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Plot of the covariates' effects

Source: R/covariates_plot.R
covariates_plot.Rd

covariates_plot() produces a plot of the covariates' effects (\(\hat\beta\)) with confidence intervals, or of the Hazard Ratios (\(\exp(\hat\beta)\)) with confidence intervals.

Usage

covariates_plot(
  fitted_model,
  confidence_lev = 0.95,
  plot_options = list(),
  ...
)

Arguments

fitted_model

A list returned by the function fit2ts or fit1ts.

confidence_lev

The level of confidence for the CIs. Default is 0.95 (\(\alpha = 0.05\)).

plot_options

A list of options for the plot:

  • HR A Boolean. If TRUE the HRs with their CIs will be plotted. Default is FALSE (plot the beta with their CIs).

  • symmetric_ci A Boolean. Default is TRUE. If a plot of the HRs is required (HR == TRUE), then plot symmetrical Confidence Intervals, based on the SEs for the HRs calculated by delta method. If FALSE, then CIs are obtained by exponentiating the CIs for the betas.

  • main The title of the plot.

  • ylab The label for the y-axis.

  • ylim A vector with two elements defining the limits for the y-axis.

  • col_beta The color for the plot of the covariates' effects.

  • pch The symbol for plotting the point estimates.

  • cex_main The magnification factor for the main of the plot.

...

further arguments passed to plot()

Value

A plot of the covariates' effects. The different covariates are plotted on the x-axis, and on the y-axis the effects on the coefficient- or on the HR-scale are plotted. The main estimate is represented by a point and the CIs are added as vertical bars.

Examples

# Create some fake data - the bare minimum
id <- 1:20
u <- c(5.43, 3.25, 8.15, 5.53, 7.28, 6.61, 5.91, 4.94, 4.25, 3.86, 4.05, 6.86,
       4.94, 4.46, 2.14, 7.56, 5.55, 7.60, 6.46, 4.96)
s <- c(0.44, 4.89, 0.92, 1.81, 2.02, 1.55, 3.16, 6.36, 0.66, 2.02, 1.22, 3.96,
       7.07, 2.91, 3.38, 2.36, 1.74, 0.06, 5.76, 3.00)
ev <- c(1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1)
x1 <- c(0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0)

fakedata <- as.data.frame(cbind(id, u, s, ev, x1))
covs <- subset(fakedata, select = c("x1"))
fakedata2ts <- prepare_data(u = fakedata$u,
                            s_out = fakedata$s,
                            ev = fakedata$ev,
                            ds = .5,
                            individual = TRUE,
                            covs = covs)
#> `s_in = NULL`. I will use `s_in = 0` for all observations.
#> `s_in = NULL`. I will use `s_in = 0` for all observations.
# Fit a fake model - not optimal smoothing
fakemod <- fit2ts(fakedata2ts,
                  optim_method = "grid_search",
                  lrho = list(seq(1 ,1.5 ,.5),
                              seq(1 ,1.5 ,.5)))
# Covariates plot with default options
covariates_plot(fakemod)

#> [1] 1

# Plot the hazard ratios instead
covariates_plot(fakemod,
                plot_options = list(
                HR = TRUE))

#> [1] 1

# Change confidence level
covariates_plot(fakemod,
                confidence_lev = .99)

#> [1] 1