osnnet: Observation Specific Neural Networks
In schiffner/locClass: Collection of Local Classification Methods

Description Usage Arguments Details Value References See Also Examples

A localized version of single-hidden-layer neural networks, possibly with skip-layer connections.

osnnet(x, ...)

## S3 method for class 'formula'
osnnet(formula, data, ..., subset, na.action,
  contrasts = NULL)

## Default S3 method:
osnnet(x, y, wf = c("biweight", "cauchy", "cosine",
  "epanechnikov", "exponential", "gaussian", "optcosine", "rectangular",
  "triangular"), bw, k, nn.only = TRUE, size, Wts, mask = rep(TRUE,
  length(wts)), linout = FALSE, entropy = FALSE, softmax = FALSE,
  censored = FALSE, skip = FALSE, rang = 0.7, decay = 0, maxit = 100,
  trace = TRUE, MaxNWts = 1000, abstol = 1e-04, reltol = 1e-08,
  reps = 1, ...)

`x`	(Required if no `formula` is given as principal argument.) A `matrix` or `data.frame` or `Matrix` containing the explanatory variables.
`formula`	A `formula` of the form `response ~ x1 + x2 + ...`.
`data`	A `data.frame` from which variables specified in `formula` are to be taken.
`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 `NA`s are found. The default action is for the procedure to fail. 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.
`y`	(Required if no `formula` is given as principal argument.) A `matrix` or `data.frame` of target values for examples.
`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`.)
`size`	Number of units in the hidden layer. Can be zero if there are skip-layer units.
`Wts`	Initial parameter vector. If missing chosen at random.
`mask`	Logical vector indicating which parameters should be optimized (default all).
`linout`	Switch for linear output units. Default logistic output units.
`entropy`	Switch for entropy (= maximum conditional likelihood) fitting. Default by least-squares.
`softmax`	Switch for softmax (log-linear model) and maximum conditional likelihood fitting. `linout`, `entropy`, `softmax` and `censored` are mutually exclusive.
`censored`	A variant on `softmax`, in which non-zero targets mean possible classes. Thus for `softmax` a row of `(0, 1, 1)` means one example each of classes 2 and 3, but for `censored` it means one example whose class is only known to be 2 or 3.
`skip`	Switch to add skip-layer connections from input to output.
`rang`	Initial random weights on [-`rang`, `rang`]. Value about 0.5 unless the inputs are large, in which case it should be chosen so that `rang` * max(`\|x\|`) is about 1.
`decay`	Parameter for weight decay. Default 0.
`maxit`	Maximum number of iterations. Default 100.
`trace`	Switch for tracing optimization. Default `TRUE`.
`MaxNWts`	The maximum allowable number of weights. There is no intrinsic limit in the code, but increasing `MaxNWts` will probably allow fits that are very slow and time-consuming.
`abstol`	Stop if the fit criterion falls below `abstol`, indicating an essentially perfect fit.
`reltol`	Stop if the optimizer is unable to reduce the fit criterion by a factor of at least `1 - reltol`.
`reps`	Neural networks are fitted repeatedly (`reps` times) for different initial values and the solution with largest likelihood value is kept. Defaults to 1. (`reps` larger one does not make sense if `Wts` is specified.)
`...`	Arguments passed to or from other methods.

This is a localized version of neural networks where a neural network is fitted for each test observation based on the training data near the trial point. It is based on the function nnet from package nnet.

The name of the window function (wf) can be specified as a character string. In this case the window function is generated internally in predict.osnnet. Currently supported are "biweight", "cauchy", "cosine", "epanechnikov", "exponential", "gaussian", "optcosine", "rectangular" and "triangular".

Moreover, it is possible to generate the window functions mentioned above in advance (see wfs) and pass them to osnnet.

Any other function implementing a window function can also be used as wf argument. This allows the user to try own window functions. See help on wfs for details.

If the predictor variables include factors, the formula interface must be used in order to get a correct model matrix.

An object of class "osnnet.formula" or "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.osnnet, nnet.

Other observation_specific nnet: predict.osnnet

samp <- c(sample(1:50,25), sample(51:100,25), sample(101:150,25))
fit <- osnnet(Species ~ ., data = iris, subset = samp, size = 2, 
               rang = 0.1, maxit = 200, bw = 0.5, reps = 2)
pred <- predict(fit)