biglm                 package:biglm                 R Documentation

_B_o_u_n_d_e_d _m_e_m_o_r_y _l_i_n_e_a_r _r_e_g_r_e_s_s_i_o_n

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

     'biglm' creates a linear model object that uses only 'p^2' memory
     for 'p' variables. It can be updated with more data using
     'update'. This allows linear regression on data sets larger than
     memory.

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

     biglm(formula, data, weights=NULL, sandwich=FALSE)
     ## S3 method for class 'biglm':
     update(object, moredata,...)
     ## S3 method for class 'biglm':
     vcov(object,...)
     ## S3 method for class 'biglm':
     coef(object,...)
     ## S3 method for class 'biglm':
     summary(object,...)

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

 formula: A model formula

 weights: A one-sided, single term formula specifying weights

sandwich: 'TRUE' to compute the Huber/White sandwich covariance matrix
          (uses 'p^4' memory rather than 'p^2')

  object: A 'biglm' object

    data: Data frame that must contain all variables in 'formula' and
          'weights'

moredata: Additional data to add to the model

     ...: Additional arguments for future expansion

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

     The model formula must not contain any data-dependent terms, as
     these will not be consistent when updated.  Factors are permitted,
     but the levels of the factor must be the same across all data
     chunks (empty factor levels are ok).

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

     An object of class 'biglm'

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

     Algorithm AS274  Applied Statistics (1992) Vol.41,  No. 2

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

     lm

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

     data(trees)
     ff<-log(Volume)~log(Girth)+log(Height)

     chunk1<-trees[1:10,]
     chunk2<-trees[11:20,]
     chunk3<-trees[21:31,]

     a <- biglm(ff,chunk1)
     a <- update(a,chunk2)
     a <- update(a,chunk3)

     summary(a)

