highDmsm

Multi state data with 3 states, 100 continuous biomarkers

datasets

Provides a suite of methods for detecting influential
subjects in longitudinal datasets, particularly when observations
occur at irregular time points. The methods identify individuals
whose response trajectories deviate significantly from the population
pattern, enabling detection of anomalies or subjects exerting undue
influence on model outcomes.

Atanu Bhattacharjee

flassomsm

Penalized Estimation for Multi-State Models with Lasso and Fused
Penalties

Gajendra Kumar Vishwakarma

Abhipsa Tripathy

highDmsm function

A dataframe with 500 rows and 107 columns including all the states and covariates<dl>
<dt>id</dt>
<dd>ID of subjects</dd><dt>state1</dt>
<dd>Time in days from transplantation to state-1 or last follow up</dd><dt>status1</dt>
<dd>Status of state-1</dd><dt>state2</dt>
<dd>Time in days from transplantation to state-2 or last follow up</dd><dt>status2</dt>
<dd>Status of state-1</dd><dt>state3,status3</dt>
<dd>Time in days from transplant to the respective states along with its status</dd><dt>v1,...,v100</dt>
<dd>The 100 biomarkers attached to the dataset</dd>
</dl>

Format

Multi state data with high dimensional covariates — highDmsm

A dataframe with 500 rows and 107 columns including all the states and covariates<dl>
<dt>id</dt>
<dd>ID of subjects</dd>

<dt>state1</dt>
<dd>Time in days from transplantation to state-1 or last follow up</dd>

<dt>status1</dt>
<dd>Status of state-1</dd>

<dt>state2</dt>
<dd>Time in days from transplantation to state-2 or last follow up</dd>

<dt>status2</dt>
<dd>Status of state-1</dd>

<dt>state3,status3</dt>
<dd>Time in days from transplant to the respective states along with its status</dd>

<dt>v1,...,v100</dt>
<dd>The 100 biomarkers attached to the dataset</dd>


</dl>

highDmsm: Multi state data with high dimensional covariates

Description

Usage

Arguments

Format

Examples