spoD: spoD: Determine spontaneous response level or the optimal...
In toxtestD: Experimental design for binary toxicity tests

Description Usage Arguments Value Author(s) References Examples

The function "spoD" offers two services. In the planning process the number of individuals to test under control conditions is calculated, together with a proposal for partitioning the total data set into subgroups in order to identify the amount of biological variation between experiments. In the analysis process, the spontaneous lethality together with its 95% confidence interval and the biological variation are computed from the users data.

 spoD(n = 500, SL.p = 5, SLmin = NA, SLmax = NA, 
      bio.sd.p = 2.008, maxCI = 2.5, 
      analysis = FALSE, SLdataset = NA,
      print.result = "01_spontaneous lethality.txt")

`n`	maximally possible number (integer) of test organisms. Limiting this number is necessary to avoid non-essential calculations and thereby save computing time. The program will invite the user to increase the number if the number is not high enough to estimate the SL with the specified precision.
`SL.p`	A rough guess of the spontaneous lethality (SL) in %. It is possible to specify SL.p either as single number or as an interval between 0-100% by using SLmin and SLmax. At least SL.p or (SLmin,SLmax) must be specified.
`SLmin`	see SL.p
`SLmax`	see SL.p
`bio.sd.p`	(optional): an assumption about the biological contribution the standard deviation of the estimated SL The default of 2.008% holds for the Fish Embryo Toxicity test. The optimal number of partitions of the sample under control conditions will be determined using the bio.sd.p specified.
`maxCI`	the maximally accepted absolute difference in percent between mean SL and its confidence limits. Default: 2.5%.
`analysis`	defaults to FALSE, indicating that the function does planning. To analyze an SLdataset as described below, choose analysis=TRUE.
`SLdataset`	the data frame containing the spontaneous data to analyze. It has columns titled "n" and "bearer". Column "n" contains the total number of observations, column "bearer" contains the number of organisms which are carriers (in the case of FET the counts of dead or lethal malformed eggs). Each row contains the outcome from one experimental run.
`print.result`	If omitted, the result is written to "01_spontaneous lethality.txt" in the calling directory, if a file name is given, the result is written to that file, if FALSE, nothing is written

if analyse=FALSE:

`targetSL`	assumed spontaneous lethality in %, typically close or identical to `SL.p`
`ntarget`	total number of organisms to test, based on the point estimate `SL.p`
`optnum`	optimal number of separate sub-tests, based on `SL.p`
`nopt`	number of organisms per sub-test, based on `SL.p`
`maxSL`	spontaneous lethality associated with `maxn`
`Intmin`	lower limit of interval for assumed spontaneous lethality
`Intmax`	upper limit of interval for assumed spontaneous lethality
`maxn`	maximal number of organisms to test, if an interval was given for the assumed spontaneous lethality
`optmax`	optimal number of separate sub-tests, based on `Int.min,Int.max`
`noptmax`	number of organisms per sub-test, based on `Int.min,Int.max`

with analyse=TRUE:

`SL`	estimated spontaneous lethality in %
`CIlo`	lower limit of 95% confidence interval for `SL`
`CIup`	upper limit of 95% confidence interval for `SL`
`sdSL`	standard deviation of `SL`

Nadia Keddig & Werner Wosniok

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

 #spontaneous lethality

  #1a: planning
  spoD(n=600,SL.p=3.5,SLmin=NA,SLmax=NA,bio.sd.p=2.008)
  spoD(n=600,SL.p=NA,SLmin=3,SLmax=4)
  spoD(n=600,SL.p=3.5,SLmin=NA,SLmax=NA,bio.sd.p=2.008,print.result="spoDa.txt")
  spoD(n=600,SL.p=3.5,SLmin=NA,SLmax=NA,bio.sd.p=2.008,print.result=FALSE)

  #1b: analysis
  SLdataset <- data.frame(n=rep(60,times=4),bearer=c(1,5,8,3))
  spoD(analysis=TRUE,SLdataset=SLdataset)
  spoD(analysis=TRUE,SLdataset=SLdataset,print.result="spoDb.txt")
  spoD(analysis=TRUE,SLdataset=SLdataset,print.result=FALSE)