# Confidence interval plots for natural effect components

### Description

Obtain effect decomposition confidence interval plots for natural effect models.

### Usage

1 2 3 4 5 6 7 |

### Arguments

`x` |
a fitted natural effect model object. |

`xRef` |
a vector including reference levels for the exposure, |

`covLev` |
a vector including covariate levels at which natural effect components need to be evaluated (see details). |

`level` |
the confidence level required. |

`transf` |
transformation function to be applied internally on the (linear hypothesis) estimates and their confidence intervals (e.g. |

`ylabels` |
character vector containing the labels for the (linear hypothesis) estimates to be plotted on the y-axis. |

`yticks.at` |
numeric vector containing the y-coordinates (from 0 to 1) to draw the tick marks for the different estimates and their corresponding confidence intervals. |

`...` |
additional arguments. |

`ci.type` |
the type of bootstrap intervals required (see |

### Details

This function yields confidence interval plots for the natural effect components.
These causal parameter estimates are first internally extracted from the `neModel`

object by applying the effect decomposition function `neEffdecomp(x, xRef, covLev)`

.

### Examples

1 2 3 4 5 6 7 8 9 10 11 | ```
data(UPBdata)
impData <- neImpute(UPB ~ att * negaff + educ + gender + age,
family = binomial, data = UPBdata)
neMod <- neModel(UPB ~ att0 * att1 + educ + gender + age,
family = binomial, expData = impData, se = "robust")
plot(neMod)
plot(neMod, transf = exp,
ylabels = c("PDE", "TDE", "PIE", "TIE", "TE"))
plot(neMod, level = 0.9, xRef = c(-1, 0))
``` |