View source: R/plot_PAF_continuous.R
plot_continuous | R Documentation |
Plot hazard ratios, odds ratios or risk ratios comparing differing values of a continuous exposure to a reference level
plot_continuous(
model,
riskfactor,
data,
S = 10,
ref_val = NA,
ci_level = 0.95,
min_risk_q = 0.1,
plot_region = TRUE,
plot_density = TRUE,
n_x = 10000,
theylab = "OR",
qlist = seq(from = 0.001, to = 0.999, by = 0.001),
interact = FALSE
)
model |
A fitted model (either glm, clogit or coxph) |
riskfactor |
The string name of a continuous exposure or risk factor represented in the data and model |
data |
Data frame used to fit the model |
S |
Default 10. The integer number of random samples used to calculate average differences in linear predictors. Only relevant to set when interact=TRUE |
ref_val |
The reference value used in plotting. If left at NA, the median value of the risk factor is used |
ci_level |
Numeric. A number between 0 and 1 specifying the confidence level |
min_risk_q |
Default .1. A number between 0 and 1 representing the desired risk quantile for the continuous risk factor |
plot_region |
Default TRUE. Logical specifying whether the targeted region corresponding to an intervention setting the continuous risk factor at a quantile min_risk_q or lower is to be plotted |
plot_density |
Default TRUE. Logical specifying whether density of distribution of risk factor is to be added to the plot |
n_x |
Default 10000. How many values of riskfactor will be used to plot spline (when interact=FALSE) |
theylab |
Default "OR". Y-axis label of the plot |
qlist |
Vector of quantile values for q, corresponding to the plotted values of PAF_q for each risk factor/exposure |
interact |
Default "FALSE". Set to TRUE spline models enter as interactions. |
A ggplot2 plotting object
Ferguson, J., Maturo, F., Yusuf, S. and O’Donnell, M., 2020. Population attributable fractions for continuously distributed exposures. Epidemiologic Methods, 9(1)
library(survival)
library(splines)
model_continuous <- glm(formula = case ~ region * ns(age, df = 5) +
sex * ns(age, df = 5) + education +exercise + ns(diet, df = 3) +
alcohol + stress + ns(lipids,df = 3) + ns(waist_hip_ratio, df = 3) +
high_blood_pressure, family = "binomial", data = stroke_reduced)
plot_continuous(model_continuous,riskfactor="diet",data=stroke_reduced)
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