rmna                 package:rmutil                 R Documentation

_C_r_e_a_t_e _a _r_e_p_e_a_t_e_d _O_b_j_e_c_t, _R_e_m_o_v_i_n_g _N_A_s

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

     'rmna' forms an object of class, 'repeated', from a 'response'
     object and possibly time-varying or intra-individual covariate
     ('tvcov'), and time-constant or inter-individual covariate
     ('tccov') objects, removing any observations where response and
     covariate values have NAs. Subjects must be in the same order in
     all (three) objects to be combined.

     Such objects can be printed and plotted. Methods are available for
     extracting the response, the numbers of observations per
     individual, the times, the weights, the units of
     measurement/Jacobian, the nesting variable, the covariates, and
     their names: 'response', 'nobs', 'times', 'weights', 'delta',
     'nesting', 'covariates', and 'names'.

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

     rmna(response, ccov=NULL, tvcov=NULL)

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

response: An object of class, 'response' (created by 'restovec'),
          containing the response variable information.

    ccov: An object of class, 'tccov' (created by 'tcctomat'),
          containing the time-constant or inter-individual covariate
          information.

   tvcov: An object of class, 'tvcov' (created by 'tvctomat'),
          containing the time-varying or intra-individual covariate
          information.

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

     Returns an object of class, 'repeated', containing a list of the
     response object ('z$response', so that, for example, the response
     vector is 'z$response$y'; see 'restovec'), and possibly the two
     classes of covariate objects ('z$ccov' and 'z$tvcov'; see
     'tcctomat' and 'tvctomat').

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

     J.K. Lindsey

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

     'DataMethods', 'covariates', 'covind', 'delta', 'dftorep', 'lvna',
     'names', 'nesting', 'nobs', 'read.list', 'read.surv', 'response',
     'resptype', 'restovec', 'tcctomat', 'times', 'transform',
     'tvctomat', 'units', 'weights'

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

     y <- matrix(rnorm(20),ncol=5)
     tt <- c(1,3,6,10,15)
     print(resp <- restovec(y,times=tt))
     x <- c(0,0,1,1)
     tcc <- tcctomat(x)
     z <- matrix(rpois(20,5),ncol=5)
     tvc <- tvctomat(z)
     print(reps <- rmna(resp, tvcov=tvc, ccov=tcc))
     response(reps)
     response(reps, nind=2:3)
     times(reps)
     nobs(reps)
     weights(reps)
     covariates(reps)
     covariates(reps,names="x")
     covariates(reps,names="z")
     names(reps)
     nesting(reps)
     # because individuals are the only nesting, this is the same as
     covind(reps)
     #
     # use in glm
     rm(y,x,z)
     glm(y~x+z,data=as.data.frame(reps))
     #
     # binomial
     y <- matrix(rpois(20,5),ncol=5)
     print(respb <- restovec(y,totals=y+matrix(rpois(20,5),ncol=5),times=tt))
     print(repsb <- rmna(respb, tvcov=tvc, ccov=tcc))
     response(repsb)
     #
     # censored data
     y <- matrix(rweibull(20,2,5),ncol=5)
     print(respc <- restovec(y,censor=matrix(rbinom(20,1,0.9),ncol=5),times=tt))
     print(repsc <- rmna(respc, tvcov=tvc, ccov=tcc))
     # if there is no censoring, censor indicator is not printed
     response(repsc)
     #
     # nesting clustered within individuals
     nest <- c(1,1,2,2,2)
     print(respn <- restovec(y,censor=matrix(rbinom(20,1,0.9),ncol=5),
             times=tt,nest=nest))
     print(repsn <- rmna(respn, tvcov=tvc, ccov=tcc))
     response(respn)
     times(respn)
     nesting(respn)

