buildModel             package:quantmod             R Documentation

_B_u_i_l_d _q_u_a_n_t_m_o_d _m_o_d_e_l _g_i_v_e_n _s_p_e_c_i_f_i_e_d _f_i_t_t_i_n_g _m_e_t_h_o_d

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

     Construct and attach a fitted model of type 'method' to 'quantmod'
     object.

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

     buildModel(x, method, training.per, ...)

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

       x: An object of class 'quantmod' created with 'specifyModel' or
          an R formula 

training.per: character vector representing dates in ISO 8601 format
          CCYY-MM-DD or CCYY-MM-DD HH:MM:SS of length 2

  method: A character string naming the fitting method. See details
          section for available methods, and  how to create new
          methods.

     ...: Additional arguments to method call 

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

     Currently available methods include:

     lm, glm, loess, step, ppr, rpart[rpart], tree[tree],
     randomForest[randomForest], mars[mda], polymars[polspline],
     lars[lars], rq[quantreg], lqs[MASS], rlm[MASS], svm[e1071], and
     nnet[nnet].

     The 'training.per' _should_ match the undelying date format of the
     time-series data used in modelling. Any other style may not return
     what you expect.

     Additional methods wrappers can be created to allow for modelling 
     using custom functions.  The only requirements are for a wrapper 
     function to be constructed taking parameters 'quantmod', 
     'training.data', and ....  The function must return the  fitted
     model object and have a predict method available.   It is possible
     to add predict methods if non exist by  adding an S3 method for
     predictModel. The ' buildModel.skeleton' function can be used for
     new methods.

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

     An object of class 'quantmod' with fitted model attached

_N_o_t_e:

     See 'buildModel.skeleton' for information on adding additional
     methods

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

     Jeffrey Ryan

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

     'specifyModel' 'tradeModel'

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

     ## Not run: 
     getSymbols('QQQQ',src='yahoo')
     q.model = specifyModel(Next(OpCl(QQQQ)) ~ Lag(OpHi(QQQQ),0:3))
     buildModel(q.model,method='lm',training.per=c('2006-08-01','2006-09-30'))
     ## End(Not run)

