nnet                  package:nnet                  R Documentation

_F_i_t _N_e_u_r_a_l _N_e_t_w_o_r_k_s

_D_e_s_c_r_i_p_t_i_o_n:

     Fit single-hidden-layer neural network, possibly with skip-layer
     connections.

_U_s_a_g_e:

     nnet.formula(formula, data=NULL, weights, ...,
                  subset, na.action=na.fail, contrasts=NULL)
     nnet.default(x, y, weights, size, Wts, mask,
                  linout=FALSE, entropy=FALSE, softmax=FALSE, censored=FALSE,
                  skip=FALSE, rang=0.7, decay=0, maxit=100,
                  Hess=FALSE, trace=TRUE, MaxNWts=1000, abstol=1.0e-4,
                  reltol=1.0e-8)

_A_r_g_u_m_e_n_t_s:

 formula: A formula of the form `class ~ x1 + x2 + ...'  

       x: matrix or data frame of `x' values for examples. 

       y: matrix or data frame of target values for examples. 

 weights: (case) weights for each example - if missing defaults to 1. 

    size: number of units in the hidden layer. Can be zero if there are
          skip-layer units. 

    data: Data frame from which variables specified in  `formula' are
          preferentially 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. 

     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. 

    Hess: If true, the Hessian of the measure of fit at the best set of
          weights found is returned as component `Hessian'. 

   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
          (and perhaps uninterruptable under Windows). 

  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'. 

_D_e_t_a_i_l_s:

     If the response in `formula' is a factor, an appropriate
     classification network is constructed; this has one output and
     entropy fit if the number of levels is two, and a number of
     outputs equal to the number of classes and a softmax output stage
     for more levels.  If the response is not a factor, it is passed on
     unchanged to `nnet.default'.

     A quasi-Newton optimizer is used, written in `C'.

_V_a_l_u_e:

     object of class `nnet' or `nnet.formula'. Mostly internal
     structure, but has components

     wts: the best set of weights found 

   value: value of fitting criterion plus weight decay term. 

fitted.values: the fitted values for the training data. 

residuals: the residuals for the training data. 

_S_e_e _A_l_s_o:

     `predict.nnet', `nnet.Hess'

_E_x_a_m_p_l_e_s:

     data(iris3)
     # use half the iris data
     ir <- rbind(iris3[,,1],iris3[,,2],iris3[,,3])
     targets <- class.ind( c(rep("s", 50), rep("c", 50), rep("v", 50)) )
     samp <- c(sample(1:50,25), sample(51:100,25), sample(101:150,25))
     ir1 <- nnet(ir[samp,], targets[samp,], size=2, rang=0.1, 
                 decay=5e-4, maxit=200)
     test.cl <- function(true, pred){
             true <- max.col(true)
             cres <- max.col(pred)
             table(true, cres)
     }
     test.cl(targets[-samp,], predict(ir1, ir[-samp,]))

     # or
     ird <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]),
             species=c(rep("s",50), rep("c", 50), rep("v", 50)))
     ir.nn2 <- nnet(species ~ ., data=ird, subset=samp, size=2, rang=0.1, 
                 decay=5e-4, maxit=200)
     table(ird$species[-samp], predict(ir.nn2, ird[-samp,], type="class"))

