VADeaths              package:datasets              R Documentation

_D_e_a_t_h _R_a_t_e_s _i_n _V_i_r_g_i_n_i_a (_1_9_4_0)

_D_e_s_c_r_i_p_t_i_o_n:

     Death rates per 1000 in Virginia in 1940.

_U_s_a_g_e:

     VADeaths

_F_o_r_m_a_t:

     A matrix with 5 rows and 4 columns.

_D_e_t_a_i_l_s:

     The death rates are measured per 1000 population per year.  They
     are cross-classified by age group (rows) and population group
     (columns).  The age groups are: 50-54, 55-59, 60-64, 65-69, 70-74
     and the population groups are Rural/Male, Rural/Female, Urban/Male
     and Urban/Female.

     This provides a rather nice 3-way analysis of variance example.

_S_o_u_r_c_e:

     Molyneaux, L.,  Gilliam, S. K., and  Florant, L. C.(1947)
     Differences in Virginia death rates by color, sex, age, and rural
     or urban residence. _American Sociological Review_, *12*, 525-535.

_R_e_f_e_r_e_n_c_e_s:

     McNeil, D. R. (1977) _Interactive Data Analysis_. Wiley.

_E_x_a_m_p_l_e_s:

     require(stats); require(graphics)
     n <- length(dr <- c(VADeaths))
     nam <- names(VADeaths)
     d.VAD <- data.frame(
      Drate = dr,
      age = rep(ordered(rownames(VADeaths)),length=n),
      gender= gl(2,5,n, labels= c("M", "F")),
      site =  gl(2,10,  labels= c("rural", "urban")))
     coplot(Drate ~ as.numeric(age) | gender * site, data = d.VAD,
            panel = panel.smooth, xlab = "VADeaths data - Given: gender")
     summary(aov.VAD <- aov(Drate ~ .^2, data = d.VAD))
     opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
     plot(aov.VAD)
     par(opar)

