condMeanVar: Conditional Mean and Variance
In smd: Sparse Multivariate Diffusions

Description Usage Arguments Details Value Author(s) See Also

condMeanVar is a method which returns the conditional mean and, optionally, the covariance matrix of X_{t_0+t} given X_{t_0} = x_{t_0} for N pairs of x_{t_0} and t.

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## S4 method for signature 'OUModel'
condMeanVar(object, parameters,
	x, t, var = FALSE, ...)

`object`	An object of class `OUModel`.
`parameters`	A `list` of parameters which are compatible with `object`. If `parameters` are not specified, the parameters from `object` are used.
`x`	If `x` is specified, `t` must be specified as well (and vice versa). `x` must be a N x p `matrix`, with rows representing x_{t_0}-vectors.
`t`	If `t` is specified, `x` must be specified as well (and vice versa). `t` may be a `numeric` vector of length N with t-values corresponding to the rows in `x`. `t` may also be a `numeric` (of length 1), in which case the same value of t is used for all calculations.
`var`	If `var = FALSE` only the conditional means are computed. If `var = TRUE` both the conditional means and conditional covariance matrices are computed.
`...`	Other arguments.

If x and t are missing, the data and time-distance between observations from object are used, and the conditional means and, optionally, the conditional covariance matrices of X_{t_2}|X_{t_1} = x_{t_1} up to X_{t_n}|X_{t_(n-1)} = x_{t_(n-1)} are computed.

If var = FALSE, the conditional means are returned as columns in a p x N matrix.

If var = TRUE, the conditional means and covariance matrices are returned in a list with two elements. The first element, "condMean", contains the conditional mean vectors as columns in a p x N matrix. The second element, "condVar", contains the conditional covariance matrices in a p x p x N array.

Nina Munkholt, nina.m@math.ku.dk

MultDiffModel, OUModel

smd documentation built on May 2, 2019, 5:56 p.m.