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Last data update: 2014.03.03

R: Displaying 'gbp' Class

print.gbp

R Documentation

Displaying 'gbp' Class

Description

print.gbp enables users to see a compact group-level (unit-level) estimation result of gbp function.

Usage

## S3 method for class 'gbp'
print(x, sort = TRUE, ...)

Arguments

`x`	a resultant object of `gbp` function.
`sort`	`TRUE` or `FALSE` flag. If `TRUE`, the result will appear by the order of `se` for Gaussian, or of `n` for Binomial and Poisson data. If `FALSE`, it will do by the order of data input. Default is `TRUE`.
`...`	further arguments passed to other methods.

Details

As for the argument x, if the result of gbp is designated to b like "b <- gbp(z, n, model = "binomial")", the argument x is supposed to be b.

We do not need to type "print(b, sort = TRUE)" but "b" itself is enough to call print(b, sort = TRUE). But if we want to see the result NOT sorted by the order of se for Gaussian, or of n for Binomial and Poisson data, print(b, sort = FALSE) will show the result by the order of data input.

Value

print(gbp.object) will display:

`obs.mean`	sample mean of each group
`se`	if Gaussian data, standard error of each group
`n`	if Binomial or Poisson data, total number of trials of each group
`X`	a covariate vector or matrix if designated. NA if not
`prior.mean`	numeric if entered, NA if not entered
`prior.mean.hat`	estimate of prior mean by a regression if prior mean is not assigned a priori. The variable name on the display will be "prior.mean"
`prior.mean.AR`	the posterior mean(s) of the expected random effects, if the acceptance-rejection method is used for the binomial model. The variable name on the display will be "prior.mean".
`shrinkage`	shrinkage estimate of each group (adjusted posterior mean)
`shrinkage.AR`	the posterior mean of the shrinkage factor, if the acceptance-rejection method is used for the binomial model. The variable name on the display will be "shrinkage".
`low.intv`	lower bound of 100*confidence.lvl% posterior interval
`post.mean`	posterior mean of each group
`upp.intv`	upper bound of 100*confidence.lvl% posterior interval
`post.sd`	posterior standard deviation of each group

Author(s)

Hyungsuk Tak, Joseph Kelly, and Carl Morris

Examples


  data(hospital)

  z <- hospital$d
  n <- hospital$n
  y <- hospital$y
  se <- hospital$se
  
  ###################################################################################
  # We do not have any covariates and do not know a mean of the prior distribution. #
  ###################################################################################

    ###############################################################
    # Gaussian Regression Interactive Multilevel Modeling (GRIMM) #
    ###############################################################

    g <- gbp(y, se, model = "gaussian")
    g
    print(g, sort = FALSE)

    ###############################################################
    # Binomial Regression Interactive Multilevel Modeling (BRIMM) #
    ###############################################################

    b <- gbp(z, n, model = "binomial")
    b
    print(b, sort = FALSE)

    ##############################################################
    # Poisson Regression Interactive Multilevel Modeling (PRIMM) #
    ##############################################################

    p <- gbp(z, n, mean.PriorDist = 0.03, model = "poisson")
    p
    print(p, sort = FALSE)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(Rgbp)
Loading required package: sn
Loading required package: stats4

Attaching package: 'sn'

The following object is masked from 'package:stats':

    sd

Loading required package: mnormt
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/Rgbp/print.gbp.Rd_%03d_medium.png", width=480, height=480)
> ### Name: print.gbp
> ### Title: Displaying 'gbp' Class
> ### Aliases: print.gbp
> ### Keywords: methods
> 
> ### ** Examples
> 
> 
>   data(hospital)
> 
>   z <- hospital$d
>   n <- hospital$n
>   y <- hospital$y
>   se <- hospital$se
>   
>   ###################################################################################
>   # We do not have any covariates and do not know a mean of the prior distribution. #
>   ###################################################################################
> 
>     ###############################################################
>     # Gaussian Regression Interactive Multilevel Modeling (GRIMM) #
>     ###############################################################
> 
>     g <- gbp(y, se, model = "gaussian")
>     g
Summary for each group (sorted by the descending order of se): 

     obs.mean  se prior.mean shrinkage low.intv post.mean upp.intv post.sd
1       -2.07 2.8     0.0184     0.916   -1.883  -0.15703   1.4226   0.842
2       -0.22 2.8     0.0184     0.915   -1.648  -0.00188   1.6277   0.836
3        0.58 1.6     0.0184     0.777   -1.326   0.14385   1.6871   0.768
4       -1.87 1.4     0.0184     0.740   -2.125  -0.47276   0.9273   0.777
5       -0.74 1.4     0.0184     0.732   -1.704  -0.18514   1.2291   0.747
6       -1.97 1.4     0.0184     0.726   -2.183  -0.52667   0.8627   0.776
7       -1.90 1.4     0.0184     0.723   -2.158  -0.51309   0.8736   0.772
8        2.31 1.3     0.0184     0.711   -0.703   0.68108   2.3684   0.783
9       -0.14 1.2     0.0184     0.677   -1.441  -0.03269   1.3531   0.713
10      -1.21 1.2     0.0184     0.677   -1.897  -0.37787   0.9703   0.730
11      -1.43 1.2     0.0184     0.670   -2.000  -0.45937   0.8814   0.733
12       1.56 1.1     0.0184     0.647   -0.759   0.56249   2.0931   0.726
13       0.00 1.1     0.0184     0.631   -1.334   0.01162   1.3548   0.686
14       0.41 1.1     0.0184     0.622   -1.146   0.16644   1.5340   0.683
15       0.08 1.0     0.0184     0.604   -1.268   0.04280   1.3618   0.671
16      -2.15 1.0     0.0184     0.599   -2.426  -0.85010   0.4629   0.736
17      -0.34 1.0     0.0184     0.595   -1.460  -0.12682   1.1574   0.667
18       0.86 1.0     0.0184     0.595   -0.911   0.35945   1.7437   0.676
19       0.01 1.0     0.0184     0.590   -1.284   0.01497   1.3128   0.662
20       1.11 1.0     0.0184     0.575   -0.768   0.48195   1.8740   0.673
21      -0.08 1.0     0.0184     0.565   -1.301  -0.02437   1.2392   0.648
22       0.61 0.9     0.0184     0.550   -0.941   0.28488   1.5873   0.644
23       2.05 0.9     0.0184     0.550   -0.336   0.93347   2.4303   0.704
24       0.57 0.9     0.0184     0.539   -0.942   0.27281   1.5578   0.637
25       1.10 0.9     0.0184     0.533   -0.684   0.52319   1.8622   0.648
26      -2.42 0.8     0.0184     0.499   -2.700  -1.20357   0.0732   0.706
27      -0.38 0.8     0.0184     0.462   -1.367  -0.19598   0.9318   0.586
28       0.07 0.8     0.0184     0.442   -1.068   0.04718   1.1678   0.570
29       0.96 0.7     0.0184     0.436   -0.548   0.54962   1.7388   0.582
30      -0.21 0.7     0.0184     0.381   -1.169  -0.12306   0.9034   0.529
31       1.14 0.6     0.0184     0.352   -0.254   0.74563   1.8226   0.529
Mean          1.2     0.0184     0.614   -1.346   0.01842   1.3681   0.692
>     print(g, sort = FALSE)
Summary for each group: 

     obs.mean  se prior.mean shrinkage low.intv post.mean upp.intv post.sd
1       -2.07 2.8     0.0184     0.916   -1.883  -0.15703   1.4226   0.842
2       -0.22 2.8     0.0184     0.915   -1.648  -0.00188   1.6277   0.836
3        0.58 1.6     0.0184     0.777   -1.326   0.14385   1.6871   0.768
4       -1.87 1.4     0.0184     0.740   -2.125  -0.47276   0.9273   0.777
5       -0.74 1.4     0.0184     0.732   -1.704  -0.18514   1.2291   0.747
6       -1.97 1.4     0.0184     0.726   -2.183  -0.52667   0.8627   0.776
7       -1.90 1.4     0.0184     0.723   -2.158  -0.51309   0.8736   0.772
8        2.31 1.3     0.0184     0.711   -0.703   0.68108   2.3684   0.783
9       -0.14 1.2     0.0184     0.677   -1.441  -0.03269   1.3531   0.713
10      -1.21 1.2     0.0184     0.677   -1.897  -0.37787   0.9703   0.730
11      -1.43 1.2     0.0184     0.670   -2.000  -0.45937   0.8814   0.733
12       1.56 1.1     0.0184     0.647   -0.759   0.56249   2.0931   0.726
13       0.00 1.1     0.0184     0.631   -1.334   0.01162   1.3548   0.686
14       0.41 1.1     0.0184     0.622   -1.146   0.16644   1.5340   0.683
15       0.08 1.0     0.0184     0.604   -1.268   0.04280   1.3618   0.671
16      -2.15 1.0     0.0184     0.599   -2.426  -0.85010   0.4629   0.736
17      -0.34 1.0     0.0184     0.595   -1.460  -0.12682   1.1574   0.667
18       0.86 1.0     0.0184     0.595   -0.911   0.35945   1.7437   0.676
19       0.01 1.0     0.0184     0.590   -1.284   0.01497   1.3128   0.662
20       1.11 1.0     0.0184     0.575   -0.768   0.48195   1.8740   0.673
21      -0.08 1.0     0.0184     0.565   -1.301  -0.02437   1.2392   0.648
22       0.61 0.9     0.0184     0.550   -0.941   0.28488   1.5873   0.644
23       2.05 0.9     0.0184     0.550   -0.336   0.93347   2.4303   0.704
24       0.57 0.9     0.0184     0.539   -0.942   0.27281   1.5578   0.637
25       1.10 0.9     0.0184     0.533   -0.684   0.52319   1.8622   0.648
26      -2.42 0.8     0.0184     0.499   -2.700  -1.20357   0.0732   0.706
27      -0.38 0.8     0.0184     0.462   -1.367  -0.19598   0.9318   0.586
28       0.07 0.8     0.0184     0.442   -1.068   0.04718   1.1678   0.570
29       0.96 0.7     0.0184     0.436   -0.548   0.54962   1.7388   0.582
30      -0.21 0.7     0.0184     0.381   -1.169  -0.12306   0.9034   0.529
31       1.14 0.6     0.0184     0.352   -0.254   0.74563   1.8226   0.529
Mean          1.2     0.0184     0.614   -1.346   0.01842   1.3681   0.692
> 
>     ###############################################################
>     # Binomial Regression Interactive Multilevel Modeling (BRIMM) #
>     ###############################################################
> 
>     b <- gbp(z, n, model = "binomial")
>     b
Summary for each group (sorted by  the ascending order of n): 

     obs.mean    n prior.mean shrinkage low.intv post.mean upp.intv post.sd
1      0.0448   67     0.0285     0.914   0.0187    0.0299   0.0437 0.00640
2      0.0294   68     0.0285     0.913   0.0178    0.0286   0.0419 0.00619
3      0.0238  210     0.0285     0.772   0.0176    0.0275   0.0393 0.00556
4      0.0430  256     0.0285     0.736   0.0214    0.0323   0.0454 0.00612
5      0.0335  269     0.0285     0.726   0.0198    0.0299   0.0419 0.00564
6      0.0438  274     0.0285     0.722   0.0218    0.0328   0.0458 0.00614
7      0.0432  278     0.0285     0.719   0.0218    0.0326   0.0456 0.00610
8      0.0136  295     0.0285     0.707   0.0149    0.0241   0.0354 0.00525
9      0.0288  347     0.0285     0.672   0.0192    0.0286   0.0398 0.00527
10     0.0372  349     0.0285     0.671   0.0213    0.0314   0.0433 0.00564
11     0.0391  358     0.0285     0.665   0.0218    0.0321   0.0442 0.00573
12     0.0177  396     0.0285     0.643   0.0159    0.0247   0.0353 0.00497
13     0.0278  431     0.0285     0.623   0.0193    0.0283   0.0389 0.00503
14     0.0249  441     0.0285     0.618   0.0184    0.0272   0.0376 0.00493
15     0.0273  477     0.0285     0.599   0.0192    0.0280   0.0384 0.00491
16     0.0455  484     0.0285     0.595   0.0246    0.0354   0.0480 0.00600
17     0.0304  494     0.0285     0.590   0.0203    0.0293   0.0398 0.00499
18     0.0220  501     0.0285     0.587   0.0174    0.0258   0.0359 0.00474
19     0.0277  505     0.0285     0.585   0.0195    0.0282   0.0385 0.00486
20     0.0204  540     0.0285     0.569   0.0167    0.0250   0.0349 0.00465
21     0.0284  563     0.0285     0.558   0.0199    0.0285   0.0385 0.00477
22     0.0236  593     0.0285     0.546   0.0181    0.0263   0.0360 0.00458
23     0.0150  602     0.0285     0.542   0.0142    0.0223   0.0321 0.00458
24     0.0238  629     0.0285     0.531   0.0182    0.0263   0.0359 0.00451
25     0.0204  636     0.0285     0.528   0.0167    0.0247   0.0342 0.00447
26     0.0480  729     0.0285     0.494   0.0277    0.0384   0.0508 0.00591
27     0.0306  849     0.0285     0.456   0.0217    0.0297   0.0389 0.00439
28     0.0274  914     0.0285     0.438   0.0203    0.0279   0.0366 0.00416
29     0.0213  940     0.0285     0.431   0.0172    0.0244   0.0328 0.00400
30     0.0293 1193     0.0285     0.374   0.0219    0.0290   0.0372 0.00391
31     0.0201 1340     0.0285     0.347   0.0166    0.0231   0.0305 0.00354
Mean           517     0.0285     0.609   0.0193    0.0285   0.0393 0.00509
>     print(b, sort = FALSE)
Summary for each group: 

     obs.mean    n prior.mean shrinkage low.intv post.mean upp.intv post.sd
1      0.0448   67     0.0285     0.914   0.0187    0.0299   0.0437 0.00640
2      0.0294   68     0.0285     0.913   0.0178    0.0286   0.0419 0.00619
3      0.0238  210     0.0285     0.772   0.0176    0.0275   0.0393 0.00556
4      0.0430  256     0.0285     0.736   0.0214    0.0323   0.0454 0.00612
5      0.0335  269     0.0285     0.726   0.0198    0.0299   0.0419 0.00564
6      0.0438  274     0.0285     0.722   0.0218    0.0328   0.0458 0.00614
7      0.0432  278     0.0285     0.719   0.0218    0.0326   0.0456 0.00610
8      0.0136  295     0.0285     0.707   0.0149    0.0241   0.0354 0.00525
9      0.0288  347     0.0285     0.672   0.0192    0.0286   0.0398 0.00527
10     0.0372  349     0.0285     0.671   0.0213    0.0314   0.0433 0.00564
11     0.0391  358     0.0285     0.665   0.0218    0.0321   0.0442 0.00573
12     0.0177  396     0.0285     0.643   0.0159    0.0247   0.0353 0.00497
13     0.0278  431     0.0285     0.623   0.0193    0.0283   0.0389 0.00503
14     0.0249  441     0.0285     0.618   0.0184    0.0272   0.0376 0.00493
15     0.0273  477     0.0285     0.599   0.0192    0.0280   0.0384 0.00491
16     0.0455  484     0.0285     0.595   0.0246    0.0354   0.0480 0.00600
17     0.0304  494     0.0285     0.590   0.0203    0.0293   0.0398 0.00499
18     0.0220  501     0.0285     0.587   0.0174    0.0258   0.0359 0.00474
19     0.0277  505     0.0285     0.585   0.0195    0.0282   0.0385 0.00486
20     0.0204  540     0.0285     0.569   0.0167    0.0250   0.0349 0.00465
21     0.0284  563     0.0285     0.558   0.0199    0.0285   0.0385 0.00477
22     0.0236  593     0.0285     0.546   0.0181    0.0263   0.0360 0.00458
23     0.0150  602     0.0285     0.542   0.0142    0.0223   0.0321 0.00458
24     0.0238  629     0.0285     0.531   0.0182    0.0263   0.0359 0.00451
25     0.0204  636     0.0285     0.528   0.0167    0.0247   0.0342 0.00447
26     0.0480  729     0.0285     0.494   0.0277    0.0384   0.0508 0.00591
27     0.0306  849     0.0285     0.456   0.0217    0.0297   0.0389 0.00439
28     0.0274  914     0.0285     0.438   0.0203    0.0279   0.0366 0.00416
29     0.0213  940     0.0285     0.431   0.0172    0.0244   0.0328 0.00400
30     0.0293 1193     0.0285     0.374   0.0219    0.0290   0.0372 0.00391
31     0.0201 1340     0.0285     0.347   0.0166    0.0231   0.0305 0.00354
Mean           517     0.0285     0.609   0.0193    0.0285   0.0393 0.00509
> 
>     ##############################################################
>     # Poisson Regression Interactive Multilevel Modeling (PRIMM) #
>     ##############################################################
> 
>     p <- gbp(z, n, mean.PriorDist = 0.03, model = "poisson")
>     p
Summary for each group (sorted by  the ascending order of n): 

     obs.mean    n prior.mean shrinkage low.intv post.mean upp.intv post.sd
1      0.0448   67       0.03     0.911   0.0199    0.0313   0.0454 0.00653
2      0.0294   68       0.03     0.910   0.0189    0.0299   0.0435 0.00631
3      0.0238  210       0.03     0.765   0.0185    0.0285   0.0407 0.00566
4      0.0430  256       0.03     0.728   0.0225    0.0335   0.0467 0.00619
5      0.0335  269       0.03     0.718   0.0208    0.0310   0.0432 0.00573
6      0.0438  274       0.03     0.714   0.0229    0.0339   0.0472 0.00621
7      0.0432  278       0.03     0.711   0.0228    0.0338   0.0469 0.00617
8      0.0136  295       0.03     0.699   0.0157    0.0250   0.0366 0.00534
9      0.0288  347       0.03     0.663   0.0200    0.0296   0.0410 0.00536
10     0.0372  349       0.03     0.662   0.0222    0.0325   0.0446 0.00571
11     0.0391  358       0.03     0.656   0.0228    0.0331   0.0454 0.00579
12     0.0177  396       0.03     0.633   0.0165    0.0255   0.0363 0.00506
13     0.0278  431       0.03     0.613   0.0200    0.0292   0.0400 0.00511
14     0.0249  441       0.03     0.608   0.0191    0.0280   0.0387 0.00502
15     0.0273  477       0.03     0.589   0.0199    0.0289   0.0394 0.00499
16     0.0455  484       0.03     0.585   0.0256    0.0364   0.0491 0.00601
17     0.0304  494       0.03     0.580   0.0211    0.0302   0.0409 0.00506
18     0.0220  501       0.03     0.577   0.0180    0.0266   0.0369 0.00483
19     0.0277  505       0.03     0.575   0.0202    0.0290   0.0395 0.00494
20     0.0204  540       0.03     0.559   0.0173    0.0258   0.0358 0.00474
21     0.0284  563       0.03     0.548   0.0206    0.0293   0.0395 0.00485
22     0.0236  593       0.03     0.535   0.0187    0.0270   0.0369 0.00466
23     0.0150  602       0.03     0.532   0.0147    0.0230   0.0329 0.00466
24     0.0238  629       0.03     0.521   0.0188    0.0271   0.0368 0.00460
25     0.0204  636       0.03     0.518   0.0173    0.0254   0.0351 0.00455
26     0.0480  729       0.03     0.484   0.0286    0.0393   0.0516 0.00587
27     0.0306  849       0.03     0.446   0.0223    0.0303   0.0397 0.00445
28     0.0274  914       0.03     0.428   0.0208    0.0285   0.0374 0.00423
29     0.0213  940       0.03     0.421   0.0176    0.0249   0.0335 0.00407
30     0.0293 1193       0.03     0.364   0.0223    0.0296   0.0379 0.00397
31     0.0201 1340       0.03     0.338   0.0170    0.0235   0.0310 0.00360
Mean           517       0.03     0.600   0.0201    0.0293   0.0403 0.00517
>     print(p, sort = FALSE)
Summary for each group: 

     obs.mean    n prior.mean shrinkage low.intv post.mean upp.intv post.sd
1      0.0448   67       0.03     0.911   0.0199    0.0313   0.0454 0.00653
2      0.0294   68       0.03     0.910   0.0189    0.0299   0.0435 0.00631
3      0.0238  210       0.03     0.765   0.0185    0.0285   0.0407 0.00566
4      0.0430  256       0.03     0.728   0.0225    0.0335   0.0467 0.00619
5      0.0335  269       0.03     0.718   0.0208    0.0310   0.0432 0.00573
6      0.0438  274       0.03     0.714   0.0229    0.0339   0.0472 0.00621
7      0.0432  278       0.03     0.711   0.0228    0.0338   0.0469 0.00617
8      0.0136  295       0.03     0.699   0.0157    0.0250   0.0366 0.00534
9      0.0288  347       0.03     0.663   0.0200    0.0296   0.0410 0.00536
10     0.0372  349       0.03     0.662   0.0222    0.0325   0.0446 0.00571
11     0.0391  358       0.03     0.656   0.0228    0.0331   0.0454 0.00579
12     0.0177  396       0.03     0.633   0.0165    0.0255   0.0363 0.00506
13     0.0278  431       0.03     0.613   0.0200    0.0292   0.0400 0.00511
14     0.0249  441       0.03     0.608   0.0191    0.0280   0.0387 0.00502
15     0.0273  477       0.03     0.589   0.0199    0.0289   0.0394 0.00499
16     0.0455  484       0.03     0.585   0.0256    0.0364   0.0491 0.00601
17     0.0304  494       0.03     0.580   0.0211    0.0302   0.0409 0.00506
18     0.0220  501       0.03     0.577   0.0180    0.0266   0.0369 0.00483
19     0.0277  505       0.03     0.575   0.0202    0.0290   0.0395 0.00494
20     0.0204  540       0.03     0.559   0.0173    0.0258   0.0358 0.00474
21     0.0284  563       0.03     0.548   0.0206    0.0293   0.0395 0.00485
22     0.0236  593       0.03     0.535   0.0187    0.0270   0.0369 0.00466
23     0.0150  602       0.03     0.532   0.0147    0.0230   0.0329 0.00466
24     0.0238  629       0.03     0.521   0.0188    0.0271   0.0368 0.00460
25     0.0204  636       0.03     0.518   0.0173    0.0254   0.0351 0.00455
26     0.0480  729       0.03     0.484   0.0286    0.0393   0.0516 0.00587
27     0.0306  849       0.03     0.446   0.0223    0.0303   0.0397 0.00445
28     0.0274  914       0.03     0.428   0.0208    0.0285   0.0374 0.00423
29     0.0213  940       0.03     0.421   0.0176    0.0249   0.0335 0.00407
30     0.0293 1193       0.03     0.364   0.0223    0.0296   0.0379 0.00397
31     0.0201 1340       0.03     0.338   0.0170    0.0235   0.0310 0.00360
Mean           517       0.03     0.600   0.0201    0.0293   0.0403 0.00517
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>