PsumtSim (version 0.4)

compPowerCatSelectivity: Compute number of simulated neurons with a significant effect of category using a bootstrapped F-ratio test.

Description

Compute number of simulated neurons with a significant effect of category using a bootstrapped F-ratio test.

Usage

compPowerCatSelectivity(respRates, normDistribution = FALSE, showProgress = FALSE,
numTrialsPerCat = 15, numBootIters = 1000, numRuns = 1000, alpha = 0.05)

Arguments

respRates

Vector of rates of responses in each category to be simulated.

normDistribution

TRUE if normally distributed response and background counts should be used, otherwise Poisson distributed counts are used. Default is FALSE.

showProgress

TRUE if run count should be printed. Default is FALSE.

numTrialsPerCat

Number of trials of responses and backgrounds for each category. Default is 15.

numBootIters

Number of boot iterations, passed as 'R' argument to boot function. Default is 1000.

numRuns

Number of simulations to perform followed by bootstrap testing. Default is 1000.

alpha

Significance level, alpha, to use in counting if simulated results are significant. Default is 0.05.

Value

Number of trials, out of numRuns which were detected as significant in the bootstrap testing.

References

Steinmetz, P.N. & Thorp, C.K. (2013) Testing for effects of different stimuli on neuronal firing relative to background activity. Journal of Neural Engineering, Sept. 2013.

Examples

## Not run: 
# # 
# # Compute power of the F-ratio test for category selectivity over
# # a range of plausible simulation parameters, placing results
# # in a dataframe. This computes values a subset of which were used to 
# # generate figure 5 of Steinmetz & Thorp, 2013.
# #
# 
# if (exists('res',inherits=FALSE)) rm('res')
# 
# numRuns<-1000
# 
# for (numCats in c(5,10,20,30)) {
#   for (anovaP in c(0.01,0.05)) {
#     for (meanRate in seq(0.5,10,by=0.5)) {
#       for (fracChange in c(0.1,0.25,0.5,1)) { 
#         minRate<-meanRate*(1-fracChange)
#         maxRate<-meanRate*(1+fracChange)
#         respLevels<-seq(minRate,maxRate,length.out=numCats)
#         
#         print(paste(numCats,anovaP,meanRate,fracChange))
#                 
#         numSig<-compPowerCatSelectivity(respLevels,
#                                         alpha=anovaP, numRuns=numRuns,
#                                         showProgress=TRUE)
# 
#         df1<-data.frame(numCats=numCats,
#                         fracChange=fracChange, anovaP=anovaP, meanRate=meanRate,
#                         frac=numSig/numRuns)
#       
#         if (!exists('res',inherits=FALSE)) res<-df1
#         else res<-rbind(res,df1)
#       }
#     }
#   }
# }
# 
# ## End(Not run)

#
# Example of smaller simulation corresponding to circle at 0.5 fractional 
# change in figure 5A of Steinmetz & Thorp, 2013.
#
numRuns<-5
numCats<-30
anovaP<-0.05
meanRate<-0.5
fracChange<-0.5
minRate<-meanRate*(1-fracChange)
maxRate<-meanRate*(1+fracChange)
respLevels<-seq(minRate,maxRate,length.out=numCats)
        
print(paste(numCats,anovaP,meanRate,fracChange))
                
numSig<-compPowerCatSelectivity(respLevels,
                                    alpha=anovaP, numRuns=numRuns,
                                    showProgress=TRUE)

df1<-data.frame(numCats=numCats,
                  fracChange=fracChange, anovaP=anovaP, meanRate=meanRate,
                  frac=numSig/numRuns)