# NOT RUN {
# a Brownian motion model with one regime
modelBM <- PCM(model = "BM", k = 2)
# print the model
modelBM
# assign the model parameters at random: this will use uniform distribution
# with boundaries specified by PCMParamLowerLimit and PCMParamUpperLimit
# We do this in two steps:
# 1. First we generate a random vector. Note the length of the vector equals PCMParamCount(modelBM)
randomParams <- PCMParamRandomVecParams(modelBM, PCMNumTraits(modelBM), PCMNumRegimes(modelBM))
randomParams
# 2. Then we load this random vector into the model.
PCMParamLoadOrStore(modelBM, randomParams, 0, PCMNumTraits(modelBM), PCMNumRegimes(modelBM), TRUE)
# create a random tree of 10 tips
tree <- ape::rtree(10)
PCMMean(tree, modelBM)
# }
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