brms: addition-terms – R documentation

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addition-terms

Additional Response Information

Description

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

Usage

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(...)

Arguments

`x`	A vector; usually a variable defined in the data. Allowed values depend on the function: `resp_se` and `resp_weights` require positive numeric values. `resp_trials`, `resp_thres`, and `resp_cat` require positive integers. `resp_dec` requires `0` and `1`, or alternatively `'lower'` and `'upper'`. `resp_subset` requires `0` and `1`, or alternatively `FALSE` and `TRUE`. `resp_cens` requires `'left'`, `'none'`, `'right'`, and `'interval'` (or equivalently `-1`, `0`, `1`, and `2`) to indicate left, no, right, or interval censoring.
`sigma`	Logical; Indicates whether the residual standard deviation parameter `sigma` should be included in addition to the known measurement error. Defaults to `FALSE` for backwards compatibility, but setting it to `TRUE` is usually the better choice.
`scale`	Logical; Indicates whether weights should be scaled so that the average weight equals one. Defaults to `FALSE`.
`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 `NA` even for non-interval censored observations to avoid accidental exclusion of these observations.
`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 `resp_vreal`, vectors of real values. For `resp_vint`, vectors of integer values.

Details

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.

Value

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

Examples

## 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)

brms

Bayesian Regression Models using 'Stan'

v2.15.0

GPL-2

Authors

Paul-Christian Bürkner [aut, cre], Jonah Gabry [ctb], Sebastian Weber [ctb], Andrew Johnson [ctb], Martin Modrak [ctb]

Initial release

2021-03-10