ros                   package:NADA                   R Documentation

_R_e_g_r_e_s_s_i_o_n _o_n _O_r_d_e_r _S_t_a_t_i_s_t_i_c_s

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

     'ros' is an implementation of a Regression on Order Statistics
     (ROS) designed for multiply censored analytical chemistry data.

     The method assumes data contains zero to many left censored
     (less-than) values.

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

      ros(obs, censored, forwardT="log", reverseT="exp")

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

     obs: A numeric vector of observations.  This includes both
          censored and uncensored observations. 

censored: A logical vector indicating TRUE where an observation in
          'obs' is censored (a less-than value) and FALSE otherwise. 

forwardT: A name of a function to use for transformation prior to
          performing the ROS fit.  Defaults to 'log'. 

reverseT: A name of a function to use for reversing the transformation
          after performing the ROS fit. Defaults to 'exp'. 

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

     By default, 'ros' performs a log transformation prior to, and
     after operations over the data.  This can be changed by specifying
     a forward and reverse transformation function using the 'forwardT'
     and 'reverseT' parameters.  No transformation will be performed if
     either 'forwardT' or 'reverseT' are set to 'NULL'. 

     The procedure first computes the Weibull-type plotting positions
     of the combined uncensored and censored observations using a
     formula designed for multiply-censored data (see 'hc.ppoints'). A
     linear regression is formed using the plotting positions of the
     uncensored observations and their normal quantiles.  This model is
     then used to estimate the concentration of the censored
     observations as a function of their normal quantiles.  Finally,
     the observed uncensored values are combined with modeled censored
     values to corporately estimate summary statistics of the entire
     population. By combining the uncensored values with modeled
     censored values, this method is more resistant of any
     non-normality of errors, and reduces any transformation errors
     that may be incurred.

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

     'ros' returns an object of class c("ros", "lm").

     'print' displays a simple summary of the ROS model.
     'as.data.frame' converts the modeled data in a ROS model to a data
     frame. Note that this discards all linear-model information from
     the object.

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

     Lopaka(Rob) Lee <rclee@usgs.gov>

     Dennis Helsel <dhelsel@usgs.gov>

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

     Lee and Helsel (2005) Statistical analysis of environmental data
     containing multiple detection limits: S-language software for
     regression on order statistics, Computers in Geoscience vol. 31,
     pp. 1241-1248.

     Lee and Helsel (2005) Baseline models of trace elements in major
     aquifers of the United States. Applied Geochemistry vol. 20, pp.
     1560-1570.

     Dennis R. Helsel (2005), Nondetects And Data Analysis: John Wiley
     and Sons, New York.

     Dennis R. Helsel (1990), Less Than Obvious: Statistical Methods
     for, Environmental Science and Technology, vol.24, no. 12, pp.
     1767-1774

     Dennis R. Helsel and Timothy A. Cohn (1988), Estimation of
     descriptive statistics for multiply censored water quality data,
     Water Resources Research vol. 24, no. 12, pp.1997-2004

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

     'splitQual',  'predict',  'plot',  'ros-class',  'ros-methods',
     'plot-methods', 'mean-methods', 'sd-methods', 'quantile-methods',
     'median-methods', 'predict-methods', 'summary-methods'

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

         obs      = c(0.5,    0.5,   1.0,  1.5,   5.0,    10,   100)
         censored = c(TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE)

         myros = ros(obs, censored) 

         plot(myros)
         summary(myros)
         mean(myros); sd(myros)
         quantile(myros); median(myros)
         as.data.frame(myros)

