Calculate the log likelihood of the binomial function (and its derivatives)

Usage

llikBinom(x, size, prob, full = FALSE)

Arguments

x: Number of successes
size: Size of trial
prob: probability of success
full: Add the data frame showing x, mean, sd as well as the fx and derivatives

Value

data frame with fx for the pdf value of with dProb that has the derivatives with respect to the parameters at the observation time-point

Details

In an rxode2() model, you can use llikBinom() but you have to use all arguments. You can also get the derivative of prob with llikBinomDprob()

Author

Matthew L. Fidler

Examples

# \donttest{
llikBinom(46:54, 100, 0.5)
#>          fx dProb
#> 1 -2.848030   -16
#> 2 -2.709193   -12
#> 3 -2.610102    -8
#> 4 -2.550679    -4
#> 5 -2.530876     0
#> 6 -2.550679     4
#> 7 -2.610102     8
#> 8 -2.709193    12
#> 9 -2.848030    16

llikBinom(46:54, 100, 0.5, TRUE)
#>    x size prob        fx dProb
#> 1 46  100  0.5 -2.848030   -16
#> 2 47  100  0.5 -2.709193   -12
#> 3 48  100  0.5 -2.610102    -8
#> 4 49  100  0.5 -2.550679    -4
#> 5 50  100  0.5 -2.530876     0
#> 6 51  100  0.5 -2.550679     4
#> 7 52  100  0.5 -2.610102     8
#> 8 53  100  0.5 -2.709193    12
#> 9 54  100  0.5 -2.848030    16

# In rxode2 you can use:

et <- et(46:54)
et$size <- 100
et$prob <-0.5

model <- function() {
  model({
    fx <- llikBinom(time, size, prob)
    dProb <- llikBinomDprob(time, size, 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: 9 × 5
#>    time    fx dProb  size  prob
#>   <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1    46 -2.85   -16   100   0.5
#> 2    47 -2.71   -12   100   0.5
#> 3    48 -2.61    -8   100   0.5
#> 4    49 -2.55    -4   100   0.5
#> 5    50 -2.53     0   100   0.5
#> 6    51 -2.55     4   100   0.5
#> # ℹ 3 more rows
# }