imageplot.bma              package:BMA              R Documentation

_I_m_a_g_e_s _o_f _m_o_d_e_l_s _u_s_e_d _i_n _B_a_y_e_s_i_a_n _m_o_d_e_l _a_v_e_r_a_g_i_n_g

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

     Creates an image of the models selected using 'bicreg', 'bic.glm'
     or 'bic.surv'.

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

     imageplot.bma(bicreg.out,color="default",...) 

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

bicreg.out: An object of type 'bicreg', 'bic.glm' or 'bic.surv'

   color: The color of the plot. The value "default" uses the current
          default R color scheme for 'image'.  The value
          "blackandwhite" produces a black and white image.

     ...: Other parameters to be passed to the 'image' and 'axis'
          functions.

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

     Creates an image of the models selected using 'bicreg', 'bic.glm'
     or 'bic.surv'.

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

     Adrian E. Raftery raftery@AT@stat.washington.edu and Hana
     Sevcikova

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

     Clyde, M. (1999) Bayesian Model Averaging and Model Search
     Strategies (with discussion). In Bayesian Statistics 6. J.M.
     Bernardo, A.P. Dawid, J.O. Berger, and A.F.M. Smith eds. Oxford
     University Press, pages 157-185.

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

     'bicreg', 'bic.glm', 'bic.surv'

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

     # logistic regression using bic.glm
     library("MASS")
     data(birthwt)
     y<- birthwt$lo
     x<- data.frame(birthwt[,-1])
     x$race<- as.factor(x$race)
     x$ht<- (x$ht>=1)+0
     x<- x[,-9]
     x$smoke <- as.factor(x$smoke)
     x$ptl<- as.factor(x$ptl)
     x$ht <- as.factor(x$ht)
     x$ui <- as.factor(x$ui)

     glm.out1<- bic.glm(x, y, strict = TRUE, OR = 20, glm.family="binomial")
     imageplot.bma(glm.out1)


     # logistic regression using glib
     library("MASS")
     data(birthwt)
     y<- birthwt$lo
     x<- data.frame(birthwt[,-1])
     x$race<- as.factor(x$race)
     x$ht<- (x$ht>=1)+0
     x<- x[,-9]
     x$smoke <- as.factor(x$smoke)
     x$ptl<- as.factor(x$ptl)
     x$ht <- as.factor(x$ht)
     x$ui <- as.factor(x$ui)

     glib.birthwt<- glib(x,y, error="binomial", link = "logit")
     glm.birthwt<- as.bic.glm(glib.birthwt)
     imageplot.bma(glm.birthwt)

