sitar  R Documentation 
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 four fixed and random effects (a, b, c and d) defining how individual growth curves differ from the mean curve.
sitar(
x,
y,
id,
data,
df,
knots,
fixed = NULL,
random = "a + b + c",
pdDiag = FALSE,
a.formula = ~1,
b.formula = ~1,
c.formula = ~1,
d.formula = ~1,
bounds = 0.04,
start,
xoffset = "mean",
bstart = xoffset,
returndata = FALSE,
verbose = FALSE,
correlation = NULL,
weights = NULL,
subset = NULL,
method = "ML",
na.action = na.fail,
control = nlmeControl(msMaxIter = 100, returnObject = TRUE),
keep.data = TRUE
)
## S3 method for class 'sitar'
update(object, ..., evaluate = TRUE)
x 
vector of ages. 
y 
vector of measurements. 
id 
factor of subject identifiers. 
data 
data frame containing variables 
df 
degrees of freedom for cubic regression spline (0 or more, see Details). 
knots 
vector of values for knots (default 
fixed 
character string specifying a, b, c, d fixed effects (default

random 
character string specifying a, b, c, d random effects (default

pdDiag 
logical which if TRUE fits a diagonal random effects covariance matrix, or if FALSE (default) a general covariance matrix. 
a.formula 
formula for fixed effect a (default 
b.formula 
formula for fixed effect b (default 
c.formula 
formula for fixed effect c (default 
d.formula 
formula for fixed effect d (default 
bounds 
span of 
start 
optional numeric vector of initial estimates for the fixed
effects, or list of initial estimates for the fixed and random effects (see

xoffset 
optional value of offset for 
bstart 
optional starting value for fixed effect 
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 
verbose 
optional logical value to print information on the evolution
of the iterative algorithm (see 
correlation 
optional 
weights 
optional 
subset 
optional expression indicating the subset of the rows of data
that should be used in the fit (see 
method 
character string, either "REML" or "ML" (default) (see

na.action 
function for when the data contain NAs (see

control 
list of control values for the estimation algorithm (see

keep.data 
logical to control saving 
object 
object of class 
... 
further parameters for 
evaluate 
logical to control evaluation. If TRUE (default) the
expanded 
The SITAR model usually has up to three random effects (a, b and c), termed
size, timing and intensity respectively. df
sets the degrees of freedom
for the mean spline curve, taking values from 1 (i.e. linear) upwards. In
addition there is a random effect for the slope, d, which is fitted when
df = 0
, and combined with a, it provides the classic random intercept random
slope model, which is similar to the 1 df spline model. In addition d can be
fitted, along with a, b and c, to extend
SITAR to model variability in the adult slope of the growth curve.
xoffset
allows the origin of x
to be varied, while
bstart
specifies the starting value for b
, both of which can
affect the model fit and particularly b
. The values of bstart
,
knots
and bounds
are offset by xoffset
for fitting
purposes, and similarly for fixed effect b
.
The formulae a.formula
, b.formula
, c.formula
and d.formula
allow for cov.names and
can include functions and interactions. make.names
is used to
ensure that the names of the corresponding model terms are valid. The
modified not the original names need to be specified in predict.sitar
.
update
updates the model by taking the object
call, adding any
new parameters and replacing changed ones. Where feasible the fixed and
random effects of the model being updated are suitably modified and passed
via the start
argument.
An object inheriting from class sitar
representing the
nonlinear mixedeffects model fit, with all the components returned by
nlme
(see nlmeObject
for a full description) plus the
following components:
fitnlme 
the function returning the predicted value of 
data 
copy of 
constants 
data frame of mean abcd values for unique combinations
of covariates (excluding 
call.sitar 
the internal 
xoffset 
the value of 
ns 
the 
Generic functions such as print
, plot
, anova
and
summary
have methods to show the results of the fit. The functions
residuals
, coef
, fitted
, fixed.effects
,
random.effects
, predict
, getData
, getGroups
,
getCovariate
and getVarCov
can be used to extract some of its
components.
Tim Cole tim.cole@ucl.ac.uk
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 (timing) are positively associated with age at menarche
(m2 < update(m1, a.formula = ~abs(men), b.formula = ~abs(men), c.formula = ~abs(men)))
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