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 risk factor to a reference

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 = 1000, 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 1000. 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 Oâ€™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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