plot.gformula_binary_eof function

Plot method for objects of class "gformula_binary_eof"

This function generates graphs of the mean simulated vs. observed values at each time point of the time-varying covariates under the natural course. For categorical covariates, the observed and simulated probability of each level are plotted at each time point.

## S3 method for class 'gformula_binary_eof'
plot(
  x,
  covnames = NULL,
  ncol = NULL,
  nrow = NULL,
  common.legend = TRUE,
  legend = "bottom",
  xlab = NULL,
  ylab_cov = NULL,
  ...
)

Arguments

• x: Object of class "gformula_binary_eof".
• covnames: Vector of character strings specifying the names of the time-varying covariates to be plotted. The ordering of covariates given here is used in the plot grid. Time-varying covariates of type "categorical time" cannot be included. By default, this argument is set equal to the covnames argument used in gformula_binary_eof, where covariates of type "categorical time" are removed.
• ncol: Number of columns in the plot grid. By default, two columns are used when there is at least two plots.
• nrow: Number of rows in the plot grid. By default, a maximum of six rows is used and additional plots are included in subsequent pages.
• common.legend: Logical scalar indicating whether to include a legend. The default is TRUE.
• legend: Character string specifying the legend position. Valid values are "top", "bottom", "left", "right", and "none". The default is "bottom".
• xlab: Character string for the x axes of all plots. By default, this argument is set to the time_name argument specified in gformula_binary_eof.
• ylab_cov: Vector of character strings for the y axes of the plots for the covariates. This argument must be the same length as covnames. The i-th element of this argument corresponds to the plot for the i-th element of covnames.
• ...: Other arguments, which are passed to ggarrange.

Returns

An object of class "ggarrange". See documentation of ggarrange.

Examples

## Estimating the effect of threshold interventions on the mean of a binary
## end of follow-up outcome

outcome_type <- 'binary_eof'
id <- 'id_num'
time_name <- 'time'
covnames <- c('cov1', 'cov2', 'treat')
outcome_name <- 'outcome'
histories <- c(lagged, cumavg)
histvars <- list(c('treat', 'cov1', 'cov2'), c('cov1', 'cov2'))
covtypes <- c('binary', 'zero-inflated normal', 'normal')
covparams <- list(covmodels = c(cov1 ~ lag1_treat + lag1_cov1 + lag1_cov2 +
                                  cov3 + time,
                                cov2 ~ lag1_treat + cov1 + lag1_cov1 +
                                  lag1_cov2 + cov3 + time,
                                treat ~ lag1_treat + cumavg_cov1 +
                                  cumavg_cov2 + cov3 + time))
ymodel <- outcome ~  treat + cov1 + cov2 + lag1_cov1 + lag1_cov2 + cov3
intervention1.treat <- list(static, rep(0, 7))
intervention2.treat <- list(threshold, 1, Inf)
int_descript <- c('Never treat', 'Threshold - lower bound 1')
nsimul <- 10000
ncores <- 2

gform_bin_eof <- gformula(obs_data = binary_eofdata,
                          outcome_type = outcome_type, id = id,
                          time_name = time_name, covnames = covnames,
                          outcome_name = outcome_name, covtypes = covtypes,
                          covparams = covparams, ymodel = ymodel,
                          intervention1.treat = intervention1.treat,
                          intervention2.treat = intervention2.treat,
                          int_descript = int_descript, histories = histories,
                          histvars = histvars, basecovs = c("cov3"),
                          seed = 1234, parallel = TRUE, nsamples = 5,
                          nsimul = nsimul, ncores = ncores)
plot(gform_bin_eof)

See Also

gformula_binary_eof