addition-terms {brms} | R Documentation |

Provide additional information on the response variable in brms models, such as censoring, truncation, or known measurement error.

resp_se(x, sigma = FALSE) resp_weights(x, scale = FALSE) resp_trials(x) resp_thres(x, gr = NA) resp_cat(x) resp_dec(x) resp_cens(x, y2 = NA) resp_trunc(lb = -Inf, ub = Inf) resp_mi(sdy = NA) resp_rate(denom) resp_subset(x) resp_vreal(...) resp_vint(...)

`x` |
A vector; usually a variable defined in the data. Allowed values
depend on the function: |

`sigma` |
Logical; Indicates whether the residual standard deviation
parameter |

`scale` |
Logical; Indicates whether weights should be scaled
so that the average weight equals one. Defaults to |

`gr` |
A vector of grouping indicators. |

`y2` |
A vector specifying the upper bounds in interval censoring.
Will be ignored for non-interval censored observations. However, it
should NOT be |

`lb` |
A numeric vector or single numeric value specifying the lower truncation bound. |

`ub` |
A numeric vector or single numeric value specifying the upper truncation bound. |

`sdy` |
Optional known measurement error of the response treated as standard deviation. If specified, handles measurement error and (completely) missing values at the same time using the plausible-values-technique. |

`denom` |
A vector of positive numeric values specifying the denominator values from which the response rates are computed. |

`...` |
For |

These functions are almost solely useful when
called in formulas passed to the brms package.
Within formulas, the `resp_`

prefix may be omitted.
More information is given in the 'Details' section
of `brmsformula`

.

A list of additional response information to be processed further by brms.

## Not run: ## Random effects meta-analysis nstudies <- 20 true_effects <- rnorm(nstudies, 0.5, 0.2) sei <- runif(nstudies, 0.05, 0.3) outcomes <- rnorm(nstudies, true_effects, sei) data1 <- data.frame(outcomes, sei) fit1 <- brm(outcomes | se(sei, sigma = TRUE) ~ 1, data = data1) summary(fit1) ## Probit regression using the binomial family n <- sample(1:10, 100, TRUE) # number of trials success <- rbinom(100, size = n, prob = 0.4) x <- rnorm(100) data2 <- data.frame(n, success, x) fit2 <- brm(success | trials(n) ~ x, data = data2, family = binomial("probit")) summary(fit2) ## Survival regression modeling the time between the first ## and second recurrence of an infection in kidney patients. fit3 <- brm(time | cens(censored) ~ age * sex + disease + (1|patient), data = kidney, family = lognormal()) summary(fit3) ## Poisson model with truncated counts fit4 <- brm(count | trunc(ub = 104) ~ zBase * Trt, data = epilepsy, family = poisson()) summary(fit4) ## End(Not run)

[Package *brms* version 2.15.0 Index]