| Version: | 2.0.13 |
| Title: | Log-Likelihood Functions for 'rxode2' |
| Maintainer: | Matthew L. Fidler <matthew.fidler@gmail.com> |
| Depends: | R (≥ 4.0.0) |
| Suggests: | covr, testthat (≥ 3.0.0) |
| Imports: | Rcpp (≥ 1.0.8), checkmate, RcppParallel |
| Description: | Provides the log-likelihoods with gradients from 'stan' (Carpenter et al (2015), <doi:10.48550/arXiv.1509.07164>) needed for generalized log-likelihood estimation in 'nlmixr2' (Fidler et al (2019) <doi:10.1002/psp4.12445>). This is split of to reduce computational burden of recompiling 'rxode2' (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) which runs the 'nlmixr2' models during estimation. |
| BugReports: | https://github.com/nlmixr2/rxode2ll/issues/ |
| NeedsCompilation: | yes |
| License: | GPL (≥ 3) |
| URL: | https://nlmixr2.github.io/rxode2ll/, https://github.com/nlmixr2/rxode2ll/ |
| RoxygenNote: | 7.2.3 |
| Biarch: | true |
| LinkingTo: | Rcpp (≥ 1.0.8), RcppEigen (≥ 0.3.3.9.2), StanHeaders (≥ 2.21.0.7), BH (≥ 1.78.0.0), RcppParallel |
| Encoding: | UTF-8 |
| Language: | en-US |
| Config/testthat/edition: | 3 |
| Packaged: | 2024-12-15 01:27:21 UTC; matt |
| Author: | Matthew L. Fidler 
[aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2024-12-15 03:40:02 UTC |
Calculate the log likelihood of the binomial function (and its derivatives)
Description
Calculate the log likelihood of the binomial function (and its derivatives)
Usage
llikBeta(x, shape1, shape2, full = FALSE)
Arguments
x |
Observation |
shape1, shape2 |
non-negative parameters of the Beta distribution. |
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
dShape1 and dShape2 that has the derivatives with respect to the parameters at
the observation time-point
Author(s)
Matthew L. Fidler
Examples
x <- seq(1e-4, 1 - 1e-4, length.out = 21)
llikBeta(x, 0.5, 0.5)
llikBeta(x, 1, 3, TRUE)
Calculate the log likelihood of the binomial function (and its derivatives)
Description
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
Author(s)
Matthew L. Fidler
Examples
llikBinom(46:54, 100, 0.5)
llikBinom(46:54, 100, 0.5, TRUE)
log likelihood of Cauchy distribution and it's derivatives (from stan)
Description
log likelihood of Cauchy distribution and it's derivatives (from stan)
Usage
llikCauchy(x, location = 0, scale = 1, full = FALSE)
Arguments
x |
Observation |
location, scale |
location and scale parameters. |
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
dLocation and dScale that has the derivatives with respect to the parameters at
the observation time-point
Author(s)
Matthew L. Fidler
Examples
x <- seq(-3, 3, length.out = 21)
llikCauchy(x, 0, 1)
llikCauchy(x, 3, 1, full=TRUE)
log likelihood and derivatives for chi-squared distribution
Description
log likelihood and derivatives for chi-squared distribution
Usage
llikChisq(x, df, full = FALSE)
Arguments
x |
variable that is distributed by chi-squared distribution |
df |
degrees of freedom (non-negative, but can be non-integer). |
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 dDf
that has the derivatives with respect to the df parameter
the observation time-point
Author(s)
Matthew L. Fidler
Examples
llikChisq(1, df = 1:3, full=TRUE)
llikChisq(1, df = 6:9)
log likelihood and derivatives for exponential distribution
Description
log likelihood and derivatives for exponential distribution
Usage
llikExp(x, rate, full = FALSE)
Arguments
x |
variable that is distributed by exponential distribution |
rate |
vector of rates. |
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 dRate
that has the derivatives with respect to the rate parameter
the observation time-point
Author(s)
Matthew L. Fidler
Examples
llikExp(1, 1:3)
llikExp(1, 1:3, full=TRUE)
log likelihood and derivatives for F distribution
Description
log likelihood and derivatives for F distribution
Usage
llikF(x, df1, df2, full = FALSE)
Arguments
x |
variable that is distributed by f distribution |
df1, df2 |
degrees of freedom. |
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 dDf1 and dDf2
that has the derivatives with respect to the df1/df2 parameters at
the observation time-point
Author(s)
Matthew L. Fidler
Examples
x <- seq(0.001, 5, length.out = 100)
llikF(x^2, 1, 5)
log likelihood and derivatives for Gamma distribution
Description
log likelihood and derivatives for Gamma distribution
Usage
llikGamma(x, shape, rate, full = FALSE)
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
Author(s)
Matthew L. Fidler
Examples
llikGamma(1, 1, 10)
log likelihood and derivatives for Geom distribution
Description
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. |
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
Author(s)
Matthew L. Fidler
Examples
llikGeom(1:10, 0.2)
Calculate the log likelihood of the negative binomial function (and its derivatives)
Description
Calculate the log likelihood of the negative binomial function (and its derivatives)
Usage
llikNbinom(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
Author(s)
Matthew L. Fidler
Examples
llikNbinom(46:54, 100, 0.5)
llikNbinom(46:54, 100, 0.5, TRUE)
Calculate the log likelihood of the negative binomial function (and its derivatives)
Description
Calculate the log likelihood of the negative binomial function (and its derivatives)
Usage
llikNbinomMu(x, size, mu, full = FALSE)
Arguments
x |
Number of successes |
size |
Size of trial |
mu |
mu parameter for negative binomial |
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
Author(s)
Matthew L. Fidler
Examples
llikNbinomMu(46:54, 100, 40)
llikNbinomMu(46:54, 100, 40, TRUE)
Log likelihood for normal distribution
Description
Log likelihood for normal distribution
Usage
llikNorm(x, mean = 0, sd = 1, full = FALSE)
Arguments
x |
Observation |
mean |
Mean for the likelihood |
sd |
Standard deviation for the likelihood |
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 dMean and
dSd that has the derivatives with respect to the parameters at
the observation time-point
Author(s)
Matthew L. Fidler
Examples
llikNorm(0)
llikNorm(seq(-2,2,length.out=10), full=TRUE)
log-likelihood for the Poisson distribution
Description
log-likelihood for the Poisson distribution
Usage
llikPois(x, lambda, full = FALSE)
Arguments
x |
non negative integers |
lambda |
non-negative means |
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
dLambda that has the derivatives with respect to the parameters at
the observation time-point
Author(s)
Matthew L. Fidler
Log likelihood of T and it's derivatives (from stan)
Description
Log likelihood of T and it's derivatives (from stan)
Usage
llikT(x, df, mean = 0, sd = 1, full = FALSE)
Arguments
x |
Observation |
df |
degrees of freedom ( |
mean |
Mean for the likelihood |
sd |
Standard deviation for the likelihood |
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 dDf
dMean and dSd that has the derivatives with respect to the parameters at
the observation time-point
Author(s)
Matthew L. Fidler
Examples
x <- seq(-3, 3, length.out = 21)
llikT(x, 7, 0, 1)
llikT(x, 15, 0, 1, full=TRUE)
log likelihood and derivatives for Unif distribution
Description
log likelihood and derivatives for Unif distribution
Usage
llikUnif(x, alpha, beta, full = FALSE)
Arguments
x |
variable distributed by a uniform distribution |
alpha |
is the lower limit of the uniform distribution |
beta |
is the upper limit of the distribution |
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
Author(s)
Matthew L. Fidler
Examples
llikUnif(1, -2, 2)
log likelihood and derivatives for Weibull distribution
Description
log likelihood and derivatives for Weibull distribution
Usage
llikWeibull(x, shape, scale, full = FALSE)
Arguments
x |
variable distributed by a Weibull distribution |
shape, scale |
shape and scale parameters, the latter defaulting to 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
Author(s)
Matthew L. Fidler
Examples
llikWeibull(1, 1, 10)