log(f(y, theta, ...))
with respect to theta[[i]]
/theta[[i]]
and theta[[j]]
.numDerivLogf(f, isLogf = FALSE, logZero = .Machine$double.xmin,
logInf = .Machine$double.xmax/2, method = "Richardson",
method.args = list())numDeriv2Logf(f, isLogf = FALSE, logZero = .Machine$double.xmin,
logInf = .Machine$double.xmax/2, method = "Richardson",
method.args = list())
function(y, theta, ...)
, where theta
is a list of parameters.
A joint probability density function.TRUE
if f
is already log(f)
.log(f)
should return if f
evaluates to 0
.log(f)
should return if f
evaluates to Inf
.numDerivLogf
returns function(y, theta, i, ...)
which evaluates to the first derivative of log(f(y, theta, ...))
with respect to theta[[i]]
.numDeriv2Logf
returns function(y, theta, i, j, ...)
which evaluates to the second derivative of log(f(y, theta, ...))
with respect to theta[[i]]
and theta[[j]]
.
NaN
s if the log evaluates to (negative) Inf
so you may want to specify logZero
and logInf
.buildf
, DerivLogf
, fisherI