dataPrep: Reformat a user-provided dataframe in a generic form...

In ebmtnprof/rties: Modeling Interpersonal Dynamics

Reformat a user-provided dataframe in a generic form appropriate for rties modeling

Description

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.

Usage

dataPrep(
  basedata,
  dyadId,
  personId,
  obs_name,
  dist_name,
  time_name,
  time_lag = NULL,
  lagMax = NULL
)

Arguments

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).

Value

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

Examples

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) 
 

ebmtnprof/rties documentation built on Aug. 25, 2022, 7:47 p.m.