trls.influence            package:spatial            R Documentation

_R_e_g_r_e_s_s_i_o_n _d_i_a_g_n_o_s_t_i_c_s _f_o_r _t_r_e_n_d _s_u_r_f_a_c_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     This function provides the basic quantities which are used in
     forming a variety of diagnostics for checking the quality of
     regression fits for trend surfaces calculated by 'surf.ls'.

_U_s_a_g_e:

     trls.influence(object)
     ## S3 method for class 'trls':
     plot(x, border = "red", col = NA, pch = 4, cex = 0.6,
          add = FALSE, div = 8, ...)

_A_r_g_u_m_e_n_t_s:

object, x: Fitted trend surface model from 'surf.ls' 

     div: scaling factor for influence circle radii in 'plot.trls' 

     add: add influence plot to existing graphics if 'TRUE' 

border, col, pch, cex, ...: additional graphical parameters 

_V_a_l_u_e:

     'trls.influence' returns a list with components:

       r: raw residuals as given by 'residuals.trls' 

     hii: diagonal elements of the Hat matrix 

 stresid: standardised residuals 

      Di: Cook's statistic 

_R_e_f_e_r_e_n_c_e_s:

     Unwin, D. J., Wrigley, N. (1987) Towards a general-theory of
     control point distribution effects in trend surface models.
     _Computers and Geosciences,_ *13*, 351-355.

     Venables, W. N. and Ripley, B. D. (2002) _Modern Applied
     Statistics with S._ Fourth edition.  Springer.

_S_e_e _A_l_s_o:

     'surf.ls', 'influence.measures', 'plot.lm'

_E_x_a_m_p_l_e_s:

     library(MASS)  # for eqscplot
     data(topo, package = "MASS")
     topo2 <- surf.ls(2, topo)
     infl.topo2 <- trls.influence(topo2)
     (cand <- as.data.frame(infl.topo2)[abs(infl.topo2$stresid) > 1.5, ])
     cand.xy <- topo[as.integer(rownames(cand)), c("x", "y")]
     trsurf <- trmat(topo2, 0, 6.5, 0, 6.5, 50)
     eqscplot(trsurf, type = "n")
     contour(trsurf, add = TRUE, col = "grey")
     plot(topo2, add = TRUE, div = 3)
     points(cand.xy, pch = 16, col = "orange")
     text(cand.xy, labels = rownames(cand.xy), pos = 4, offset = 0.5)