log likelihood and derivatives for Geom distribution

## Usage

llikGeom(x, prob, full = FALSE)

## Arguments

x

variable distributed by a geom distribution

prob

probability of success in each trial. 0 < prob <= 1.

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 llikGeom() but you have to use the x and rate arguments. You can also get the derivative of prob with llikGeomDprob().

## Author

Matthew L. Fidler

## Examples


# \donttest{

llikGeom(1:10, 0.2)
#>           fx dProb
#> 1  -1.832581  3.75
#> 2  -2.055725  2.50
#> 3  -2.278869  1.25
#> 4  -2.502012  0.00
#> 5  -2.725156 -1.25
#> 6  -2.948299 -2.50
#> 7  -3.171443 -3.75
#> 8  -3.394586 -5.00
#> 9  -3.617730 -6.25
#> 10 -3.840873 -7.50

et  <- et(1:10)
et$prob <- 0.2 model <- function() { model({ fx <- llikGeom(time, prob) dProb <- llikGeomDprob(time, prob) }) } rxSolve(model, et) #> #> #> #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ── Solved rxode2 object ── #> ── Parameters ($params): ──
#> # A tibble: 1 × 0
#> ── Initial Conditions (\$inits): ──
#> named numeric(0)
#> ── First part of data (object): ──
#> # A tibble: 10 × 4
#>    time    fx dProb  prob
#>   <dbl> <dbl> <dbl> <dbl>
#> 1     1 -1.83  3.75   0.2
#> 2     2 -2.06  2.5    0.2
#> 3     3 -2.28  1.25   0.2
#> 4     4 -2.50  0      0.2
#> 5     5 -2.73 -1.25   0.2
#> 6     6 -2.95 -2.5    0.2
#> # ℹ 4 more rows
# }