`plot.Rd`

The various causalRisk plot methods produce paneled ggplot2 objects depicting
each set of estimates or effect measures of interest and their associated
confidence intervals. In the case of the `cumrisk`

and `cumcount`

methods,
the results are plotted over time, wheras for the `hr`

method the plot simply
displays a single data point for each hazard ratio since by construction the
ratios are constant over time.

Note that the `plot`

and `forest_plot`

methods for the `hr`

class are aliases
for each other.

```
# S3 method for cumrisk
plot(
...,
rd = FALSE,
effect_measure_type = NULL,
overlay = FALSE,
smooth = FALSE,
ncol = 2,
panel_dim = 1,
time_lab,
alpha = 0.05,
scales = "free_y",
stepribbon = TRUE,
legend_title = "Treatment \nGroups",
colors = "Dark2",
ref = 1,
boot_method = "normal"
)
# S3 method for cumcount
plot(
...,
cd = FALSE,
effect_measure_type = NULL,
overlay = FALSE,
smooth = FALSE,
ncol = 2,
panel_dim = 1,
time_lab,
alpha = 0.05,
scales = "free_y",
stepribbon = TRUE,
legend_title = "Treatment \nGroups",
colors = "Dark2",
ref = 1,
boot_method = "normal"
)
# S3 method for hr
plot(..., alpha = 0.05, legend_title = "Treatment \nGroups", colors = "Dark2")
```

- ...
Objects with an associated method for creating plots supplied as either as seperate arguments or as a list.

- rd
A flag to indicate whether the risk difference should be plotted or the individual risk functions (deprecated, now use the

`effect_measure_ttype`

parameter)- effect_measure_type
Either

`NULL`

to display the original estimates of interrest or a string identifying the type of effect measure to be displayed from among the following options for a given method.`cumrisk`

:`"RD"`

(risk difference),`"RR"`

(risk ratio),`"logRR"`

(logarithm of the risk ratio), or`"AR"`

(attributable risk)`cumcount`

:`"CD"`

(count difference) or`"CR"`

(count ratio)

Note that when a given input results object was created using bootstrap estimation and

`boot_method`

is`"log-normal"`

, then only effect measures with support on the positive real line can be used (i.e.`"RR"`

or`"CR"`

, depending on the method). See the*Cumulative risk effect measure types*and*Cumulative count effect measure types*sections for the definitions of the various effect measures.- overlay
A flag to indicate that the risk difference curves should be overlayed rather than paneled

- smooth
A flag to indicate the use of LOESS smoothing for risk difference

- ncol
The number of columns for the faceting.

- panel_dim
The number of dimensions for the faceting (either 1 or 2). Panels are faceted using the first (and if panel_dim = 2) and second elements of the labels vector in the supplied objects.

- time_lab
A numeric vector denoting the x-axis tick marks to be labeled. Default is the

`ggplot2`

default. Setting the parameter to`NULL`

suppressed labeling.- alpha
The desired significance level of the confidence intervals.

- scales
A

`ggplot2`

option for facet_grid/facet_wrap that controls the scales on facted plots. It can be fixed ("fixed"), free ("free"), or free in one dimension ("free.x", "free.y"). The default is "free.y."- stepribbon
A flag to determine whether to force the confidence intervals to be plotted using the stepribbon geom. The default is

`TRUE.`

- legend_title
A string used to title the legend, default is

`"Treatment Groups"`

- colors
Set colors for resulting ggplot object. Either a vector of strings which are interpreted as RGB codes or a sinlge string that identifies a color brewer palette. If set to

`NULL`

, the methods will use grey shades via the color brewer palette 'Dark2'.- ref
Identifies the treatment group in the supplied object(s) to be used as a referent category for risk differecence curves. Defaults to

`1`

.- boot_method
The specific bootstrap approach used to compute confidence intervals (default method = "normal" for a normal approximation on the risk scale, other choices include "log normal" for normal approximation on the log scale).

- cd
A flag to indicate whether the count difference should be plotted or the individual count functions (deprecated, now use the

`effect_measure_type`

parameter)

A `ggplot2`

object that can be further modified by the user before
plotting.

`plot(cumrisk)`

: Plot a panel of (possibly overlayed) cumulative risk functions or cumulative risk effect measures and confidence intervals.`plot(cumcount)`

: Plot a panel of (possibly overlayed) cumulative count functions or cumulative count effect measures and confidence intervals.`plot(hr)`

: Plot hazard ratios for analyses relative to their respective control groups.

Mean: \(\mathrm{E}[Y^{1}] - \mathrm{E}[Y^{0}]\)

Variance with IPW: \( \mathrm{Var}[Y^{1}] + \mathrm{Var}[Y^{0}] + \frac{2}{n} \mathrm{E}[Y^{1}] \, \mathrm{E}[Y^{0}] \)

Variance otherwise: \(\mathrm{Var}[Y^{1}] + \mathrm{Var}[Y^{0}]\)

Mean: \(\frac{\mathrm{E}[Y^{1}]}{\mathrm{E}[Y^{0}]}\)

Variance with IPW: \( \frac{\mathrm{Var}[Y^{1}]}{(\mathrm{E}[Y^{0}])^{2}} + \frac{\mathrm{Var}[Y^{0}]\,(\mathrm{E}[Y^{1}])^2} {(\mathrm{E}[Y^{0}])^{4}} + \frac{2\,(\mathrm{E}[Y^{1}])^2}{n\,(\mathrm{E}[Y^{0}])^2} \)

Variance otherwise: \( \frac{\mathrm{Var}[Y^{1}]}{(\mathrm{E}[Y^{0}])^{2}} + \frac{\mathrm{Var}[Y^{0}]\,(\mathrm{E}[Y^{1}])^2} {(\mathrm{E}[Y^{0}])^{4}} \)

Mean: \(\mathrm{log}\frac{\mathrm{E}[Y^{1}]}{\mathrm{E}[Y^{0}]}\)

Variance with IPW: \( \frac{\mathrm{Var}[Y^{1}]}{(\mathrm{E}[Y^{1}])^{2}} + \frac{\mathrm{Var}[Y^{0}]}{(\mathrm{E}[Y^{0}])^{2}} + \frac{2}{n} \)

Variance otherwise: \( \frac{\mathrm{Var}[Y^{1}]}{(\mathrm{E}[Y^{1}])^{2}} + \frac{\mathrm{Var}[Y^{0}]}{(\mathrm{E}[Y^{0}])^{2}} \)

Mean: \(\frac{\mathrm{E}[Y^{1}] - \mathrm{E}[Y^{0}]}{\mathrm{E}[Y^{1}]}\)

Variance with IPW: \( \frac{\mathrm{Var}[Y^{0}]}{(\mathrm{E}[Y^{1}])^{2}} + \frac{\mathrm{Var}[Y^{1}]\,(\mathrm{E}[Y^{0}])^{2}} {(\mathrm{E}[Y^{1}])^{4}} + \frac{2\,(\mathrm{E}[Y^{0}])^{2}}{n\,(\mathrm{E}[Y^{1}])^{2}} \)

Variance otherwise: \( \frac{\mathrm{Var}[Y^{0}]}{(\mathrm{E}[Y^{1}])^{2}} + \frac{\mathrm{Var}[Y^{1}]\,(\mathrm{E}[Y^{0}])^{2}} {(\mathrm{E}[Y^{1}])^{4}} \)

Mean: \(\mathrm{E}[Y^{1}] - \mathrm{E}[Y^{0}]\)

Variance with IPW: \( \mathrm{Var}[Y^{1}] + \mathrm{Var}[Y^{0}] + \frac{2}{n} \mathrm{E}[Y^{1}] \, \mathrm{E}[Y^{0}] \)

Variance otherwise: \(\mathrm{Var}[Y^{1}] + \mathrm{Var}[Y^{0}]\)

Mean: \(\frac{\mathrm{E}[Y^{1}]}{\mathrm{E}[Y^{0}]}\)

Variance with IPW: \( \frac{\mathrm{Var}[Y^{1}]}{(\mathrm{E}[Y^{0}])^{2}} + \frac{\mathrm{Var}[Y^{0}]\,(\mathrm{E}[Y^{1}])^2} {(\mathrm{E}[Y^{0}])^{4}} + \frac{2\,(\mathrm{E}[Y^{1}])^2}{n\,(\mathrm{E}[Y^{0}])^2} \)

Variance otherwise: \( \frac{\mathrm{Var}[Y^{1}]}{(\mathrm{E}[Y^{0}])^{2}} + \frac{\mathrm{Var}[Y^{0}]\,(\mathrm{E}[Y^{1}])^2} {(\mathrm{E}[Y^{0}])^{4}} \)