| 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"),
alpha = TRUE,
beta = alpha,
n.chains = 4,
n.iter = 2000,
n.burnin = n.iter/2,
n.thin = ceiling((n.iter - n.burnin)/1000),
bugs.directory = getOption("bugs.directory"),
debug = FALSE,
bugs.code.file = "model.txt",
clearWD = TRUE,
bugsWD = "bugsWD",
code.only = FALSE,
ini.mult = 2,
org = FALSE,
program = "BRugs",
...)
## S3 method for class 'MethComp':
summary(object, alpha=0.05, ...)
## S3 method for class 'MethComp':
print(x, across, digits=3, alpha=0.05,... )
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. |
alpha |
Logical. If FALSE the bias between methods will be
assumed to be 0. If beta is TRUE when alpha is
FALSE, the resulting model assumes proportional bias, which is
a very strong assumption. |
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 be sampled — 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 file. |
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. |
bugsWD |
Name of the folder where the bugs files are put. The code file is also put in this folder. |
bugs.code.file |
Where should the bugs code go? |
code.only |
Should MethComp just create a bugs code file and a set
of inits? |
ini.mult |
Numeric. What factor should be used to randomly perturb the initial values for the variance componets, see below in details. |
org |
Logical. Should the posterior of the original model parameters be
returned too? If TRUE, the MethComp object will have
an attribute, original, with the posterior of the parameters
in the model actually simulated. |
program |
Which program should be used for the MCMC simulation. Possible values are "brugs","openbugs","ob" (openBUGS), "winbugs","wb" (WinBUGS). |
... |
Additional arguments passed on to bugs. |
object |
A MethComp object |
alpha |
1 minus the the confidence level |
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 mcmc.list object of the relevant parametes, i.e. the
posteriors of the conversion aprametrs and the variance components transformed
to the scales of each of the methods.
Furthermore, the object have the following attibutes:
random |
Character vector indicatin which renadom effects ("ir","mi") were included in the model. |
methods |
Characater vector with the method names. |
data |
The dataframe used in the analysis. This is
use in plot.MethComp when plotting points. |
mcmc.par |
A list giving the number of chains etc. used to generate the object. |
original |
If org=TRUE, an mcmc.list object
with the posterior of the original model parameters, i.e.
the variance components and the unidentifiable mean parameters. |
If code.only==TRUE, a list containing the initial values is
generated.
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,
plot.MethComp,
print.MethComp
data( ox )
str( ox )
MethComp( ox, code.only=TRUE, bugs.code.file="ox-ex.bug", random=c("mi") )
shell( "type bugsWD\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.
# options( bugs.directory="c:/Program Files/WinBUGS14/" )
# options( bugs.directory="c:/Stat/Bugs/WinBUGS14/")
# ox.res <- MethComp( ox, random=c("mi","ir"), n.iter=100 )
data( ox.MC )
str( ox.MC )
print( ox.MC )