make_N, make_Ni, make_Y, make_Yi {HQM}

Occurance and Exposure on grids

Description

Auxiliary functions that help automate the process of calculating integrals with occurances or exposure processes.

Usage

make_N(data, data.id, breaks_X, breaks_s, ss, XX, delta)
make_Ni(breaks_s, size_s_grid, ss, delta, n)
make_Y(data, data.id, X_lin, breaks_X, breaks_s,
        size_s_grid, size_X_grid, int_s, int_X, event_time = 'years', n)
make_Yi(data, data.id, X_lin, breaks_X, breaks_s,
        size_s_grid, size_X_grid, int_s,int_X, event_time = 'years', n)

Arguments

data

A data frame of time dependent data points. Missing values are allowed.

data.id

An id data frame obtained from to_id.

breaks_X

Marker value grid points where the function will be evaluated.

breaks_s

Time value grid points where the function will be evaluated.

ss

Vector with event times.

XX

Vector of last observed marker values.

delta

0-1 vector of whether events happened.

size_s_grid

Size of the time grid.

size_X_grid

Size of the marker grid.

n

Number of individuals.

X_lin

Linear interpolation of observed marker values evaluated on the marker grid.

int_s

Position of the observed time values on the time grid.

int_X

Position of the linear interpolated marker values on the marker grid.

event_time

String of the column name with the event times.

Details

Implements matrices for the computation of integrals with occurences and exposures of the form

\int f(s)Z(s)Z(s+t)ds, \int f(s) Z(s)ds, \int f(s) dN(s).

where N is a 0-1 counting process, Z the exposure and f an arbitrary function.

Value

The functions make_N and make_Y return a matrix on the time grid and marker grid for occurence and exposure, respectively, while make_Ni and make_Yi return a matrix on the time grid for evey individual again for occurence and exposure, respectively.

See Also

h_xt, g_xt, get_alpha

Examples

pbc2_id = to_id(pbc2)
size_s_grid <- size_X_grid <- 100
n = max(as.numeric(pbc2$id))
s = pbc2$year
X = pbc2$serBilir
XX = pbc2_id$serBilir
ss <- pbc2_id$years
delta <- pbc2_id$status2
br_s = seq(0, max(s), max(s)/( size_s_grid-1))
br_X = seq(min(X), max(X), (max(X)-min(X))/( size_X_grid-1))

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

int_X <- findInterval(X_lin, br_X)
int_s = rep(1:length(br_s), n)

N <- make_N(pbc2, pbc2_id, br_X, br_s, ss, XX, delta)
Y <- make_Y(pbc2, pbc2_id, X_lin, br_X, br_s,
            size_s_grid, size_X_grid, int_s, int_X, event_time = 'years', n)
Yi <- make_Yi(pbc2, pbc2_id, X_lin, br_X, br_s,
              size_s_grid, size_X_grid, int_s, int_X, event_time = 'years', n)
Ni  <- make_Ni(br_s, size_s_grid, ss, delta, n)