mix_mode.md
In BayesMultiMode: Bayesian Mode Inference

mix_mode() returns expected results with dist = shifted_poisson and flat modes

Code
  modes$mode_estimates
Output
  [1]  0  1 10

Code
  summary(mix)
Output
  Estimated mixture distribution.
  - Mixture type: discrete
  - Number of components: 2
  - Distribution family: shifted_poisson
  - Number of distribution variables: 2
  - Names of variables: lambda kappa
  - Parameter estimates:
    pars (numeric vector, dim 6): 
     eta1    eta2 lambda1 lambda2  kappa1  kappa2 
      0.5     0.5     0.1     1.0    10.0     0.0

Code
  summary(modes)
Output
  Modes of a shifted_poisson mixture with 2 components.
  - Number of modes found: 3
  - Mode estimation technique: discrete algorithm
  - Estimates of mode locations:
    mode_estimates (numeric vector, dim 3): 
  [1]  0  1 10

mix_mode() function returns expected results with dist = poisson

Code
  modes$mode_estimates
Output
  [1] 0 9

Code
  summary(mix)
Output
  Estimated mixture distribution.
  - Mixture type: discrete
  - Number of components: 2
  - Distribution family: poisson
  - Number of distribution variables: 1
  - Names of variables: lambda
  - Parameter estimates:
    pars (numeric vector, dim 4): 
     eta1    eta2 lambda1 lambda2 
      0.5     0.5     0.1    10.0

Code
  summary(modes)
Output
  Modes of a poisson mixture with 2 components.
  - Number of modes found: 2
  - Mode estimation technique: discrete algorithm
  - Estimates of mode locations:
    mode_estimates (numeric vector, dim 2): 
  [1] 0 9

mix_mode() function returns expected results with arbitrary function

Code
  modes$mode_estimates
Output
  [1]  0 18

Code
  summary(mix)
Output
  Estimated mixture distribution.
  - Mixture type: discrete
  - Number of components: 2
  - Distribution family: NA
  - Number of distribution variables: 2
  - Names of variables: mu size
  - Parameter estimates:
    pars (numeric vector, dim 6): 
   eta1  eta2   mu1   mu2 size1 size2 
    0.5   0.5  20.0   5.0  20.0   0.5

Code
  summary(modes)
Output
  Modes of a discrete mixture with 2 components.
  - Number of modes found: 2
  - Mode estimation technique: discrete algorithm
  - Estimates of mode locations:
    mode_estimates (numeric vector, dim 2): 
  [1]  0 18

mix_mode() function returns expected results with dist = skew_normal

Code
  modes$mode_estimates
Output
  [1] 0.002088749 5.999997076

Code
  summary(mix)
Output
  Estimated mixture distribution.
  - Mixture type: continuous
  - Number of components: 2
  - Distribution family: skew_normal
  - Number of distribution variables: 3
  - Names of variables: xi omega alpha
  - Parameter estimates:
    pars (numeric vector, dim 8): 
    eta1   eta2    xi1    xi2 omega1 omega2 
     0.8    0.2    0.0    6.0    1.0    2.0 
  ... (2 more elements)

Code
  summary(modes)
Output
  Modes of a skew_normal mixture with 2 components.
  - Number of modes found: 2
  - Mode estimation technique: Modal Expectation-Maximization (MEM) algorithm
  - Estimates of mode locations:
    mode_estimates (numeric vector, dim 2): 
  [1] 0 6

mix_mode() function returns expected results with an arbitrary function

Code
  modes$mode_estimates
Output
  [1] 0.00182144 5.88332478

Code
  summary(mix)
Output
  Estimated mixture distribution.
  - Mixture type: continuous
  - Number of components: 2
  - Distribution family: NA
  - Number of distribution variables: 4
  - Names of variables: mu sigma xi nu
  - Parameter estimates:
    pars (numeric vector, dim 10): 
    eta1   eta2    mu1    mu2 sigma1 sigma2 
     0.8    0.2    0.0    6.0    1.0    2.0 
  ... (4 more elements)

Code
  summary(modes)
Output
  Modes of a continuous mixture with 2 components.
  - Number of modes found: 2
  - Mode estimation technique: Modal Expectation-Maximization (MEM) algorithm
  - Estimates of mode locations:
    mode_estimates (numeric vector, dim 2): 
  [1] 0 6

mix_mode() function returns expected results

Code
  modes$mode_estimates
Output
  [1] 0.006915293 4.999402022

Code
  summary(mix)
Output
  Estimated mixture distribution.
  - Mixture type: continuous
  - Number of components: 2
  - Distribution family: normal
  - Number of distribution variables: 2
  - Names of variables: mu sigma
  - Parameter estimates:
    pars (numeric vector, dim 6): 
    eta1   eta2    mu1    mu2 sigma1 sigma2 
     0.8    0.2    0.0    5.0    1.0    2.0

Code
  summary(modes)
Output
  Modes of a normal mixture with 2 components.
  - Number of modes found: 2
  - Mode estimation technique: fixed-point algorithm
  - Estimates of mode locations:
    mode_estimates (numeric vector, dim 2): 
  [1] 0 5