View source: R/vis.mirror.pt.R

vis.mirror.pt | R Documentation |

A mirror plot for comparing two different `"probtrans"`

objects. Useful
for comparing predicted probabilities for different levels of a covariate,
or for different subgroups at some prediction time horizon.

```
vis.mirror.pt(
x,
titles,
size_titles = 5,
horizon = NULL,
breaks_x_left,
breaks_x_right,
from = 1,
cols,
ord,
xlab = "Time",
ylab = "Probability",
legend.pos = "right"
)
```

`x` |
A list of two |

`titles` |
A character vector c("Title for left", "Title for right") |

`size_titles` |
Numeric, size of the title text |

`horizon` |
Numeric, position denoting (in time) where to symmetrically mirror the plots. Default is maximum follow-up of from both plots. |

`breaks_x_left` |
Numeric vector specifying axis breaks on the left plot |

`breaks_x_right` |
Numeric vector specifying axis breaks on the right plot |

`from` |
The starting state from which the probabilities are used to plot |

`cols` |
A vector specifying colors for the different transitions;
default is a palette from green to red, when type= |

`ord` |
A vector of length equal to the number of states, specifying the
order of plotting in case type= |

`xlab` |
A title for the x-axis, default is "Time" |

`ylab` |
A title for the y-axis, default is "Probability" |

`legend.pos` |
Position of the legend, default is "right" |

A ggplot2 object.

Edouard F. Bonneville e.f.bonneville@lumc.nl

`plot.probtrans`

```
library(ggplot2)
data("aidssi")
head(aidssi)
si <- aidssi
# Prepare transition matrix
tmat <- trans.comprisk(2, names = c("event-free", "AIDS", "SI"))
# Run msprep
si$stat1 <- as.numeric(si$status == 1)
si$stat2 <- as.numeric(si$status == 2)
silong <- msprep(
time = c(NA, "time", "time"),
status = c(NA, "stat1", "stat2"),
data = si, keep = "ccr5", trans = tmat
)
# Run cox model
silong <- expand.covs(silong, "ccr5")
c1 <- coxph(Surv(time, status) ~ ccr5WM.1 + ccr5WM.2 + strata(trans),
data = silong)
# 1. Prepare reference patient data - both CCR5 genotypes
WW <- data.frame(
ccr5WM.1 = c(0, 0),
ccr5WM.2 = c(0, 0),
trans = c(1, 2),
strata = c(1, 2)
)
WM <- data.frame(
ccr5WM.1 = c(1, 0),
ccr5WM.2 = c(0, 1),
trans = c(1, 2),
strata = c(1, 2)
)
# 2. Make msfit objects
msf.WW <- msfit(c1, WW, trans = tmat)
msf.WM <- msfit(c1, WM, trans = tmat)
# 3. Make probtrans objects
pt.WW <- probtrans(msf.WW, predt = 0)
pt.WM <- probtrans(msf.WM, predt = 0)
# Mirror plot split at 10 years - see vignette for more details
vis.mirror.pt(
x = list(pt.WW, pt.WM),
titles = c("WW", "WM"),
horizon = 10
)
```

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