meanscore             package:meanscore             R Documentation

_M_e_a_n _S_c_o_r_e _M_e_t_h_o_d _f_o_r _M_i_s_s_i_n_g _C_o_v_a_r_i_a_t_e _D_a_t_a _i_n _L_o_g_i_s_t_i_c _R_e_g_r_e_s_s_i_o_n _M_o_d_e_l_s

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

     Weighted logistic regression using the Mean Score method

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

             meanscore(x=x,y=y,z=z,factor=NULL,print.all=FALSE)

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

       x: matrix of predictor variables, one column of which contains
          some missing values (NA)

       y: response variable (binary 0-1)

       z: matrix of the surrogate or auxiliary variables  which must be
          categorical 

          OPTIONAL ARGUMENTS

print.all: logical value determining all output to be printed.  The
          default is False (F).

  factor: factor variables; if the columns of the matrix of predictor
          variables have names, supply these names,  otherwise supply
          the column numbers. MS.NPREV will fit  separate coefficients
          for each level of the factor variables.

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

     The response, predictor and surrogate variables  must be numeric.
     The function will automatically call the CODING function to recode
     the z matrix  to give a 'new.z' vector which takes a unique value
     for each combination (type help('coding') for further
     particulars), as follows:

       z1  z2  z3  new.z
        0   0   0      1
        1   0   0      2
        0   1   0      3
        1   1   0      4
        0   0   1      5
        1   0   1      6
        0   1   1      7
        1   1   1      8

     The values of this new.z are reported as 'new.z' see  'coding'.

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

     A list called "parameters" containing the following  will be
     returned:

     est: the vector of estimates of the regression coefficients

      se: the vector of standard errors of the estimates

       z: Wald statistic for each coefficient

  pvalue: 2-sided p-value (H0: coeff=0) 

          when print.all = TRUE, it will also return the following
          lists:

    Ihat: the Fisher information matrix

   varsi: variance of the score for each (ylevel,zlevel) stratum

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

     Reilly,M and M.S. Pepe. 1995. A mean score method for missing and
     auxiliary 
      covariate data in regression models. _Biometrika_ *82:*299-314

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

     'ms.nprev','coding', 'ectopic','simNA','glm'.

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

     ## Not run: 
     THE SIMULATED DATASET EXAMPLE
     ## End(Not run)

     ## Not run: 
     We use the simulated dataset which is stored in the matrix simNA.
     You can load the dataset using:
     ## End(Not run)

     data(simNA) 

     help (simNA)
     #gives a detailed description of the data.
           
     ## Not run: To analyze this data using the meanscore function:

     meanscore(y=simNA[,1],z=simNA[,2],x=simNA[,3])

     ## Not run: This will give the following:

     [1] "For calls to ms.nprev, input n1 or prev in the following order!!"
          ylevel z new.z  n1  n2
     [1,]      0 0     0 310 150
     [2,]      0 1     1 166  85
     [3,]      1 0     0 177  86
     [4,]      1 1     1 347 179

     $parameters
                       est         se          z    pvalue
     (Intercept) 0.0493998 0.07155138  0.6904103 0.4899362
     x           1.0188437 0.10187094 10.0013188 0.0000000
     ## End(Not run)
     ## Not run: 
     If you extract the complete cases (n=500) to a matrix called
     "complete", using
     ## End(Not run)

     complete=simNA[!is.na(simNA[,3]),]

     ## Not run: then 
     summary(glm(complete[,1]~complete[,3], family="binomial"))

     ## Not run: gives the following results:

     ## Not run: 
     Coefficients:
                   Estimate Std. Error z value Pr(>|z|)    
     (Intercept)    0.05258    0.09879   0.532    0.595    
     complete[, 3]  1.01942    0.12050   8.460   <2e-16 ***
     ## End(Not run)

     ## Not run: 
     Notice that the Mean Score estimates above had smaller 
     standard errors, reflecting the additional information
     in the incomplete observations used in the analysis.
     Also note that since z is a surrogate for x, it is not 
     used in the complete case analysis.
     ## End(Not run)
      

     ## Not run: THE ECTOPIC DATASET EXAMPLE

     ## Not run: This is a real-data example of an application of Mean Score
     to a case-control study of the association between ectopic 
     pregnancy and sexually transmitted diseases (see Reilly and 
     Pepe, 1995). To learn more about the dataset, type help(ectopic). 

     The data frame called "ectopic" is in the data subfolder
     of the meanscore library. You can load the data by typing:
     ## End(Not run)
     data(ectopic)

     ## Not run: 
     The following lines will reproduce the results presented in Table 3 
     of Reilly & Pepe (1995)
     ## End(Not run)

     # use gonnorhoea, contracept and sexpatr as auxiliary variables
     ectopic.z=ectopic[,3:5]

     # the auxiliary variables defined above and the chlamydia antibody status 
     # are the predictor variables in the logistic regression model          
     ectopic.x=ectopic[,2:5]    

     meanscore(x=ectopic.x,z=ectopic.z,y=ectopic[,1])

