MarihuanaAlcohol             package:cmm             R Documentation

_M_a_r_i_h_u_a_n_a _a_n_d _a_l_c_o_h_o_l _u_s_e _d_u_r_i_n_g _a_d_o_l_e_s_c_e_n_c_e, _f_i_v_e-_w_a_v_e _p_a_n_e_l

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

     Panel study with five time points. A group of 269 youths were
     interviewed at ages 13, 14, 15, 16, and 17, and asked (among other
     things) about their marijuana and alcohol use (Eliot, Huizinga &
     Menard, 1989). The data are tabulated in Bergsma, Croon, and
     Hagenaars (2009, p. 128). 208 observations do not have missing
     values.

     Sections 4.2 and 4.4 in Bergsma, Croon, and Hagenaars (2009).

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

     data(MarihuanaAlcohol)

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

     A data frame with 269 observations on the following variables.

     '_G_e_n_d_e_r' (factor): 1 = Male; 2 = Female.

     '_M_1' Use of marihuana at time 1 (ordered): 1 = Never; 2 =
          Occasionally; 3 = Frequently.

     '_M_2' Use of marihuana at time 2 (ordered): see 'M1'.

     '_M_3' Use of marihuana at time 3 (ordered): see 'M1'.

     '_M_4' Use of marihuana at time 4 (ordered): see 'M1'.

     '_M_5' Use of marihuana at time 5 (ordered): see 'M1'.

     '_A_1' Use of alcohol at time 1 (ordered): see 'M1'.

     '_A_2' Use of alcohol at time 2 (ordered): see 'M1'.

     '_A_3' Use of alcohol at time 3 (ordered): see 'M1'.

     '_A_4' Use of alcohol at time 4 (ordered): see 'M1'.

     '_A_5' Use of alcohol at time 5 (ordered): see 'M1'.

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

     US National Youth Survey (NYS): teenage marijuana and alcohol use
     (Elliot, Huizinga and Menard, 1989)

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

     Bergsma, W. P., Croon, M. A., & Hagenaars, J. A. P. (2009).
     _Marginal models for dependent, clustered, and longitudinal
     categorical data._ New York: Springer.

     Elliot, D. S., Huizinga, D. & Menard, S. (1989). Multiple problem
     youth: Delinquency, substance use, and metal health problems. New
     York: Springer.

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

     data(MarihuanaAlcohol)

     # Table MA: marginal loglinear analysis  (BCH Section 4.2.1)
     # listwise deletion of missing values and deletion of Gender and Alcohol use
     dat <- MarihuanaAlcohol[-row(MarihuanaAlcohol)[is.na(MarihuanaAlcohol)],2:6]

     # at yields the vectorized 5x3 table MA (marijuana use x age)
     at <- MarginalMatrix( c("M1", "M2", "M3", "M4", "M5"), list(c("M1"), c("M2"), c("M3"), c("M4"), c("M5")), c(3, 3, 3, 3, 3) )
     obscoeff <- SampleStatistics(dat, list("log", at), CoefficientDimensions = c(5,3), Labels = c("Age", "M"), ShowCoefficients = FALSE, ShowParameters = TRUE)

