View source: R/multivariate_constructors.R
Autoregressive.Multivariate.Normal.Distribution | R Documentation |
Creates a distribution where all the variables, after arbitrary transformations, follow a normal distribution with a Continuous Autoregressive 1 (CAR-1) correlation structure
Autoregressive.Multivariate.Normal.Distribution(
rho,
times,
mu = 0,
sds = 1,
lower = -Inf,
upper = Inf,
var.names = NULL,
transformations = NULL
)
rho |
The autoregressive correlation coefficient |
times |
The times for each observation in the distribution. The correlation between any two observations at times t1 and t2 is rho^(abs(t1-t2)) |
mu , sds |
The mean and standard deviation vectors. If given a scalar value, assumes that the mean/sd is the same for every variable in the distribution |
lower |
The upper and lower bounds for each variable (on the original scale, NOT the transformed scale). If only one value is given, assumed to apply to all variables in the distribution |
upper |
The upper and lower bounds for each variable (on the original scale, NOT the transformed scale). If only one value is given, assumed to apply to all variables in the distribution |
var.names |
The names of the variables in the distribution. If NULL, uses the names of mu, the row names of sigma, or the column names of sigma, in that order |
transformations |
Objects of class transformation to be applied to each variable. Can be either an object of class transformation, the character name of a defined transformation, or a list of transformation objects. If only one transformation is supplied, assumed to apply to all variables. If this is a named list, any variables missing transformation are assmed to not be transformed |
Other Distribution Constructors:
Bernoulli.Distribution()
,
Beta.Distribution()
,
Binomial.Distribution()
,
Canonical.Mixture.Distribution()
,
Compound.Symmetry.Multivariate.Normal.Distribution()
,
Constant.Distribution()
,
Discrete.Set.Distribution()
,
Empiric.Distribution()
,
Logitnormal.Distribution()
,
Logitnormal.Mixture()
,
Logituniform.Distribution()
,
Lognormal.Distribution()
,
Lognormal.Mixture()
,
Loguniform.Distribution()
,
Multivariate.Correlated.Uniform.Distribution()
,
Multivariate.Logitnormal.Distribution()
,
Multivariate.Lognormal.Distribution()
,
Multivariate.Normal.Distribution()
,
Normal.Distribution()
,
Normal.Mixture()
,
Smoothed.Empiric.Distribution()
,
Transformed.Multivariate.Normal.Distribution()
,
Transformed.Normal.Distribution()
,
Transformed.Normal.Mixture()
,
Uniform.Distribution()
,
Univariate.Canonical.Distribution()
,
join.distributions()
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