Package 'inlatools'

Title: Diagnostic Tools for INLA Models
Description: Several functions which can be useful to choose sensible priors and diagnose the fitted model.
Authors: Thierry Onkelinx [aut, cre] (<https://orcid.org/0000-0001-8804-4216>, Research Institute for Nature and Forest (INBO)), Research Institute for Nature and Forest (INBO) [cph, fnd]
Maintainer: Thierry Onkelinx <[email protected]>
License: GPL-3
Version: 0.0.3
Built: 2024-12-03 06:14:02 UTC
Source: https://github.com/inbo/inlatools

Help Index


The generalised Poisson distribution

Description

The generalised Poisson distribution

Usage

dgpoisson(y, mu, phi)

rgpoisson(n, mu, phi)

Arguments

y

a vector of positive integers for which to calculate the density

mu

a vector of averages for which to calculate the density

phi

a single overdispersion parameter

n

the number of simulated values

Value

a matrix with the density for each combination of y (rows) and mu (cols)

See Also

Other statistics: dispersion(), fitted,inla-method, get_observed(), residuals,inla-method

Other statistics: dispersion(), fitted,inla-method, get_observed(), residuals,inla-method


Calculate a measure for dispersion

Description

The measure is calculated as the average of the squared Pearson residuals

Usage

dispersion(observed, fitted, variance)

Arguments

observed

the observed values

fitted

the fitted values

variance

the variance of the fitted values

See Also

Other statistics: dgpoisson(), fitted,inla-method, get_observed(), residuals,inla-method

Examples

library(INLA)
set.seed(20181202)
model <- inla(
  poisson ~ 1,
  family = "poisson",
  data = data.frame(
    poisson = rpois(20, lambda = 10),
    base = 1
  ),
  control.predictor = list(compute = TRUE)
)
dispersion(
  observed = get_observed(model),
  fitted = fitted(model),
  variance = fitted(model)
)

Use simulations to check for overdispersion or underdispersion

Description

Use simulations to check for overdispersion or underdispersion

Usage

dispersion_check(object, nsim = 1000)

## S4 method for signature 'inla'
dispersion_check(object, nsim = 1000)

Arguments

object

the INLA model

nsim

the number of simulation

See Also

Other checks: distribution_check(), fast_aggregation_check(), fast_distribution_check(), get_anomaly()

Examples

library(INLA)
set.seed(20181202)
model <- inla(
  poisson ~ 1,
  family = "poisson",
  data = data.frame(
    poisson = rpois(20, lambda = 10),
    base = 1
  ),
  control.predictor = list(compute = TRUE)
)
dc <- dispersion_check(model)
str(dc)

Use simulations to compare the observed distribution with the modelled distribution

Description

Use simulations to compare the observed distribution with the modelled distribution

Usage

distribution_check(object, nsim = 1000, seed = 0L)

## S4 method for signature 'inla'
distribution_check(object, nsim = 1000, seed = 0L)

Arguments

object

the INLA model

nsim

the number of simulation

seed

See the same argument in ?inla.qsample for further information. In order to produce reproducible results, you ALSO need to make sure the RNG in R is in the same state, see example below. When seed is non-zero, num.threads is forced to "1:1" and parallel.configs is set to FALSE, since parallel sampling would not produce a reproducible sequence of pseudo-random numbers.

See Also

Other checks: dispersion_check(), fast_aggregation_check(), fast_distribution_check(), get_anomaly()

Examples

library(INLA)
model <- inla(
  poisson ~ 1,
  family = "poisson",
  data = data.frame(
    poisson = rpois(20, lambda = 10),
    base = 1
  ),
  control.predictor = list(compute = TRUE),
  control.compute = list(config = TRUE)
)
distribution_check(model, seed = 20181202)

Fast aggregation check Compare the observed and modelled aggregated values.

Description

Fast aggregation check Compare the observed and modelled aggregated values.

Usage

fast_aggregation_check(
  object,
  grouping_vars,
  fun = sum,
  remove_na = TRUE,
  nsim = 1000
)

## S4 method for signature 'inla'
fast_aggregation_check(
  object,
  grouping_vars,
  fun = sum,
  remove_na = TRUE,
  nsim = 1000
)

Arguments

object

the INLA model

grouping_vars

character vector of variable names to group by.

fun

function to apply to the aggregated values.

remove_na

logical. Indicated whether to remove observations where the response variable is a missing values.

nsim

the number of simulation

See Also

Other checks: dispersion_check(), distribution_check(), fast_distribution_check(), get_anomaly()


Use simulations to compare the observed distribution with the modelled distribution

Description

This check uses the fitted values and thus ignores the uncertainty on the predictions

Usage

fast_distribution_check(object, nsim = 1000)

## S4 method for signature 'inla'
fast_distribution_check(object, nsim = 1000)

## S4 method for signature 'list'
fast_distribution_check(object, nsim = 1000)

Arguments

object

the INLA model

nsim

the number of simulation

See Also

Other checks: dispersion_check(), distribution_check(), fast_aggregation_check(), get_anomaly()

Examples

library(INLA)
set.seed(20181202)
model <- inla(
  poisson ~ 1,
  family = "poisson",
  data = data.frame(
    poisson = rpois(20, lambda = 10),
    base = 1
  ),
  control.predictor = list(compute = TRUE)
)
fast_distribution_check(model)

Extract the fitted values from an INLA model

Description

Extract the fitted values from an INLA model

Usage

## S4 method for signature 'inla'
fitted(object, ...)

Arguments

object

an object for which the extraction of model fitted values is meaningful.

...

other arguments.

See Also

Other statistics: dgpoisson(), dispersion(), get_observed(), residuals,inla-method

Examples

library(INLA)
set.seed(20181202)
model <- inla(#'   poisson ~ 1,
  poisson ~ 1,
  family = "poisson",
  data = data.frame(
    poisson = rpois(20, lambda = 10),
    base = 1
  ),
  control.predictor = list(compute = TRUE)
)
fitted(model)

Generate dummy data with several distributions

Description

All distributions share the same latent variable ηij=a+bi\eta_{ij} = a + b_i with bi=N(0,σr)b_i = N(0, \sigma_r).

Usage

generate_data(
  a = 0,
  sigma_random = 0.5,
  n_random = 20,
  n_replicate = 10,
  nb_size = 1,
  b_size = 5,
  zero_inflation = 0.5
)

Arguments

a

the intercept of the latent variable

sigma_random

The standard error for the random effect σr\sigma_r.

n_random

the number of random effect levels (groups).

n_replicate

the number of observation per random effect level.

nb_size

the size parameter of the negative binomial distribution. Passed to the size parameter of stats::rnbinom().

b_size

the size parameter of the binomial distribution. Passed to the size parameter of stats::rbinom().

zero_inflation

the probability the the observed value stems for the a point mass in zero.

Details

  • The Poisson distribution uses λ=eηij\lambda = e^{\eta_{ij}}.

  • The negation binomial distribution uses μ=eηij\mu = e^{\eta_{ij}}.

  • The binomial distribution uses πij=eηij/(eηij+1)\pi_{ij} = e^{\eta_{ij}}/(e^{\eta_{ij}}+ 1).

Value

A data.frame

  • ìd the id of the random effect.

  • eta the latent variable.

  • zero_inflation use the point mass in zero.

  • poisson the Poisson distributed variable.

  • zipoisson the zero-inflated Poisson distributed variable.

  • negbin the negative binomial distributed variable.

  • zinegbin the zero-inflated negative binomial distributed variable.

  • binom the binomial distributed variable.

See Also

Other utils: plot.dispersion_check(), plot.distribution_check()

Examples

set.seed(20181202)
head(generate_data())

Get a set of anomalies of the model

Description

Returns a named list with an element called observations and one element for every random effect. The random effect components use the name of the random effect.

Usage

get_anomaly(object, n = 10)

## S4 method for signature 'inla'
get_anomaly(object, n = 20)

Arguments

object

the INLA model

n

the number of anomalies per criterion. Defaults to 10.

Details

observations is a subset of the original data.frame. It contains the rows with the n largest and n smallest values of the Pearson residuals. The random effect components contain a subset of the random effects. Here we select the rows with the n largest and n lowest values of the mean.

See Also

Other checks: dispersion_check(), distribution_check(), fast_aggregation_check(), fast_distribution_check()

Examples

library(INLA)
set.seed(20181202)
model <- inla(
  poisson ~ 1,
  family = "poisson",
  data = data.frame(
    poisson = rpois(20, lambda = 10),
    base = 1
  ),
  control.predictor = list(compute = TRUE)
)
dc <- get_anomaly(model, n = 2)
str(dc)

get the observed values from the model object

Description

get the observed values from the model object

Usage

get_observed(object)

Arguments

object

the INLA model

See Also

Other statistics: dgpoisson(), dispersion(), fitted,inla-method, residuals,inla-method

Examples

library(INLA)
set.seed(20181202)
model <- inla(
  poisson ~ 1,
  family = "poisson",
  data = data.frame(
    poisson = rpois(20, lambda = 10),
    base = 1
  ),
  control.predictor = list(compute = TRUE)
)
get_observed(model)

Plot the results from a dispersion check

Description

Plot the results from a dispersion check

Usage

## S3 method for class 'dispersion_check'
plot(x, y, ...)

Arguments

x

a dispersion_check object. Which is the output of ⁠\link{dispersion_check}⁠

y

currently ignored

...

currently ignored

Value

A ⁠\link[ggplot2]{ggplot}⁠ object.

See Also

Other utils: generate_data(), plot.distribution_check()

Examples

library(INLA)
set.seed(20181202)
model <- inla(
  poisson ~ 1,
  family = "poisson",
  data = data.frame(
    poisson = rpois(20, lambda = 10),
    base = 1
  ),
  control.predictor = list(compute = TRUE)
)
dc <- dispersion_check(model)
plot(dc)

Plot the results from a distribution check

Description

Plot the results from a distribution check

Usage

## S3 method for class 'distribution_check'
plot(x, y, ..., n = FALSE, scales = "fixed")

Arguments

x

a distribution_check object. Which is the output of ⁠\link{fast_distribution_check}⁠

y

currently ignored

...

currently ignored

n

display the number of observations

scales

Should scales be fixed ("fixed", the default), free ("free"), or free in one dimension ("free_x", "free_y")?

Value

A ⁠\link[ggplot2]{ggplot}⁠ object.

See Also

Other utils: generate_data(), plot.dispersion_check()

Examples

library(INLA)
set.seed(20181202)
model <- inla(
  poisson ~ 1,
  family = "poisson",
  data = data.frame(
    poisson = rpois(20, lambda = 10),
    base = 1
  ),
  control.predictor = list(compute = TRUE)
)
fdc <- fast_distribution_check(model)
plot(fdc)

Plot Simulated Random Intercepts

Description

Plot Simulated Random Intercepts

Usage

## S3 method for class 'sim_iid'
plot(
  x,
  y,
  ...,
  link = c("identity", "log", "logit"),
  baseline,
  center = c("mean", "bottom", "top"),
  quantiles = c(0.025, 0.1, 0.5, 0.9, 0.975)
)

Arguments

x

A sim_iid object. Which is the output of ⁠\link{simulate_iid}⁠.

y

Currently ignored.

...

Currently ignored.

link

Which link to use for back transformation.

baseline

Optional baseline for the time series.

center

Defines how to centre the time series to the baseline. Options are: start all time series start at the baseline; mean the average of the time series is the baseline; bottom the lowest value of the time series equals the baseline; top the highest value of the time series equals the baseline.

quantiles

Which quantiles are shown on the plot.

Value

A ⁠\link[ggplot2]{ggplot}⁠ object.

See Also

Other priors: plot.sim_rw(), select_change(), select_divergence(), select_poly(), select_quantile(), simulate_iid(), simulate_rw()

Examples

set.seed(20181202)
x <- simulate_iid(sigma = 0.25)
plot(x)
plot(x, link = "log")
plot(x, link = "logit")

Plot Simulated Random Walks

Description

Plot Simulated Random Walks

Usage

## S3 method for class 'sim_rw'
plot(
  x,
  y,
  ...,
  link = c("identity", "log", "logit"),
  baseline,
  center = c("start", "mean", "bottom", "top")
)

Arguments

x

An sim_rw object. Which is the output of ⁠\link{simulate_rw}⁠

y

Currently ignored.

...

Currently ignored.

link

Which link to use for back transformation.

baseline

Optional baseline for the time series.

center

Defines how to centre the time series to the baseline. Options are: start all time series start at the baseline; mean the average of the time series is the baseline; bottom the lowest value of the time series equals the baseline; top the highest value of the time series equals the baseline.

Value

A ⁠\link[ggplot2]{ggplot}⁠ object.

See Also

Other priors: plot.sim_iid(), select_change(), select_divergence(), select_poly(), select_quantile(), simulate_iid(), simulate_rw()

Examples

set.seed(20181202)
x <- simulate_rw(sigma = 0.05, start = -10, length = 40)
plot(x)
plot(select_quantile(x))
plot(select_quantile(x), link = "log")
plot(select_quantile(x), link = "logit")
x <- simulate_rw(sigma = 0.001, start = -10, length = 40, order = 2)
plot(x)
plot(select_quantile(x))
plot(select_quantile(x), link = "log")
plot(select_quantile(x), link = "logit")

Convert the posterior marginal of a precision to a standard deviation

Description

Convert the posterior marginal of a precision to a standard deviation

Usage

prec2sd(marg)

Arguments

marg

A matrix with columns "y" and "x" where "y" is the marginal of the precision.

Value

A data.frame with the mean, standard deviation and 2.5%, 25%, 50%, 75% and 97.5% quantiles of the posterior of the standard deviation.

Examples

stopifnot(require(INLA))
model <- inla(Sepal.Length ~ Species, data = iris, family = "gaussian")
marg <- model$marginals.hyperpar[["Precision for the Gaussian observations"]]
prec2sd(marg)

Calculate the Residuals From an INLA Model

Description

Calculate the Residuals From an INLA Model

Usage

## S4 method for signature 'inla'
residuals(
  object,
  type = c("pearson", "deviance", "working", "response", "partial"),
  ...
)

Arguments

object

An inla object.

type

Currently only Pearson residuals are available. Other types are only listed for compatibility with the default residuals function.

...

Currently ignored.

See Also

Other statistics: dgpoisson(), dispersion(), fitted,inla-method, get_observed()

Examples

library(INLA)
set.seed(20181202)
model <- inla(
  poisson ~ 1,
  family = "poisson",
  data = data.frame(
    poisson = rpois(20, lambda = 10),
    base = 1
  ),
  control.predictor = list(compute = TRUE)
)
residuals(model)

Zero altered negative binomial

Description

Zero altered negative binomial

Usage

rzanbinom(n, mu, size, prob, tol = 2e-10)

Arguments

n

number of random values to return.

mu

alternative parametrization via mean: see ‘Details’.

size

target for number of successful trials, or dispersion parameter (the shape parameter of the gamma mixing distribution). Must be strictly positive, need not be integer.

prob

the point mass of zero

tol

the tolerance for low numbers


Zero-altered Poisson

Description

Generate random numbers from a zero-altered Poisson distribution

Usage

rzapois(n, lambda, prob, tol = 2e-10)

Arguments

n

number of random values to return.

lambda

vector of (non-negative) means.

prob

the point mass of zero

tol

the tolerance for low numbers


Zero inflated negative binomial

Description

Zero inflated negative binomial

Usage

rzinbinom(n, mu, size, prob)

Arguments

n

number of random values to return.

mu

alternative parametrization via mean: see ‘Details’.

size

target for number of successful trials, or dispersion parameter (the shape parameter of the gamma mixing distribution). Must be strictly positive, need not be integer.

prob

the mass of extra zero's


Zero-inflated Poisson

Description

Generate random numbers from a zero-inflated Poisson distribution

Usage

rzipois(n, lambda, prob)

Arguments

n

number of random values to return.

lambda

vector of (non-negative) means.

prob

the mass of extra zero's


Select Fast Changing Simulations from an sim_rw Object

Description

This functions count the number of changes in direction in each simulation. It returns the subset with the highest number of direction changes

Usage

select_change(x, n = 10)

Arguments

x

An sim_rw object. Which is the output of ⁠\link{simulate_rw}⁠

n

The number of simulations to plot when only a subset is shown.

See Also

Other priors: plot.sim_iid(), plot.sim_rw(), select_divergence(), select_poly(), select_quantile(), simulate_iid(), simulate_rw()


Select Diverging Simulations from an sim_rw Object

Description

The selection will contain the most extreme simulations base on either the minimum effect or the maximum effect within the simulation.

Usage

select_divergence(x, n = 10)

Arguments

x

An sim_rw object. Which is the output of ⁠\link{simulate_rw}⁠

n

The number of simulations to plot when only a subset is shown.

See Also

Other priors: plot.sim_iid(), plot.sim_rw(), select_change(), select_poly(), select_quantile(), simulate_iid(), simulate_rw()


Select Random Walks Best Matching Some Polygon Coefficients

Description

The target coefficients will be rescaled to have norm 1. The coefficients of the simulations will be rescaled by the largest norm over all simulations.

Usage

select_poly(x, coefs = c(0, -1), n = 10)

Arguments

x

An sim_rw object. Which is the output of ⁠\link{simulate_rw}⁠

coefs

The polynomial coefficients.

n

The number of simulations to plot when only a subset is shown.

See Also

Other priors: plot.sim_iid(), plot.sim_rw(), select_change(), select_divergence(), select_quantile(), simulate_iid(), simulate_rw()


select the quantiles from an sim_rw object

Description

select the quantiles from an sim_rw object

Usage

select_quantile(x, quantiles = c(0.025, 0.1, 0.25, 0.5, 0.75, 0.975))

Arguments

x

An sim_rw object. Which is the output of ⁠\link{simulate_rw}⁠

quantiles

a vector of quantiles

See Also

Other priors: plot.sim_iid(), plot.sim_rw(), select_change(), select_divergence(), select_poly(), simulate_iid(), simulate_rw()


simulate data from a second order random walk

Description

simulate data from a second order random walk

Usage

simulate_iid(sigma = NULL, tau = NULL, n_sim = 1000)

Arguments

sigma

the standard deviation of the random intercept

tau

the precision of the random intercept

n_sim

the number of simulations

Value

a data.frame with simulated time series from the random walk

See Also

Other priors: plot.sim_iid(), plot.sim_rw(), select_change(), select_divergence(), select_poly(), select_quantile(), simulate_rw()

Examples

set.seed(20181202)
x <- simulate_iid(sigma = 0.25)
head(x)

simulate data from a second order random walk

Description

simulate data from a second order random walk

Usage

simulate_rw(
  sigma = NULL,
  tau = NULL,
  length = 10,
  start = 1,
  order = 1,
  n_sim = 1000
)

Arguments

sigma

the standard deviation of the random walk process

tau

the precision of the random walk process

length

the length of the time series

start

the starting values of the time series

order

1 for first order random walk or 2 for second order random walk

n_sim

the number of simulations

Value

a data.frame with simulated time series from the random walk

See Also

Other priors: plot.sim_iid(), plot.sim_rw(), select_change(), select_divergence(), select_poly(), select_quantile(), simulate_iid()

Examples

set.seed(20181202)
x <- simulate_rw(sigma = 0.1, start = -10, length = 40)
head(x)
y <- simulate_rw(sigma = 0.001, start = -10, length = 40, order = 2)
head(y)

Variance of the negative binomial distribution

Description

The type 1 zero-inflated negative binomial distribution is a standard negative binomial distribution with an additional point mass at zero.

The type 1 zero-inflated poisson is a standard Poisson distribution with an additional point mass at zero.

Usage

var_nbinom(mu, size)

var_zinbinom1(mu, size, zero)

var_zipois1(mu, zero)

Arguments

mu

mean of the distribution. Must be on the original scale.

size

Size of the negative binomial distribution. Must be strict positive.

zero

Probability of the point mass at zero.