Description Usage Arguments Details Value Author(s) References See Also Examples
A variance component model is fitted to method comparison data with replicate measurements in each method by item stratum. The purpose is to simplify the construction of a correct BlandAltmanplot when replicate measurements are available, and to give the REMLestimates of the relevant variance components.
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data 
A 
linked 
Logical. Are replicates linked within item across methods? 
IxR 
Logical. Should an item by repl interaction be included in the
model. This is needed when the replicates are linked within item across
methods, so it is just another name for the 
MxI 
Logical. Should the method by item interaction (matrix effect) be included in the model. 
corMxI 
Logical. Should the method by item interaction allow coorelated effects within item. Ignored if only two methods are compared. 
varMxI 
Logical. Should the method by item interaction have a variance that varies between methods. Ignored if only two methods are compared. 
IxR.pr 
Logical. Should the item by repl interaction variation be included in the prediction standard deviation? 
bias 
Logical. Should a systematic bias between methods be estimated?
If 
alpha 
Numerical. Significance level. By default the value 2 is used when computing prediction intervals, otherwise the 1alpha/2 tquantile is used. The number of d.f. is taken as the number of units minus the number of items minus the number of methods minus 1 (IM1). 
Transform 
Transformation applied to data ( 
trans.tol 
Numerical. The tolerance used to check whether the supplied transformation and its inverse combine to the identity. 
random.raters 
Logical. Should methods/raters be considered as random.
Defaults to 
lmecontrol 
A list of control parameters passed on to 
weightfunction 
Function to weigh variance components for random
raters. Defaults to 
The model fitted is:
y=alpha_m + mu_i + c_mi + a_ir + e_ir, var(c_mi)=tau_m^2, var(a_ir)=omega^2, var(e_mir)=sigma_m^2
y=alpha_m + mu_i + c_mi + a_ir + e_ir, var(c_mi)=tau_m^2, var(a_ir)=omega^2, var(e_mir)=sigma_m^2
y=alpha_m + mu_i + c_mi + a_ir + e_ir, var(c_mi)=tau_m^2, var(a_ir)=omega^2, var(e_mir)=sigma_m^2
y=alpha_m + mu_i + c_mi + a_ir + e_ir, var(c_mi)=tau_m^2, var(a_ir)=omega^2, var(e_mir)=sigma_m^2
y=alpha_m + mu_i + c_mi + a_ir + e_ir, var(c_mi)=tau_m^2, var(a_ir)=omega^2, var(e_mir)=sigma_m^2
We can only fit separate variances
for the tau's if more than two methods are compared (i.e.
nM
> 2), hence varMxI is ignored when nM
==2.
The function VC.est
is the workhorse; BA.est
just calls it.
VC.est
figures out which model to fit by lme
, extracts results
and returns estimates. VC.est
is also used as part of the fitting
algorithm in AltReg
, where each iteration step requires fit of
this model. The function VC.est
is actually just a wrapper for the
functions VC.est.fixed
that handles the case with fixed methods
(usually 2 or three) i.e. the classical method comparison problem, and
VC.est.random
that handles the situation where "methods" are merely a
random sample of raters from some population of raters; and therefore are
regarded as random.
BA.est
returns an object of class
c("MethComp","BA.est")
, a list with four elements Conv
,
VarComp
, LoA
, RepCoef
; VC.est
returns
(invisibly!) a list with elements Bias
, VarComp
, Mu
,
RanEff
. These list components are:
Conv 
3dimensional array
with dimensions "To", "From" and unnamed. The first two dimensions have the
methods compared as levels, the last one
Where "To" and "From" take the same value the value of the "sd" component is
sqrt(2) times the residual variation for the method. If

VarComp 
A matrix of variance components (on the SD scale) with methods as rows and variance components "IxR", "MxI" and "res" as columns. 
LoA 
Fourcolumn matrix with mean difference, lower and upper limit of agreement and prediction SD. Each row in the matrix represents a pair of methods. 
RepCoef 
Twocolumn matrix of repeatability SDs and repeatability coefficients. The SDs are the standard deviation of the difference between two measurements by the same method on the item under identical circumstances; the repeatability coefficient the numerical extent of the prediction interval for this difference, i.e. 2*sqrt(2) times the sd. 
Mu 
Estimates of the itemspecific parameters. 
RanEff 
Estimates of the random effects
from the model (BLUPS). This is a (possibly empty) list with possible
elements named 
The returned object has an attribute,
Transform
with the transformation applied to data before analysis,
and its inverse — see choose.trans
.
Bendix Carstensen
Carstensen, Simpson & Gurrin: Statistical models for assessing agreement in method comparison studies with replicate measurements, The International Journal of Biostatistics: Vol. 4 : Iss. 1, Article 16. http://www.bepress.com/ijb/vol4/iss1/16.
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