emforbeta.md
In glmfitmiss: Fitting GLMs with Missing Data in Both Responses and Covariates

emforbeta returns the correct structure and values with glm.fit

Code
  print(f_fit$beta)
Output
  (Intercept)    Fetoprtn     Antigen    Jaundice         Age 
  -3.85535341 -0.16438673  0.15221442 -0.79215934  0.06511526 
Code
  print(f_fit$alpha)
Output
  [1] 0.3065370 0.1163299 0.3362203 0.2409127
Code
  print(f_fit$cvcov)
Output
              [,1]          [,2]        [,3]         [,4]          [,5]
  [1,]  3.79559798 -0.4010129085 -0.45550004 -0.797461614 -0.0451347506
  [2,] -0.40101291  0.6527818508 -0.13681293  0.155614288 -0.0005940072
  [3,] -0.45550004 -0.1368129303  0.67463403  0.123308258  0.0032686704
  [4,] -0.79746161  0.1556142883  0.12330826  0.746239168  0.0014753379
  [5,] -0.04513475 -0.0005940072  0.00326867  0.001475338  0.0007286965

emforbeta returns the correct structure and values with brglmFit

Code
  print(f_fit1$beta)
Output
  (Intercept)     Numnill    Numsleep       Smoke         Set     Reftime 
  -9.04104606  1.24102525 -0.07801513  0.93381098  0.53165418  0.26581444 
Code
  print(f_fit1$alpha)
Output
   [1] 0.19732941 0.23949636 0.03326083 0.04790966 0.11429600 0.13948247
   [7] 0.01969728 0.07247232 0.08848416 0.02874141 0.01883010
Code
  print(f_fit1$cvcov)
Output
             [,1]        [,2]         [,3]         [,4]        [,5]         [,6]
  [1,] 24.7392900 -1.74394010 -1.624406105 -0.467228962 -3.94800655 -0.276717922
  [2,] -1.7439401  0.48954551  0.141931769  0.174433084  0.05433943  0.011672569
  [3,] -1.6244061  0.14193177  0.711964615  0.007109409  0.16666616  0.002847071
  [4,] -0.4672290  0.17443308  0.007109409  1.906614032 -0.22914531 -0.025837887
  [5,] -3.9480066  0.05433943  0.166666165 -0.229145314  0.84079580  0.040004634
  [6,] -0.2767179  0.01167257  0.002847071 -0.025837887  0.04000463  0.017007328