Fit a population recovery-trajectory NLME

Fits a nonlinear mixed-effects recovery curve to longitudinal panel data with subject-level random effects (partial pooling), and returns the population fixed effects together with per-subject predicted asymptote, rate, and time-to-90%-recovery.

Usage

recoveryTrajectoryLME(
  data,
  subject,
  time,
  outcome,
  model = c("exponential", "asymptotic", "logistic")
)

Arguments

data: A long-format data frame.
subject, time, outcome: Column names (character) for the grouping factor, the time variable, and the response.
model: "exponential" (default), "asymptotic" (an alias), or "logistic".

Value

An AnalysisResult (type "recovery_trajectory") whose result holds the fixed_effects, random_effects, a per-subject data frame (asymptote, rate, time_to_90), and the fitted nlme object.

Details

Models: "exponential"/"asymptotic" use the self-starting asymptotic curve \(y = A + (R_0 - A)\,e^{-e^{lrc} t}\) (SSasymp); "logistic" uses SSlogis.

References

Pinheiro & Bates 2000 (nlme); Lindstrom & Bates 1990.

Examples

set.seed(1)
df <- do.call(rbind, lapply(1:12, function(s) {
  A <- 60 + rnorm(1, 0, 5); rate <- 0.3 * exp(rnorm(1, 0, 0.2))
  t <- 0:8; data.frame(subject = s, time = t,
    y = A * (1 - exp(-rate * t)) + rnorm(9, 0, 2))
}))
fit <- recoveryTrajectoryLME(df, "subject", "time", "y")
#> Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!