tests/testthat/_snaps/distributions.md

In greta-dev/greta: Simple and Scalable Statistical Modelling in R

uniform distribution errors informatively

Code
  uniform(min = 0, max = NA)
Condition
  Error in `initialize()`:
  ! `max` must be a numeric vector of length 1
  However its class, and length are:
  `max`:
  * (class: <logical>)
  * (length: 1)

Code
  uniform(min = 0, max = head)
Condition
  Error in `initialize()`:
  ! `max` must be a numeric vector of length 1
  However its class, and length are:
  `max`:
  * (class: <function>)
  * (length: 1)

Code
  uniform(min = 1:3, max = 5)
Condition
  Error in `initialize()`:
  ! `min` must be a numeric vector of length 1
  However its class, and length are:
  `min`:
  * (class: <integer>)
  * (length: 3)

Code
  uniform(min = -Inf, max = Inf)
Condition
  Error in `initialize()`:
  ! `-Inf` must be a finite scalar
  But their values are:
  `-Inf`: -Inf

Code
  uniform(min = 1, max = 1)
Condition
  Error in `initialize()`:
  ! `max` must be greater than `min`
  Their values are:
  `min`: 1
  `max`: 1

poisson() and binomial() error informatively in glm

Code
  glm(1 ~ 1, family = poisson)
Condition
  Error in `family()`:
  ! Wrong function name provided in another model
  It looks like you're using greta's `poisson()` function in the family argument of another model.
  Maybe you want to use `family = stats::poisson`,instead?

Code
  glm(1 ~ 1, family = binomial)
Condition
  Error in `family()`:
  ! Wrong function name provided in another model
  It looks like you're using greta's `binomial()` function in the family argument of another model.
  Maybe you want to use `family = stats::binomial`,instead?

Code
  glm(1 ~ 1, family = poisson())
Condition
  Error in `poisson()`:
  ! Wrong function name provided in another model
  It looks like you're using greta's `poisson()` function in the family argument of another model.
  Maybe you want to use `family = stats::poisson`,instead?

Code
  glm(1 ~ 1, family = poisson("sqrt"))
Condition
  Error in `poisson()`:
  ! Wrong function name provided in another model
  It looks like you're using greta's `poisson()` function in the family argument of another model.
  Maybe you want to use `family = stats::poisson`,instead?

wishart distribution errors informatively

Code
  wishart(3, b)
Condition
  Error in `initialize()`:
  ! `Sigma` must be a square 2D greta array
  However, `Sigma` has dimensions "3x3x3"

Code
  wishart(3, c)
Condition
  Error in `initialize()`:
  ! `Sigma` must be a square 2D greta array
  However, `Sigma` has dimensions "3x2"

lkj_correlation distribution errors informatively

Code
  lkj_correlation(-1, dim)
Condition
  Error in `initialize()`:
  ! `eta` must be a positive scalar value, or a scalar <greta_array>

Code
  lkj_correlation(c(3, 3), dim)
Condition
  Error in `initialize()`:
  ! `eta` must be a positive scalar value, or a scalar <greta_array>

Code
  lkj_correlation(uniform(0, 1, dim = 2), dim)
Condition
  Error in `initialize()`:
  ! `eta` must be a scalar
  However `eta` had dimensions: 2x1

Code
  lkj_correlation(4, dimension = -1)
Condition
  Error in `initialize()`:
  ! `dimension` must be a positive scalar integer giving the dimension of the distribution
  `dim(target)` returns:

Code
  lkj_correlation(4, dim = c(3, 3))
Condition
  Error in `initialize()`:
  ! `dimension` must be a positive scalar integer giving the dimension of the distribution
  `dim(target)` returns:

Code
  lkj_correlation(4, dim = NA)
Condition
  Error in `initialize()`:
  ! `dimension` must be a positive scalar integer giving the dimension of the distribution
  `dim(target)` returns:

multivariate_normal distribution errors informatively

Code
  multivariate_normal(m_c, a)
Condition
  Error in `check_dimension()`:
  ! the dimension of this distribution must be at least 2, but was 1
  multivariate distributions treat each row as a separate realisation - perhaps you need to transpose something?

Code
  multivariate_normal(m_d, a)
Condition
  Error in `check_dimension()`:
  ! the dimension of this distribution must be at least 2, but was 1
  multivariate distributions treat each row as a separate realisation - perhaps you need to transpose something?

Code
  multivariate_normal(m_a, b)
Condition
  Error in `lapply()`:
  ! Dimensions of parameters not compatible with multivariate distribution parameters of multivariate distributions cannot have more than two dimensions
  object `x` has dimensions: 3x3x3

Code
  multivariate_normal(m_a, c)
Condition
  Error in `lapply()`:
  ! Object must be 2D square array
  x But it had dimension: "3x2"

Code
  multivariate_normal(m_a, d)
Condition
  Error in `check_dimension()`:
  ! distribution dimensions do not match implied dimensions
  The distribution dimension should be 3, but parameters implied dimensions: 3 vs 4
  Multivariate distributions treat each row as a separate realisation - perhaps you need to transpose something?

Code
  multivariate_normal(0, 1)
Condition
  Error in `check_dimension()`:
  ! the dimension of this distribution must be at least 2, but was 1
  multivariate distributions treat each row as a separate realisation - perhaps you need to transpose something?

Code
  multivariate_normal(m_a, a, n_realisations = -1)
Condition
  Error in `check_n_realisations()`:
  ! `n_realisations is not a positive scalar interger`
  `n_realisations` must be a positive scalar integer giving the number of rows of the output
  x We see `n_realisations` = `-1` having class: <numeric> and length `1`

Code
  multivariate_normal(m_a, a, n_realisations = c(1, 3))
Condition
  Error in `check_n_realisations()`:
  ! `n_realisations is not a positive scalar interger`
  `n_realisations` must be a positive scalar integer giving the number of rows of the output
  x We see `n_realisations` = `1` and `3` having class: <numeric> and length `2`

Code
  multivariate_normal(m_a, a, dimension = -1)
Condition
  Error in `check_multivariate_dims()`:
  ! `dimension` must be a positive scalar integer giving the dimension of the distribution
  `dim(target)` returns:

Code
  multivariate_normal(m_a, a, dimension = c(1, 3))
Condition
  Error in `check_multivariate_dims()`:
  ! `dimension` must be a positive scalar integer giving the dimension of the distribution
  `dim(target)` returns:

multinomial distribution errors informatively

Code
  multinomial(c(1), 1)
Condition
  Error in `check_dimension()`:
  ! the dimension of this distribution must be at least 2, but was 1
  multivariate distributions treat each row as a separate realisation - perhaps you need to transpose something?

Code
  multinomial(10, p_a, n_realisations = -1)
Condition
  Error in `check_n_realisations()`:
  ! `n_realisations is not a positive scalar interger`
  `n_realisations` must be a positive scalar integer giving the number of rows of the output
  x We see `n_realisations` = `-1` having class: <numeric> and length `1`

Code
  multinomial(10, p_a, n_realisations = c(1, 3))
Condition
  Error in `check_n_realisations()`:
  ! `n_realisations is not a positive scalar interger`
  `n_realisations` must be a positive scalar integer giving the number of rows of the output
  x We see `n_realisations` = `1` and `3` having class: <numeric> and length `2`

Code
  multinomial(10, p_a, dimension = -1)
Condition
  Error in `check_multivariate_dims()`:
  ! `dimension` must be a positive scalar integer giving the dimension of the distribution
  `dim(target)` returns:

Code
  multinomial(10, p_a, dimension = c(1, 3))
Condition
  Error in `check_multivariate_dims()`:
  ! `dimension` must be a positive scalar integer giving the dimension of the distribution
  `dim(target)` returns:

categorical distribution errors informatively

Code
  categorical(1)
Condition
  Error in `check_dimension()`:
  ! the dimension of this distribution must be at least 2, but was 1
  multivariate distributions treat each row as a separate realisation - perhaps you need to transpose something?

Code
  categorical(p_a, n_realisations = -1)
Condition
  Error in `check_n_realisations()`:
  ! `n_realisations is not a positive scalar interger`
  `n_realisations` must be a positive scalar integer giving the number of rows of the output
  x We see `n_realisations` = `-1` having class: <numeric> and length `1`

Code
  categorical(p_a, n_realisations = c(1, 3))
Condition
  Error in `check_n_realisations()`:
  ! `n_realisations is not a positive scalar interger`
  `n_realisations` must be a positive scalar integer giving the number of rows of the output
  x We see `n_realisations` = `1` and `3` having class: <numeric> and length `2`

Code
  categorical(p_a, dimension = -1)
Condition
  Error in `check_multivariate_dims()`:
  ! `dimension` must be a positive scalar integer giving the dimension of the distribution
  `dim(target)` returns:

Code
  categorical(p_a, dimension = c(1, 3))
Condition
  Error in `check_multivariate_dims()`:
  ! `dimension` must be a positive scalar integer giving the dimension of the distribution
  `dim(target)` returns:

dirichlet distribution errors informatively

Code
  dirichlet(1)
Condition
  Error in `check_dimension()`:
  ! the dimension of this distribution must be at least 2, but was 1
  multivariate distributions treat each row as a separate realisation - perhaps you need to transpose something?

Code
  dirichlet(alpha_a, n_realisations = -1)
Condition
  Error in `check_n_realisations()`:
  ! `n_realisations is not a positive scalar interger`
  `n_realisations` must be a positive scalar integer giving the number of rows of the output
  x We see `n_realisations` = `-1` having class: <numeric> and length `1`

Code
  dirichlet(alpha_a, n_realisations = c(1, 3))
Condition
  Error in `check_n_realisations()`:
  ! `n_realisations is not a positive scalar interger`
  `n_realisations` must be a positive scalar integer giving the number of rows of the output
  x We see `n_realisations` = `1` and `3` having class: <numeric> and length `2`

Code
  dirichlet(alpha_a, dimension = -1)
Condition
  Error in `check_multivariate_dims()`:
  ! `dimension` must be a positive scalar integer giving the dimension of the distribution
  `dim(target)` returns:

Code
  dirichlet(alpha_a, dimension = c(1, 3))
Condition
  Error in `check_multivariate_dims()`:
  ! `dimension` must be a positive scalar integer giving the dimension of the distribution
  `dim(target)` returns:

dirichlet-multinomial distribution errors informatively

Code
  dirichlet_multinomial(c(1), 1)
Condition
  Error in `check_dimension()`:
  ! the dimension of this distribution must be at least 2, but was 1
  multivariate distributions treat each row as a separate realisation - perhaps you need to transpose something?

Code
  dirichlet_multinomial(10, alpha_a, n_realisations = -1)
Condition
  Error in `check_n_realisations()`:
  ! `n_realisations is not a positive scalar interger`
  `n_realisations` must be a positive scalar integer giving the number of rows of the output
  x We see `n_realisations` = `-1` having class: <numeric> and length `1`

Code
  dirichlet_multinomial(10, alpha_a, n_realisations = c(1, 3))
Condition
  Error in `check_n_realisations()`:
  ! `n_realisations is not a positive scalar interger`
  `n_realisations` must be a positive scalar integer giving the number of rows of the output
  x We see `n_realisations` = `1` and `3` having class: <numeric> and length `2`

Code
  dirichlet_multinomial(10, alpha_a, dimension = -1)
Condition
  Error in `check_multivariate_dims()`:
  ! `dimension` must be a positive scalar integer giving the dimension of the distribution
  `dim(target)` returns:

Code
  dirichlet_multinomial(10, alpha_a, dimension = c(1, 3))
Condition
  Error in `check_multivariate_dims()`:
  ! `dimension` must be a positive scalar integer giving the dimension of the distribution
  `dim(target)` returns:

greta-dev/greta documentation built on Dec. 21, 2024, 5:03 a.m.