success (version 1.0.1)

interactive_plot: Plot a list of CUSUM charts (interactive)

Description

Create an interactive plot visualizing a combination of control charts which can be created using this package.

Usage

interactive_plot(x, unit_names, scale = FALSE, group_by = c("none", "unit",
  "type"), highlight = FALSE, manual_colors = c(), ...)

Value

An interactive plot will be produced in the current graphics device. For more information on the possibilities for interaction, see https://plotly.com/r/.

Arguments

x

A list with each item containing one cumulative sum chart.

unit_names

The unit names to be displayed in the interactive plot. Must be of equal length as x.

scale

Should charts be scaled with respect to their control limit/ maximum value? Default is FALSE.

group_by

Character indicating how to group the CUSUM charts in the plot. Possible values are c("none", "unit", "type"). Default is "none".

highlight

Should charts be highlighted on hover? Default is FALSE.

manual_colors

A character vector specifying which colors to use for the units in the data. By default, the "Set2" color set from palette() will be used.

...

Further plotting parameters

See Also

cgr_cusum, bk_cusum, bernoulli_cusum, funnel_plot

Examples

# \donttest{
require(survival)
#Extract data to construct CUSUM charts on
tdat <- subset(surgerydat, unit == 1 & entrytime < 365)
tdat2 <- subset(surgerydat, unit == 2 & entrytime < 365)

#Determine model parameters
followup <- 100
tcbaseh <- function(t) chaz_exp(t, lambda = 0.01)
exprfit <- as.formula("Surv(survtime, censorid) ~ age + sex + BMI")
tcoxmod <- coxph(exprfit, data= surgerydat)
exprfitber <- as.formula("(survtime <= followup) & (censorid == 1)~ age + sex + BMI")
glmmodber <- glm(exprfitber, data = surgerydat, family = binomial(link = "logit"))


#Construct the charts
cgr <- cgr_cusum(data = tdat, coxphmod = tcoxmod, cbaseh = tcbaseh, pb = TRUE)
cgr$h <- 8.29
bk <- bk_cusum(data = tdat, theta = log(2), coxphmod = tcoxmod, cbaseh = tcbaseh, pb = TRUE)
bk$h <- 6.23
bercus <- bernoulli_cusum(data = subset(surgerydat, unit == 1), glmmod = glmmodber,
followup = followup, theta = log(2))
bercus$h <- 3.36
bk2 <- bk_cusum(data = tdat2, theta = log(2), coxphmod = tcoxmod, cbaseh = tcbaseh, pb = TRUE)
bk2$h <- 6.23

#Create the plot
interactive_plot(list(cgr, bk, bercus, bk2), unit_names =
c("hosp1", "hosp1", "hosp1", "hosp2"))
# }