plotvgam                package:VGAM                R Documentation

_D_e_f_a_u_l_t _V_G_A_M _P_l_o_t_t_i_n_g

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

     Component functions of a 'vgam-class' object can be plotted  with
     'plotvgam()'. These are on the scale of the linear/additive
     predictor.

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

     plotvgam(x, newdata = NULL, y = NULL, residuals = NULL,
              rugplot = TRUE, se = FALSE, scale = 0, raw = TRUE,
              offset.arg = 0, deriv.arg = 0, overlay = FALSE,
              type.residuals = c("deviance", "working", "pearson", "response"),
              plot.arg = TRUE, which.term = NULL, which.cf = NULL,
              control = plotvgam.control(...), ...)

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

       x: A fitted 'VGAM' object, e.g., produced by 'vgam()', 'vglm()',
          or 'rrvglm()'. 

 newdata: Data frame. May be used to reconstruct the original data set. 

       y: Unused. 

residuals: Logical. If 'TRUE', residuals are plotted. See
          'type.residuals'

 rugplot: Logical. If 'TRUE', a rug plot is plotted at the foot of each
          plot. These values are jittered to expose ties. 

      se: Logical. If 'TRUE', approximate +-2 pointwise standard error
          bands are included in the plot.

   scale: Numerical. By default, each plot will have its own y-axis
          scale. However, by specifying a value, each plot's y-axis
          scale will be at least 'scale' wide. 

     raw: Logical. If 'TRUE', the smooth functions are those obtained
          directly by the algorithm, and are plotted without having to
          premultiply with the constraint matrices. If 'FALSE', the
          smooth functions have been premultiply by the constraint
          matrices. The 'raw' argument is directly fed into
          'predict.vgam()'. 

offset.arg: Numerical vector of length r. These are added to the
          component functions. Useful for separating out the functions
          when 'overlay' is 'TRUE'. If 'overlay' is 'TRUE' and there is
          one covariate, using the intercept values as the offsets can
          be a good idea. 

deriv.arg: Numerical. The order of the derivative. Should be assigned
          an small  integer such as 0, 1, 2. Only applying to 's()'
          terms, it plots the derivative. 

 overlay: Logical. If 'TRUE', component functions of the same covariate
          are overlaid on each other. The functions are centered, so
          'offset.arg' can be useful when 'overlay' is 'TRUE'. 

type.residuals: if 'residuals' is 'TRUE', the first possible value of
          this vector, is used to specify the type of residual. 

plot.arg: Logical. If 'FALSE', no plot is produced. 

which.term: Character or integer vector containing all terms to be
          plotted, e.g., 'which.term=c("s(age)", "s(height"))' or
          'which.term=c(2,5,9)'. By default, all are plotted. 

which.cf: An integer-valued vector specifying which linear/additive
          predictors are to be plotted. The values must be from the set
          {1,2,...,r}. By default, all are plotted. 

 control: Other control parameters. See 'plotvgam.control'. 

     ...: Other arguments that can be fed into 'plotvgam.control'. This
          includes line colors, line widths, line types, etc. 

     In the above, M is the number of linear/additive predictors, and r
     is the number of columns of the constraint matrix of interest.

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

     Many of 'plotvgam()''s options can be found in  
     'plotvgam.control', e.g., line types, line widths, colors.

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

     The original object, but with the 'preplot' slot of the object
     assigned information regarding the plot.

_N_o_t_e:

     While 'plot(fit)' will work if 'class(fit)' is '"vgam"', it is
     necessary to use 'plotvgam(fit)'  explicitly otherwise.

     'plotvgam()' is quite buggy at the moment. 'plotvgam()' works in a
     similar manner to S-PLUS's 'plot.gam()', however, there is no
     options for interactive construction of the plots yet.

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

     Thomas W. Yee

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

     Yee, T. W. and Wild, C. J. (1996) Vector generalized additive
     models. _Journal of the Royal Statistical Society, Series B,
     Methodological_, *58*, 481-493.

     Documentation accompanying the 'VGAM' package at <URL:
     http://www.stat.auckland.ac.nz/~yee> contains further information
     and examples.

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

     'vgam', 'plotvgam.control', 'predict.vgam', 'vglm'.

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

     data(coalminers)
     coalminers = transform(coalminers, Age = (age - 42) / 5)
     fit = vgam(cbind(nBnW,nBW,BnW,BW) ~ s(Age), binom2.or(zero=NULL), coalminers)
     ## Not run: 
     par(mfrow=c(1,3))
     plot(fit, se=TRUE, ylim=c(-3,2), las=1)

     plot(fit, se=TRUE, which.cf=1:2, lcol="blue", scol="red", ylim=c(-3,2))
     plot(fit, se=TRUE, which.cf=1:2, lcol="blue", scol="red", overlay=TRUE)
     ## End(Not run)

