lines.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'
lines(
x,
y,
class = "joint",
newdata,
evalPoints = 50,
evalTimes1 = NULL,
evalTimes2 = NULL,
plot_margin = 1,
cond_time = NULL,
cond_margin = 2,
plotly_object = NULL,
...
)
```

`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; 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 |

`plotly_object` |
only for |

`...` |
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.

If `class = "joint"`

, one needs to include a `plot_ly`

object
(from `plot.CopulaCenR`

with `class`

= `"joint"`

)
through the argument `plotly_object`

. 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(1:3, each=2), ind = rep(c(1,2),3),
SevScaleBL = rep(3,6), ENROLLAGE = rep(60,6),
rs2284665 = c(0,0,1,1,2,2))
# Plot marginal survival probabilities
plot(x = copula2_sp, class = "marginal",
newdata = newdata[newdata$id==1,],
plot_margin = 1, ylim = c(0.6,1),
ylab = "Marginal Survival Probability")
lines(x = copula2_sp, class = "marginal",
newdata = newdata[newdata$id==2,],
plot_margin = 1, lty = 2)
legend("bottomleft", c("id: 1","id: 2"), lty = c(1,2))
# Plot conditional survival probabilities
plot(x = copula2_sp, class = "conditional",
newdata = newdata[newdata$id==1,],
cond_margin = 2, cond_time = 5, ylim = c(0.25,1),
xlab = "years", ylab = "Conditional Survival Probability")
lines(x = copula2_sp, class = "conditional",
newdata = newdata[newdata$id==2,],
cond_margin = 2, cond_time = 5, lty = 2)
legend("bottomleft", c("id: 1","id: 2"), lty = c(1,2))
# Plot joint survival probabilities
plot3d <- plot(x = copula2_sp, class = "joint",
newdata = newdata[newdata$id==1,])
plot3d <- lines(x = copula2_sp, class = "joint",
newdata = newdata[newdata$id==2,], plotly_object = plot3d)
```

[Package *CopulaCenR* version 1.2.3 Index]