plot.gformula_continuous_eof function

Plot method for objects of class "gformula_continuous_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_continuous_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_continuous_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_continuous_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_continuous_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 treatment strategies on the mean of a continuous
## end of follow-up outcome

library('Hmisc')
id <- 'id'
time_name <- 't0'
covnames <- c('L1', 'L2', 'A')
outcome_name <- 'Y'
outcome_type <- 'continuous_eof'
covtypes <- c('categorical', 'normal', 'binary')
histories <- c(lagged)
histvars <- list(c('A', 'L1', 'L2'))
covparams <- list(covmodels = c(L1 ~ lag1_A + lag1_L1 + L3 + t0 +
                                  rcspline.eval(lag1_L2, knots = c(-1, 0, 1)),
                                L2 ~ lag1_A + L1 + lag1_L1 + lag1_L2 + L3 + t0,
                                A ~ lag1_A + L1 + L2 + lag1_L1 + lag1_L2 + L3 + t0))
ymodel <- Y ~ A + L1 + L2 + lag1_A + lag1_L1 + lag1_L2 + L3
intervention1.A <- list(static, rep(0, 7))
intervention2.A <- list(static, rep(1, 7))
int_descript <- c('Never treat', 'Always treat')
nsimul <- 10000

gform_cont_eof <- gformula(obs_data = continuous_eofdata,
                           id = id, time_name = time_name,
                           covnames = covnames, outcome_name = outcome_name,
                           outcome_type = outcome_type, covtypes = covtypes,
                           covparams = covparams, ymodel = ymodel,
                           intervention1.A = intervention1.A,
                           intervention2.A = intervention2.A,
                           int_descript = int_descript,
                           histories = histories, histvars = histvars,
                           basecovs = c("L3"), nsimul = nsimul, seed = 1234)
plot(gform_cont_eof)

See Also

gformula_continuous_eof