makeplot: Function to create balance plot for a specified diagnostic....
In confoundr: Diagnostics for Confounding of Time-Varying and Other Joint Exposures

Description Usage Arguments Value Examples

Function to create balance plot for a specified diagnostic. Takes input from balance() or apply.scope() or diagnose().

makeplot(input, diagnostic, approach, metric = "SMD", censoring, scope,
  average.over = NULL, stratum = NULL, label.exposure = "A",
  label.covariate = "C", lbound = -1, ubound = 1, ratio = 2,
  text.axis.title = 8, text.axis.y = 6.5, text.axis.x = 6.5,
  text.strip.y = 10, text.strip.x = 10, point.size = 0.75,
  zeroline.size = 0.1, refline.size = 0.1, refline.limit.a = -0.25,
  refline.limit.b = 0.25, panel.spacing.size = 0.75,
  axis.title = NULL, label.width = 15, grouptype = "none",
  shapetype = NULL, colourtype = NULL, colour_palette = "Set1",
  legend.title = "", legend.position = "bottom", text.legend = NULL)

`input`	output from balance() or diagnose() or apply.scope()
`diagnostic`	diagnostic of interest e.g. 1, 2, or 3
`approach`	adjustment method e.g. "none" or "weight" or "stratify"
`metric`	scale e.g. "D" for mean difference, "SMD" for standardized mean difference
`censoring`	use censoring indicators/weights e.g. "yes" or "no"
`scope`	report the entire trellis e.g. "all", the diagonal e.g. "recent", or a summary e.g. "average"
`average.over`	level of summary for average e.g. "values" or "history" or "time" or "distance"
`stratum`	the propensity-score stratum to plot
`label.exposure`	common label used for exposure axis in plot (default = "A")
`label.covariate`	common label used for covariate axis in plot (default = "C")
`lbound`	lower bound for mean difference or standardized mean difference (default = -1)
`ubound`	upper bound for mean difference or standardized mean difference (default = 1)
`ratio`	aspect ratio of plot (default = 2)
`text.axis.title`	font size of axis title (default = 8)
`text.axis.y`	font size of y-axis values (default = 6.5)
`text.axis.x`	font size of x-axis values (default = 6.5)
`text.strip.y`	font size of y-axis label (default = 10)
`text.strip.x`	font size of x-axis label (default = 10)
`point.size`	size of data points (default = 0.75)
`zeroline.size`	width of the line plotted at mean difference = 0 or standardized mean difference = 0 (default = 0.1)
`refline.size`	width of the lines plotted at the specified fraction of the mean difference or standardized mean difference (default = 0.1)
`refline.limit.a`	position of the lower reference line, specified as a fraction of the mean difference or standardized mean difference (default = -0.25)
`refline.limit.b`	position of the upper reference line, specified as a fraction of the mean difference or standardized mean difference (default = 0.25)
`panel.spacing.size`	space between each panel in the plot (default = 0.75)
`axis.title`	main title for plot (optional)
`label.width`	width of labels in plot (default = 15)
`grouptype`	the type of grouping variable "shape" or "colour"
`shapetype`	the variable name to assign a shape scale, either "exposure" or "history"
`colourtype`	the variable name to assign a color scale, either "exposure" or "history"
`colour_palette`	the palette used for scale_brewer. "Set1" is the default. See documentation for scale_brewer for other options: https://ropensci.github.io/plotly/ggplot2/scale_brewer.html.
`legend.title`	title for legend (optional)
`legend.position`	position of legend (default = "bottom")
`text.legend`	text to include in legend (optional)

A plot object. The trellised format is automatically chosen based on the format of the input dataframe, determined by balance() or apply.scope().

# Simulate the output of balance()
E <- as.numeric(rep(1,15))
H <- as.character(c(rep("H",3), rep("H0",6), rep("H01",6)))
name.cov <- as.character(c("l","m","n","l","l","m","m","n","n",
                           "l","l","m","m","n","n"))
time.exposure <- as.numeric(c(rep(0,3), rep(1,6), rep(2,6)))
time.covariate <- as.numeric(c(0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1))
D <- as.numeric(rnorm(15, 0.008401823, 0.1229099))
SMD <- as.numeric(rnorm(15, 0.01233356, 0.2696507))
N <- as.numeric(c(27,24,9,18,25,16,26,6,9,18,17,16,17,6,6))
Nexp <- as.numeric(c(14,12,4,9,12,8,13,3,5,9,8,8,9,3,3))

mytable <- data.frame(E, H, name.cov, time.exposure,
                      time.covariate, D, SMD, N, Nexp)

# Run the balance() function
myplot <- makeplot (input=mytable,
                    diagnostic=1,
                    approach="none",
                    censoring="no",
                    scope="all",
                    metric="SMD"
                    )