Meth.sim {MethComp}

Simulate a dataframe containing replicate measurements on the same items using different methods.

Description

Simulates a dataframe representing data from a method comparison study. It is returned as a Meth object.

Usage

Meth.sim(
  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
)

Arguments

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. If a vector length Ni is given, the values of that vector will be used as "true" means.

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.

Details

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.

Value

A Meth object, i.e. dataframe with columns meth, item, repl and y, representing results from a method comparison study.

Author(s)

Lyle Gurrin, University of Melbourne, http://www.epi.unimelb.edu.au/about/staff/gurrin-lyle

Bendix Carstensen, Steno Diabetes Center, http://BendixCarstensen.com

See Also

summary.Meth, plot.Meth, MCmcmc

Examples

  Meth.sim( Ni=4, Nr=3 )
  xx <- Meth.sim( Nm=3, Nr=5, nr=2, alpha=1:3, beta=c(0.7,0.9,1.2), m.thin=0.7 )
  summary( xx )
  plot( xx )