fitPLM                package:affyPLM                R Documentation

_F_i_t _a _P_r_o_b_e _L_e_v_e_l _M_o_d_e_l _t_o _A_f_f_y_m_e_t_r_i_x _G_e_n_e_c_h_i_p _D_a_t_a.

_D_e_s_c_r_i_p_t_i_o_n:

     This function converts an 'AffyBatch' into an 'PLMset' by fitting
     a specified robust linear model to the probe level data

_U_s_a_g_e:

     fitPLM(object,model=PM ~ -1 + probes +samples,
     variable.type=c(default="factor"),
     constraint.type=c(default="contr.treatment"),
     subset=NULL,
     background=TRUE, normalize=TRUE, background.method = "RMA.2",normalize.method = "quantile",background.param=list(),normalize.param=list(),output.param=list(),model.param=list())

_A_r_g_u_m_e_n_t_s:

  object: an 'AffyBatch'

   model: A formula describing the model to fit. This is slightly
          different from the standard method of specifying formulae in
          R. Read the description below

variable.type: a way to specify whether variables in the model are
          factors or standard variables

constraint.type: should factor variables sum to zero or have first
          variable set to zero (endpoint constraint)

  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.

background.param: A list of parameters for background routines

normalize.param: A list of parameters for normalization routines

output.param: A list of parameters controlling optional output from the
          routine.

model.param: A list of parameters controlling model procedure

_D_e_t_a_i_l_s:

     This function fits robust Probe Level linear Models to all the
     probesets in an 'AffyBatch'. This is carried out on a probeset by
     probeset basis. The user has quite a lot of control over which
     model is used and what outputs are stored. For more details please
     read the vignette.

_V_a_l_u_e:

     An 'PLMset'

_A_u_t_h_o_r(_s):

     Ben Bolstad bolstad@stat.berkeley.edu

_R_e_f_e_r_e_n_c_e_s:

     Under Preparation

_S_e_e _A_l_s_o:

     'expresso', 'rma', 'threestep'

_E_x_a_m_p_l_e_s:

     data(affybatch.example)
     Pset <- fitPLM(affybatch.example,model=PM ~ -1 + probes + samples)
     se(Pset)[1:5,]

     # A larger example testing weight image function
     data(Dilution)
     Pset <- fitPLM(Dilution,model=PM ~ -1 + probes + samples)
     image(Pset)
     boxplot(Pset) # NUSE

     #now lets try a wider class of models
     Pset <- fitPLM(Dilution,model=PM ~ -1 + probes +liver,normalize=FALSE,background=FALSE)
     coefs(Pset)[1:10,]

     Pset <- fitPLM(Dilution,model=PM ~ -1 + probes + liver + scanner,normalize=FALSE,background=FALSE)
     coefs(Pset)[1:10,]

     #try liver as a covariate
     logliver <- log2(c(20,20,10,10))
     Pset <- fitPLM(Dilution,model=PM~-1+probes+logliver+scanner,normalize=FALSE,background=FALSE,variable.type=c(logliver="covariate"))
     coefs(Pset)[1:10,]

     #try a different se.type
     Pset <- fitPLM(Dilution,model=PM~-1+probes+scanner,normalize=FALSE,background=FALSE,model.param=list(se.type=2))
     se(Pset)[1:10,]

