Description Usage Arguments Value Examples

View source: R/Disequilibrium.R

Log likelihood of market in disequilibrium model

1 2 | ```
LLikelihoodDE(theta, Y, X, transformR3toPD = TRUE, summed = TRUE,
MaskRho = FALSE)
``` |

`theta` |
A vector of parameter values to obtain the gradient at. The order of parameters is coefficients of equation 1, coefficients of equation 2, variance of equation 1, correlation of equations 1 and 2, and variance of equation 2. |

`Y` |
A vector of observed responses. |

`X` |
A list of two elements. The first element is a |

`transformR3toPD` |
A logical to determine if the covariance matrix is
transformed to an unrestricted 3 dimension real space ( |

`summed` |
A logical to determine if the negative log likelihood values are summed over observations. |

`MaskRho` |
A logical or numeric to determine if the correlation is masked. A value of FALSE means the correlation is not fixed. A value between -1 and 1 will fix the correlation to that value. |

A scalar value of the negative log likelihood if `summed = TRUE`

,
else a *N* length vector of negative log likelihood observations.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | ```
set.seed(1775)
library(MASS)
beta01 = c(1,1)
beta02 = c(-1,-1)
N = 10000
SigmaEps = diag(2)
SigmaX = diag(2)
MuX = c(0,0)
par0 = c(beta01, beta02, SigmaX[1, 1], SigmaX[1, 2], SigmaX[2, 2])
Xgen = mvrnorm(N,MuX,SigmaX)
X1 = cbind(1,Xgen[,1])
X2 = cbind(1,Xgen[,2])
X = list(X1 = X1,X2 = X2)
eps = mvrnorm(N,c(0,0),SigmaEps)
eps1 = eps[,1]
eps2 = eps[,2]
Y1 = X1 %*% beta01 + eps1
Y2 = X2 %*% beta02 + eps2
Y = pmin(Y1,Y2)
p1 = 2
p2 = 2
theta = c(beta01, beta02, log(SigmaX[1, 1]), atanh(SigmaX[1, 2]), log(SigmaX[2, 2]))
lhood = LLikelihoodDE(theta, Y, X, summed = TRUE)
head(LLikelihoodDE)
``` |

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