sitar (version 1.0.4)

sitar: Fit SITAR growth curve model

Description

SITAR is a method of growth curve analysis, based on nlme, that summarises a set of growth curves with a mean growth curve as a regression spline, plus a set of up to three fixed and random effects (a, b and c) defining how individual growth curves differ from the mean curve.

Usage

sitar(x, y, id, data, df, knots,
fixed = random, random = "a+b+c",
a.formula = ~1, b.formula = ~1, c.formula = ~1,
bounds = 0.04, start, bstart = "mean", xoffset = "mean",
returndata = FALSE, verbose = FALSE, correlation = NULL,
weights = NULL, subset = NULL, method = "ML",
na.action = na.fail,
control = nlmeControl(returnObject = TRUE), newform = TRUE)
## S3 method for class 'sitar':
update(object, ..., evaluate = TRUE)

Arguments

x

vector of ages.

y

vector of measurements.

id

factor of subject identifiers.

data

data frame containing variables x, y and id.

df

degrees of freedom for cubic regression spline.

knots

vector of values for knots (default df quantiles of x distribution).

fixed

character string specifying a, b, c fixed effects (default random).

random

character string specifying a, b, c random effects (default "a+b+c").

a.formula

formula for fixed effect a (default ~ 1).

b.formula

formula for fixed effect b (default ~ 1).

c.formula

formula for fixed effect c (default ~ 1).

bounds

span of x for regression spline, or fractional extension of range (default 0.04).

start

optional numeric vector of initial estimates for the fixed effects, or list of initial estimates for the fixed and random effects (see nlme).

bstart

optional starting value for fixed effect b (either "mean" (default), "apv" or value).

xoffset

optional value of offset for x (either "mean" (default), "apv" or value).

returndata

logical which if TRUE causes the model matrix to be returned, or if FALSE (default) the fitted model. Setting returndata TRUE is useful in conjunction with subset and subsample for simula

verbose

optional logical value to print information on the evolution of the iterative algorithm (see nlme).

correlation

optional corStruct object describing the within-group correlation structure (see nlme).

weights

optional varFunc object or one-sided formula describing the within-group heteroscedasticity structure (see nlme).

subset

optional expression indicating the subset of the rows of data that should be used in the fit (see nlme).

method

character string, either "REML" or "ML" (default) (see nlme).

na.action

function for when the data contain NAs (see nlme).

control

list of control values for the estimation algorithm (see nlme).

newform

logical to distinguish between old and new forms of sitar object - default TRUE indicates new form.

object

object of class sitar.

...

further parameters for update consisting of any of the above sitar parameters.

evaluate

logical to control evaluation. If TRUE (default) the expanded update call is passed to sitar for evaluation, while if FALSE the expanded call itself is returned.

Value

• An object inheriting from class sitar representing the nonlinear mixed-effects model fit, with all the components returned by nlme (see nlmeObject for a full description) plus the following components:
• bstart
  the value of arg bstart.
• call.sitar
  the internal sitar call that produced the object.
• fitnlme
  the function returning the predicted value of y.
• ns
  the lm object providing starting values for the B-spline curve.
• Generic functions such as print, plot, anova and summary have methods to show the results of the fit. The functions resid, coef, fitted, fixed.effects, random.effects, predict, getData, getGroups, getCovariate and getVarCov can be used to extract some of its components.

  Note that earlier versions of sitar did not return fitnlme. Both plot and predict may require it, in which case they update the SITAR object on the fly, with a message. This means the object also needs to be updated manually.

Details

xoffset is ignored unless newform is FALSE. bstart (or xoffset if !newform) allow the origin of b to be varied, which affects its random effect variance. update updates the model by taking the object call, adding any new parameters and replacing changed ones. Where possible the fixed and random effects of the model being updated are passed via the start argument.

Examples

data(heights)
##  fit simple model
(m1 <- sitar(x=age, y=height, id=id, data=heights, df=5))

##  relate random effects to age at menarche (with censored values +ve)
##  both a (size) and b (tempo) are positively associated with age at menarche
amen <- abs(heights$men)
(m2 <- update(m1, a.form=~amen, b.form=~amen, c.form=~amen))