normalize.covariates2

This function normalized the covariates in the data to lie in
the unit interval based on a principal component analysis. It is useful to
perform this step when defining the instrumental functions later on. This
function is used in <code>G.box</code>, <code>G.spline</code> and by extension <code>G.cd</code>.

Several statistical methods for analyzing survival data under various forms of dependent
censoring are implemented in the package. In addition to accounting for dependent censoring, it
offers tools to adjust for unmeasured confounding factors. The implemented approaches allow
users to estimate the dependency between survival time and dependent censoring time, based
solely on observed survival data. For more details on the methods, refer to Deresa and Van
Keilegom (2021) <doi:10.1093/biomet/asaa095>, Czado and Van Keilegom (2023)
<doi:10.1093/biomet/asac067>, Crommen et al. (2024) <doi:10.1007/s11749-023-00903-9>,
Deresa and Van Keilegom (2024) <doi:10.1080/01621459.2022.2161387>, Rutten et al. (2024+)
<doi:10.48550/arXiv.2403.11860> and Ding and Van Keilegom (2024).

Negera Wakgari Deresa

depCensoring

Statistical Methods for Survival Data with Dependent Censoring

Ilias Willems

Gilles Crommen

Jie Ding

Claudia Czado

Ingrid Van Keilegom

normalize.covariates2 function

<dl><dt>data</dt>
<dd>(optional) Data set to be used to construct the normalizing
transformation. Default is <code>data = NULL</code>.</dd>
<dt>x</dt>
<dd>(optional) Vector of covariates to be normalized alongside the data.
Default is <code>x = NULL</code>.</dd>
<dt>idxs.c</dt>
<dd>(optional) Vector of indices of covariates that are continuous.
Note that that indices are relative to the covariate vector, not the full
data set. Default value is <code>idxs.c = "all"</code>, which indicates that all
elements should be regarded as continuous. If <code>idxs.c = NULL</code>, all
elements are regarded as discrete.</dd>
<dt>norm.cov.out</dt>
<dd>(optional) The output of a previous call to this function.
Can be used to speed up computation. If both <code>data</code> and
<code>norm.cov.out</code> are supplied to the function, the function will throw an
error. Default is <code>norm.cov.out = NULL</code></dd>
<dt>...</dt>
<dd>Allows easier interchangeability between covariate normalization
functions. All arguments specified under <code>...</code> will be ignored.</dd></dl>

Arguments

Normalize the covariates of a data set to lie in the unit interval by
transforming based on PCA. — normalize.covariates2

<dl>

<dt>data</dt>
<dd>(optional) Data set to be used to construct the normalizing
transformation. Default is <code>data = NULL</code>.</dd>


<dt>x</dt>
<dd>(optional) Vector of covariates to be normalized alongside the data.
Default is <code>x = NULL</code>.</dd>


<dt>idxs.c</dt>
<dd>(optional) Vector of indices of covariates that are continuous.
Note that that indices are relative to the covariate vector, not the full
data set. Default value is <code>idxs.c = "all"</code>, which indicates that all
elements should be regarded as continuous. If <code>idxs.c = NULL</code>, all
elements are regarded as discrete.</dd>


<dt>norm.cov.out</dt>
<dd>(optional) The output of a previous call to this function.
Can be used to speed up computation. If both <code>data</code> and
<code>norm.cov.out</code> are supplied to the function, the function will throw an
error. Default is <code>norm.cov.out = NULL</code></dd>


<dt>...</dt>
<dd>Allows easier interchangeability between covariate normalization
functions. All arguments specified under <code>...</code> will be ignored.</dd>

</dl>

normalize.covariates2: Normalize the covariates of a data set to lie in the unit interval by transforming based on PCA.

Description

Usage

Arguments