lwqs {lwqs}

Wrapper function for the implementaion of lagged WQS.

Description

Wrapper function for the implementaion of lagged WQS.

Usage

lwqs(
  data,
  timevar,
  wqs_parms,
  outcome,
  ID,
  rDLM_parms = list(formula = wqs ~ s(time, by = y, bs = "cr"), random = ~(1 | id))
)

Arguments

data

Data frame containing observations in long format.

timevar

Enquoted variable name identifying the repeated measure / time variable

wqs_parms

A list containing parameters to be passed to the WQS algorithm. See gWQS package for details.

outcome

An enquoted variable name identifying the outcome measure

ID

An enquoted variable name identifying the subject identifier

rDLM_parms

(optional). A list containing parameters to be passed to the GAM algorithm. See gamm4 package for details. Parameters wqs, time, by, and id (see above) are created by the lwqs function and passed to the gamm4 function automatically.

Value

The lwqs function returns a list containing final model output and time-specific model parameters.

parameters

This list contains several objects summarizing different stages of the lagged ensemble model. The first object, res, contains output from the gWQS algorithm applied to each discreet repeated measure in the overall model; see package gWQS for details. The second output, wqstime, provides the mixture index, identified as "wqs", estimated for each subject at each discrete time point. The third item, weightstime, provides the weights estimated for each predictor at each discrete time point.

plot

This list contains two plots (as grobs) which summarize output of the lwqs algorithm.

Examples

 # identify predictor variables used in mixture
mixvars=names(lwqs_data)[5:9]

model=lwqs(data=lwqs_data,
           timevar="time",
           wqs_parms=list(formula=out ~ wqs,
              data = lwqs_data,
              mix_name=mixvars,
              b1_constr = TRUE,
              b1_pos=TRUE,
              b = 5,
              q = 5,
              validation = 0,
              family = "gaussian",
              seed = 1),
              outcome="out",
              ID="ID")