plot.qq                 package:sma                 R Documentation

_H_i_s_t_o_g_r_a_m _a_n_d _N_o_r_m_a_l _Q_u_a_n_t_i_l_e-_Q_u_a_n_t_i_l_e _p_l_o_t

_D_e_s_c_r_i_p_t_i_o_n:

     Produces a histogram and a normal Quantile-Quantile plot of the
     data. The points corresponding to genes with statistics
     less/greater than a user defined threshold are highlighted. The
     histogram and Q-Q plots are displayed on the same page.

_U_s_a_g_e:

     plot.qq(x, name, low=-5, high=5,...)

_A_r_g_u_m_e_n_t_s:

       x: a numeric vector containing the statistics whose histogram
          and Q-Q plot will be produced. Missing values (NAs) are
          allowed.

    name: title for the plots.

     low: lower threshold: points with statistic < low are colored in
          green.

    high: upper threshold: points with statistic > high are colored in
          red.

     ...: additional graphical parameters

_A_u_t_h_o_r(_s):

     Sandrine Dudoit, sandrine@stat.berkeley.edu 
      Yee Hwa Yang, yeehwa@stat.berkeley.edu

_R_e_f_e_r_e_n_c_e_s:

     Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A.
     (1983). Graphical Methods for Data Analysis. Wadsworth, Belmont,
     California. 

     Hoaglin, D. C., Mosteller, F. and Tukey, J.  W., editors (1983).
     Understanding Robust and Exploratory Data Analysis. Wiley, New
     York.

_S_e_e _A_l_s_o:

     'plot.spatial', 'plot.t2', 'stat.t2', 'hist', 'qqnorm'.

_E_x_a_m_p_l_e_s:

     data(MouseArray)
     ## mouse.setup <- init.grid()
     ## mouse.data <- init.data() ## see \emph{init.data}
     ## mouse.lratio <- stat.ma(mouse.data, mouse.setup)

     ## Calculation of t-statistics
     ## cl <- c(rep(1,3), rep(2,3))
     ## mouse.t2 <- stat.t2(mouse.lratio, cl)

     ## Diagnostic plots
     plot.qq(mouse.t2$t, "Mouse")

