Semi2                package:SASmixed                R Documentation

_O_x_i_d_e _l_a_y_e_r _t_h_i_c_k_n_e_s_s_e_s _o_n _s_e_m_i_c_o_n_d_u_c_t_o_r_s

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

     The `Semi2' data frame has 72 rows and 5 columns.

_F_o_r_m_a_t:

     This data frame contains the following columns:

     _S_o_u_r_c_e a factor with levels `1' and `2' 

     _L_o_t a factor with levels `1' to `8'

     _W_a_f_e_r a factor with levels `1' to `3'

     _S_i_t_e a factor with levels `1' to `3'

     _T_h_i_c_k_n_e_s_s a numeric vector

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

_S_o_u_r_c_e:

     Littel, R. C., Milliken, G. A., Stroup, W. W., and Wolfinger, R.
     D. (1996), SAS System for Mixed Models, SAS Institute (Data Set
     4.4).

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

     library(SASmixed)
     options(
       contrasts = c(unordered = "contr.SAS", ordered = "contr.poly"))
     data(Semi2)
     formula(Semi2)
     fm1Semi2 <- lme( Thickness ~ 1, data = Semi2,
        random = ~ 1 | Lot/Wafer )
     summary( fm1Semi2 )       # compare with output 4.13, p. 156
     VarCorr( fm1Semi2 )
     anova( fm1Semi2 )
     fm2Semi2 <- update( fm1Semi2, Thickness ~ Source )
     summary( fm2Semi2 )       # compare with output 4.15, p. 159
     VarCorr( fm2Semi2 )
     fm3Semi2 <- update( fm2Semi2, 
        random = list(Lot = pdDiag( ~ Source - 1 ), Wafer = ~ 1 ) )
     summary( fm3Semi2 )       # compare with output 4.17, p. 163
     VarCorr( fm3Semi2 )

