BMSC: Fit Bayesian Multilevel Single Case models

In bmscstan: Bayesian Multilevel Single Case Models using 'Stan'

Fit Bayesian Multilevel Single Case models

Description

BMSC fits the Bayesian Multilevel Single Case models.

Usage

BMSC(
  formula,
  data_ctrl,
  data_sc,
  cores = 1,
  chains = 4,
  iter = 4000,
  warmup,
  seed = NA,
  typeprior = "normal",
  s,
  family = "gaussian",
  ...
)

Arguments

formula	An object of class formula: a symbolic description of the model to be fitted.
data_ctrl	An object of class data.frame (or one that can be coerced to that class) containing data of all variables used in the model for the control group.
data_sc	An object of class data.frame (or one that can be coerced to that class) containing data of all variables used in the model for the Single Case
cores	The number of cores to use when executing the Markov chains in parallel. The default is 1.
chains	Number of Markov chains (defaults to 4).
iter	Number of total iterations per chain (including warmup; defaults to 4000).
warmup	A positive integer specifying number of warmup (aka burnin) iterations. This also specifies the number of iterations used for stepsize adaptation, so warmup samples should not be used for inference. The number of warmup should not be larger than iter and the default is 2000.
seed	The seed for random number generation to make results reproducible. If NA (the default), Stan will set the seed randomly.
typeprior	Set the desired prior distribution for the fixed effects. normal a normal distribution with μ = 0 and σ = 10 cauchy a cauchy distribution with μ = 0 and scale √{2}/2 student a Student's T distribution, with μ = 0, ν = 3 and σ = 10 The normal distribution is the default. The σ or scale parameters of the prior distributions can be modified by setting the dispersion parameter s.
s	is the dispersion parameter (standard deviation or scale) for the prior distribution. If NULL (the default) and typeprior = "normal" or typeprior = "student" s = 10, otherwise, if typeprior = "cauchy" s = sqrt(2)/2.
family	a description of the response distribution to be used in this model. This is a character string naming the family. By default, a linear gaussian model is applied. gaussian the dependent variable is distributed along a Gaussian distribution, with identity link function binomial the dependent variable is distributed along a Binomial distribution, with logit link function
...	further arguments to be passed to stan function.

Value

a BMSC object

Examples

 

# simulation of healthy controls data

Sigma.ctrl <- matrix(cbind(1, .7,  .7, 1) ,nrow=2)

U <- t(chol(Sigma.ctrl))

numobs <- 100

set.seed(123)

random.normal <- matrix( rnorm( n = ncol(U) * numobs, mean = 3, sd = 1),
                         nrow = ncol(U), ncol = numobs)

X = U %*% random.normal

dat.ctrl <- as.data.frame(t(X))

names(dat.ctrl) <- c("y","x")

cor(dat.ctrl)

# simulation of patient data

Sigma.pt <- matrix(cbind(1, 0,  0, 1) ,nrow=2)

U <- t(chol(Sigma.pt))

numobs <- 20

set.seed(0)

random.normal <- matrix( rnorm( n = ncol(U) * numobs, mean = 3, sd = 1),
                 nrow = ncol(U), ncol = numobs)

X = U %*% random.normal

dat.pt <- as.data.frame(t(X))

names(dat.pt) <- c("y","x")

cor(dat.pt)

# fit the single case model

mdl.reg <- BMSC(y ~ x, data_ctrl = dat.ctrl, data_sc = dat.pt, seed = 10)

# posterior-predictive check of the model

pp_check(mdl.reg)

# summarize the results

summary(mdl.reg)

# plot the results

plot(mdl.reg)

bmscstan documentation built on Sept. 5, 2022, 1:05 a.m.