foehnix: Foehn Classification Based on a Two-Component Mixture Model

In retostauffer/Rfoehnix: Objective Foehn Diagnosis

Foehn Classification Based on a Two-Component Mixture Model

Description

This is the main method of the foehnix package to estimate two-component mixture models for automated foehn classification.

Usage

foehnix(
  formula,
  data,
  switch = FALSE,
  filter = NULL,
  family = "gaussian",
  control = foehnix.control(family, switch, ...),
  ...
)

## S3 method for class 'foehnix'
logLik(object, ...)

## S3 method for class 'foehnix'
nobs(object, ...)

## S3 method for class 'foehnix'
AIC(object, ...)

## S3 method for class 'foehnix'
BIC(object, ...)

## S3 method for class 'foehnix'
IGN(object, ...)

## S3 method for class 'foehnix'
edf(object, ...)

## S3 method for class 'foehnix'
print(x, ...)

## S3 method for class 'foehnix'
formula(x, ...)

## S3 method for class 'foehnix'
summary(object, eps = 1e-04, detailed = FALSE, ...)

Arguments

formula	an object of class formula (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under 'Details'.
data	a regular (not necessarily strictly regular) time series object of class zoo containing the variables for the two-part mixture model.
switch	logical. If set to TRUE the two estimated components will be switched. This is important if the covariate for the components (left hand side of input formula is smaller for cases with foehn than for cases without foehn, e.g., when using the temperature difference as main covariate.
filter	a named list can be provided to apply a custom (simple) filter to the observations on data. Can be used to e.g., prespecify a specific wind sector for foehn winds. Please read the manual of the foehnix_filter method for more details and examples.
family	character (at the moment "gaussian" or "logistic") or an object of class foehnix.family.
control	additional control arguments, see foehnix.control.
...	forwarded to foehnix.control
object	a foehnix object (input for S3 methods)
x	a foehnix object (input for S3 methods)
eps	threshold for posterior probabilities used in summary. numeric value, default 1e-4.
detailed	boolean, default FALSE. If TRUE, additional information will be returned.

Details

The two-component mixture model can be specified via formula object where the left hand side of the formula contains the 'main' variable explaining the two components (only one variable), the right hand side of the formula specifies the concomitant variables (multiple variables allowed). As an example: let's assume that our zoo object data contains the following columns:

• ff: observed wind speed at target site

• rh: observed relative humidity at target site

• diff_t: (potential) temperature difference between target site and a station upstream of the foehn wind direction

The specification for formula could e.g. look as follows:

• ff ~ 1: the two components of the mixture model will be based on the observed wind speed (ff), no concomitant model (right hand side is simply 1).

• ff ~ rh: similar to the specification above, but using observed relative humidity (rh) as concomitant.

• ff ~ rh + diff_t: as above but with an additional second concomitant variable (observed temperature difference, diff_t).

• diff_t ~ ff + rh: using temperature difference as the main variable for the two components while ff and rh are used as concomitants. Note: in this case it will be required to set switch = TRUE as lower values of diff_t indicate less stratified conditions where the occurrence of foehn is more likely. If switch = FALSE the two components (foehn and no foehn) may be interchanged and the model will return "probabilities of not observing foehn".

Note that these are just examples and have to be adjusted given data availability, location, structure/names of the variables in the data object.

The optional input filter allows to specify simple or complex filters (see foehnix_filter for details). This allows to add additional constraints, e.g., adding a filter on the observed wind direction to ensure that only events within a specific wind sector (the main foehn wind direction) are classified as "foehn".

Value

Returns an object of class foehnix.

Log-likelihood sum (numeric).

Number of observations (integer) used to train the model.

Returns the Akaike information criterion (numeric).

Bayesian information criterion (numeric).

Ignorance (mean negative log-likelihood; numeric).

Effective degrees of freedom.

Returns the formula of the model (formula).

Object of class summary.foehnix.

Author(s)

Reto Stauffer

References

Plavcan D, Mayr GJ, Zeileis A (2014). Automatic and Probabilistic Foehn Diagnosis with a Statistical Mixture Model. Journal of Applied Meteorology and Climatology. 53(3), 652–659. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1175/JAMC-D-13-0267.1")}

Gr\"un B, Leisch F (2007). Fitting Finite Mixtures of Generalized Linear Regressions in R. Computational Statistics \& Data Analysis. 51(11), 5247–5252. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.csda.2006.08.014")}

Gr\"un B, Leisch F (2008). FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters. Journal of Statistical Software, Articles. 28(4), 1–35. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v028.i04")}

Fraley C, Raftery AE (2002). Model-Based Clustering, Discriminant Analysis, and Density Estimation. Journal of the American Statistical Association. 97(458), 611–631. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1198/016214502760047131")}

See Also

See foehnix_filter for more information about the filter option. See also: tsplot, windrose.

Foehnix family objects: foehnix.family.

S3 methods for foehnix objects: plot.foehnix, predict.foehnix, fitted.foehnix, print.foehnix, summary.foehnix, windrose.foehnix, coef.foehnix, nobs.foehnix, edf.foehnix, AIC.foehnix, BIC.foehnix, IGN.foehnix, logLik.foehnix, ...

retostauffer/Rfoehnix documentation built on June 5, 2023, 11:39 p.m.