HQM (version 0.1.1)

lin_interpolate: Linear interpolation

Description

Implements a linear interpolation between observered marker values.

Usage

lin_interpolate(t, i, data_id, data_marker, data_time)

Value

A matrix with columns \((f(t_1),...,f(t_K))\) as described above for every individual in the vector i.

Arguments

t

A vector of time values where the function should be evaluated.

i

A vector of ids of individuals for whom the marker values should be interpolated.

data_id

The vector of ids from a data frame of time dependent variables.

data_marker

The vector of marker values from a data frame of time dependent variables.

data_time

The vector of time values from a data frame of time dependent variables.

Details

Given time points \(t_1,...,t_K\) and marker values \(m_1,...,m_J\) at different time points \(t^m_1,...,t^m_J\), the function calculates a linear interpolation \(f\) with \(f(t^m_i) = m_i\) at the time points \(t_1,...,t_K\) for all indicated individuals. Returned are then \((f(t_1),...,f(t_K))\). Note that the first value is always observed at time point \(0\) and the function \(f\) is extrapolated constantly after the last observed marker value.

Examples

size_s_grid <- 100
X = pbc2$serBilir
s = pbc2$year
br_s = seq(0, max(s), max(s)/( size_s_grid-1))
pbc2_id = to_id(pbc2)

X_lin = lin_interpolate(br_s, pbc2_id$id, pbc2$id, X, s)