FAMEoutliers          package:TargetSearch          R Documentation

_F_A_M_E _o_u_t_l_i_e_r _d_e_t_e_c_t_i_o_n

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

     A function to detect retention time marker (FAME) outliers.

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

     FAMEoutliers(samples, RImatrix, pdffile = NA, startDay = NA, endDay = NA,
                  threshold = 3, group.threshold = 0.05)

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

 samples: A 'tsSample' object created by 'ImportSamples' function. 

RImatrix: A retention time matrix of the found retention time markers. 

 pdffile: A character string naming a PDF file where the FAMEs report
          will be saved. 

startDay: A numeric vector with the starting days of your day groups. 

  endDay: A numeric vector with the ending days of your day groups. 

threshold: A standard deviations cutoff to detect outliers. 

group.threshold: A numeric cutoff to detect day groups based on
          hierarchical clustering. Must be between '0..1'.

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

     If no 'pdffile' argument is given, the report will be saved on a
     file called '"TargetSearch-YYYY-MM-DD.FAME-report.pdf"', where
     'YYYY-MM-DD' is a date.

     If both 'startDay' and 'endDay' are not given, the function will
     try to detect day groups using a hierarchical clustering approach
     by cutting the tree using 'group.threshold' as cutoff height.

     Retention time markers that deviate more than 'threshold' standard
     deviations from the    mean of their day group will be identified
     as outliers.

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

     A logical matrix of the same size of 'RImatrix'. A 'TRUE' value
     indicates that the retention time marker in that particular sample
     is an outlier.

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

     Alvaro Cuadros-Inostroza, Matthew Hannah, Henning Redestig

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

     'RIcorrect', 'ImportSamples'

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

     require(TargetSearchData)
     data(TargetSearchData)

     # find the retention marker outliers of the example data and save it in "outlier.pdf"
     outliers <- FAMEoutliers(sampleDescription, RImatrix, pdffile = "outlier.pdf")

     # find the outliers (although they are reported in the output PDF file)
     apply(outliers, 1, which)

