DynNom (version 4.1.1)

DynNom.Glm: Dynamic Nomograms for Generalized Linear Models from the rms package

Description

DynNom.Glm displays the results of a Glm model object from the rms package as a dynamic nomogram in an 'RStudio' panel or web browser.

Usage

DynNom.Glm(model, data, clevel = 0.95, m.summary = c("raw", "formatted"),
                       covariate = c("slider", "numeric"))

Arguments

model

a Glm model object which accepts a variety of transformation functions such as asis, pol, lsp, rcs, catg, scored, strat and matrx defined in rms package.

data

dataframe containing the accompanying data

clevel

confidence level required

m.summary

The option to choose the format of the model output in the 'Summary Model' tab. If "raw" (the default) is chosen the result of summary(model) will be display while if "formatted" is chosen the model summary using the stargazer package will be displayed.

covariate

The option to choose the type of covariate(s) input control widget for numeric values. If "slider" (the default) is chosen a shiny application with slider control widgets are used while if "numeric" is chosen numeric values input controls will be displayed.

Value

A dynamic nomogram in a shiny application which recognises all the predictors in the model and uses them to build a sidebar panel. It sets up drop down menus for factors and sliders set at the mean and bounded by the range for covariates.

The individual predictions with a relative confidence interval are calculated using the predict function, displaying either graphically as an interactive plot in the Graphical Summary tab or a table in the Numerical Summary tab. A table of model output is also available in the Model Summary tab.

See Also

Glm, predict.Glm, rms

Examples

# NOT RUN {
# example 1 - a generalized linear model
set.seed(1)
x1 <- runif(200)
x2 <- sample(0:3, 200, TRUE)
x3 <- sample(0:2, 200, TRUE)

distance <- (x1 + x2 / 3 + rnorm(200)) ^ 2
d <- datadist(x1, x2)
options(datadist = "d")
data1 = data.frame(distance, x1, x2, x3)
model1 <- Glm(distance ~ x3 + rcs(x1, 4) * scored(x2), data = data1)
DynNom.Glm(model1, data1)

# example 2 - a poisson regression model
counts <- c(18, 17, 15, 20, 10, 20, 25, 13, 12)
outcome <- gl(3, 1, 9)
treatment <- gl(3, 3)
data2 = data.frame(counts, outcome, treatment)
model2 <- Glm((2 * counts) ~ outcome + treatment, family = poisson(), data = data2)
DynNom.Glm(model2, data2)
# }
# NOT RUN {
if (interactive()) {
# a Gamma regression model
clotting <- data.frame(
    u = c(5, 10, 15, 20, 30, 40, 60, 80, 100),
    lot1 = c(118, 58, 42, 35, 27, 25, 21, 19, 18),
    lot2 = c(69, 35, 26, 21, 18, 16, 13, 12, 12),
    cat = c(rep("A",5), rep("B",4)))
model3 <- Glm(lot1 ~ log(u) + cat, data = clotting, family = Gamma)
DynNom.Glm(model3, clotting)
}
# }