addition-terms

0th

Percentile

Additional Response Information

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)
resp_disp(x)
resp_trials(x)
resp_cat(x)
resp_dec(x)
resp_cens(x, y2 = NULL)
resp_trunc(lb = -Inf, ub = Inf)
Arguments
x
A vector; usually a variable defined in the data. Allowed values depend on the function: resp_se, resp_weights, and resp_disp 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 equivalenty -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.
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.
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

Aliases
  • addition-terms
  • resp_cat
  • resp_cens
  • resp_dec
  • resp_disp
  • resp_se
  • resp_trials
  • resp_trunc
  • resp_weights
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) ~ log_Base4_c * Trt_c, 
#             data = epilepsy, family = poisson())
# summary(fit4)
# ## End(Not run)
  
Documentation reproduced from package brms, version 1.4.0, License: GPL (>= 3)

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