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 Bland-Altman-plot when replicate measurements are available, and to give the REML-estimates of the relevant variance components.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | BA.est( data, linked=TRUE, IxR=has.repl(data),
MxI=has.repl(data),
corMxI=FALSE,
varMxI=TRUE,
IxR.pr=FALSE,
bias=TRUE, alpha=0.05,
Transform = NULL,
trans.tol = 1e-6,
random.raters = FALSE,
lmecontrol = lmeControl(msMaxIter=300),
weightfunction = c("mean", "median")
)
## S3 method for class 'BA.est'
bias( obj, ref=1, ... )
VC.est( data,
IxR = has.repl(data), linked = IxR,
MxI = has.repl(data), matrix = MxI,
corMxI = FALSE,
varMxI = TRUE,
bias = TRUE,
print = FALSE,
random.raters = FALSE,
lmecontrol = lmeControl(msMaxIter=300)
)
|
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. |
matrix |
Logical. Alias for |
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 1-alpha/2 t-quantile 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 (I-M-1). |
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 |
obj |
A |
ref |
Numeric or character. The reference method for the biases: the method with bias 0. |
print |
Logical. Should the estimated bias and variance components be printed? |
... |
Further arguments passed on. Currently ignored. |
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
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 |
3-dimensional 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 |
Four-column matrix with mean difference, lower and upper limit of agreement and prediction SD. Each row in the matrix represents a pair of methods. |
RepCoef |
Two-column 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 item-specific 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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