Description Usage Arguments Details Value Author(s) References See Also Examples
Estimates in the general model for method comparison studies with replicate measurements by each method, allowing for a linear relationship between methods, using the method of alternating regressions.
1 2 3 4 5 6 7 8 9 10 11 12 13 
data 
Data frame with the data in long format, (or a

linked 
Logical. Are the replicates linked across methods? If true, a
random 
IxR 
Logical, alias for linked. 
MxI 
Logical, should the method by item effect (matrix effect) be in the model? 
varMxI 
Logical, should the method by item effect have methodspecific variances. Ignored if only two methods are compared. See details. 
eps 
Convergence criterion, the test is the max of the relative change since last iteration in both mean and variance parameters. 
maxiter 
Maximal number of iterations. 
trace 
Should a trace of the iterations be printed? If 
sd.lim 
Estimated standard deviations below 
Transform 
A character string, or a list of two functions, each
other's inverse. The measurements are transformed by this before analysis.
Possibilities are: "exp", "log", "logit", "pctlogit" (transforms percentages
by the logit), "sqrt", "sq" (square), "cll" (complementary logminuslog),
"ll" (logminuslog). For further details see 
trans.tol 
The tolerance used to check whether the supplied
transformation and its inverse combine to the identity. Only used if

When fitting a model with both IxR and MxI interactions it may become very unstable to have different variances of the MxI random effects for each method, and hence the default option is to have a constant MxI variance across methods. On the other hand it may be grossly inadequate to assume these variances to be identical.
If only two methods are compared, it is not possible to separate different
variances of the MxI effect, and hence the varMxI
is ignored in this
case.
The model fitted is formulated as:
y_mir = alpha_m + beta_m*(mu_i+a_{ir}+c_mi) + e_mir
y_mir = alpha_m + beta_m*(mu_i+a_{ir}+c_mi) + e_mir
and the relevant parameters to report are
the estimates sds of a_{ir} and c_{mi}
multiplied with the corresonidng beta_m. Therefore, different
values of the variances for MxI and IxR are reported also when
varMxI==FALSE
. Note that varMxI==FALSE
is the default and that
this is the opposite of the default in BA.est
.
An object of class c("MethComp","AltReg")
, which is a list
with three elements:
Conv 
A 3way array with the 2 first dimensions named "To:" and "From:", with methods as levels. The third dimension is classifed by the linear parameters "alpha", "beta", and "sd". 
VarComp 
A matrix with methods as rows and variance components as columns. Entries are the estimated standard deviations. 
data 
The
original data used in the analysis, with untransformed measurements
( 
Moreover, if a
transformation was applied before analysis, an attribute "Transform" is
present; a list with two elements trans
and inv
, both of which
are functions, the first the transform, the last the inverse.
Bendix Carstensen, Steno Diabetes Center, bendix.carstensen@regionh.dk, http://BendixCarstensen.com.
B Carstensen: Comparing and predicting between several methods of measurement. Biostatistics (2004), 5, 3, pp. 399–413.
BA.est
, DA.reg
, Meth.sim
,
MethComp
1 2 3 4 5 6 7 8 9 10 11  data( ox )
ox < Meth( ox )
## Not run:
ox.AR < AltReg( ox, linked=TRUE, trace=TRUE, Transform="pctlogit" )
str( ox.AR )
ox.AR
# plot the resulting conversion between methods
plot(ox.AR,pl.type="conv",axlim=c(20,100),points=TRUE,xaxs="i",yaxs="i",pch=16)
#  or the rotated plot
plot(ox.AR,pl.type="BA",axlim=c(20,100),points=TRUE,xaxs="i",yaxs="i",pch=16)
## End(Not run)

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