nnreg                package:funfits                R Documentation

_F_i_t_s _a _s_u_r_f_a_c_e _b_a_s_e_d _o_n _a _n_e_u_r_a_l _n_e_t_w_o_r_k

_D_e_s_c_r_i_p_t_i_o_n:

     Parameters of the model are estimated by nonlinear least squares.
     The parameter space has a large number of local minimum so the
     strategy is to generate "many" parameter sets at random and
     iterate these starts with a minimization algorithm. The two
     function parameters ntries and ngrid are used in generating the
     many starting parameter sets for nonlinear least squares. Ngrind
     is the number of cubes growing geometrically over a range of
     magnitude of parameters. Ntries is the number of parameter sets
     generated at random by a uniform distribution in each cube. The
     best parameter set ( out the Ntries ) in each cube is used as the
     start of a coarse optimization.  Npol of these coarse fits are
     selected for further refinement by a minimization with smaller
     tolerance.

_U_s_a_g_e:

     nnreg(x, y, k1, k2, start, ngrind=250, ntries=100, npol=20,
     glow=-1.26, ghigh=1.26, scale=0.5, fdata, derivative=F,
     fout="nnreg.out",run=T, just.setup=F, just.read=F,
     fitted.values=F, all.fits=F, greedy=F, seed)

_A_r_g_u_m_e_n_t_s:

       x: Matrix of independent variables. 

       y: Vector of dependent variables. 

      k1: Lower limit for K, where K is the number of hidden units. 

      k2: Upper limit for K. 

   start: Starting values for parameters. 

  ngrind: Number of coarse optimizations. 

  ntries: Number of random starting values for each coarse
          optimization. 

    npol: Number of coarse fits improved, i.e polish, using smaller
          minimization tolerance. 

    glow: Lower limit for grid of initial parameter values. 

   ghigh: Upper limit for grid of initial parameter values. 

   scale: Scale factor for grid of initial parameter values. 

   fdata: Temporary UNIX file name for the data. 

derivative: Return the derivative evaluated at the data points. 

    fout: Temporary UNIX file name for the output. 

     run: Runs the fitting program. 

just.setup: Sets up the input files but does not run the fitting
          program. 

just.read: Does no fitting, just reads in the results from a previous
          fit. 

fitted.values: Computes fitted values and residuals. 

all.fits: Includes all polished fits in the output file not just the
          best one. 

  greedy: A value of zero fits the full model by nonlinear least
          squares. A positive value uses the greedy algorithm to fit
          hidden units in chunks of size greedy, sequentially adding
          hidden units fit to the residuals of the previous fit. 

    seed: Seed used in generating the random parameter starts. 

_V_a_l_u_e:

     Object of class nnreg. The component model is a list of the
     parameters for each fitted model. Columns of the components:
     residuals and predicted values, correspond to the different fitted
     models. Each component model is of class netfit. The best model
     number as judged by minimizing the GCV statistic is also returned.

   model: Component model of class netfit. Includes a list of the
          dimension of the x matrix, the number of hidden units used in
          the model, the mean of each column of the x matrix, the mean
          of the y values, the standard deviation of each column of the
          x matrix, the standard deviation of the y values, the number
          of parameters in the model and the parameters of model.  

 summary: Partial Fortan program output. Summary of the nnreg fit.
          Includes a  summary of the polished values. 

fitted.values: Predicted values from the fit. 

residuals: Residuals from the fit. 

    call: Call to the function. 

       x: Matrix of independent variables. 

       y: Vector of dependent variables. 

       n: Number of observations or length of y. 

   nfits: Number of different model specifications. 

    lags: Time lags used in the x matrix, if a time series model. 

    seed: Seed used in generating the random parameter starts. 

best.model: Number of the best model based on GCV with cost=2. 

_S_i_d_e _E_f_f_e_c_t_s:

     This function does the bulk of the computation using a stand-alone
     FORTRAN program running in the UNIX shell. This operation is
     transparent to the user. For large problems the input files can be
     setup using this function and the fitting program can be run
     separately in the background.

_R_e_f_e_r_e_n_c_e_s:

     S. Ellner, D.W. Nychka, and A.R. Gallant. 1992. LENNS, a program
     to estimate the dominant Lyapunov exponent of noisy nonlinear
     systems from time series  data. Institute of Statistics Mimeo
     Series #2235, Statistics Department, North Carolina State
     University, Raleigh, NC 27695-8203.

     D.W. Nychka, S. Ellner, D. McCaffrey, and A.R. Gallant. 1992.
     Finding Chaos in Noisy Systems. J. R. Statist. Soc. B 54:399-426.

_S_e_e _A_l_s_o:

     predict.nnreg, predict.netfit, plot.nnreg, summary.nnreg,
     print.nnreg

_E_x_a_m_p_l_e_s:

     nnreg(ozone$x,ozone$y,1,2) -> fit # fitting a surface to ozone 
     # measurements, from 1 to 2 hidden units
     plot(fit) # plots fit and residuals

     nnreg(as.matrix(BD[,1:4]),BD[,5],2,4) -> fit # fitting DNA strand
     # displacement amplification surface to various buffer compositions
     plot(fit) # plots fit and residuals

