Description Usage Arguments Value Author(s) See Also Examples

View source: R/predictL.lcmm.R

This function provides a matrix containing the class-specific predicted
trajectories computed in the latent process scale, that is the latent
process underlying the curvilinear outcome(s), for a profile of covariates
specified by the user. This function applies only to `lcmm`

and
`multlcmm`

objects. The function `plot.predict`

provides directly
the plot of these class-specific predicted trajectories. The function
`predictY`

provides the class-specific predicted trajectories computed
in the natural scale of the outcome(s).

1 |

`x` |
an object inheriting from class |

`newdata` |
data frame containing the data from which predictions are
computed. The data frame should include at least all the covariates listed
in x$Xnames2. Names in the data frame should be exactly x$Xnames2 that are
the names of covariates specified in |

`var.time` |
A character string containing the name of the variable that corresponds to time in the data frame (x axis in the plot). |

`na.action` |
Integer indicating how NAs are managed. The default is 1 for 'na.omit'. The alternative is 2 for 'na.fail'. Other options such as 'na.pass' or 'na.exclude' are not implemented in the current version. |

`confint` |
logical indicating if confidence should be provided. Default to FALSE. |

`...` |
further arguments to be passed to or from other methods. They are ignored in this function. |

An object of class `predictL`

with values :

- `pred`

: a matrix containing the class-specific predicted values in
the latent process scale, the lower and the upper limits of the confidence
intervals (if calculated).

- `times`

: the `var.time`

variable from `newdata`

Cecile Proust-Lima, Viviane Philipps

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
#### Prediction from a 2-class model with a Splines link function
## Not run:
## fitted model
m<-lcmm(Ydep2~Time*X1,mixture=~Time,random=~Time,classmb=~X2+X3,
subject='ID',ng=2,data=data_lcmm,link="splines",B=c(
-0.175, -0.191, 0.654, -0.443,
-0.345, -1.780, 0.913, 0.016,
0.389, 0.028, 0.083, -7.349,
0.722, 0.770, 1.376, 1.653,
1.640, 1.285))
summary(m)
## predictions for times from 0 to 5 for X1=0
newdata<-data.frame(Time=seq(0,5,length=100),
X1=rep(0,100),X2=rep(0,100),X3=rep(0,100))
predictL(m,newdata,var.time="Time")
## predictions for times from 0 to 5 for X1=1
newdata$X1 <- 1
predictY(m,newdata,var.time="Time")
## End(Not run)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.