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(...), varxij = 1, ...)

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

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

       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' then residuals are plotted. See
          'type.residuals'

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

      se: Logical. If 'TRUE' then 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' then the smooth functions are those
          obtained directly by the algorithm, and are plotted without
          having to premultiply with the constraint matrices. If
          'FALSE' then 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 then 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' then 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' then the first possible value
          of this vector, is used to specify the type of residual. 

plot.arg: Logical. If 'FALSE' then 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. 

  varxij: Positive integer. Used if 'xij' of 'vglm.control' was used,
          this chooses which inner argument the component is plotted
          against. This argument is related to 'raw=TRUE' and terms
          such as 'NS(dum1,dum2)' and constraint matrices that have
          more than one column. The default would plot the smooth
          against 'dum1' but setting 'varxij=2' could mean plotting the
          smooth against 'dum2'. See the 'VGAM' website for further
          information.

_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.

_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:

     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)

