| MethComp {MethComp} | R Documentation |
A model linking each of a number of methods of measurement linearly to the
"true" value is set up in BUGS and run via the function
bugs from the R2WinBUGS package.
MethComp( data,
random = c("mi", "ir"),
beta = TRUE,
n.chains = 3,
n.iter = 2000,
n.burnin = n.iter/2,
n.thin = ceiling((n.iter - n.burnin)/1000),
bugs.directory = options("bugs.directory")[[1]],
debug = FALSE,
clearWD = TRUE,
bugs.code.file = "qwzx.bug",
code.only = FALSE,
... )
## S3 method for class 'MethComp':
summary(object, ...)
## S3 method for class 'MethComp':
print(x, across, digits=3, ... )
data |
Data frame with variables meth, item, repl
and y. y represents a measurement on an item
(typically patient or sample) by method meth, in replicate
repl. |
random |
Which random effects should be included in the
model?. Enter NULL if none is desired. |
beta |
Logical. Should a slope other than 1 be allowed? If
FALSE the bias between methods will be assumed constant. |
n.chains |
How many chains should be run by WinBUGS — passed on
to bugs. |
n.iter |
How many total iterations — passed on to bugs. |
n.burnin |
How many of these should be burn-in — passed on to bugs. |
n.thin |
How many should samples — passed on to bugs. |
bugs.directory |
Where is WinBUGS (>=1.4) installed — passed on
to bugs. The default is to use a parameter from
options(). If you use this routinely, this is most conveniently set in
your .Rprofile. |
debug |
Should WinBUGS remain open after running — passed on to
bugs. |
clearWD |
Should the working directory be cleared for junk files after
the running of WinBUGS — passed on to bugs. |
bugs.code.file |
Where should the bugs code go? |
code.only |
Should MethComp just create a bugs code file and a set
of inits? |
... |
Additional arguments passed on to bugs. |
object |
A MethComp object |
x |
A MethComp object |
across |
Should the summary of conversion formulae be printed with $α$, $β$ and prediction sd. across or down? |
digits |
Number of digits after the decimal point when printing. |
The model set up for an observation y_mir is:
y_mir = alpha_m + beta_m*(mu_i+b_ir+c_mi) + e_mir
where $b_{ir}$ is a random item by repl interaction (included if
"ir" %in% random) and $c_{mi}$ is a random meth by item
interaction (included if "mi" %in% random). The mu_i's are
parameters in the model but are not monitored — only the alphas,
betas and the variances of b_{ir},
c_{mi} and e_{mir} are monitored and
returned. The estimated parameters are only determined up to a linear
transformation of the mus, but the linear functions linking
methods are invariant. The identifiable conversion parameters are:
alpha_m|k=alpha_m-alpha_k beta_m/beta_k, beta_m|k=beta_m/beta_k
The posteriors of these are derived and included in the posterior, which
also will contain the posterior of the variance components (the sd's, that is).
Furthermore, the posterior of the point where the conversion lines intersects
the identity as well as the prediction sd's between any pairs of methods are
included.
The function summary.MethComp method gives estimates of the conversion
parameters that are consistent. Clearly,
median(beta.1.2)=1/median(beta.2.1)
because the inverse is a monotone transformation, but there is no guarantee that
median(alpha.1.2)=median(-alpha.2.1/beta.2.1)
and hence no guarantee that the parameters derived as posterior medians
produce conversion lines that are the same in both directions. Therefore,
summary.MethComp computes the estimate for α_{2cdot 1}
{alpha.2.1} as
(median(alpha.1.2)-median(alpha.2.1)/ median(beta.2.1))/2
and the estimate of alpha.1.2 correspondingly. The resulting parameter estimates defines the same lines.
If code.only==FALSE, an object of class MethComp which is
a list with three components:
summary |
Matrix with a summary of the posterior of the variance components and the parameters linking the methods. |
posterior |
Dataframe with the posterior samples of the interesting parameters. |
org.summary |
Summary of the original parameters as monitored by
WinBUGS. |
random |
A character sting indicationg which random effects are in the model. |
methods |
A character string of the names of the methods. |
data |
The original data frame used in the computations. This is
intended for us in plot.MethComp. |
Bendix Carstensen, Steno Diabetes Center, http://www.biostat.ku.dk/~bxc, Lyle Gurrin, University of Melbourne, http://www.epi.unimelb.edu.au/about/staff/gurrin-lyle.
B Carstensen: Comparing and predicting between several methods of measurement, Biostatistics, 5, pp 399-413, 2004
BA.plot, \code{plot.MethComp}
data( ox )
str( ox )
MethComp( ox, code.only=TRUE, bugs.code.file="ox-ex.bug", random=c("mi") )
shell( "type ox-ex.bug" ) # only works on windows
### These next lines only work if you properly name the path to WinBUGS
### What is written here is not necessarily correct on your machine.
library(R2WinBUGS)
# options( bugs.directory="c:/Program Files/WinBUGS14/" )
options( bugs.directory="c:/Stat/Bugs/WinBUGS14/")
ox.res <- MethComp( ox, random=c("mi"), n.iter=100 )
str( ox.res )
str( ox.res[[2]] )
print( ox.res )