| AltReg {MethComp} | R Documentation |
Estimates in the general model for method comapriosn studies with replicate measurements by each method, allowing for a linear relatioinship between methods.
AltReg( dfr,
linked = FALSE,
eps = 0.001,
maxiter = 50,
int.loc = 0,
trace = FALSE)
dfr |
Data frame with the data in the usual meth format,
i.e. variables meth, item, repl and y |
linked |
Logical. Are the replicates linked across methods. If
true, a random item by repl is included in the model. |
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. |
int.loc |
Scalar. The location where the intercept is evaluated when returning the linear conversion paramaters between methods. |
trace |
Should a trace of the iterations be printed? If
TRUE iteration number, convergence criterion and current
estimates of means and sds are printed. |
A matrix with one row per method compared.
There are columns for intercept and slope for each of the methods,
as well as columns for each of the three variance components.
Suppose methods are labelled m1, m2 and m3.
Prediction of a measurement y1 by method m1 from an
observation y2 by method m2 is obtained as
y1= A + B y2 where A and B are from the row
labelled m1, columns labelled a m1 and labelled
b m1, respectivlely.
Bendix Carstense, bxc@steno.dk
~put references to the literature/web site here ~
dfr <- sim.meth( Ni=30,
Nm=3,
beta=c(0.9,0.8,1.1),
sigma.mi =c(4,5,8),
sigma.ir =3,
sigma.mir=c(5,4,3),
m.thin=1,
i.thin=1 )
dfr$meth <- factor( dfr$meth, labels=paste( "m",1:3,sep="" ) )
str(dfr)
tab.repl(dfr)
plotMeth(dfr,var.names=TRUE)
AltReg( dfr, linked=TRUE, trace=TRUE )