gastempt (version 0.7.0)

nlme_gastempt: Simplified population fit of gastric emptying data

Description

Compute coefficients v0, tempt and kappa of a mixed model fit to a linexp function with one grouping variable

Usage

nlme_gastempt(d, pnlsTol = 0.001, model = linexp, variant = 1)

Value

A list of class nlme_gastempt with elements coef, summary, plot, pnlsTol, message

• coef is a data frame with columns:

  • record Record descriptor, e.g. patient ID

  • v0 Initial volume at t=0

  • tempt Emptying time constant

  • kappa Parameter kappa for model = linexp

  • beta Parameter beta for model = powexp

  • t50 Half-time of emptying

  • slope_t50 Slope in t50; typically in units of ml/minute

  On error, coef is NULL

• nlme_result Result of the nlme fit; can be used for addition processing, e.g. to plot residuals or via summary to extract AIC. On error, nlme_result is NULL.

• plot A ggplot graph of data and prediction. Plot of raw data is returned even when convergence was not achieved.

• pnlsTol Effective value of pnlsTo after convergence or failure.

• message String "Ok" on success, and the error message of nlme on failure.

Arguments

d

A data frame with columns

• record Record descriptor as grouping variable, e.g. patient ID

• minute Time after meal or start of recording.

• vol Volume of meal or stomach

pnlsTol

The value of pnlsTol at the initial iteration. See nlmeControl When the model does not converge, pnlsTol is multiplied by 5 and the iteration repeated until convergence or pnlsTol >= 0.5. The effective value of pnlsTol is returned in a separate list item. When it is known that a data set converges badly, it is recommended to set the initial pnlsTol to a higher value, but below 0.5, for faster convergence.

model

linexp (default) or powexp

variant

For both models, there are 3 variants

• variant = 1 The most generic version with independent estimates of all three parameters per record (random = v0 + tempt + kappa ~ 1 | record). The most likely to fail for degenerate cases. If this variant converges, use it.

• variant = 2 Diagonal random effects (random = pdDiag(v0 + tempt + kappa) ~ 1; groups = ~record ). Better convergence in critical cases. Note: I never found out why I have to use the groups parameter instead of the |; see also p. 380 of Pinheiro/Bates.

• variant = 3 Since parameters kappa and beta respectively are the most difficult to estimate, these are fixed in this variant (random = v0 + tempt ~ 1). This variant converges in all reasonable cases, but the estimates of kappa and beta cannot be use for secondary between-group analysis. If you are only interested in t50, you can use this safe version.

Examples

suppressWarnings(RNGversion("3.5.0"))
set.seed(4711)
d = simulate_gastempt(n_record = 10, kappa_mean = 0.9, kappa_std = 0.3,
                      model = linexp)$data
fit_d = nlme_gastempt(d)
# fit_d$coef # direct access
coef(fit_d) # better use accessor function
coef(fit_d, signif = 3) # Can also set number of digits
# Avoid ugly ggplot shading (not really needed...)
library(ggplot2)
theme_set(theme_bw() + theme(panel.spacing = grid::unit(0,"lines")))
# fit_d$plot  # direct access is possible
plot(fit_d) # better use accessor function