`plotErrorModel`

creates a plot showing the systematic error model.

plotErrorModel( logRr, seLogRr, trueLogRr, title, legacy = FALSE, fileName = NULL )

logRr | A numeric vector of effect estimates on the log scale. |
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seLogRr | The standard error of the log of the effect estimates. Hint: often the standard error = (log(<lower bound 95 percent confidence interval>) - log(<effect estimate>))/qnorm(0.025). |

trueLogRr | The true log relative risk. |

title | Optional: the main title for the plot |

legacy | If true, a legacy error model will be fitted, meaning standard deviation is linear on the log scale. If false, standard deviation is assumed to be simply linear. |

fileName | Name of the file where the plot should be saved, for example 'plot.png'.
See the function |

A Ggplot object. Use the `ggsave`

function to save to file.

Creates a plot with the true effect size on the x-axis, and the mean plus and minus the standard deviation shown on the y-axis. Also shown are simple error models fitted at each true relative risk in the input.

data <- simulateControls(n = 50 * 3, mean = 0.25, sd = 0.25, trueLogRr = log(c(1, 2, 4))) plotErrorModel(data$logRr, data$seLogRr, data$trueLogRr)