Package 'inlatools' reference manual

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:	2024-11-03 05:52:51 UTC
Source:	https://github.com/inbo/inlatools

The generalised Poisson distribution

Description

The generalised Poisson distribution

Usage

dgpoisson(y, mu, phi)

rgpoisson(n, mu, phi)
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)

Calculate a measure for dispersion

Description

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

Usage

dispersion(observed, fitted, variance)
dispersion(observed, fitted, variance)

Arguments

`observed`	the observed values
`fitted`	the fitted values
`variance`	the variance of the fitted values

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)
)
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)
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

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)
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)
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.

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)

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
)
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

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)
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

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

Arguments

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

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)
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 $\eta_{ij} = a + b_i$ with $b_i = N(0, \sigma_r)$ .

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
)
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 $\sigma_r$ .
`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 $\lambda = e^{\eta_{ij}}$ .
The negation binomial distribution uses $\mu = e^{\eta_{ij}}$ .
The binomial distribution uses $\pi_{ij} = e^{\eta_{ij}}/(e^{\eta_{ij}}+ 1)$ .

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.

Examples

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

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)
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)
get_observed(object)

Arguments

object

the INLA model

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)
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, ...)
## 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.

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)
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")
## 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.

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)
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)
)
## 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.

Examples

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

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")

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

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)
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)
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)
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)
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)
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)
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.

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)
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.

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)
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.

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))
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

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)
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

Examples

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

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)
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)
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.

Package 'inlatools'

Help Index

The generalised Poisson distribution

Description

Usage

Arguments

Value

See Also

Calculate a measure for dispersion

Description

Usage

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Examples

Use simulations to check for overdispersion or underdispersion

Description

Usage

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Examples

Use simulations to compare the observed distribution with the modelled distribution

Description

Usage

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Examples

Fast aggregation check Compare the observed and modelled aggregated values.

Description

Usage

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See Also

Use simulations to compare the observed distribution with the modelled distribution

Description

Usage

Arguments

See Also

Examples

Extract the fitted values from an INLA model

Description

Usage

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Examples

Generate dummy data with several distributions

Description

Usage

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Details

Value

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Examples

Get a set of anomalies of the model

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Usage

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Details

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Examples

get the observed values from the model object

Description

Usage

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Examples

Plot the results from a dispersion check

Description

Usage

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Value

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Examples

Plot the results from a distribution check

Description

Usage

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Value

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Examples

Plot Simulated Random Intercepts

Description

Select Fast Changing Simulations from an `sim_rw` Object

Select Diverging Simulations from an `sim_rw` Object

select the quantiles from an `sim_rw` object