| AltReg {MethComp} | R Documentation |
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.
AltReg( data,
linked = FALSE,
IxR = linked,
MxI = TRUE,
varMxI = FALSE,
eps = 0.001,
maxiter = 50,
trace = FALSE,
sd.lim = 0.01,
Transform = NULL,
trans.tol = 1e-6 )
data |
Data frame with the data in long format,
(or a Meth object)
i.e. it must have columns 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. |
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 method-specific 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
TRUE iteration number, convergence criterion and current
estimates of means and sds are printed. |
sd.lim |
Estimated standard deviations below sd.lim are
disregarded in the evaluation of convergence. See details. |
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 log-minus-log), "ll"
(log-minus-log). For further details see
choose.trans. |
trans.tol |
The tolerance used to check whether the supplied
transformation and its inverse combine to the identity.
Only used if Transform is a list of two functions. |
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
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 3-way 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 standrd deviations. |
data |
The original data used in the analysis, with untransformed measuremnts. This is needed for plotting purposes. |
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, bxc@steno.dk
B Carstensen: Comparing and predicting between several methods of measurement. Biostatistics (2004), 5, 3, pp. 399–413.
BA.est
DA.reg
Meth.sim
MethComp
dfm <- Meth.sim( 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 )
levels(dfm$meth) <- paste( "m",1:3,sep="" )
str(dfm)
summary(dfm)
plot(dfm,var.names=TRUE)
# AltReg( dfm, linked=TRUE, trace=TRUE )
# AltReg( dfm, linked=TRUE, varMxI=TRUE, trace=TRUE )
data( ox )
ox.AR <- AltReg( ox, linked=TRUE, trace=TRUE, Transform="pctlogit" )
str( ox.AR )
ox.AR