![]() However, it is returning the same values of peak correlation and lag repeated throughout output dataframe. Then I used following function to find peak correlation: Find_Abs_Max_CCF = maxcor-maxcor*e & absres$abscor % group_by(files) %>% group_map(~Find_Abs_Max_CCF(datframe$f, dataframe$m, 0.05)) #obtaining observations of first file entry I have so far could measure the peak cross-correlation of the files individually: file1 % filter(file = unique(dataframe$`Begin File`)) ![]() More specifically I want the output to be a dataframe with three columns: recording file names, peak correlation score between female and male, and the lag value (at which peak correlation occurred). Installation: PC: Unzip the mod and put the folder named 'game' from the mod in your 'CorruptionTime-0.03-pc' folder. ACCF was converted to a Multi-donor Trust fund in 2017 with contributions from. I want to find at which lags the female and male are most correlated for all different recordings. Corruption in Brazil is endemic and has increased since the end of the military transition regime in 1985, a year marked by an indirect presidential election, which was followed by the 1988. The AfDB established the ACCF in April 2014 with an initial contribution of 4.725 million from the Government of Germany to support African countries build their resilience to the negative impacts of climate change and transition to sustainable low-carbon growth. ![]() The 'files' column represent recording file names, 'Time' represents discretised time bins of 0.1 seconds, the 'Male' and 'Female' column represents whether the male and female are calling (1) or not (0) during that time bin.
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