poprofile              package:ordinal              R Documentation

_P_r_o_d_u_c_e _P_r_e_d_i_c_t_e_d _O_r_d_i_n_a_l _T_i_m_e _P_r_o_f_i_l_e_s _f_o_r _P_l_o_t_t_i_n_g

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

     'poprofile' is used for plotting predicted marginal ordinal
     profiles over time for models obtained from dynamic models. These
     are obtained from a function supplied by the user. It produces
     output for plotting highest probabilities and cumulative
     probabilities for predicted marginal ordinal profiles
     corresponding to a model fitted with 'kalord'.

     See 'moprofile' for plotting marginal ordinal profiles, and
     'ioprofile' for plotting individual ordinal profiles.

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

     plot(poprofile(mu,pintercept,preg,pinitial=NULL,depend="independence",
                    times=NULL,distribution="multinomial",
                    curve.type="probability"),main="Predicted profile",
          xlab=NULL,ylab=NULL,xlim=NULL,ylim=NULL,lty=NULL,add=F,axes=F,
          bty="n",at=NULL,touch=F,...)

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

      mu: The location regression as a function of the parameters and
          the times for the desired covariate values. The regression
          function must not contain intercepts.

pintercept: Intercept coefficients for the regression model.

    preg: Remaining coefficients for the regression model: one for each
          time-constant and time-varying covariate, or one for each
          unknown parameter in their order of appearance.

pinitial: A coefficient for the initial parameter, if it is 'NULL' then
          this parameter will be fixed at zero.

  depend: Type of dependence. Choices are 'independence' (default),
          'Markov', 'serial', and 'frailty'.

   times: Vector of time points at which profiles are to be plotted.

distribution: Specifies the parameterization of the logistic
          distribution used in the Pareto distribution. Choices are
          binary, multinomial, continuation-ratio, and
          proportional-odds.

curve.type: Specifies the type of curves to be plotted. Must either be
          "probability" for highest probabilities or "cumulative" for
          cumulative probabilities.

    main: A main title for the plot.

    xlab: A label for the x-axis.

    ylab: A label for the y-axis.

    xlim: The x limits (min,max) of the plot.

    ylim: The y limits (min,max) of the plot.

     lty: A vector of integers or character strings specifying the line
          type to be used as the default in plotting lines. For further
          information, see 'par'.

     pch: A vector of integers or single characters specifying symbols
          to be used as the default in plotting points. For further
          information, see 'par'.

     add: If TRUE, the graph is added to an existing plot.

    axes: If FALSE, axes are not drawn around the plot.

     bty: A character string which determined the type of box which is
          drawn about plots. For further information, see 'par'.

      at: The points at which tick-marks are to be drawn. For further
          information, see 'axis'.

   touch: If TRUE, the x-axis and y-axis will touch each other.

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

     'poprofile' returns information ready for plotting by
     'plot.poprofile'.

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

     P.J. Lindsey

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

     'ioprofile', 'kalord', 'moprofile', 'plot.ordinal'.

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

     library(ordinal)

     #
     # Binary data
     #
     data(cardiac.indiv)

     y <- restovec(cardiac.indiv[,1:4],type="ordinal")

     cov <- tcctomat(as.matrix(cardiac.indiv[,5:10]))

     w <- rmna(y,ccov=cov)

     rm(cardiac.indiv,y,cov)

     # Time-constant and time-varying covariate with a frailty dependence.
     z <- kalord(w,distribution="binary",mu=~age+ren+cop+dia+sex+pmi+times,
                 ptvc=c(4.43357,-0.03128,-0.62602,-0.37679,-0.32969,-0.17013,
                        -0.12209,-0.09095),pinit=0.1196,dep="frailty")
     ren <- rep(0,4)
     cop <- rep(0,4)
     dia <- rep(0,4)
     sex <- rep(0,4)
     pmi <- rep(0,4)
     times <- 1:4

     # Predicted highest probability profiles.
     par(mfrow=c(2,2))
     for(i in c(1,25,50,80)) {
       age <- rep(i,4)
       mu <- finterp(attr(z$mu,"formula"),.intercep=F)
       prof <- poprofile(mu,pint=4.43362,preg=c(4.43362,-0.03128,-0.62602,-0.37679,
                                                -0.32969,-0.17013,-0.12209,-0.09095),
                         pinit=NULL,times=times,dist="binary",curve="prob")
       plot(prof,main=paste("Age: ",i,sep=""))
     }
     par(mfrow=c(1,1))

     # Predicted cumulative probability profiles.
     par(mfrow=c(2,2))
     for(i in c(1,25,50,80)) {
       age <- rep(i,4)
       mu <- finterp(attr(z$mu,"formula"),.intercep=F)
       prof <- poprofile(mu,pint=4.43362,preg=c(-0.03128,-0.62602,-0.37679,-0.32969,
                                                -0.17013,-0.12209,-0.09095),
                         pinit=NULL,times=times,dist="binary",curve="cum")
       plot(prof,main=paste("Age: ",i,sep=""))
     }
     par(mfrow=c(1,1))

     rm(w,z,ren,cop,dia,sex,pmi,times,mu,i,age,prof)

     #
     # Ordinal data
     #
     data(tmi2)

     y <- restovec(tmi2[,1:4],type="ordinal")

     cov <- tcctomat(tmi2[,5],name="distance")

     w <- rmna(y,ccov=cov)

     rm(tmi2,y,cov)

     # Proportional-odds model with time-constant covariate with a Markov dependence.
     z <- kalord(w,distribution="proportional-odds",ccov=~distance,
                 preg=c(-1.89,11.652,-0.199),pinit=3.111,pdep=0.217,dep="Markov")

     mu <- function(p) rep(p[1]*distance,4)

     # Predicted highest probability profiles.
     par(mfrow=c(1,2))
     for(distance in 1:2)
       plot(poprofile(mu,pint=c(-1.89,11.655),preg=-0.199,pinit=NULL,times=1:4,
                      dist="prop",curve="prob"),main=paste("Distance: ",distance,sep=""))
     par(mfrow=c(1,1))

     # Predicted cumulative probability profiles.
     par(mfrow=c(1,2))
     for(distance in 1:2)
       plot(poprofile(mu,pint=c(-1.89,11.655),preg=-0.199,pinit=NULL,times=1:4,
                      dist="prop",curve="cum"),main=paste("Distance: ",distance,sep=""))
     par(mfrow=c(1,1))

     rm(w,z,mu,distance)

