nlive.splines: Automated Estimation of the Linear Mixed Model with Splines#'

In nlive: Automated Estimation of Sigmoidal and Piecewise Linear Mixed Models

Automated Estimation of the Linear Mixed Model with Splines#'

Description

The nlive.splines function allows to fit a Linear Mixed Models with the function of time approximated with natural cubic splines or B-splines in the context of longitudinal Gaussian outcomes. This function was designed to be intuitive enough to the less sophisticated users, while using the existing hlme() function from the lcmm R package as well as the existing ns() and bs() functions from the splines R package. #' CAUTIONS REGARDING THE USE OF THE FUNCTION

Usage

nlive.splines(
  dataset,
  ID,
  time,
  formula,
  random,
  splines = NULL,
  knots = NULL,
  Boundary.knots = NULL,
  traj.marg = FALSE
)

Arguments

dataset	data frame containing the variables ID, outcome, time, var.all, and all other var. arguments.
ID	name of the variable representing the grouping structure specified with " (e.g., "ID" representing the unique identifier of participants).
time	name of the variable representing the timescale specified with " (e.g., "time"), which can be negative or positive.
formula	two-sided linear formula object for the fixed-effects in the linear mixed model. The response outcome is on the left of ~ and the covariates are separated by + on the right of ~. By default, an intercept is included. If no intercept, -1 should be the first term included on the right of ~.
random	optional one-sided formula for the random-effects in the linear mixed model. Covariates with a random-effect are separated by +. Default is an intercept. If no intercept, -1 should be the first term included.
splines	"ns" for Natural Cubic Splines. "bs" for Cubic B-splines.
knots	inner knots that define the spline. Typical values are the mean or median for one knot, quantiles for more knots. Default is two equally spaced inner knots (i.e., 33th, 66th percentiles).
Boundary.knots	boundary points at which to impose the natural boundary conditions and anchor the spline basis. Default to the range of the data.
traj.marg	optional logical indicating if the marginal estimated trajectory should be plotted for the most common profile of covariates, if any. Default to FALSE. #' @return An object of from the existing lcmm R package containing the results of the fit of the data by a linear mixed model and with the function of time approximated using natural cubic splines.

Details

traj.marg: if "TRUE", this argument automatically plots the estimated marginal trajectories of the longitudinal outcome for the most common profile of covariates, if any (i.e., ref "1" for binary variables and mean values for continuous variables). Thus, users must ensure that continuous variables are centered on the mean.

Author(s)

Maude Wagner, Ana W. Capuano, Cecile Proust-lima

[e-mail]

References

Proust-Lima, C., Philipps, V., & Liquet, B. (2017). Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm. Journal of Statistical Software, 78(2), 1–56. https://doi.org/10.18637/jss.v078.i02 Perperoglou, A., Sauerbrei, W., Abrahamowicz, M. et al. A review of spline function procedures in R. BMC Med Res Methodol 19, 46 (2019). https://doi.org/10.1186/s12874-019-0666-3

Examples

#### Fitting a linear mixed model with ns splines and 2 inner knots (33th, 66th percentiles)
## Not run: 
head(dataCog)
lmm.ns.fit = nlive.splines(dataCog, ID="ID", outcome="cognition", time="time", splines.type="ns")

## End(Not run)
#### plot(lmm.ns.fit): diagnostic plots to assess the goodness-of-fit of lmm.ns.fit

nlive documentation built on April 12, 2025, 1:33 a.m.