cprobit {cprobit} | R Documentation |

Apply the three-step workflow for the analysis of two repeated outcomes from each subject

```
cprobit(
formula,
dat,
index,
transform = NULL,
lambda = NA,
resid_pval_threshold = 0.05
)
## S3 method for class 'cprobit'
summary(object, plot = FALSE, ...)
## S3 method for class 'cprobit'
print(x, ...)
```

`formula` |
Formula for the model. Do not convert data type within the
formula (e.g., |

`dat` |
A |

`index` |
Names of variables indicating subject and case ID. Case ID must be coded as integers 1 and 2. |

`transform` |
Whether a Box-Cox transformation should be applied to the
outcome, taking value |

`lambda` |
Value of the Box-Cox transformation parameter to use. Default
is |

`resid_pval_threshold` |
The threshold for the Lilliefors p-value of the residuals to determine whether a Box-Cox transformation on the outcome is necessary. Default is 0.05. |

`object` |
Model fitted using |

`plot` |
Wether residual qq-plots should be plotted. Default is |

`...` |
Additional arguments affecting the summary produced (not yet implemented). |

`x` |
Model fitted using |

Specify the formula for the repeated measurements instead of the
change in the outcome, but without any time-invariant component that would
have been eliminated after taking the difference. Interaction between two
variables can be specified in the formula using `*`

or `:`

, but
users need to create their own variable for interaction involving three or
more variables.

If `transform = NULL`

, the workflow will determine the need for a
Box-Cox transforamtion on the outcome (i.e., Step 3) based on the residual
diagnostics in Step 2. A Box-Cox transforamtion will be used if the p-value
of the Lilliefors test is smaller than `resid_pval_threshold`

(default
is 0.05). If `transform = TRUE`

, analyses will always be performed on
both the observed and Box-Cox transformed outcomes. If ```
transform =
FALSE
```

, analysis will only be performed on the observed outcomes.

Returns a list.

GEP Box, DR Cox. An Analysis of Transformations. Journal of the Royal Statistical Society. Series B (Methodological). 1964;26:211–52.

DM Hawkins, S Weisberg. Combining the box-cox power and generalised log transformations to accommodate nonpositive responses in linear and mixed-effects linear models. South African Stat J. 2017;51:317–28.

HW Lilliefors. On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown. J Am Stat Assoc. 1967;62:399.

Y Ning, NC Støer, PJ Ho, SL Kao, KY Ngiam, EYH Khoo, SC Lee, ES Tai, M Hartman, M Reilly, CS Tan. Robust estimation of the effect of an exposure on the change in a continuous outcome. BMC Medical Research Methodology (in press).

```
# Apply the three-step workflow to assess the association between the
# baseline glucose variability and the change in the glucose variability in
# the subsequent two days.
# Although age and gender are available, they do not need to be explicitly
# adjusted for in the cprobit model.
data(bg_variability)
head(bg_variability)
model <- cprobit(formula = y ~ t + t:sd0, dat = bg_variability,
index = c("subject_id", "case_id"))
summary(model, plot = TRUE)
```

[Package *cprobit* version 1.0.2 Index]