log likelihood and derivatives for Gamma distribution
Arguments
- x
variable that is distributed by gamma distribution
- shape
this is the distribution's shape parameter. Must be positive.
- rate
this is the distribution's rate parameters. Must be positive.
- full
Add the data frame showing x, mean, sd as well as the fx and derivatives
Value
data frame with fx for the log pdf value of with dProb
that has the derivatives with respect to the prob parameters at
the observation time-point
Details
In an rxode2() model, you can use llikGamma() but you have to
use the x and rate arguments. You can also get the derivative of shape or rate with
llikGammaDshape() and llikGammaDrate().
Examples
# \donttest{
llikGamma(1, 1, 10)
#> fx dShape dRate
#> 1 -7.697415 2.879801 -0.9
# You can use this in `rxode2` too:
et <- et(seq(0.001, 1, length.out=10))
et$shape <- 1
et$rate <- 10
model <- function() {
model({
fx <- llikGamma(time, shape, rate)
dShape<- llikGammaDshape(time, shape, rate)
dRate <- llikGammaDrate(time, shape, rate)
})
}
rxSolve(model, et)
#>
#>
#>
#>
#> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’
#> ── Solved rxode2 object ──
#> ── Parameters ($params): ──
#> # A tibble: 1 × 0
#> ── Initial Conditions ($inits): ──
#> named numeric(0)
#> ── First part of data (object): ──
#> # A tibble: 10 × 6
#> time fx dShape dRate rate shape
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.001 2.29 -4.03 0.099 10 1
#> 2 0.112 1.18 0.691 -0.012 10 1
#> 3 0.223 0.0726 1.38 -0.123 10 1
#> 4 0.334 -1.04 1.78 -0.234 10 1
#> 5 0.445 -2.15 2.07 -0.345 10 1
#> 6 0.556 -3.26 2.29 -0.456 10 1
#> # ℹ 4 more rows
# }