nlf                   package:pomp                   R Documentation

_F_i_t _M_o_d_e_l _t_o _D_a_t_a _U_s_i_n_g _N_o_n_l_i_n_e_a_r _F_o_r_e_c_a_s_t_i_n_g (_N_L_F)

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

     Calls an optimizer to maximize the nonlinear forecasting (NLF)
     goodness of fit, by simulating data from a model, fitting a
     nonlinear autoregressive model to the simulated time series (which
     may be multivariate) and using the fitted model to predict some or
     all variables in the data time series. NLF is an indirect
     inference method using a quasi-likelihood as the objective
     function.

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

      nlf(object, start, est, lags, period = NA, tensor = FALSE,
          nconverge=1000, nasymp=1000, seed = 1066, nrbf = 4,
          method = "subplex", skip.se = FALSE, verbose = FALSE, gr = NULL,
          bootstrap=FALSE, bootsamp = NULL,
          lql.frac = 0.1, se.par.frac = 0.1, eval.only = FALSE, ...)

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

  object: A 'pomp' object, with the data and model to fit to it. 

   start: Named numeric vector with guessed parameters. 

     est: Vector containing the names or indices of parameters to be
          estimated. 

    lags: A vector specifying the lags to use when constructing the
          nonlinear autoregressive prediction model. The first lag is
          the prediction interval. 

  period: numeric; 'period=NA' means the model is nonseasonal. period>0
          is the period of seasonal forcing in 'real time'. 

  tensor: logical; if FALSE, the fitted model is a generalized additive
          model with time mod period as one of the predictors, i.e., a
          gam with time-varying intercept.  If TRUE, the fitted model
          is a gam with lagged state variables as predictors and
          time-periodic coefficients, constructed using tensor products
          of basis functions of state variables with basis functions of
          time. 

nconverge: Number of convergence timesteps to be discarded from the
          model simulation. 

  nasymp: Number of asymptotic timesteps to be recorded from the model
          simulation. 

    seed: Integer specifying the random number seed to use. When
          fitting, it is usually best to always run the simulations
          with the same sequence of random numbers, which is
          accomplished by setting 'seed' to an integer. If you want a
          truly random simulation, set 'seed=NULL'. 

    nrbf: A scalar specifying the number of radial basis functions to
          be used at each lag.

  method: Optimization method. Choices are 'subplex' and any of the
          methods used by 'optim'. 

 skip.se: Logical; if 'TRUE', skip the computation of standard errors. 

 verbose: Logical; if 'TRUE', the negative log quasilikelihood and
          parameter values are printed at each iteration of the
          optimizer. 

      gr: optional; passed to 'optim' if 'optim' is used.

bootstrap: Logical; if 'TRUE' the indices in 'bootsamp' will determine
          which of the conditional likelihood values be used in
          computing the quasi-loglikelihood. 

bootsamp: Vector of integers; used to have the quasi-loglikelihood
          evaluated using a bootstrap re-sampling of the data set. 

lql.frac: target fractional change in log quasi-likelihood for
          quadratic standard error estimate 

se.par.frac: initial parameter-change fraction for quadratic standard
          error estimate 

eval.only: logical; if 'TRUE', no optimization is attempted and the
          quasi-loglikelihood value is evaluated at the 'start'
          parameters. 

     ...: Arguments that will be passed to 'optim' or 'subplex' in the
          'control' list. 

_D_e_t_a_i_l_s:

     This is functionally a wrapper for 'nlf.objfun', which does the
     statistical heavy lifting and should be consulted for details.

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

     A list corresponding to the output from the optimizer, except that
     the full parameter vector is returned (not just the ones fitted),
     the log quasilikelihood (LQL) (_not_ -LQL) is reported, xstart is
     included, and asymptotic Wald standard errors based on M-estimator
     theory are returned for each fitted parameter.

_A_u_t_h_o_r(_s):

     Stephen P. Ellner spe2 at cornell dot edu and Bruce E. Kendall
     kendall at bren dot ucsb dot edu

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

     The following papers describe and motivate the NLF approach to
     model fitting:

     Ellner, S. P., Bailey, B. A., Bobashev, G. V., Gallant, A. R.,
     Grenfell, B. T. and Nychka D. W. (1998) Noise and nonlinearity in
     measles epidemics: combining mechanistic and statistical
     approaches to population modeling. _American Naturalist_ *151*,
     425-440.

     Kendall, B. E., Briggs, C. J., Murdoch, W. W., Turchin, P.,
     Ellner, S. P., McCauley, E., Nisbet, R. M. and Wood S. N. (1999)
     Why do populations cycle? A synthesis of statistical and
     mechanistic modeling approaches. _Ecology_ *80*, 1789-1805.
     Available online at <URL:
     http://www2.bren.ucsb.edu/~kendall/pubs/1999Ecology.pdf>

     Kendall, B. E., Ellner, S. P., McCauley, E., Wood, S. N., Briggs,
     C. J., Murdoch, W. W. and Turchin, P. (2005) Population cycles in
     the pine looper moth (_Bupalus piniarius_): dynamical tests of
     mechanistic hypotheses. _Ecological Monographs_ *75*, 259-276.
     Available online at <URL:
     http://repositories.cdlib.org/postprints/818/>

