plot.CopulaCenR {CopulaCenR} | R Documentation |

Plotting for CopulaCenR fits from `ic_spTran_copula`

, `rc_spCox_copula`

,
`ic_par_copula`

and `rc_par_copula`

.

```
## S3 method for class 'CopulaCenR'
plot(
x,
y,
class = "joint",
newdata,
evalPoints = 50,
evalTimes1 = NULL,
evalTimes2 = NULL,
plot_margin = 1,
cond_time = NULL,
cond_margin = 2,
type = "l",
xlab = "years",
ylab = "survival probability",
cex.main = 1.4,
cex.lab = 1.4,
cex.axis = 1.4,
legend = TRUE,
...
)
```

`x` |
an object of |

`y` |
new data frame with colname names |

`class` |
one of "joint", "conditional" or "marginal" |

`newdata` |
new data frame (ignored if |

`evalPoints` |
number of time points to be evaluated in both margins; default is 50 |

`evalTimes1` |
a vector of times for margin 1 to be evaluated; default is NULL; will override evalPoints if non-NULL |

`evalTimes2` |
a vector of times for margin 2 to be evaluated |

`plot_margin` |
for |

`cond_time` |
for |

`cond_margin` |
for |

`type` |
type of plot with default |

`xlab` |
a title for the x axis. |

`ylab` |
a title for the x axis. |

`cex.main` |
cex for main. |

`cex.lab` |
cex for lab. |

`cex.axis` |
cex for axis. |

`legend` |
whether to show legend with default |

`...` |
further arguments |

y must be a data frame with columns `id`

(subject id),
`ind`

(1,2 for two margins) and `covariates`

.

The argument class determines the plot:
`"joint"`

for joint survival probabilities,
`"conditional"`

for conditional probabilities and
`"marginal"`

for marginal probabilities.

The function evaluates on a series of time points
(given by `evalPoints`

or `evalTimes`

;
`evalTimes`

will override `evalPoints`

).
By default, the time points are automatically
selected by specifying the number of points (`evalPoints = 50`

).
Users can also provide the specific time points through `evalTimes1`

and
`evalTimes2`

for the two margins, respectively.
When `class`

`= "conditional"`

, only `evalTimes1`

is needed
and the evaluation times are actually `evalTimes1`

plus `cond_time`

.

If `class`

`= "conditional"`

, one needs to specify the margin
that has the event (by `cond_margin`

)
and time when the event has occurred (by `cond_time`

).
For example, if `cond_margin = 2`

and `cond_time = 5`

,
then the function produces the conditional survival probability
(after time 5) in margin 1 given that margin 2 has got an event by time 5.
This measurement is useful for predicting the second event
given the first event has occurred. See the example for details.

If `class = "marginal"`

, one needs to specify which margin to plot
through the argument `plot_margin`

. See the example for details.

a 3D joint survival distribution plot if `class = "joint"`

;
a 2D survival distribution plot if `class`

= `"marginal"`

or `"conditional"`

.

```
data(AREDS)
# fit a Copula2-Sieve model
copula2_sp <- ic_spTran_copula(data = AREDS, copula = "Copula2",
l = 0, u = 15, m = 3, r = 3,
var_list = c("ENROLLAGE","rs2284665","SevScaleBL"))
newdata = data.frame(id = rep(1, each=2), ind = rep(c(1,2),1),
SevScaleBL = rep(3,2), ENROLLAGE = rep(60,2),
rs2284665 = c(0,0))
# Plot joint survival probabilities
plot(x = copula2_sp, class = "joint", newdata = newdata)
# Plot conditional survival probabilities
plot(x = copula2_sp, class = "conditional", newdata = newdata,
cond_margin = 2, cond_time = 5, ylim = c(0.25,1),
ylab = "Conditional Survival Probability")
# Plot marginal survival probabilities
plot(x = copula2_sp, class = "marginal", newdata = newdata,
plot_margin = 1, ylim = c(0.6,1),
ylab = "Marginal Survival Probability")
```

[Package *CopulaCenR* version 1.2.3 Index]