Description Usage Arguments Details Value Author(s) See Also Examples

Produces point estimates, interval estimates, and p values for an arbitrary functional (mean, geometric mean,
proportion, median, quantile, odds) of a variable of class `integer`

, `numeric`

, `Surv`

, when regressed on an arbitrary number of covariates. Multiple Partial F-tests can be specified using the `U`

function.

1 2 3 4 5 6 7 | ```
regress(fnctl, formula, data, intercept = fnctl!="hazard",
strata = rep(1,n),weights=rep(1,n),id=1:n,ties="efron",subset=rep(TRUE,n),
robustSE = TRUE, conf.level = 0.95, exponentiate = fnctl!="mean",
replaceZeroes, useFdstn = TRUE, suppress = FALSE, na.action, method = "qr",
model.f = TRUE, model.x = FALSE, model.y = FALSE, qr = TRUE,
singular.ok = TRUE, contrasts = NULL, offset,control = list(...),
init, ..., version=FALSE)
``` |

`fnctl` |
a character string indicating the functional (summary measure of the distribution) for which inference is desired. Choices include |

`formula` |
an object of class |

`data` |
a data frame, matrix, or other data structure with matching names to those entered in |

`intercept` |
a logical value indicating whether a intercept exists or not. |

`strata` |
vector indicating a variable to be used for stratification in proportional hazards regression. |

`weights` |
vector indicating optional weights for weighted regression. |

`id` |
vector with ids for the variables. If any ids are repeated, runs a clustered regression. |

`ties` |
One of |

`subset` |
vector indicating a subset to be used for all inference. |

`robustSE` |
a logical indicator that standard errors are to be computed using the Huber-White sandwich estimator. |

`conf.level` |
a numeric scalar indicating the level of confidence to be used in computing confidence intervals. The default is 0.95. |

`exponentiate` |
a logical indicator that the regression parameters should be exponentiated. This is by default true for all functionals except the mean. |

`replaceZeroes` |
if not |

`useFdstn` |
a logical indicator that the F distribution should be used for test statistics instead of the chi squared distribution even in logistic and proportional hazard regression models. When using the F distribution, the degrees of freedom are taken to be the sample size minus the number of parameters, as it would be in a linear regression model. |

`suppress` |
if |

`na.action, method, model.f, model.x, model.y, qr, singular.ok, offset, contrasts, control` |
optional arguments that are passed to the functionality of |

`init` |
optional argument that are passed to the functionality of |

`...` |
other arbitrary parameters. |

`version` |
if |

Regression models include linear regression (for the “mean” functional), logistic regression (for the “odds”
functional), Poisson regression (for the “rate” functional). Proportional hazards regression is currently not supported in the `regress`

function. Objects created using the `U`

function can also be passed in. If the `U`

call involves a partial formula of the form `~ var1 + var2`

, then `regress`

will return a multiple-partial F-test involving `var1`

and `var2`

. The multiple partial tests must be the last terms specified in the model (i.e. no other predictors can follow them).

An object of class uRegress is returned. Parameter estimates, confidence intervals, and p values are contained in a matrix $augCoefficients.

Scott S. Emerson, M.D., Ph.D., Andrew J. Spieker, Brian D. Williamson, Travis Hee Wai

Functions for fitting linear models (`lm`

), generalized linear models (`glm`

), proportional hazards models (`coxph`

), and generalized estimating equations (`geeglm`

). Also see the function to specify multiple-partial F-tests, `U`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
# Loading required libraries
library(survival)
library(sandwich)
# Reading in a dataset
mri <- read.table("http://www.emersonstatistics.com/datasets/mri.txt",header=TRUE)
# Creating a Surv object to reflect time to death
mri$ttodth <- Surv(mri$obstime,mri$death)
# Attaching the mri dataset
attach(mri)
# Linear regression of atrophy on age
regress("mean", atrophy~age, data=mri)
## Linear regression of atrophy on male and race and their interaction,
## with a multiple-partial F-test on the race-age interaction
regress("mean", atrophy~ male + U(ra=~race*age), data=mri)
## Linear regression of atrophy on age, male, race (as a dummy variable), chf,
## and diabetes. There are two multiple partial F-tests and both are named
regress("mean", atrophy~age+male+U(rc=~dummy(race)+chf)+U(md=~male+diabetes), data=mri)
``` |

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