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Last data update: 2014.03.03

R: Calculate planning quantities for MCPMod

planMM

R Documentation

Calculate planning quantities for MCPMod

Description

Calculates the optimal model contrasts, the critical value and the contrast correlation matrix, i.e. the quantities necessary to conduct the multiple contrast test for a given candidate set of dose-response models.

Usage

planMM(models, doses, n, off = 0.1 * max(doses), scal = 1.2 * max(doses),
       std = TRUE, alpha = 0.025, twoSide = FALSE,
       control = mvtnorm.control(), cV = TRUE, muMat = NULL)

Arguments

`models`	A list of candidate models
`doses`	A numeric vector giving the doses to be administered.
`n`	The vector of sample sizes per group. In case just one number is specified, it is assumed that all group sample sizes are equal to this number.
`off`	Offset parameter for the linear in log model (default 10 perc of the maximum dose).
`scal`	Scale parameter for the beta model (default 20 perc. larger than maximum dose).
`std`	Optional logical indicating, whether standardized version of the models should be assumed.
`alpha`	Level of significance (default: 0.025)
`twoSide`	Logical indicating whether a two sided or a one-sided test should be performed. By default FALSE, so one-sided testing.
`control`	A list of options for the `pmvt` and `qmvt` functions as produced by `mvtnorm.control`
`cV`	Logical indicating whether critical value should be calculated
`muMat`	An optional matrix with means in the columns and given dimnames (dose levels and names of contrasts). If specified the `models` argument should not be specified, see examples below.

Value

An object of class planMM with the following components:

`contMat`	Matrix of optimal contrasts.
`critVal`	The critical value for the test (if calculated)
`muMat`	Matrix of (non-normalized) model means
`corMat`	Matrix of the contrast correlations.

References

Bornkamp B., Pinheiro J. C., and Bretz, F. (2009). MCPMod: An R Package for the Design and Analysis of Dose-Finding Studies, Journal of Statistical Software, 29(7), 1–23

Bretz, F., Pinheiro, J., and Branson, M. (2005), Combining Multiple Comparisons and Modeling Techniques in Dose-Response Studies, Biometrics, 61, 738–748

Pinheiro, J. C., Bornkamp, B., and Bretz, F. (2006). Design and analysis of dose finding studies combining multiple comparisons and modeling procedures, Journal of Biopharmaceutical Statistics, 16, 639–656

Examples

# Example from JBS paper
doses <- c(0,10,25,50,100,150)
models <- list(linear = NULL, emax = 25,                               
               logistic = c(50, 10.88111), exponential= 85,            
               betaMod=matrix(c(0.33,2.31,1.39,1.39), byrow=TRUE, nrow=2))
plM <- planMM(models, doses, n = rep(50,6), alpha = 0.05, scal=200)
plot(plM)

## Not run: 
# example, where means are directly specified
# doses   
dvec <- c(0, 10, 50, 100)
# mean vectors
mu1 <- c(1, 2, 2, 2)
mu2 <- c(1, 1, 2, 2)
mu3 <- c(1, 1, 1, 2)
mMat <- cbind(mu1, mu2, mu3)
dimnames(mMat)[[1]] <- dvec
planMM(muMat = mMat, doses = dvec, n = 30)

## End(Not run)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.

> library(MCPMod)
Loading required package: mvtnorm
Loading required package: lattice
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/MCPMod/planMM.Rd_%03d_medium.png", width=480, height=480)
> ### Name: planMM
> ### Title: Calculate planning quantities for MCPMod
> ### Aliases: planMM print.planMM
> ### Keywords: design
> 
> ### ** Examples
> 
> # Example from JBS paper
> doses <- c(0,10,25,50,100,150)
> models <- list(linear = NULL, emax = 25,                               
+                logistic = c(50, 10.88111), exponential= 85,            
+                betaMod=matrix(c(0.33,2.31,1.39,1.39), byrow=TRUE, nrow=2))
> plM <- planMM(models, doses, n = rep(50,6), alpha = 0.05, scal=200)
> plot(plM)
> 
> ## Not run: 
> ##D # example, where means are directly specified
> ##D # doses   
> ##D dvec <- c(0, 10, 50, 100)
> ##D # mean vectors
> ##D mu1 <- c(1, 2, 2, 2)
> ##D mu2 <- c(1, 1, 2, 2)
> ##D mu3 <- c(1, 1, 1, 2)
> ##D mMat <- cbind(mu1, mu2, mu3)
> ##D dimnames(mMat)[[1]] <- dvec
> ##D planMM(muMat = mMat, doses = dvec, n = 30)
> ## End(Not run)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>