dataPrep | R Documentation |
The dataframe must be in a specific format and include several specific variables. See the "overview_data_prep" vignette for complete details on the necessary format and follow it closely if you'd like to avoid error messages. That vignette also includes information on how to structure the data if you have two variables within people (rather than two people within dyads) or have indistinguishable dyads.
dataPrep( basedata, dyadId, personId, obs_name, dist_name, time_name, time_lag = NULL, lagMax = NULL )
basedata |
A user-provided dataframe that includes all variables needed for an rties analysis. |
dyadId |
The name of the column in the dataframe that has the dyad-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. |
time_name |
The name of the column in the dataframe that indicates sequential temporal observations. |
time_lag |
An optional argument for the number of lags for the lagged observable. If a number is provided, the observed variable is lagged that amount. The other option is to use "absMaxCC". In this case the maximum cross-correlation is found for each dyad and the lag at which that occurs is used to lag their observed variables. |
lagMax |
An optional argument for the maximum number of lags to be considered for the lagged observable. If a number is provided, it is used as the range of lags to consider. The default is 10*log10(N/m) where N is the number of observations and m the number of series (e.g., the default for the ccf function). |
The function returns a dataframe that has all the variables needed for modeling system dynamics, each renamed to a generic variable name, which are:
id = person id
dyad = dyad id
obs = observed state variable
dist1 = 0/1 variable where the 1's indicate the 1's in the original distinguishing variable
time = the variable indicating temporal sequence
dist0 = 0/1 variable where the 1's indicate the 0's in the original distinguishing variable
obs_deTrend = the observed state variable with each person's linear trend removed
p_ = all the same variables, but for a person's partner rather than themselves
data <- rties_ExampleDataShort newData <- dataPrep(basedata=data, dyadId="couple", personId="person", obs_name="dial", dist_name="female", time_name="time", time_lag=2) head(newData)
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