setD: setD: Determining the optimal number of objects per treatment
In toxtestD: Experimental design for binary toxicity tests

Description Usage Arguments Value Author(s) References Examples

"setD" estimates the minimal sample size given the users precision requirements including the accepted errors of the first and second type.

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setD(nmax, SL.p, immunity.p = 0, risk.type = 2, 
     target.EC.p = 10, plot = FALSE, alpha.p = 5, 
     beta.p = 20, print.result = "02.sample size.txt")

`nmax`	maximum available number of organisms that can be tested per treatment. The calculation of the optimal number will only be done if this number is high enough to generate the response of at least one organism under control conditions (nmax*SL.p > 1). Otherwise a warning message is issued.
`SL.p`	spontaneous lethality in percent. The function "spoD" can be used to calculate SL.p from own experimental data.
`immunity.p`	Immunity in percent. A population of biological objects might contain a subpopulation which shows no reaction at all (is immune). immunity.p gives the size of the subpopulation in percent of total population size. The concentration-response curve always has [100% - IY] as maximum. EC values are affected by immunity.p only if risktype=3 is used.
`risk.type`	Choose one of (1,2,3) to select a risk type (see reference for more detail): 1: Total risk (TR): The total risk is the total response expressed as percentage of affected biological units among all treated units. Spontaneous lethality and immunity are ignored. 2: Added risk (AR): The reference frame is restricted below and above by spontaneous lethality (SL) and immunity (IY). Only the response above the SL is considered as an effect. Using AR, the total response associated with a target effect of size xx and a spontaneous lethality SL is xx + SL. 3: Extra risk (ER): The reference frame is the interval from SL to (100%-IY). Using ER, the total response associated with a target effect of size xx is SL + 0.01 * xx * (100%-SL-IY).
`target.EC.p`	The target response in percent (e.g. 10% to calculate EC10). Only a single value is allowed. Note that the interpretation of target.EC depends on the risktype setting.
`plot`	There are three possibilities: plot = FALSE: no plots. plot ="single": Creates one plot showing the distributions under no treatment and under treated conditions with the optimal number of cases. Additionally, the actual rates of the type I and type II error are given. plot ="all": In addition to the "single" plot this option provides a sample size estimation for all possible target values. This gives an impression which possibilities of detection exist under the chosen conditions. This option may need a lot of computer capacity and time. It should not be activated in general.
`alpha.p`	alpha & beta: size of the type I and type II in percent. The default global settings for alpha and beta are alpha = 5 percent and beta = 20 percent. Increasing the default error rates is not recommended. The accepted error rates are global parameters and affect all calculations.
`beta.p`	see alpha.p
`print.result`	If omitted, the result is written to "02.sample size.txt" in the calling directory, if a file name is given, the result is written to that file, if FALSE, nothing is written

`number.organisms`	sample size per treatment, substance and run
`spontaneous.lethality`	assumed spontaneous lethality in %
`immunity`	assumed immunity in %
`delta.to.zero`	total response (= `target.EC` + `spontaneous.lethality`)
`risk.type`	risk type, see arguments
`target.EC`	the target response in percent
`exact.alpha`	exact alpha error when testing H0: p(response) = `spontaneous.lethality`, given the parameters assumed and number of organisms = `number.organisms`
`exact.beta`	exact beta error, given the parameters assumed and number of organisms = `number.organisms`

Nadia Keddig & Werner Wosniok

Optimal test design for binary response data: the example of the Fish Embryo Toxicity Test. Submitted.

 # sample size calculation per treatment and experimental run
 setD(nmax=350,SL.p=5.5,immunity.p=0,risk.type=2,target.EC=10,
      alpha.p=5,beta.p=20,plot="single")
 setD(nmax=350,SL.p=3,target.EC=5,plot="FALSE")
 setD(nmax=350,SL.p=3,target.EC=5,plot="FALSE",print.result="setD.txt")
 setD(nmax=350,SL.p=3,target.EC=5,plot="FALSE",print.result=FALSE)