qgcomp (version 2.0.0)

plot.qgcompfit: plot.qgcompfit: default plotting method for a qgcompfit object

Description

Plot a quantile g-computation object. For qgcomp.noboot, this function will create a butterfly plot of weights. For qgcomp.boot, this function will create a box plot with smoothed line overlaying that represents a non-parametric fit of a model to the expected outcomes in the population at each quantile of the joint exposures (e.g. '1' represents 'at the first quantile for every exposure')

Usage

# S3 method for qgcompfit
plot(
  x,
  suppressprint = FALSE,
  pointwisebars = TRUE,
  modelfitline = TRUE,
  modelband = TRUE,
  flexfit = TRUE,
  pointwiseref = 1,
  ...
)

Arguments

x

"qgcompfit" object from `qgcomp.noboot`, `qgcomp.boot`, `qgcomp.cox.noboot`, `qgcomp.cox.boot`, `qgcomp.zi.noboot` or `qgcomp.zi.boot` functions

suppressprint

If TRUE, suppresses the plot, rather than printing it by default (it can be saved as a ggplot2 object (or list of ggplot2 objects if x is from a zero- inflated model) and used programmatically) (default = FALSE)

pointwisebars

(boot.gcomp only) If TRUE, adds 95% error bars for pointwise comparisons of E(Y|joint exposure) to the smooth regression line plot

modelfitline

(boot.gcomp only) If TRUE, adds fitted (MSM) regression line of E(Y|joint exposure) to the smooth regression line plot

modelband

If TRUE, adds 95% prediction bands for E(Y|joint exposure) (the MSM fit)

flexfit

(boot.gcomp only) if TRUE, adds flexible interpolation of predictions from underlying (conditional) model

pointwiseref

(boot.gcomp only) integer: which category of exposure (from 1 to q) should serve as the referent category for pointwise comparisons? (default=1)

...

unused

See Also

qgcomp.noboot, qgcomp.boot, and qgcomp

Examples

# NOT RUN {
set.seed(12)
dat <- data.frame(x1=(x1 <- runif(100)), x2=runif(100), x3=runif(100), z=runif(100),
                  y=runif(100)+x1+x1^2)
ft <- qgcomp.noboot(y ~ z + x1 + x2 + x3, expnms=c('x1','x2','x3'), data=dat, q=4)
ft
plot(ft)
# examining fit
plot(ft$fit, which=1) # residual vs. fitted is not straight line!

# using non-linear outcome model
ft2 <- qgcomp.boot(y ~ z + x1 + x2 + x3 + I(x1*x1), expnms=c('x1','x2','x3'), 
data=dat, q=4, B=10)
ft2
plot(ft2$fit, which=1) # much better looking fit diagnostics suggests
# it is better to include interaction term for x
plot(ft2) # the msm predictions don't match up with a smooth estimate
# of the expected outcome, so we should consider a non-linear MSM
# using non-linear marginal structural model
ft3 <- qgcomp.boot(y ~ z + x1 + x2 + x3 + I(x1*x1), expnms=c('x1','x2','x3'), 
data=dat, q=4, B=10, degree=2)
# plot(ft3$fit, which=1) - not run - this is identical to ft2 fit
plot(ft3) # the MSM estimates look much closer to the smoothed estimates
# suggesting the non-linear MSM fits the data better and should be used
# for inference about the effect of the exposure
# }