ClusterVAR (version 0.0.8)

numberPredictableObservations: Determine the number of observations that can be predicted

Description

numberPredictableObservations is a function to determine the number of observations in a given dataset that can be predicted based on the availability of previous observations, considering a specified time-lag.

Usage

numberPredictableObservations(Data, yVars, Beep, Day = NULL, ID,
                              xContinuous = NULL, xFactor = NULL, Lags, ...)

Value

Predictable observations per subject

The number of predictable observations for each endogenous variable per subject, considering a specified time-lag.

Total predictable observations

The total number of predictable observations summed over all subjects in the dataset for each endogenous variable, considering a specified time-lag.

Arguments

Data

The data provided in a data.frame.

yVars

An integer vector specifying the position of the column(s) in dataframe Data that contain the endogenous variables (= the VAR time series).

Beep

An integer specifying the position of the column in dataframe Data that contains the time-point.

Day

Optional. An integer specifying the position of the column in dataframe Data that contains the variable that indicates the day of measurement. If Day is supplied here, measurements on the previous day are not used to predict measurements on the current day. Instead, the first Lags observations within each day are excluded from the calculation of VAR coefficients.

ID

An integer specifying the position of the column in dataframe Data that contains the ID variable for every participant.

xContinuous

Optional argument. An integer vector specifying the position of the column(s) in dataframe Data that contain the continuous exogenous variable(s), if present. Exogenous variables are also known as covariates or as moderators for the within-person mean.

xFactor

Optional argument. An integer vector specifying the position of the column(s) in dataframe Data that contain the categorical exogenous variable(s), if present. Exogenous variables are also known as covariates or as moderators for the within-person mean.

Lags

An integer or integer vector specifying the number of VAR(p) lags to consider. Needs to be a sequence of subsequent integers. The maximum number supported is Lags = 3.

...

Additional arguments passed to the function.

Author

Anja Ernst & Jonas Haslbeck

Details

This function determines the number of observations in a given dataset that can be predicted based on previous observations. For instance, in a lag-1 model, if an observation is missing, the observation at the next time-point cannot be predicted. Similarly, in a lag-2 model, if an observation is missing, the observations at the next two time-points cannot be predicted. The output gives the number of predictable observations for each of the endogenous variables that was specified under yVars. The number of predictable observations is the same for all endogenous variables.

Examples

# \donttest{
head(SyntheticData)

Obs <- numberPredictableObservations(Data = SyntheticData, yVars = 1:4,
                                      Beep = 9, Day = 10,  ID = 5, Lags = 1:3)

Obs

Obs$`Predictable observations per subject`$`1 Lag`
# }