gen_Xn_longitudinal: Design Matrix Generator in Longitudinal Setting

In bayesassurance: Bayesian Assurance Computation

Design Matrix Generator in Longitudinal Setting

Description

Constructs design matrix using inputs that correspond to a balanced longitudinal study design. Used for power and sample size analysis in the Bayesian setting.

Usage

gen_Xn_longitudinal(ids, from, to, num_repeated_measures, poly_degree = 1)

Arguments

ids	vector of unique subject ids, usually of length 2 for study design purposes.
from	start time of repeated measures for each subject
to	end time of repeated measures for each subject
num_repeated_measures	desired length of the repeated measures sequence. Should be a non-negative number, will be rounded up if fractional.
poly_degree	degree of polynomial in longitudinal model, set to 1 by default.

Value

Xn: a design matrix that can be used to assess the Bayesian assurance through Monte Carlo sampling using functions presented in this package.

See Also

gen_Xn

Examples

## Example 1
## We pass in a vector of subject IDs and specify the start and end
## timepoints along with the desired length of the sequence.
## The resulting design matrix contains vectors of
## ones with lengths that correspond to the number of repeated
## measures for each unique subject.

ids <- c(1,2,3,4)
gen_Xn_longitudinal(ids, from = 1, to = 10, num_repeated_measures = 4)

## Example 2
## If we wish to fit a longitudinal model of a higher degree (e.g. 
## parabolic, cubic), we need to adjust the `poly_degree` variable

# parabolic
ids <- c(1,2,3,4)
gen_Xn_longitudinal(ids, from = 1, to = 10, num_repeated_measures = 4,
poly_degree = 2)

# cubic
ids <- c(1,2,3,4)
gen_Xn_longitudinal(ids, from = 1, to = 10, num_repeated_measures = 4,
poly_degree = 3)

bayesassurance documentation built on June 17, 2022, 5:05 p.m.