DynNom (version 5.0.2)

DNbuilder: Publishing a dynamic nomogram

Description

DNbuilder is a generic function which builds required scripts to publish a dynamic nomogram on a web server such as the https://www.shinyapps.io/. This application can then access through a URL and be used independent of R. DNbuilder supports a large number of model objects from a variety of packages.

Usage

DNbuilder(model, data = NULL, clevel = 0.95, m.summary = c("raw", "formatted"),
  covariate = c("slider", "numeric"), ptype = c("st", "1-st"),
  DNtitle = NULL, DNxlab = NULL, DNylab = NULL, DNlimits = NULL,
  KMtitle = NULL, KMxlab = NULL, KMylab = NULL)
DNbuilder.core(model, data, clevel, m.summary, covariate,
  DNtitle, DNxlab, DNylab, DNlimits)
DNbuilder.surv(model, data, clevel, m.summary, covariate,
  ptype, DNtitle, DNxlab, DNylab, KMtitle, KMxlab, KMylab)

Value

A new folder called 'DynNomapp' will be created in the current working directory which contains all the required scripts to deploy this dynamic nomogram on a host server such as the https://www.shinyapps.io/. This folder includes ui.R, server.R, global.R and data.RData which needs to publish the app. A user guide text file (README.txt) will be also added to explain how to deploy the app using these files.

Arguments

model

an lm, glm, coxph, ols, Glm, lrm, cph, mgcv::gam or gam::gam model objects.

data

a dataframe of the accompanying dataset for the model (if required).

clevel

a confidence level for constructing the confidence interval. If not specified, a 95% level will be used.

m.summary

an option to choose the type of the model output represented in the 'Summary Model' tab. "raw" (the default) returns an unformatted summary of the model; "formatted" returns a formatted table of the model summary using stargazer package.

covariate

an option to choose the type of input control widgets used for numeric values. "slider" (the default) picks out sliderInput; "numeric" picks out numericInput.

ptype

an option for coxph or cph model objects to choose the type of plot which displays in "Survival plot" tab. "st" (the default) returns plot of estimated survivor probability (S(t)). "1-st" returns plot of estimated failure probability (1-S(t)).

DNtitle

a character vector used as the app's title. If not specified, "Dynamic Nomogram" will be used.

DNxlab

a character vector used as the title for the x-axis in "Graphical Summary" tab. If not specified, "Probability" will be used for logistic model and Cox proportional model objects; or "Response variable" for other model objects.

DNylab

a character vector used as the title for the y-axis in "Graphical Summary" tab (default is NULL).

DNlimits

a vector of 2 numeric values used to set x-axis limits in "Graphical Summary" tab. Note: This also removes the 'Set x-axis ranges' widget in the sidebar panel.

KMtitle

a character vector used as KM plot's title in "Survival plot" tab. If not specified, "Estimated Survival Probability" for ptype = "st" and "Estimated Probability" for ptype = "1-st" will be used.

KMxlab

a character vector used as the title for the x-axis in "Survival plot" tab. If not specified, "Follow Up Time" will be used.

KMylab

a character vector used as the title for the y-axis in "Survival plot" tab. If not specified, "S(t)" for ptype = "st" and "F(t)" for ptype = "1-st" will be used.

Author

Amirhossein Jalali, Davood Roshan, Alberto Alvarez-Iglesias, John Newell

Maintainer: Amirhossein Jalali <[e-mail]>

Please cite as:

Jalali, A., Roshan, D., Alvarez-Iglesias, A., Newell, J. (2019). Visualising statistical models using dynamic nomograms. R package version 5.0.

References

Banks, J. 2006. Nomograms. Encyclopedia of Statistical Sciences. 8.
Easy web applications in R. https://shiny.rstudio.com/
Frank E Harrell Jr (2017). rms: Regression Modeling Strategies. R package version 4.5-0. https://CRAN.R-project.org/package=rms/

See Also

DynNom, getpred.DN

Examples

if (FALSE) {
# Simple linear regression models
fit1 <- lm(uptake ~ Plant + conc + Plant * conc, data = CO2)
DNbuilder(fit1)

t.data <- datadist(swiss)
options(datadist = 't.data')
ols(Fertility ~ Agriculture + Education + rcs(Catholic, 4), data = swiss) %>%
  DNbuilder(clevel = 0.9, m.summary="formatted")

# Generalized regression models
fit2 <- glm(Survived ~ Age + Class + Sex,
  data = as.data.frame(Titanic), weights = Freq, binomial("probit"))
DNbuilder(fit2, DNtitle="Titanic", DNxlab = "Probability of survival")

counts <- c(18, 17, 15, 20, 10, 20, 25, 13, 12)
outcome <- gl(3, 1, 9)
treatment <- gl(3, 3)
d <- datadist(treatment, outcome)
options(datadist = "d")
Glm((2 * counts) ~ outcome + treatment, family = poisson(),
  data = data.frame(counts, outcome, treatment)) %>%
  DNbuilder()

# Proportional hazard models
coxph(Surv(time, status) ~ age + strata(sex) + ph.ecog, data = lung) %>%
  DNbuilder()

data.kidney <- kidney
data.kidney$sex <- as.factor(data.kidney$sex)
levels(data.kidney$sex) <- c("male", "female")
coxph(Surv(time, status) ~ age + strata(sex) + disease, data.kidney) %>%
  DNbuilder(ptype = "1-st")

d <- datadist(veteran)
options(datadist = "d")
fit3 <- cph((Surv(time/30, status)) ~ rcs(age, 4) * strat(trt) + diagtime +
  strat(prior) + lsp(karno, 60), veteran)
DNbuilder(fit3, DNxlab = "Survival probability",
  KMtitle="Kaplan-Meier plot", KMxlab = "Time (Days)", KMylab = "Survival probability")

# Generalized additive models
mgcv::gam(Fertility ~ s(Agriculture) + Education + s(Catholic), data=swiss) %>%
  DNbuilder(DNlimits = c(0, 110), m.summary="formatted")

fit4 <- gam::gam(Fertility ~ Education + Catholic + s(Agriculture), fit=FALSE, data=swiss)
DNbuilder(fit4)
}
if (interactive()) {
  data(rock)
  lm(area~I(log(peri)), data = rock) %>%
    DNbuilder()
}