| sim.meth {MethComp} | R Documentation |
A dataframe is simulated that represents data from a method comparison study based on parameters specified by the user.
sim.meth( Ni = 100,
Nm = 2,
Nr = 3,
nr = Nr,
alpha = rep(0,Nm),
beta = rep(1,Nm),
mu.range = c(0, 100),
sigma.mi = rep(5,Nm),
sigma.ir = 2.5,
sigma.mir = rep(5,Nm),
m.thin = 1,
i.thin = 1 )
Ni |
The number of items (patient, animal, sample, unit etc.) |
Nm |
The number of methods of measurement. |
Nr |
The (maximal) number of replicate measurements for each (item,method) pair. |
nr |
The minimal number of replicate measurements for each
(item,method) pair. If nr<Nr, the number of replicates for
each (meth,item) pair is uniformly distributed on the points
nr:Nr, otherwise nr is ignored. Different number of
replicates is only meaningful if replicates are not linked, hence
nr is also ignored when sigma.ir>0. |
alpha |
A vector of method-specific intercepts for the linear equation relating the "true" underlying item mean measurement to the mean measurement on each method. |
beta |
A vector of method-specific slopes for the linear equation relating the "true" underlying item mean measurement to the mean measurement on each method. |
mu.range |
The range across items of the "true" mean measurement. Item means are uniformly spaced across the range. |
sigma.mi |
A vector of method-specific standard deviations for a method by item random effect. Some or all components can be zero. |
sigma.ir |
Method-specific standard deviations for the item by replicate random effect. |
sigma.mir |
A vector of method-specific residual standard deviations for a method by item by replicate random effect (residual variation). All components must be greater than zero. |
m.thin |
Fraction of the observations from each method to keep. |
i.thin |
Fraction of the observations from each item to keep. If both
m.thin and i.thin are given the thinning is by their
componentwise product. |
Data are simulated according to the following model for an observation y_mir:
y_mir = alpha_m + beta_m*(mu_i+b_ir+c_mi) + e_mir
where $b_{ir}$ is a random item by repl interaction
(with standard deviation for method m the corresponding component of the
vector sigma_ir), c_mi is a random meth
by item interaction (with standard deviation for method m the
corresponding component of the vector sigma_mi) and
e_mir is a residual error term (with standard deviation
for method $m$ the corresponding component of the vector
sigma_mir). The mu_i's are uniformly spaced
in a range specified by mu.range.
A dataframe with columns meth, item, repl and y,
representing results from a method comparison study.
Lyle Gurrin, University of Melbourne, http://www.epi.unimelb.edu.au/about/staff/gurrin-lyle, Bendix Carstensen, Steno Diabetes Center, http://www.biostat.ku.dk/~bxc
sim.meth( Ni=4, Nr=3 ) xx <- sim.meth( Nm=3, Nr=5, nr=2, alpha=1:3, beta=c(0.7,0.9,1.2), m.thin=0.7 ) tab.repl( xx ) plot.meth( xx )