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Simulate Longitudinal Data from a Claret-Bruno Model

Usage

SimLongitudinalClaretBruno(
  times = c(-100, -50, 0, 50, 100, 150, 250, 350, 450, 550)/365,
  sigma = 0.01,
  mu_b = log(60),
  mu_g = log(c(0.9, 1.1)),
  mu_c = log(c(0.25, 0.35)),
  mu_p = log(c(1.5, 2)),
  omega_b = 0.2,
  omega_g = 0.2,
  omega_c = 0.2,
  omega_p = 0.2,
  link_dsld = 0,
  link_ttg = 0,
  link_identity = 0,
  link_growth = 0,
  scaled_variance = TRUE
)

Arguments

times

(numeric)
the times to generate observations at.

sigma

(number)
the variance of the longitudinal values.

mu_b

(numeric)
the mean population baseline sld value.

mu_g

(numeric)
the mean population growth rate.

mu_c

(numeric)
the mean population resistance rate.

mu_p

(numeric)
the mean population growth inhibition.

omega_b

(number)
the population standard deviation for the baseline sld value.

omega_g

(number)
the population standard deviation for the growth rate.

omega_c

(number)
the population standard deviation for the resistance rate.

omega_p

(number)
the population standard deviation for the growth inhibition.

(number)
the link coefficient for the derivative contribution.

(number)
the link coefficient for the time-to-growth contribution.

(number)
the link coefficient for the SLD Identity contribution.

(number)
the link coefficient for the growth parameter contribution.

scaled_variance

(logical)
whether the variance should be scaled by the expected value (see the "Statistical Specifications" vignette for more details)

Slots

sigma

(numeric)
See arguments.

mu_b

(numeric)
See arguments.

mu_g

(numeric)
See arguments.

mu_c

(numeric)
See arguments.

mu_p

(numeric)
See arguments.

omega_b

(numeric)
See arguments.

omega_g

(numeric)
See arguments.

omega_c

(numeric)
See arguments.

omega_p

(numeric)
See arguments.

link_dsld

(numeric)
See arguments.

link_ttg

(numeric)
See arguments.

link_identity

(numeric)
See arguments.

link_growth

(numeric)
See arguments.

scaled_variance

(logical)
See arguments.