osmultinom: Observation Specific Multinomial Log-linear Models
In locClass: Collection of Local Classification Methods

Description Usage Arguments Details Value References See Also

A localized version of multinomial regression.

1 2	osmultinom(formula, data, subset, na.action, contrasts = NULL, censored = FALSE, model = FALSE, ...)

`formula`	A formula expression as for regression models, of the form `response ~ predictors`. The response should be a factor or a matrix with K columns. If `censored = FALSE` this is required to be a zero-one indicator matrix. A log-linear model is fitted, with coefficients zero for the first class. An offset can be included: it should be a numeric matrix with K columns if the response is either a matrix with K columns or a factor with K > 2 classes, or a numeric vector for a response factor with 2 levels. See the documentation of `formula()` for other details.
`data`	An optional data frame in which to interpret the variables occurring in `formula`.
`subset`	An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.)
`na.action`	A function to specify the action to be taken if NAs are found. The default action is first the `na.action` setting of `options` and second `na.fail` if that is unset. An alternative is `na.omit`, which leads to rejection of cases with missing values on any required variable. (NOTE: If given, this argument must be named.)???
`contrasts`	A list of contrasts to be used for some or all of the factors appearing as variables in the model formula.
`censored`	Logical. If the response is a matrix with K > 2 classes, interpret the entries as one for possible classes, zero for impossible classes. Defaults to `FALSE`.
`model`	Logical. If `TRUE`, the model frame is saved as component `model` of the returned object.???
`...`	Additional arguments for `osnnet`, including the window function and bandwidth parameters used to generate observation weights: `wf` A window function which is used to calculate weights that are introduced into the fitting process. Either a character string or a function, e.g. `wf = function(x) exp(-x)`. For details see the documentation for `wfs`. `bw` (Required only if `wf` is a string.) The bandwidth parameter of the window function. (See `wfs`.) `k` (Required only if `wf` is a string.) The number of nearest neighbors of the decision boundary to be used in the fitting process. (See `wfs`.) `nn.only` (Required only if `wf` is a string indicating a window function with infinite support and if `k` is specified.) Should only the `k` nearest neighbors or all observations receive positive weights? (See `wfs`.) `itr` Number of iterations for model fitting, defaults to 3. See also the Details section.

This is a localized version of multinomial regression where a multinomial regression model is fitted for each test observation based on the training data near the trial point. It is based on the function multinom from package nnet. osnnet is called internally.

In osmultinom no fitting is done. In the prediction step an individual model for every test observation is fitted. Observations weights that reflect the importance of training observations for the fit at a particular test observation are calculated internally in predict.osmultinom. For this reason not all types of response in formula are allowed and osmultinom does not take all arguments that can be passed to multinom. As response in formula factors and matrices are allowed. If censored = FALSE only zero-one class indicator matrices are allowed. Argument weights is missing since observations weights are calculated internally in predict.osmultinom. summ that specifies a method to summarize rows of the model matrix is missing since this requires adjustment of the case weights. Also Hess is not supported.

An object of class osmultinom, inheriting from "osnnet", a list containing the following components:

`x`	A `matrix` containing the explanatory variables.
`y`	If argument `y` was a factor a class inicator matrix, otherwise a `matrix` that contains `y` unchanged.
`...`
`mask`	The `mask` vector used.
`maxit`	The `maxit` argument used.
`trace`	The `trace` argument used.
`abstol`	The `abstol` argument used.
`reltol`	The `reltol` argument used.
`lev`	If `y` is a factor the class labels (levels of `y`).
`wf`	The window function used. Always a function, even if the input was a string.
`bw`	(Only if `wf` is a string or was generated by means of one of the functions documented in `wfs`.) The bandwidth used, `NULL` if `bw` was not specified.
`k`	(Only if `wf` is a string or was generated by means of one of the functions documented in `wfs`.) The number of nearest neighbors used, `NULL` if `k` was not specified.
`nn.only`	(Logical. Only if `wf` is a string or was generated by means of one of the functions documented in `wfs` and if `k` was specified.) `TRUE` if only the `k` nearest neighbors recieve a positive weight, `FALSE` otherwise.
`adaptive`	(Logical.) `TRUE` if the bandwidth of `wf` is adaptive to the local density of data points, `FALSE` if the bandwidth is fixed.
`variant`	(Only if `wf` is a string or one of the window functions documented in `wfs` is used, for internal use only). An integer indicating which weighting scheme is implied by `bw`, `k` and `nn.only`.
`call`	The (matched) function call.

Czogiel, I., Luebke, K., Zentgraf, M. and Weihs, C. (2007), Localized linear discriminant analysis. In Decker, R. and Lenz, H.-J., editors, Advances in Data Analysis, volume 33 of Studies in Classification, Data Analysis, and Knowledge Organization, pages 133–140, Springer, Berlin Heidelberg.

Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

predict.osmultinom, osnnet, nnet.

locClass documentation built on May 2, 2019, 5:21 p.m.