Description Usage Arguments Value See Also
View source: R/input_model_mvnormal_iw.R
Multivariate Normal - Inverse Wishart model. Fits num_models
Multivariate
Normal distributions to a data frame according to Multivariate Normal - Inverse Wishart model.
If prior is not passed to the function, classic Multivariate Normal model with sample covariance
is used.
1 | input_model_mvnormal_iw(data, num_models, prior)
|
data |
Data frame of events with attributes to model jointly. |
num_models |
Number of distributions to fit. |
prior |
List of lists containing priors for mean and covariance matrix. Should
contain entries 'mean' and 'sigma'. Inner list corresponding to entry 'mean'
should specify parameters for the multivariate normal prior of the mean.
It's entries should be 'mean' (vector) and 'sigma' (matrix) of the appropriate size.
List corresponding to the outer list key 'sigma' should specify parameters for the Inverse Wishart
prior of the covariance matrix. Keys in this list should be 'a' (scalar)
and 'S' (matrix). For example:
|
List of num_models
S3 classes of type 'mvnormal'.
The class 'mvnormal' has the following methods defined: mean
and
sample
. These both sample from the posterior distribution of the mean
of the multivariate distribution fit.
Other input models: input_model_bernoulli
,
input_model_normal_ig
,
input_model_normal
,
input_model_poisson
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