| makeCrossCorr {rties} | R Documentation |
Calculates cross-correlations for a given variable and returns a dataframe with the largest absolute cross-correlation and its lag added for each dyad (e.g., it returns either the most negative or most positive cross-correlation, whichever is larger in absolute terms).
Description
Calculates cross-correlations for a given variable and returns a dataframe with the largest absolute cross-correlation and its lag added for each dyad (e.g., it returns either the most negative or most positive cross-correlation, whichever is larger in absolute terms).
Usage
makeCrossCorr(basedata, dyadId, personId, obs_name, dist_name)
Arguments
basedata |
The original dataframe provided by the user that includes all variables needed for an rties analysis, including potential system and control variables, etc. |
dyadId |
The name of the column in the dataframe that has the couple-level identifier. |
personId |
The name of the column in the dataframe that has the person-level identifier. |
obs_name |
The name of the column in the dataframe that has the time-varying observable (e.g., the variable for which dynamics will be assessed). |
dist_name |
The name of the column in the dataframe that has a variable that distinguishes the partners (e.g., sex, mother/daughter, etc) that is numeric and scored 0/1. |
Value
A cross-sectional version of the original dataframe with maximal absolute-value cross-correlations and their lags added for each dyad.
Examples
data <- rties_ExampleDataShort
newData <- makeCrossCorr(basedata=data, dyadId="couple", personId="person",
obs_name="dial", dist_name="female")
head(newData)