threestepPLM package:affyPLM R Documentation _T_h_r_e_e _S_t_e_p _e_x_p_r_e_s_s_i_o_n _m_e_a_s_u_r_e_s _r_e_t_u_r_n_e_d _a_s _a _P_L_M_s_e_t _D_e_s_c_r_i_p_t_i_o_n: This function converts an 'AffyBatch' into an 'PLMset' using a three step expression measure. _U_s_a_g_e: threestepPLM(object,subset=NULL, normalize=TRUE,background=TRUE,background.method="RMA.2",normalize.method="quantile",summary.method="median.polish",background.param = list(),normalize.param=list(),output.param=list(), model.param=list()) _A_r_g_u_m_e_n_t_s: object: an 'AffyBatch' subset: a vector with the names of probesets to be used. If NULL then all probesets are used. normalize: logical value. If 'TRUE' normalize data using quantile normalization background: logical value. If 'TRUE' background correct using RMA background correction background.method: name of background method to use. normalize.method: name of normalization method to use. summary.method: name of summary method to use. background.param: list of parameters for background correction methods normalize.param: list of parameters for normalization methods output.param: list of parameters for output methods model.param: list of parameters for model methods _D_e_t_a_i_l_s: This function computes the expression measure using threestep methods. It returns a 'PLMset'. The most important difference is that the PLMset allows you to access the residuals which the 'threestep' function does not do. _V_a_l_u_e: An 'PLMset' _A_u_t_h_o_r(_s): Ben Bolstad bmb@bmbolstad.com _R_e_f_e_r_e_n_c_e_s: Bolstad, BM (2004) _Low Level Analysis of High-density Oligonucleotide Array Data: Background, Normalization and Summarization_. PhD Dissertation. University of California, Berkeley. _S_e_e _A_l_s_o: 'expresso', 'rma', 'threestep', 'rmaPLM', 'fitPLM' _E_x_a_m_p_l_e_s: data(affybatch.example) # should be equivalent to rma() ## Not run: eset <- threestepPLM(affybatch.example)