planMod {DoseFinding}R Documentation

Evaluate performance metrics for fitting dose-response models


This function evaluates, the performance metrics for fitting dose-response models (using asymptotic approximations or simulations). Note that some metrics are available via the print method and others only via the summary method applied to planMod objects. The implemented metrics are

A plot method exists to summarize dose-response and dose estimations graphically.


planMod(model, altModels, n, sigma, S, doses, asyApprox = TRUE,
        simulation = FALSE, alpha = 0.025, tau = 0, p = 0.5,
        pLB = 0.25, pUB = 0.75, nSim = 100, cores = 1,
        showSimProgress = TRUE, bnds, addArgs = NULL)

## S3 method for class 'planMod'
plot(x, type = c("dose-response", "ED", "TD"),
     p, Delta, placAdj = FALSE, xlab, ylab, ...)

## S3 method for class 'planMod'
summary(object, digits = 3, len = 101,
        Delta, p, dLB = 0.05, dUB = 0.95, ...)



Character vector determining the dose-response model(s) to be used for fitting the data. When more than one dose-response model is provided the best fitting model is chosen using the AIC. Built-in models are "linlog", "linear", "quadratic", "emax", "exponential", "sigEmax", "betaMod" and "logistic" (see drmodels).


An object of class ‘⁠Mods⁠’, defining the true mean vectors under which operating characteristics should be calculated.

n, sigma, S

Either a vector ‘⁠n⁠’ and ‘⁠sigma⁠’ or ‘⁠S⁠’ need to be specified. When ‘⁠n⁠’ and ‘⁠sigma⁠’ are specified it is assumed computations are made for a normal homoscedastic ANOVA model with group sample sizes given by ‘⁠n⁠’ and residual standard deviation ‘⁠sigma⁠’, i.e. the covariance matrix used for the estimates is thus sigma^2*diag(1/n) and the degrees of freedom are calculated as sum(n)-nrow(contMat). When a single number is specified for ‘⁠n⁠’ it is assumed this is the sample size per group and balanced allocations are used.

When ‘⁠S⁠’ is specified this will be used as covariance matrix for the estimates.


Doses to use

asyApprox, simulation

Logicals determining, whether asymptotic approximations or simulations should be calculated. If multiple models are specified in ‘⁠model⁠’ asymptotic approximations are not available.

alpha, tau

Significance level for the one-sided confidence interval for model-based contrast of best dose vs placebo. Tau is the threshold to compare the confidence interval limit to. CI(MaxDCont) gives the percentage that the bound of the confidence interval was larger than tau.

p, pLB, pUB

p determines the type of EDp to estimate. pLB and pUB define the bounds for the EDp estimate. The performance metric Pr(Id-ED) gives the percentage that the estimated EDp was within the true EDpLB and EDpUB.


Number of simulations


Number of cores to use for simulations. By default 1 cores is used, note that cores > 1 will have no effect Windows, as the mclapply function is used internally.


In case of simulations show the progress using a progress-bar.


Bounds for non-linear parameters. This needs to be a list with list entries corresponding to the selected bounds. The names of the list entries need to correspond to the model names. The defBnds function provides the default selection.


See the corresponding argument in function fitMod. This argument is directly passed to fitMod.


An object of class planMod


Type of plot to produce


Additional arguments determining what dose estimate to plot, when ‘⁠type = "ED"⁠’ or ‘⁠type = "TD"⁠


When ‘⁠type = "dose-response"⁠’, this determines whether dose-response estimates are shown on placebo-adjusted or original scale

xlab, ylab

Labels for the plot (ylab only applies for ‘⁠type = "dose-response"⁠’)


Number of equally spaced points to determine the mean-squared error on a grid (cRMSE).

dLB, dUB

Which quantiles to use for calculation of lengthTDCI and lengthEDpCI. By default dLB = 0.05 and dUB = 0.95, so that this corresponds to a 90% interval.

object, digits

object: A planMod object. digits: Digits in summary output


Additional arguments (currently ignored)


Bjoern Bornkamp



See Also



## Not run: 
doses <- c(0,10,25,50,100,150)
fmodels <- Mods(linear = NULL, emax = 25,
                logistic = c(50, 10.88111), exponential= 85,
                doses = doses, addArgs=list(scal = 200),
                placEff = 0, maxEff = 0.4)
sigma <- 1
n <- rep(62, 6)*2

model <- "quadratic"
pObj <- planMod(model, fmodels, n, sigma, doses=doses,
               simulation = TRUE, 
               alpha = 0.025, nSim = 200, 
               p = 0.5, pLB = 0.25, pUB = 0.75)
## to get additional metrics (e.g. Eff-vs-ANOVA, cRMSE, lengthTDCI, ...)
summary(pObj, p = 0.5, Delta = 0.3)
plot(pObj, type = "TD", Delta=0.3)
plot(pObj, type = "ED", p = 0.5)

## End(Not run)

[Package DoseFinding version 1.0-2 Index]