salinity             package:robustbase             R Documentation

_S_a_l_i_n_i_t_y _D_a_t_a

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

     This is a data set consisting of measurements of water salinity
     (i.e., its salt concentration) and river discharge taken in North
     Carolina's Pamlico Sound; This dataset was listed by Ruppert and
     Carroll (1980).  In Carrol and Ruppert (1985) the physical
     background of the data is described.  They indicated that
     observations 5 and 16 correspond to periods of very heavy
     discharge and showed that the discrepant observation 5 was masked
     by observations 3 and 16, i.e., only after deletion of these
     observations it was possible to identify the influential
     observation 5.

     This data set is a prime example of the masking effect.

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

     data(salinity)

_F_o_r_m_a_t:

     A data frame with 28 observations on the following 4 variables.

     '_X_1' Lagged Salinity

     '_X_2' Trend

     '_X_3' Discharge

     '_Y' Salinity

_S_o_u_r_c_e:

     P. J. Rousseeuw and A. M. Leroy (1987) _Robust Regression and
     Outlier Detection_; Wiley, p.82, table 5.

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

     data(salinity)
     summary(lm.sali  <-        lm(Y ~ . , data = salinity))
     summary(rlm.sali <- MASS::rlm(Y ~ . , data = salinity))
     summary(lts.sali <-    ltsReg(Y ~ . , data = salinity))

     salinity.x <- data.matrix(salinity[, 1:3])
     c_sal <- covMcd(salinity.x)
     plot(c_sal, "tolEllipsePlot")

