple assets, care should be
#' taken to ensure that the length of the weights vector matches the number of
#' observations (excluding cases ignored due to NAs).
#'
#' \code{lars.criterion} selects the criterion (one of "Cp" or "cv") to
#' determine the best fitted model for \code{variable.selection="lars"}. The
#' "Cp" statistic (defined in page 17 of Efron et al. (2004)) is calculated
#' using \code{\link[lars]{summary.lars}}. While, "cv" computes the K-fold
#' cross-validated mean squared prediction error using
#' \code{\link[lars]{cv.lars}}.
#'
#' @param decay a scalar in (0, 1] to specify the decay factor for "DLS".
#' Default is 0.95.
#' @param weights an optional vector of weights to be used in the fitting
#' process for \code{fit.method="LS","Robust"}, or
#' \code{variable.selection="subsets"}. Should be \code{NULL} or a numeric
#' vector. The length of \code{weights} must be the same as the number of
#' observations. The weights must be nonnegative and it is strongly
#' recommended that they be strictly positive.
#' @param model,x,y,qr logicals passed to \code{lm} for
#' \code{fit.method="LS"}. If \code{TRUE} the corresponding components of the
#' fit (the model frame, the model matrix, the response, the QR decomposition)
#' are returned.
#' @param nrep the number of random subsamples to be drawn for
#' \code{fit.method="Robust"}. If the data set is small and "Exhaustive"
#' resampling is being used, the value of \code{nrep} is ignored.
#' @param tuning.chi tuning constant for the function used to compute the M-scale
#' used for the initial S-estimator. If missing, it is computed inside \code{lmrobdet.control} to match
#' the value of \code{bb} according to the family of rho functions specified in \code{family}.
#' @param bb tuning constant (between 0 and 1/2) for the M-scale used to compute the initial S-estimator. It
#' determines the robustness (breakdown point) of the resulting MM-estimator, which is
#' \code{bb}. Defaults to 0.5.
#' @param tuning.psi tuning parameters for the regression M-estimator computed with a rho function
#' as specified with argument \code{family}. If missing, it is computed inside \code{lmrobdet.control} to match
#' the value of \code{efficiency} according to the family of rho functions specified in \code{family}.
#' Appropriate values for \code{tuning.psi} for a given desired efficiency for Gaussian errors
#' can be constructed using the functions \code{\link[RobStatTM]{bisquare}}, \code{\link[RobStatTM]{mopt}} and \code{\link[RobStatTM]{opt}}.
#' @param efficiency desired asymptotic efficiency of the final regression M-estimator. Defaults to 0.85.
#' @param max.it maximum number of IRWLS iterations for the MM-estimator
#' @param refine.tol relative convergence tolerance for the S-estimator
#' @param rel.tol relative convergence tolerance for the IRWLS iterations for the MM-estimator
#' @param refine.PY number of refinement steps for the Pen~a-Yohai candidates
#' @param solve.tol relative tolerance for inversion
#' @param trace.lev positive values (increasingly) provide details on the progress of the MM-algorithm
#' @param compute.rd logical value indicating whether robust leverage distances need to be computed.
#' @param family string specifying the name of the family of loss function to be used (current valid
#' options are "bisquare", "optimal" and "modopt" from the RobStatTM package). Incomplete entries will be matched to
#' the current valid options.
#' @param corr.b logical value indicating whether a finite-sample correction should be applied
#' to the M-scale parameter \code{bb}.
#' @param split.type determines how categorical and continuous variables are split. See
#' \code{\link[robustbase]{splitFrame}}.
#' @param initial string specifying the initial value for the M-step of the MM-estimator. Valid
#' options are \code{'S'}, for an S-estimator and \code{'MS'} for an M-S estimator which is
#' appropriate when there are categorical explanatory variables in the model.
#' @param psc_keep For pyinit, proportion of observations to remove based on PSCs. The effective proportion of removed
#' observations is adjusted according to the sample size to be \code{prosac*(1-p/n)}. See \code{\link[pyinit]{pyinit}}.
#' @param resid_keep_method For pyinit, how to clean the data based on large residuals. If
#' \code{"threshold"}, all observations with scaled residuals larger than \code{C.res} will
#' be removed, if \code{"proportion"}, observations with the largest \code{prop} residuals will
#' be removed. See \code{\link[pyinit]{pyinit}}.
#' @param resid_keep_thresh See parameter \code{resid_keep_method} above. See \code{\link[pyinit]{pyinit}}.
#' @param resid_keep_prop See parameter \code{resid_keep_method} above. See \code{\link[pyinit]{pyinit}}.
#' @param py_maxit Maximum number of iterations. See \code{\link[pyinit]{pyinit}}.
#' @param py_eps Relative tolerance for convergence. See \code{\link[pyinit]{pyinit}}.
#' @param mscale_maxit Maximum number of iterations for the M-scale algorithm. See \code{\link[pyinit]{pyinit}}.
#' @param mscale_tol Convergence tolerance for the M-scale algorithm. See \code{\link[pyinit]{pyinit}}.
#' @param mscale_rho_fun String indicating the loss function used for the M-scale. See \code{\link[pyinit]{pyinit}}.
#' @param scope defines the range of models examined in the \code{"stepwise"}
#' search. This should be either a single formula, or a list containing
#' components \code{upper} and \code{lower}, both formulae. See
#' \code{\link[stats]{step}} for how to specify the formulae and usage.
#' @param scale optional parameter for \code{variable.selection="stepwise"}.
#' The argument is passed to \code{\link[stats]{step}} or
#' \code{\link[RobStatTM]{step.lmrobdetMM}} as appropriate.
#' @param direction the mode of \code{"stepwise"} search, can be one of "both",
#' "backward", or "forward", with a default of "both". If the \code{scope}
#' argument is missing the default for \code{direction} is "backward".
#' @param trace If positive (or, not \code{FALSE}), info is printed during the
#' running of \code{\link[stats]{step}}, \code{\link[lars]{lars}} or
#' \code{\link[lars]{cv.lars}} as relevant. Larger values may give more
#' detailed information. Default is \code{FALSE}.
#' @param steps the maximum number of steps to be considered for
#' \code{"stepwise"}. Default is 1000 (essentially as many as required). It is
#' typically used to stop the process early.
#' @param k the multiple of the number of degrees of freedom used for the
#' penalty in \code{"stepwise"}. Only \code{k = 2} gives the genuine AIC.
#' \code{k = log(n)} is sometimes referred to as BIC or SBC. Default is 2.
#' @param nvmin minimum size of subsets to examine for \code{"subsets"}.
#' Default is 1.
#' @param nvmax maximum size of subsets to examine for \code{"subsets"}.
#' Default is 8.
#' @param force.in index to columns of design matrix that should be in all
#' models for \code{"subsets"}. Default is \code{NULL}.
#' @param force.out index to columns of design matrix that should be in no
#' models for \code{"subsets"}. Default is \code{NULL}.
#' @param method one of "exhaustive", "forward", "backward" or "seqrep"
#' (sequential replacement) to specify the type of subset search/selection.
#' Required if \code{variable selection="subsets"} is chosen. Default is
#' "exhaustive".
#' @param really.big option for \code{"subsets"}; Must be \code{TRUE} to
#' perform exhaustive search on more than 50 variables.
#' @param type option for \code{"lars"}. One of "lasso", "lar",
#' "forward.stagewise" or "stepwise". The names can be abbreviated to any
#' unique substring. Default is "lasso".
#' @param normalize option for \code{"lars"}. If \code{TRUE}, each variable is
#' standardized to have unit L2 norm, otherwise they are left alone. Default
#' is \code{TRUE}.
#' @param eps option for \code{"lars"}; An effective zero.
#' @param max.steps Limit the number of steps taken for \code{"lars"}; the
#' default is \code{8 * min(m, n-intercept)}, with \code{m} the number of
#' variables, and \code{n} the number of samples. For \code{type="lar"} or
#' \code{type="stepwise"}, the maximum number of steps is
#' \code{min(m,n-intercept)}. For \code{type="lasso"} and especially
#' \code{type="forward.stagewise"}, there can be many more terms, because
#' although no more than \code{min(m,n-intercept)} variables can be active
#' during any step, variables are frequently droppped and added as the
#' algorithm proceeds. Although the default usually guarantees that the
#' algorithm has proceeded to the saturated fit, users should check.
#' @param plot.it option to plot the output for \code{\link[lars]{cv.lars}}.
#' Default is \code{FALSE}.
#' @param lars.criterion an option to assess model selection for the
#' \code{"lars"} method; one of "Cp" or "cv". See details. Default is "Cp".
#' @param K number of folds for computing the K-fold cross-validated mean
#' squared prediction error for \code{"lars"}. Default is 10.
#'
#' @return A list of the above components. This is only meant to be used by
#' \code{fitTsfm}.
#'
#' @author Sangeetha Srinivasan
#'
#' @references
#' Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004). Least angle
#' regression. The Annals of statistics, 32(2), 407-499.
#'
#' @seealso \code{\link{fitTsfm}}, \code{\link[stats]{lm}},
#' \code{\link[RobStatTM]{lmrobdetMM}}, \code{\link[stats]{step}},
#' \code{\link[leaps]{regsubsets}}, \code{\link[lars]{lars}} and
#' \code{\link[lars]{cv.lars}}
#'
#' @examples
#' \dontrun{
#' # check argument list passed by fitTsfm.control
#' tsfm.ctrl <- fitTsfm.control(method="exhaustive", nvmin=2)
#' print(tsfm.ctrl)
#' }
#'
#' # used internally by fitTsfm in the example below
#' # load data
#' data(managers, package = 'PerformanceAnalytics')
#' # Make syntactically valid column names
#' colnames(managers)
#' colnames(managers) <- make.names( colnames(managers))
#' colnames(managers)
#'
#' fit <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
#' factor.names=colnames(managers[,(7:9)]),
#' data=managers, variable.selection="subsets",
#' method="exhaustive", nvmin=2)
#'
#' @export
fitTsfm.control <- function(
decay=0.95,
weights,
model=TRUE,
x=FALSE,
y=FALSE,
qr=TRUE,
nrep=NULL,
### from lmrobdet.control
bb = 0.5,
efficiency = 0.95,
family = "mopt",
tuning.psi,
tuning.chi,
compute.rd = FALSE,
corr.b = TRUE,
split.type = "f",
initial = "S",
max.it = 100,
refine.tol = 1e-07,
rel.tol = 1e-07,
refine.PY = 10,
solve.tol = 1e-07,
trace.lev = 0,
psc_keep = 0.5,
resid_keep_method = "threshold",
resid_keep_thresh = 2,
resid_keep_prop = 0.2,
py_maxit = 20,
py_eps = 1e-05,
mscale_maxit = 50,
mscale_tol = 1e-06,
mscale_rho_fun = "bisquare",
### end lmrobdet.control
scope,
scale,
direction,
steps=1000,
k=2,
nvmin=1,
nvmax=8,
force.in=NULL,
force.out=NULL,
method,
really.big=FALSE,
type,
normalize=TRUE,
eps=.Machine$double.eps,
max.steps,
plot.it=FALSE,
lars.criterion="Cp",
K=10) {
# get the user-specified arguments (that have no defaults)
# this part of the code was adapted from stats::lm
call <- match.call()
m <- match(c("weights","scope","scale","direction","method","type",
"max.steps"), names(call), 0L)
# drop unused levels
if (!is.null(call) && sum(m>0) == 0) {
args <- list()
} else {
args <- as.list(call[m, drop=TRUE])
}
# check input validity for some of the arguments
if (decay<=0 || decay>1) {
stop("Invalid args: Decay factor should be in (0,1]")
}
if (!is.logical(model) || length(model) != 1) {
stop("Invalid args: control parameter 'model' must be logical")
}
if (!is.logical(x) || length(x) != 1) {
stop("Invalid args: control parameter 'x' must be logical")
}
if (!is.logical(y) || length(y) != 1) {
stop("Invalid args: control parameter 'y' must be logical")
}
if (!is.logical(qr) || length(qr) != 1) {
stop("Invalid args: control parameter 'qr' must be logical")
}
if (!is.logical(really.big) || length(really.big) != 1) {
stop("Invalid args: control parameter 'really.big' must be logical")
}
if (!is.logical(normalize) || length(normalize) != 1) {
stop("Invalid args: control parameter 'normalize' must be logical")
}
if (!is.logical(plot.it) || length(plot.it) != 1) {
stop("Invalid args: control parameter 'plot.it' must be logical")
}
if (nvmin <= 0 || round(nvmin) != nvmin) {
stop("Control parameter 'nvmin' must be a positive integer")
}
if (nvmax < nvmin || nvmin < length(force.in)) {
stop("Invaid args: nvmax should be >= nvmin and nvmin
should be >= length(force.in)")
}
if (!is.logical(normalize) || length(normalize) != 1) {
stop("Invalid args: control parameter 'normalize' must be logical")
}
if (!(lars.criterion %in% c("Cp","cv"))) {
stop("Invalid args: lars.criterion must be 'Cp' or 'cv'.")
}
# return list of arguments with defaults if they are unspecified
result <- c(args, list(
decay=decay,
model=model,
x=x,
y=y,
qr=qr,
nrep=nrep,
bb = bb,
efficiency=efficiency,
family = family,
compute.rd = compute.rd,
corr.b = corr.b,
split.type = split.type,
initial=initial,
max.it = max.it,
refine.tol = refine.tol,
rel.tol = rel.tol,
refine.PY = refine.PY,
solve.tol = solve.tol,
trace.lev = trace.lev,
psc_keep = psc_keep,
resid_keep_method = resid_keep_method,
resid_keep_thresh = resid_keep_thresh,
resid_keep_prop = resid_keep_prop,
py_maxit = py_maxit,
py_eps = py_eps,
mscale_maxit = mscale_maxit,
mscale_tol = mscale_tol,
mscale_rho_fun = mscale_rho_fun,
steps=steps,
k=k,
nvmin=nvmin,
nvmax=nvmax,
force.in=force.in,
force.out=force.out,
really.big=really.big,
normalize=normalize,
eps=eps,
plot.it=plot.it,
lars.criterion=lars.criterion,
K=K))
return(result)
}
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Defines functions fitTsfm.control
Documented in fitTsfm.control
#' @title List of control parameters for \code{fitTsfm}
#'
#' @description Creates a list of control parameters for \code{\link{fitTsfm}}.
#' All control parameters that are not passed to this function are set to
#' default values. This function is meant for internal use only!!
#'
#' @details This control function is used to process optional arguments passed
#' via \code{...} to \code{fitTsfm}. These arguments are validated and defaults
#' are set if necessary before being passed internally to one of the following
#' functions: \code{\link[stats]{lm}}, \code{\link[RobStatTM]{lmrobdetMM}},
#' \code{\link[stats]{step}}, \code{\link[leaps]{regsubsets}},
#' \code{\link[lars]{lars}} and \code{\link[lars]{cv.lars}}. See their
#' respective help files for more details. The arguments to each of these
#' functions are listed above in approximately the same order for user
#' convenience.
#'
#' The scalar \code{decay} is used by \code{\link{fitTsfm}} to compute
#' exponentially decaying weights for \code{fit.method="DLS"}. Alternately, one
#' can directly specify \code{weights}, a weights vector, to be used with
#' "LS" or "Robust". Especially when fitting multiple assets, care should be
#' taken to ensure that the length of the weights vector matches the number of
#' observations (excluding cases ignored due to NAs).
#'
#' \code{lars.criterion} selects the criterion (one of "Cp" or "cv") to
#' determine the best fitted model for \code{variable.selection="lars"}. The
#' "Cp" statistic (defined in page 17 of Efron et al. (2004)) is calculated
#' using \code{\link[lars]{summary.lars}}. While, "cv" computes the K-fold
#' cross-validated mean squared prediction error using
#' \code{\link[lars]{cv.lars}}.
#'
#' @param decay a scalar in (0, 1] to specify the decay factor for "DLS".
#' Default is 0.95.
#' @param weights an optional vector of weights to be used in the fitting
#' process for \code{fit.method="LS","Robust"}, or
#' \code{variable.selection="subsets"}. Should be \code{NULL} or a numeric
#' vector. The length of \code{weights} must be the same as the number of
#' observations. The weights must be nonnegative and it is strongly
#' recommended that they be strictly positive.
#' @param model,x,y,qr logicals passed to \code{lm} for
#' \code{fit.method="LS"}. If \code{TRUE} the corresponding components of the
#' fit (the model frame, the model matrix, the response, the QR decomposition)
#' are returned.
#' @param nrep the number of random subsamples to be drawn for
#' \code{fit.method="Robust"}. If the data set is small and "Exhaustive"
#' resampling is being used, the value of \code{nrep} is ignored.
#' @param tuning.chi tuning constant for the function used to compute the M-scale
#' used for the initial S-estimator. If missing, it is computed inside \code{lmrobdet.control} to match
#' the value of \code{bb} according to the family of rho functions specified in \code{family}.
#' @param bb tuning constant (between 0 and 1/2) for the M-scale used to compute the initial S-estimator. It
#' determines the robustness (breakdown point) of the resulting MM-estimator, which is
#' \code{bb}. Defaults to 0.5.
#' @param tuning.psi tuning parameters for the regression M-estimator computed with a rho function
#' as specified with argument \code{family}. If missing, it is computed inside \code{lmrobdet.control} to match
#' the value of \code{efficiency} according to the family of rho functions specified in \code{family}.
#' Appropriate values for \code{tuning.psi} for a given desired efficiency for Gaussian errors
#' can be constructed using the functions \code{\link[RobStatTM]{bisquare}}, \code{\link[RobStatTM]{mopt}} and \code{\link[RobStatTM]{opt}}.
#' @param efficiency desired asymptotic efficiency of the final regression M-estimator. Defaults to 0.85.
#' @param max.it maximum number of IRWLS iterations for the MM-estimator
#' @param refine.tol relative convergence tolerance for the S-estimator
#' @param rel.tol relative convergence tolerance for the IRWLS iterations for the MM-estimator
#' @param refine.PY number of refinement steps for the Pen~a-Yohai candidates
#' @param solve.tol relative tolerance for inversion
#' @param trace.lev positive values (increasingly) provide details on the progress of the MM-algorithm
#' @param compute.rd logical value indicating whether robust leverage distances need to be computed.
#' @param family string specifying the name of the family of loss function to be used (current valid
#' options are "bisquare", "optimal" and "modopt" from the RobStatTM package). Incomplete entries will be matched to
#' the current valid options.
#' @param corr.b logical value indicating whether a finite-sample correction should be applied
#' to the M-scale parameter \code{bb}.
#' @param split.type determines how categorical and continuous variables are split. See
#' \code{\link[robustbase]{splitFrame}}.
#' @param initial string specifying the initial value for the M-step of the MM-estimator. Valid
#' options are \code{'S'}, for an S-estimator and \code{'MS'} for an M-S estimator which is
#' appropriate when there are categorical explanatory variables in the model.
#' @param psc_keep For pyinit, proportion of observations to remove based on PSCs. The effective proportion of removed
#' observations is adjusted according to the sample size to be \code{prosac*(1-p/n)}. See \code{\link[pyinit]{pyinit}}.
#' @param resid_keep_method For pyinit, how to clean the data based on large residuals. If
#' \code{"threshold"}, all observations with scaled residuals larger than \code{C.res} will
#' be removed, if \code{"proportion"}, observations with the largest \code{prop} residuals will
#' be removed. See \code{\link[pyinit]{pyinit}}.
#' @param resid_keep_thresh See parameter \code{resid_keep_method} above. See \code{\link[pyinit]{pyinit}}.
#' @param resid_keep_prop See parameter \code{resid_keep_method} above. See \code{\link[pyinit]{pyinit}}.
#' @param py_maxit Maximum number of iterations. See \code{\link[pyinit]{pyinit}}.
#' @param py_eps Relative tolerance for convergence. See \code{\link[pyinit]{pyinit}}.
#' @param mscale_maxit Maximum number of iterations for the M-scale algorithm. See \code{\link[pyinit]{pyinit}}.
#' @param mscale_tol Convergence tolerance for the M-scale algorithm. See \code{\link[pyinit]{pyinit}}.
#' @param mscale_rho_fun String indicating the loss function used for the M-scale. See \code{\link[pyinit]{pyinit}}.
#' @param scope defines the range of models examined in the \code{"stepwise"}
#' search. This should be either a single formula, or a list containing
#' components \code{upper} and \code{lower}, both formulae. See
#' \code{\link[stats]{step}} for how to specify the formulae and usage.
#' @param scale optional parameter for \code{variable.selection="stepwise"}.
#' The argument is passed to \code{\link[stats]{step}} or
#' \code{\link[RobStatTM]{step.lmrobdetMM}} as appropriate.
#' @param direction the mode of \code{"stepwise"} search, can be one of "both",
#' "backward", or "forward", with a default of "both". If the \code{scope}
#' argument is missing the default for \code{direction} is "backward".
#' @param trace If positive (or, not \code{FALSE}), info is printed during the
#' running of \code{\link[stats]{step}}, \code{\link[lars]{lars}} or
#' \code{\link[lars]{cv.lars}} as relevant. Larger values may give more
#' detailed information. Default is \code{FALSE}.
#' @param steps the maximum number of steps to be considered for
#' \code{"stepwise"}. Default is 1000 (essentially as many as required). It is
#' typically used to stop the process early.
#' @param k the multiple of the number of degrees of freedom used for the
#' penalty in \code{"stepwise"}. Only \code{k = 2} gives the genuine AIC.
#' \code{k = log(n)} is sometimes referred to as BIC or SBC. Default is 2.
#' @param nvmin minimum size of subsets to examine for \code{"subsets"}.
#' Default is 1.
#' @param nvmax maximum size of subsets to examine for \code{"subsets"}.
#' Default is 8.
#' @param force.in index to columns of design matrix that should be in all
#' models for \code{"subsets"}. Default is \code{NULL}.
#' @param force.out index to columns of design matrix that should be in no
#' models for \code{"subsets"}. Default is \code{NULL}.
#' @param method one of "exhaustive", "forward", "backward" or "seqrep"
#' (sequential replacement) to specify the type of subset search/selection.
#' Required if \code{variable selection="subsets"} is chosen. Default is
#' "exhaustive".
#' @param really.big option for \code{"subsets"}; Must be \code{TRUE} to
#' perform exhaustive search on more than 50 variables.
#' @param type option for \code{"lars"}. One of "lasso", "lar",
#' "forward.stagewise" or "stepwise". The names can be abbreviated to any
#' unique substring. Default is "lasso".
#' @param normalize option for \code{"lars"}. If \code{TRUE}, each variable is
#' standardized to have unit L2 norm, otherwise they are left alone. Default
#' is \code{TRUE}.
#' @param eps option for \code{"lars"}; An effective zero.
#' @param max.steps Limit the number of steps taken for \code{"lars"}; the
#' default is \code{8 * min(m, n-intercept)}, with \code{m} the number of
#' variables, and \code{n} the number of samples. For \code{type="lar"} or
#' \code{type="stepwise"}, the maximum number of steps is
#' \code{min(m,n-intercept)}. For \code{type="lasso"} and especially
#' \code{type="forward.stagewise"}, there can be many more terms, because
#' although no more than \code{min(m,n-intercept)} variables can be active
#' during any step, variables are frequently droppped and added as the
#' algorithm proceeds. Although the default usually guarantees that the
#' algorithm has proceeded to the saturated fit, users should check.
#' @param plot.it option to plot the output for \code{\link[lars]{cv.lars}}.
#' Default is \code{FALSE}.
#' @param lars.criterion an option to assess model selection for the
#' \code{"lars"} method; one of "Cp" or "cv". See details. Default is "Cp".
#' @param K number of folds for computing the K-fold cross-validated mean
#' squared prediction error for \code{"lars"}. Default is 10.
#'
#' @return A list of the above components. This is only meant to be used by
#' \code{fitTsfm}.
#'
#' @author Sangeetha Srinivasan
#'
#' @references
#' Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004). Least angle
#' regression. The Annals of statistics, 32(2), 407-499.
#'
#' @seealso \code{\link{fitTsfm}}, \code{\link[stats]{lm}},
#' \code{\link[RobStatTM]{lmrobdetMM}}, \code{\link[stats]{step}},
#' \code{\link[leaps]{regsubsets}}, \code{\link[lars]{lars}} and
#' \code{\link[lars]{cv.lars}}
#'
#' @examples
#' \dontrun{
#' # check argument list passed by fitTsfm.control
#' tsfm.ctrl <- fitTsfm.control(method="exhaustive", nvmin=2)
#' print(tsfm.ctrl)
#' }
#'
#' # used internally by fitTsfm in the example below
#' # load data
#' data(managers, package = 'PerformanceAnalytics')
#' # Make syntactically valid column names
#' colnames(managers)
#' colnames(managers) <- make.names( colnames(managers))
#' colnames(managers)
#'
#' fit <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
#' factor.names=colnames(managers[,(7:9)]),
#' data=managers, variable.selection="subsets",
#' method="exhaustive", nvmin=2)
#'
#' @export
fitTsfm.control <- function(
decay=0.95,
weights,
model=TRUE,
x=FALSE,
y=FALSE,
qr=TRUE,
nrep=NULL,
### from lmrobdet.control
bb = 0.5,
efficiency = 0.95,
family = "mopt",
tuning.psi,
tuning.chi,
compute.rd = FALSE,
corr.b = TRUE,
split.type = "f",
initial = "S",
max.it = 100,
refine.tol = 1e-07,
rel.tol = 1e-07,
refine.PY = 10,
solve.tol = 1e-07,
trace.lev = 0,
psc_keep = 0.5,
resid_keep_method = "threshold",
resid_keep_thresh = 2,
resid_keep_prop = 0.2,
py_maxit = 20,
py_eps = 1e-05,
mscale_maxit = 50,
mscale_tol = 1e-06,
mscale_rho_fun = "bisquare",
### end lmrobdet.control
scope,
scale,
direction,
steps=1000,
k=2,
nvmin=1,
nvmax=8,
force.in=NULL,
force.out=NULL,
method,
really.big=FALSE,
type,
normalize=TRUE,
eps=.Machine$double.eps,
max.steps,
plot.it=FALSE,
lars.criterion="Cp",
K=10) {
# get the user-specified arguments (that have no defaults)
# this part of the code was adapted from stats::lm
call <- match.call()
m <- match(c("weights","scope","scale","direction","method","type",
"max.steps"), names(call), 0L)
# drop unused levels
if (!is.null(call) && sum(m>0) == 0) {
args <- list()
} else {
args <- as.list(call[m, drop=TRUE])
}
# check input validity for some of the arguments
if (decay<=0 || decay>1) {
stop("Invalid args: Decay factor should be in (0,1]")
}
if (!is.logical(model) || length(model) != 1) {
stop("Invalid args: control parameter 'model' must be logical")
}
if (!is.logical(x) || length(x) != 1) {
stop("Invalid args: control parameter 'x' must be logical")
}
if (!is.logical(y) || length(y) != 1) {
stop("Invalid args: control parameter 'y' must be logical")
}
if (!is.logical(qr) || length(qr) != 1) {
stop("Invalid args: control parameter 'qr' must be logical")
}
if (!is.logical(really.big) || length(really.big) != 1) {
stop("Invalid args: control parameter 'really.big' must be logical")
}
if (!is.logical(normalize) || length(normalize) != 1) {
stop("Invalid args: control parameter 'normalize' must be logical")
}
if (!is.logical(plot.it) || length(plot.it) != 1) {
stop("Invalid args: control parameter 'plot.it' must be logical")
}
if (nvmin <= 0 || round(nvmin) != nvmin) {
stop("Control parameter 'nvmin' must be a positive integer")
}
if (nvmax < nvmin || nvmin < length(force.in)) {
stop("Invaid args: nvmax should be >= nvmin and nvmin
should be >= length(force.in)")
}
if (!is.logical(normalize) || length(normalize) != 1) {
stop("Invalid args: control parameter 'normalize' must be logical")
}
if (!(lars.criterion %in% c("Cp","cv"))) {
stop("Invalid args: lars.criterion must be 'Cp' or 'cv'.")
}
# return list of arguments with defaults if they are unspecified
result <- c(args, list(
decay=decay,
model=model,
x=x,
y=y,
qr=qr,
nrep=nrep,
bb = bb,
efficiency=efficiency,
family = family,
compute.rd = compute.rd,
corr.b = corr.b,
split.type = split.type,
initial=initial,
max.it = max.it,
refine.tol = refine.tol,
rel.tol = rel.tol,
refine.PY = refine.PY,
solve.tol = solve.tol,
trace.lev = trace.lev,
psc_keep = psc_keep,
resid_keep_method = resid_keep_method,
resid_keep_thresh = resid_keep_thresh,
resid_keep_prop = resid_keep_prop,
py_maxit = py_maxit,
py_eps = py_eps,
mscale_maxit = mscale_maxit,
mscale_tol = mscale_tol,
mscale_rho_fun = mscale_rho_fun,
steps=steps,
k=k,
nvmin=nvmin,
nvmax=nvmax,
force.in=force.in,
force.out=force.out,
really.big=really.big,
normalize=normalize,
eps=eps,
plot.it=plot.it,
lars.criterion=lars.criterion,
K=K))
return(result)
}
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