SDEsim3: Simulated Stochastic Lotka-Volterra Eqns
In DiffusionRgqd: Inference and Analysis for Generalized Quadratic Diffusions
Description
The dataset contains discretely sampled observations for a simulated stochastic differential equation (SDE) with dynamics:
dX_t = (1.5X_t-0.4X_tY_t)dt+sqrt(0.05X_t)dW_t
dY_t = (-1.5Y_t+0.4X_tY_t-0.2Y_t^2)dt+sqrt(0.1X_t)dB_t
where dW_t and dB_t are standard Brownian motions, t is time and X_0 = 5, Y_0 = 5.
Usage
1
data("SDEsim3")
Format
A data frame with 1001 observations on the following 3 variables.
timeA numeric vector of time nodes at which means are calculated (time[i+1]-time[i] = 1/4).
mxMean Xt trajectory of the diffusion.
myMean Yt trajectory of the diffusion.
DiffusionRgqd documentation built on May 2, 2019, 3:26 a.m.
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Description
The dataset contains discretely sampled observations for a simulated stochastic differential equation (SDE) with dynamics:
dX_t = (1.5X_t-0.4X_tY_t)dt+sqrt(0.05X_t)dW_t
dY_t = (-1.5Y_t+0.4X_tY_t-0.2Y_t^2)dt+sqrt(0.1X_t)dB_t
where dW_t and dB_t are standard Brownian motions, t is time and X_0 = 5, Y_0 = 5.
Usage
1 | data("SDEsim3")
|
Format
A data frame with 1001 observations on the following 3 variables.
timeA numeric vector of time nodes at which means are calculated (
time[i+1]-time[i] = 1/4).mxMean Xt trajectory of the diffusion.
myMean Yt trajectory of the diffusion.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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