MCPtest: Perform model-based multiple contrast test

Description

Perform a multiple contrast test with model-based (optimal) contrasts for the dose variable (or contrasts specified via contMat) and possible additional covariates.

Usage

MCPtest(formula, data, models, addCovars = ~1, 
        alpha = 0.025, contMat = NULL, critV = NULL, pVal = TRUE,
        alternative = c("one.sided", "two.sided"),
        direction = c("increasing", "decreasing"),
        na.action = na.fail, mvtcontrol = mvtnorm.control(),
        std = TRUE, off, scal)

Arguments

formula

A formula object specifying the response and the dose variable (in the form response ~ dose). Additional covariates need to be specified via the addCovars argument, see below for details.

data

Dose Response data frame containing the variables needed for performing the multiple contrast test.

models

A candidate models list. A list specifying the model shapes to be included in the contrast matrix. The names of the list entries should be equal to the names of the model functions. The list entries should be equal to prior estimates for s

addCovars

Formula specifying additional (linear) covariates

alpha

Significance level for the multiple contrast test

contMat

Optional matrix containing the optimal contrasts in the columns. If specified the code does not calculate the optimal contrasts.

critV

Critical value, if NULL, no critical value will be calculated, and the test decision will be based on the p-values. If critV = TRUE the critical value will be calculated (the test decision will be based on the critical value). If critV is equa

pVal

Optional logical determining whether p-values should be calculated, defaults to TRUE. If the critical value is supplied, p-values will not be calculated.

alternative

Character determining the alternative for the multiple contrast trend test.

direction

Character determining the trend direction of the data, which one wants to investigate (e.g., if one wants to investigate whether the response gets larger with increasing dose direction should be equal to "increasing"). When the contrast matrix

na.action

A function which indicates what should happen when the data contain NAs.

mvtcontrol

A list specifying additional control parameters for the qmvt and pmvt calls in the code, see also mvtnorm.control for details.

std

Optional logical value determining, whether standardized versions should be assumed for calculation of the optimal contrasts. If FALSE all model parameters need to be specified in the models argument (also location and scale parameters).

off

Fixed offset parameter needed when the linear in log model is used. See also documentation of the linear in log model: "linlog". When off = NULL by default (maximum dose)*0.1 is used for off.

scal

Fixed scale parameter needed when the beta model is used. See also documentation of the beta model: "betaMod". When scal = NULL by default (maximum dose)*1.2 is used for scal.

Value

An object of class MCPtest containing a list with values
contMatThe contrast matrix (either calculated by MCPtest or handed over as an argument). The contrasts are in the columns of the matrix.
tStatThe individual contrast t-statistics (with associated p-values)
alphaThe significance level
twoSideNumeric specifying, whether two-sided or one-sided testing has been performed
critValThe critical value (either calculated by MCPtest or handed over as an argument)

Examples

Run this code

## example without covariates
data(biom)
modlist <- list(emax = 0.05, linear = NULL, logistic = c(0.5, 0.1))
fit1 <- MCPtest(resp ~ dose, biom, modlist)
## now calculate critical value (but not p-values)
fit2 <- MCPtest(resp ~ dose, biom, modlist, critV = TRUE, pVal = FALSE)
## now hand over critical value
fit3 <- MCPtest(resp ~ dose, biom, modlist, critV = 2.24)

## example with covariates
data(IBScovars)
modlist <- list(emax = 0.05, linear = NULL, logistic = c(0.5, 0.1))
MCPtest(resp ~ dose, IBScovars, modlist, addCovars = ~gender)

## example with contrast matrix handed over
data(biom)
## calculate a contrast matrix
mu1 <- c(1, 2, 2, 2, 2)                      
mu2 <- c(1, 1, 2, 2, 2)                      
mu3 <- c(1, 1, 1, 2, 2)                      
mMat <- cbind(mu1, mu2, mu3)              
dimnames(mMat)[[1]] <- sort(unique(biom$dose))  
pM <- planMM(muMat = mMat, doses = doses, n = 20, cV = FALSE)
## perform MCP analysis
MCPtest(resp~dose, data = biom, contMat = pM$contMat)

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