context("fit_ggcr")
test_that("fit_ggcr output", {
data(dipper_tiny)
clo <- c(10) # numbers of clones
nu <- 1000 # number of updates
ni <- 5000 # number of iterations
nt <- 5 # thinning
nc <- 2 # number of chains
initmeans <- c(0.15,0.5,4.9,1.4,-0.4) # means of normally distributed priors for parameters
initprec <- 1000 # common precision of the normal priors for the first number of clones
post_inf <- fit_ggcr(dipper_tiny,clo,nu,ni,nt,nc,initmeans,initprec)
a <- coef(post_inf[[length(clo)]])[1]
b <- coef(post_inf[[length(clo)]])[2]
k <- coef(post_inf[[length(clo)]])[3]
rounded_res <- round(c(a,b,k),2)
expect_equivalent(rounded_res, c(0.12,0.49,4.90))
})
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