LLSimpute              package:BNArray              R Documentation

_l_o_c_a_l _l_e_a_s_t _s_q_u_a_r_e_s _i_m_p_u_t_a_t_i_o_n

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

     Impute missing values using local least squares algorithm.

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

     LLSimpute(df.all, df.ori, n.changed)

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

  df.all: an object of 'data.frame' which contains whole microarray
          data

  df.ori: an object of 'data.frame' which contains  microarray data  of
          differentially expressed genes

n.changed: the number of differentially expressed genes 

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

     LLSimpute employs local least squares algorithm to impute missing
     values in microarray data.  Local least squares algorithm
     estimates missing values in a gene express vector according to 
     the vector's most similar complete express vectors. The missing
     values are computed as the linear  combination of the
     corresponding data in the nearest neighbor genes. Normally the
     coefficients could  be computed but in rare cases when one gene
     misses too many values in total experiments, they take values
     'NA'. Therefore, after calling 'LLSimpute', we have to
     'FinalImpute' function to  ensure a complete database for
     constructing Bayesian networks.

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

     A data.frame object is returned with imputed data.

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

     Xiaohui Chen cxh1984@interchange.ubc.ca, 
      Kaida Ning aprilnkd@hotmail.com, 
      Ming Chen mchen@zju.edu.cn.

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

     Hyunsoo Kim, Gene H. Golub and Haesum Park (2005) Missing value
     estimation for  DNA microarray gene expression data: local least
     squares imputation, Bioinformatcs, 21, 187-198

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

     'FinalImpute'

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

     ## Not run: 
             library(BNArray)
             data(total.data)
             setwd("/home")

     ##SHOULD call LLSimpute first
             ori.compact<-LLSimpute(total.data$df.all,total.data$df.ori,total.data$n.changed)## End(Not run)

