addition-terms.Rmd

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_cat(x)
resp_dec(x)
resp_cens(x, y2 = NULL)
resp_trunc(lb = -Inf, ub = Inf)
resp_mi(sdy = NULL)

#### 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
and resp_cat require positive integers. resp_dec requires 0 and 1, or alternatively
'lower' and 'upper'; 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.
y2 A vector specifying the upper bounds in interval censoring.
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.
addition-terms 7
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.
#### 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 vector containing additional information on the response variable in an appropriate format.
See Also
brm, brmsformula
#### Examples
```{r}
## 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_c,
data = epilepsy, family = poisson())
summary(fit4)
## End(Not run)
```