Commit db85a966 authored by Jakob Wirbel's avatar Jakob Wirbel
Browse files

update documentation.

parent 2f8ddb68
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lrn_liblinear.R
\encoding{UTF-8}
\name{LearnerClassifLiblineaR}
\alias{LearnerClassifLiblineaR}
\title{LiblineaR Classification Learner}
\description{
LiblineaR Classification Learner
LiblineaR Classification Learner
}
\details{
Type of SVC depends on \code{type} argument:
\itemize{
\item \code{0} L2-regularized logistic regression (primal)
\item \code{1} L2-regularized L2-loss support vector classification (dual)
\item \code{3} L2-regularized L1-loss support vector classification (dual)
\item \code{2} L2-regularized L2-loss support vector classification (primal)
\item \code{4} Support vector classification by Crammer and Singer
\item \code{5} L1-regularized L2-loss support vector classification
\item \code{6} L1-regularized logistic regression
\item \code{7} L2-regularized logistic regression (dual)
}
If number of records > number of features, \code{type = 2} is faster
than \code{type = 1}
(Hsu et al. 2003).
Note that probabilistic predictions are only available for
types \code{0}, \code{6}, and \code{7}.
The default \code{epsilon} value depends on the \code{type} parameter,
see [LiblineaR::LiblineaR].
}
\section{Super classes}{
\code{\link[mlr3:Learner]{mlr3::Learner}} -> \code{\link[mlr3:LearnerClassif]{mlr3::LearnerClassif}} -> \code{LearnerClassifLiblineaR}
}
\section{Methods}{
\subsection{Public methods}{
\itemize{
\item \href{#method-new}{\code{LearnerClassifLiblineaR$new()}}
\item \href{#method-clone}{\code{LearnerClassifLiblineaR$clone()}}
}
}
\if{html}{
\out{<details ><summary>Inherited methods</summary>}
\itemize{
\item \out{<span class="pkg-link" data-pkg="mlr3" data-topic="Learner" data-id="base_learner">}\href{../../mlr3/html/Learner.html#method-base_learner}{\code{mlr3::Learner$base_learner()}}\out{</span>}
\item \out{<span class="pkg-link" data-pkg="mlr3" data-topic="Learner" data-id="format">}\href{../../mlr3/html/Learner.html#method-format}{\code{mlr3::Learner$format()}}\out{</span>}
\item \out{<span class="pkg-link" data-pkg="mlr3" data-topic="Learner" data-id="help">}\href{../../mlr3/html/Learner.html#method-help}{\code{mlr3::Learner$help()}}\out{</span>}
\item \out{<span class="pkg-link" data-pkg="mlr3" data-topic="Learner" data-id="predict">}\href{../../mlr3/html/Learner.html#method-predict}{\code{mlr3::Learner$predict()}}\out{</span>}
\item \out{<span class="pkg-link" data-pkg="mlr3" data-topic="Learner" data-id="predict_newdata">}\href{../../mlr3/html/Learner.html#method-predict_newdata}{\code{mlr3::Learner$predict_newdata()}}\out{</span>}
\item \out{<span class="pkg-link" data-pkg="mlr3" data-topic="Learner" data-id="print">}\href{../../mlr3/html/Learner.html#method-print}{\code{mlr3::Learner$print()}}\out{</span>}
\item \out{<span class="pkg-link" data-pkg="mlr3" data-topic="Learner" data-id="reset">}\href{../../mlr3/html/Learner.html#method-reset}{\code{mlr3::Learner$reset()}}\out{</span>}
\item \out{<span class="pkg-link" data-pkg="mlr3" data-topic="Learner" data-id="train">}\href{../../mlr3/html/Learner.html#method-train}{\code{mlr3::Learner$train()}}\out{</span>}
}
\out{</details>}
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-new"></a>}}
\if{latex}{\out{\hypertarget{method-new}{}}}
\subsection{Method \code{new()}}{
#' Creates a new instance of this [R6][R6::R6Class] class.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{LearnerClassifLiblineaR$new()}\if{html}{\out{</div>}}
}
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-clone"></a>}}
\if{latex}{\out{\hypertarget{method-clone}{}}}
\subsection{Method \code{clone()}}{
The objects of this class are cloneable with this method.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{LearnerClassifLiblineaR$clone(deep = FALSE)}\if{html}{\out{</div>}}
}
\subsection{Arguments}{
\if{html}{\out{<div class="arguments">}}
\describe{
\item{\code{deep}}{Whether to make a deep clone.}
}
\if{html}{\out{</div>}}
}
}
}
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/add_meta_pred.r
\encoding{UTF-8}
\name{add.meta.pred}
\alias{add.meta.pred}
\title{Add metadata as predictors}
\usage{
add.meta.pred(siamcat, pred.names,
std.meta = TRUE,
feature.type='normalized',
verbose = 1)
add.meta.pred(siamcat, pred.names, std.meta = TRUE,
feature.type='normalized', verbose = 1)
}
\arguments{
\item{siamcat}{object of class \link{siamcat-class}}
\item{pred.names}{vector of names of the variables within the metadata to be
added to the feature matrix as predictors}
\item{pred.names}{vector of names of the variables within the metadata to
be added to the feature matrix as predictors}
\item{std.meta}{boolean, should added metadata features be standardized?,
defaults to \code{TRUE}}
\item{feature.type}{string, on which type of features should the function
work? Can be either \code{"original"}, \code{"filtered"}, or
\item{feature.type}{string, on which type of features should the function
work? Can be either \code{"original"}, \code{"filtered"}, or
\code{"normalized"}. Please only change this paramter if you know what
you are doing!}
\item{verbose}{integer, control output: \code{0} for no output at all,
\item{verbose}{integer, control output: \code{0} for no output at all,
\code{1} for only information about progress and success, \code{2} for
normal level of information and \code{3} for full debug information,
defaults to \code{1}}
}
\value{
an object of class \link{siamcat-class} with metadata added to the
features
features
}
\description{
This function adds metadata to the feature matrix to be later
used as predictors
used as predictors
}
\details{
This functions adds one or several metadata variables to the set
......@@ -58,9 +57,8 @@ siamcat_age_added <- add.meta.pred(siamcat_example, pred.names=c('Age'))
# Add Age and BMI as potential predictors
# Additionally, prevent standardization of the added features
siamcat_meta_added <- add.meta.pred(siamcat_example,
pred.names=c('Age', 'BMI'),
std.meta=FALSE)
siamcat_meta_added <- add.meta.pred(siamcat_example,
pred.names=c('Age', 'BMI'), std.meta=FALSE)
}
\keyword{SIAMCAT}
\keyword{add.meta.pred}
......@@ -24,5 +24,6 @@ Assign a new list containing trained models to a SIAMCAT object
}
\examples{
data(siamcat_example)
siamcat_example <- train.model(siamcat_example, method='lasso')
model_list(siamcat_example) <- model_list(siamcat_example)
}
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/association_plot.R
\encoding{UTF-8}
\name{association.plot}
\alias{association.plot}
\title{Visualize associations between features and classes}
\usage{
association.plot(siamcat, fn.plot=NULL, color.scheme = "RdYlBu",
sort.by = "fc", max.show = 50, plot.type = "quantile.box",
panels = c("fc", "auroc"), prompt=TRUE, verbose = 1)
}
\arguments{
\item{siamcat}{object of class \link{siamcat-class}}
\item{fn.plot}{string, filename for the pdf-plot. If \code{fn.plot} is
\code{NULL}, the plot will be produced in the active graphics device.}
\item{color.scheme}{valid R color scheme or vector of valid R colors (must
be of the same length as the number of classes), defaults to \code{'RdYlBu'}}
\item{sort.by}{string, sort features by p-value (\code{"p.val"}), by fold
change (\code{"fc"}) or by prevalence shift (\code{"pr.shift"}),
defaults to \code{"fc"}}
\item{max.show}{integer, how many associated features should be shown,
defaults to \code{50}}
\item{plot.type}{string, specify how the abundance should be plotted, must
be one of these: \code{c("bean", "box", "quantile.box", "quantile.rect")},
defaults to \code{"quantile.box"}}
\item{panels}{vector, name of the panels to be plotted next to the
abundances, possible entries are \code{c("fc", "auroc", "prevalence")},
defaults to \code{c("fc", "auroc")}}
\item{prompt}{boolean, turn on/off prompting user input when not plotting
into a pdf-file, defaults to TRUE}
\item{verbose}{integer, control output: \code{0} for no output at all,
\code{1} for only information about progress and success, \code{2} for
normal level of information and \code{3} for full debug information,
defaults to \code{1}}
}
\value{
Does not return anything, but instead produces association plot
}
\description{
This function visualizes different measures of association
between features and the label, computed previously with
the \link{check.associations} function
}
\details{
This function visualizes the results of the computations carried
out in the \link{check.associations} function. It produces a plot of the
top \code{max.show} associated features at a user-specified significance
level \code{alpha}.
For binary classification problems, the plot will show the distribution of
the log10-transformed abundances for both classes, a P-value from the
significance test, and user-selected panels for the effect size (AU-ROC,
prevalence shift, or generalized fold change). For regression problems,
the plot will show the Spearman correlation, the significance, and the
linear model effect size.
}
\examples{
# Example data
data(siamcat_example)
# Simple example
association.plot(siamcat_example, fn.plot = "./assoc_plot.pdf")
# Plot associations as box plot
association.plot(siamcat_example,
fn.plot = "./assoc_plot_box.pdf",
plot.type = "box"
)
# Additionally, sort by p-value instead of by fold change
association.plot(siamcat_example,
fn.plot = "./assoc_plot_fc.pdf",
plot.type = "box", sort.by = "p.val"
)
# Custom colors
association.plot(siamcat_example,
fn.plot = "./assoc_plot_blue_yellow.pdf",
plot.type = "box", color.scheme = c("cornflowerblue", "#ffc125")
)
}
\keyword{SIAMCAT}
\keyword{association.plot}
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/check_associations.r
\encoding{UTF-8}
\name{check.associations}
\alias{check.associations}
\title{Check and visualize associations between features and classes}
\usage{
check.associations(siamcat, fn.plot=NULL, color.scheme = "RdYlBu",
alpha =0.05, mult.corr = "fdr", sort.by = "fc",
detect.lim = 1e-06, pr.cutoff = 1e-6, max.show = 50,
plot.type = "quantile.box",
panels = c("fc","auroc"), prompt = TRUE,
feature.type = 'filtered', paired=NULL, verbose = 1)
check.associations(siamcat, formula="feat~label", test='wilcoxon',
alpha=0.05, mult.corr="fdr", log.n0=1e-06, pr.cutoff=1e-06,
probs.fc=seq(.1, .9, .05), paired=NULL, feature.type='filtered',
verbose = 1)
}
\arguments{
\item{siamcat}{object of class \link{siamcat-class}}
\item{fn.plot}{string, filename for the pdf-plot. If \code{fn.plot} is
\code{NULL}, the plot will be produced in the active graphics device.}
\item{formula}{string, formula used for testing, see Details for more
information, defaults to \code{"feat~label"}}
\item{color.scheme}{valid R color scheme or vector of valid R colors (must be
of the same length as the number of classes), defaults to \code{'RdYlBu'}}
\item{test}{string, statistical test used for the association testing, can
be either \code{'wilcoxon'} or \code{'lm'}, see Details for more
information, defaults to \code{'wilcoxon'}}
\item{alpha}{float, significance level, defaults to \code{0.05}}
\item{mult.corr}{string, multiple hypothesis correction method, see
\item{mult.corr}{string, multiple hypothesis correction method, see
\code{\link[stats]{p.adjust}}, defaults to \code{"fdr"}}
\item{sort.by}{string, sort features by p-value (\code{"p.val"}), by fold
change (\code{"fc"}) or by prevalence shift (\code{"pr.shift"}),
defaults to \code{"fc"}}
\item{detect.lim}{float, pseudocount to be added before log-transformation of
the data, defaults to \code{1e-06}. Will be ignored if
\item{log.n0}{float, pseudo-count to be added before log-transformation of
the data, defaults to \code{1e-06}. Will be ignored if
\code{feature.type} is \code{"normalized"}.}
\item{pr.cutoff}{float, cutoff for the prevalence computation, defaults to
\item{pr.cutoff}{float, cutoff for the prevalence computation, defaults to
\code{1e-06}}
\item{max.show}{integer, how many associated features should be shown,
defaults to \code{50}}
\item{plot.type}{string, specify how the abundance should be plotted, must be
one of these: \code{c("bean", "box", "quantile.box", "quantile.rect")},
defaults to \code{"quantile.box"}}
\item{probs.fc}{numeric vector, quantiles used to calculate the generalized
fold change between groups, see Details for more information,
defaults to \code{seq(.1, .9, .05)}}
\item{panels}{vector, name of the panels to be plotted next to the
abundances, possible entries are \code{c("fc", "auroc",
"prevalence")}, defaults to \code{c("fc", "auroc")}}
\item{prompt}{boolean, turn on/off prompting user input when not plotting
into a pdf-file, defaults to TRUE}
\item{paired}{character, column name of the meta-variable containing
information for a paired test, defaults to \code{NULL}}
\item{feature.type}{string, on which type of features should the function
work? Can be either \code{c()"original", "filtered", or "normalized")}.
Please only change this paramter if you know what you are doing!
Please only change this parameter if you know what you are doing!
If \code{feature.type} is \code{"normalized"}, the normalized abundances
will not be log10-transformed.}
\item{paired}{character, column name of the meta-variable containing
information for a paired test}
\item{verbose}{integer, control output: \code{0} for no output at all,
\code{1} for only information about progress and success, \code{2} for
normal level of information and \code{3} for full debug information,
\item{verbose}{integer, control output: \code{0} for no output at all,
\code{1} for only information about progress and success, \code{2} for
normal level of information and \code{3} for full debug information,
defaults to \code{1}}
}
\value{
object of class \link{siamcat-class} with the slot
\code{associations} filled
object of class \link{siamcat-class} with the slot
\code{associations} filled
}
\description{
This function computes different measures of association between
features and the label and visualizes the results
This function computes different measures of association
between features and the label and stores the results in the
\code{association} slot of the SIAMCAT object
}
\details{
For each feature, this function calculates different measures of
association between the feature and the label. In detail, these
associations are: \itemize{
\item Significance as computed by a Wilcoxon test followed by multiple
hypothesis testing correction.
\item AUROC (Area Under the Receiver Operating Characteristics Curve)
as a non-parameteric measure of enrichment (corresponds to the effect
size of the Wilcoxon test).
\item The generalized Fold Change (gFC) is a pseudo fold change
which is calculated as geometric mean of the differences between the
quantiles for the different classes found in the label.
\item The prevalence shift between the two different classes found in
the label.
}
Finally, the function produces a plot of the top \code{max.show}
associated features at a user-specified significance level \code{alpha},
showing the distribution of the log10-transformed abundances for both
classes, and user-selected panels for the effect (AU-ROC, Prevalence
Shift, and Fold Change).
\section{Statistical testing}{
The function uses the Wilcoxon test as default statistical test for binary
classification problems. Alternatively, a simple linear model (as
implemented in \link[stats]{lm}) can be used as well. For regression
problems, the function defaults to the linear model.
}
\section{Effect sizes}{
The function calculates several measures for the effect size of the
assocations between microbial features and the label. For binary
classification problems, these associations are: \itemize{
\item AUROC (area under the Receiver Operating Characteristics curve) as a
non-parametric measure of enrichment,
\item the generalized fold change (gFC), a pseudo-fold change which is
calculated as geometric mean of the differences between quantiles across
both groups,
\item prevalence shift (difference in prevalence between the two groups).}
For regression problems, the effect sizes are: \itemize{
\item Spearman correlation between the feature and the label.}
}
\section{Confounder-corrected testing}{
To correct for possible confounders while testing for association, the
function uses linear mixed effect models as implemented in the
\link{lmerTest} package. To do so, the test formula needs to be adjusted
to include the confounder. For example, when correcting for the metadata
information \code{Gender}, the formula would be:
\code{'feat~label+(1|Gender)'} (see also the example below).
Please note that modifying the formula parameter in this function might
lead to unexpected results!
}
\examples{
# Example data
data(siamcat_example)
# Simple example
siamcat_example <- check.associations(siamcat_example,
fn.plot='./assoc_plot.pdf')
# Plot associations as box plot
siamcat_example <- check.associations(siamcat_example,
fn.plot='./assoc_plot_box.pdf', plot.type='box')
siamcat_example <- check.associations(siamcat_example)
# Additionally, sort by p-value instead of by fold change
siamcat_example <- check.associations(siamcat_example,
fn.plot='./assoc_plot_fc.pdf', plot.type='box', sort.by='p.val')
# Confounder-corrected testing (corrected for Gender)
siamcat_example <- check.associations(siamcat_example,
formula='feat~label+(1|Gender)', test='lm')
# Custom colors
siamcat_example <- check.associations(siamcat_example,
fn.plot='./assoc_plot_blue_yellow.pdf', plot.type='box',
color.scheme=c('cornflowerblue', '#ffc125'))
# Paired testing
\donttest{siamcat_paired <- check.associations(siamcat_paired,
paired='Individual_ID')}
}
\keyword{SIAMCAT}
\keyword{check.associations}
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/check_confounders.r
\encoding{UTF-8}
\name{check.confounders}
\alias{check.confounders}
\title{Check for potential confounders in the metadata}
......@@ -12,36 +13,39 @@ feature.type='filtered', verbose = 1)
\item{fn.plot}{string, filename for the pdf-plot}
\item{meta.in}{vector, specific metadata variable names to analyze,
\item{meta.in}{vector, specific metadata variable names to analyze,
defaults to NULL (all metadata variables will be analyzed)}
\item{feature.type}{string, on which type of features should the function
work? Can be either \code{c()"original", "filtered", or "normalized")}.
Please only change this paramter if you know what you are doing!}
\item{verbose}{integer, control output: \code{0} for no output at all,
\code{1} for only information about progress and success, \code{2} for
normal level of information and \code{3} for full debug information,
\item{verbose}{integer, control output: \code{0} for no output at all,
\code{1} for only information about progress and success, \code{2} for
normal level of information and \code{3} for full debug information,
defaults to \code{1}}
}
\value{
Does not return anything, but outputs plots to specified pdf file
}
\description{
Checks potential confounders in the metadata and produces
some visualizations
Checks potential confounders in the metadata and visualize the
results
}
\details{
This function checks for associations between class labels and
potential confounders (e.g. Age, Sex, or BMI) that are present in the
metadata. Statistical testing is performed with Fisher's exact test or
Wilcoxon test, while associations are visualized either as barplot or
Q-Q plot, depending on the type of metadata.
This function checks for associations between class labels and
potential confounders (e.g. Age, Sex, or BMI) that are present in the
metadata. Statistical testing is performed with Fisher's exact test or
Wilcoxon test, while associations are visualized either as barplot or
Q-Q plot, depending on the type of metadata.
Additionally, it evaluates associations among metadata variables using
conditional entropy and associations with the label using generalized
linear models, producing a correlation heatmap and appropriate
quantitative barplots, respectively.
Additionally, it evaluates associations among metadata variables using
conditional entropy and associations with the label using generalized
linear models, producing a correlation heatmap and appropriate
quantitative barplots, respectively.
Please note that the confounder check is currently only available for binary
classification problems!
}
\examples{
# Example data
......
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/create_data_split.r
\encoding{UTF-8}
\name{create.data.split}
\alias{create.data.split}
\title{Split a dataset into training and a test sets.}
\usage{
create.data.split(siamcat, num.folds = 2, num.resample = 1,
stratify = TRUE, inseparable = NULL, verbose = 1)
create.data.split(siamcat, num.folds = 2, num.resample = 1,
stratify = TRUE, inseparable = NULL, verbose = 1)
}
\arguments{
\item{siamcat}{object of class \link{siamcat-class}}
\item{num.folds}{integer number of cross-validation folds (needs to be
\item{num.folds}{integer number of cross-validation folds (needs to be
\code{>=2}), defaults to \code{2}}
\item{num.resample}{integer, resampling rounds (values \code{<= 1}
\item{num.resample}{integer, resampling rounds (values \code{<= 1}
deactivate resampling), defaults to \code{1}}
\item{stratify}{boolean, should the splits be stratified so that an equal
proportion of classes are present in each fold?, defaults to \code{TRUE}}
\item{stratify}{boolean, should the splits be stratified so that an equal
proportion of classes are present in each fold?, will be ignored for
regression tasks, defaults to \code{TRUE}}
\item{inseparable}{string, name of metadata variable to be inseparable,
defaults to \code{NULL}, see Details below}
\item{verbose}{integer, control output: \code{0} for no output at all,
\item{verbose}{integer, control output: \code{0} for no output at all,
\code{1} for only information about progress and success, \code{2} for
normal level of information and \code{3} for full debug information,
defaults to \code{1}}
}
\value{
object of class \link{siamcat-class} with the \code{data_split}-slot
filled
filled
}
\description{
This function prepares the cross-validation by splitting the
data into \code{num.folds} training and test folds for
\code{num.resample} times.
This function prepares the cross-validation by splitting the
data into \code{num.folds} training and test folds for
\code{num.resample} times.
}
\details{
This function splits the labels within a \link{siamcat-class} object
and prepares the internal cross-validation for the model training (see
\link{train.model}).
This function splits the labels within a \link{siamcat-class}
object and prepares the internal cross-validation for the model training
(see \link{train.model}).
The function saves the training and test instances for the different
cross-validation folds within a list in the \code{data_split}-slot of the
\link{siamcat-class} object, which is a list with four entries:
\itemize{
\item \code{num.folds} - the number of cross-validation folds
\item \code{num.resample} - the number of repetitions for the
cross-validation
\item \code{training.folds} - a list containing the indices for the
training instances
\item \code{test.folds} - a list containing the indices for the
test instances }
The function saves the training and test instances for the different
cross-validation folds within a list in the \code{data_split}-slot of the
\link{siamcat-class} object, which is a list with four entries: \itemize{
\item \code{num.folds} - the number of cross-validation folds
\item \code{num.resample} - the number of repetitions for the
cross-validation
\item \code{training.folds} - a list containing the indices for the
training instances
\item \code{test.folds} - a list containing the indices for the
test instances }
If provided, the data split will take into account a metadata variable
for the data split (by providing the \code{inseparable} argument). For
example, if the data contains several samples for the same individual,
it would make sense to keep data from the same individual within the
same fold.
If \code{inseparable} is given, the \code{stratify} argument will be
ignored.
If provided, the data split will take into account a metadata variable
for the data split (by providing the \code{inseparable} argument). For
example, if the data contains several samples for the same individual,
it makes sense to keep data from the same individual within the
same fold.
If \code{inseparable} is given, the \code{stratify} argument will be
ignored.
}
\examples{
data(siamcat_example)
# simple working example
siamcat_split <- create.data.split(siamcat_example,
num.folds=10,
num.resample=5,
stratify=TRUE)
siamcat_split <- create.data.split(siamcat_example, num.folds=10,
num.resample=5, stratify=TRUE)
}
\keyword{SIAMCAT}
\keyword{create.data.split}
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/create_label.r
\encoding{UTF-8}
\name{create.label}
\alias{create.label}
\title{Create a label list}
\usage{
create.label(label, case,
meta=NULL, control=NULL,
p.lab = NULL, n.lab = NULL,
remove.meta.column=FALSE,
verbose=1)
create.label(label, case, meta=NULL, control=NULL,
p.lab = NULL, n.lab = NULL, remove.meta.column=FALSE, verbose=1)
}
\arguments{
\item{label}{named vector to create the label or the name of the metadata
......@@ -19,7 +17,7 @@ variable is binary, the other label will be used as a negative one. If the
variable has multiple values, all the other values will be used a negative
label (testing one vs rest).}
\item{meta}{metadata dataframe object or an object of class
\item{meta}{metadata dataframe object or an object of class
\link[phyloseq]{sample_data-class}}
\item{control}{name of a label or vector with names that will be used as a
......@@ -42,9 +40,9 @@ metadata should be retained. Please note that if this is set to
\code{TRUE}, the function will return a list as result. Defaults to
\code{FALSE}}
\item{verbose}{integer, control output: \code{0} for no output at all,
\code{1} for only information about progress and success, \code{2} for
normal level of information and \code{3} for full debug information,
\item{verbose}{integer, control output: \code{0} for no output at all,
\code{1} for only information about progress and success, \code{2} for
normal level of information and \code{3} for full debug information,
defaults to \code{1}}