# Prediction After Quantile Regression

### Description

This function returns predictions for an object of class “`ctqr`

”.

### Usage

1 2 |

### Arguments

`object` |
a |

`newdata` |
optional data frame in which to look for variables with which to predict. It must include all the covariates that enter the quantile regression model. If omitted, the fitted values are used. |

`se.fit` |
logical. If |

`...` |
for future methods. |

### Details

This function produces predicted values obtained by evaluating the regression function at `newdata`

(which defaults to `model.frame(object)`).

### Value

If `se = FALSE`, a matrix of fitted values, with rows corresponding to different observations, and one column for each value of `object$p`. If `se = TRUE`, a list with two items:

`fit` |
a matrix of fitted values, as described above. |

`se.fit` |
a matrix of estimated standard errors. |

### Author(s)

Paolo Frumento

### See Also

`ctqr`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
# Using simulated data
n <- 1000
x1 <- runif(n)
x2 <- runif(n)
t <- 1 + x1 + x2 + runif(n, -1,1)
c <- rnorm(n,3,1)
y <- pmin(t,c)
d <- (t <= c)
model <- ctqr(Surv(y,d) ~ x1 + x2, p = c(0.25,0.5))
pred <- predict(model) # the same as fitted(model)
predict(model, newdata = data.frame(x1 = c(0.2,0.6), x2 = c(0.1,0.9)), se.fit = TRUE)
``` |

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