Nothing

```
peakSignificance <-
function(power_o, lambda, power_e, times, frequencies, peakFreq, peakPower, accuracy){
#Number of trials for estimation of false alarm probability P.
#The variance of the estimator will be P(1-P)/trials and at most 0.25/trials
trials = max(10,ceiling(0.25/accuracy^2));
#Preliminary preparations
series_size = length(times);
times = sort(times);
time_step = (times[series_size] - times[1])/(series_size-1);
EpeakPower = ps_ouss(peakFreq, power_o=power_o, lambda=lambda, power_e=power_e, time_step=time_step, series_size=series_size);
Epowers = ps_ouss(frequencies, power_o=power_o, lambda=lambda, power_e=power_e, time_step=time_step, series_size=series_size);
countPositives = 0;
# countPositivesLocal = 0;
# DEPRECATED: ONLY VALID IN THE INFINITE TIME SERIES LIMIT
#Generate all exponentially distributed variables corresponding to power estimates in a hypothetical periodogram
NF = length(frequencies);
rexps = sapply(1:NF, FUN=function(m){ if(Epowers[m]==0){ return(rep(0,trials)); }else{ return(rexp(n=trials,rate=1/Epowers[m])); } } );
for(m in 1:length(frequencies)){
if(Epowers[m]==0){
rexps[,m] = rep(0, trials);
}else{
rexps[,m] = rexp(n=trials, rate=1/Epowers[m]);
}
}
# DEPRECATED: ONLY VALID IN THE INFINITE TIME SERIES LIMIT
#Evaluate trial periodograms, keeping track of how many ended up having peaks at least as extreme as the case given
for(n in 1:trials){
m = which.max(rexps[n,]); # detect peak in current trial
r_peakFreq = frequencies[m];
r_peakPower = rexps[n,m];
if(r_peakPower^2/Epowers[m] >= peakPower^2/EpeakPower){
countPositives = countPositives + 1;
}
# if(r_peakPower/Epowers[m] >= peakPower/EpeakPower){
# countPositivesLocal = countPositivesLocal + 1;
# }
}
# Alternative:
# generate OUSS series with given parameters, calculate periodograms and test for the presence of 'more extreme' peaks
# for(n in 1:trials){
# spectrum = LombScarglePeriodogram(times, generate_ouss(times,0,power_o=power_o,lambda=lambda,power_e=power_e),frequencies);
# m = which.max(spectrum$powers); # detect peak in current trial periodogram
# r_peakFreq = frequencies[m];
# r_peakPower = (spectrum$powers)[m];
# if((r_peakPower^2/Epowers[m] >= peakPower^2/EpeakPower){
# countPositives = countPositives + 1;
# }
# if(r_peakPower/Epowers[m] >= peakPower/EpeakPower){
# countPositivesLocal = countPositivesLocal + 1;
# }
# }
#Estimated false alarm probability is fraction of random peridograms with "more extreme" peaks than case given
# return(list(P=countPositives/trials,
# Plocal=countPositivesLocal/trials));
return(countPositives/trials)
}
```

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