View source: R/model_averaging_fits.R
ma_dichotomous_fit | R Documentation |
Fit a model averaged dichotomous BMD model.
ma_dichotomous_fit( D, Y, N, model_list = integer(0), fit_type = "laplace", BMD_TYPE = "extra", BMR = 0.1, point_p = 0.01, alpha = 0.05, samples = 21000, burnin = 1000 )
D |
doses matrix |
Y |
response matrix |
N |
number of replicates matrix |
model_list |
a list of configurations for the single models (priors, model type) |
fit_type |
the method used to fit (laplace, mle, or mcmc) |
BMD_TYPE |
BMD_TYPE specifies the type of benchmark dose analysis to be performed. For continuous models, there are four types of BMD definitions that are commonly used. |
BMR |
This option specifies the benchmark response BMR. The BMR is defined in relation to the BMD calculation requested (see BMD). By default, the "BMR = 0.1." |
point_p |
This option is only used for hybrid BMD calculations. It defines a probability that is the cutpoint for observations. It is the probability that observations have this probability, or less, of being observed at the background dose. |
alpha |
Alpha is the specified nominal coverage rate for computation of the lower bound on the BMDL and BMDU, i.e., one computes a 100\times(1-α)\% . For the interval (BMDL,BMDU) this is a 100\times(1-2α)\% . By default, it is set to 0.05. |
samples |
the number of samples to take (MCMC only) |
burnin |
the number of burnin samples to take (MCMC only) |
a model object containing a list of single models
Individual_Model_X
: Here X
is a number 1≤q X ≤q n, where n
is the number of models in the model average. For each X
, this is an individual model
fit identical to what is returned in 'single_continuous_fit
.'
ma_bmd
: The CDF of the model averaged BMD distribution.
posterior_probs
: The posterior model probabilities used in the MA.
bmd
: The BMD and the 100\times(1-2α)\% confidence intervals.
mData <- matrix(c(0, 2,50, 1, 2,50, 3, 10, 50, 16, 18,50, 32, 18,50, 33, 17,50),nrow=6,ncol=3,byrow=TRUE) D <- mData[,1] Y <- mData[,2] N <- mData[,3] model = ma_dichotomous_fit(D,Y,N) summary(model)
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