# Re: [R] question on xyplot

Date: Fri 18 Mar 2005 - 01:26:34 EST

As promised here is my reply. I am assuming that your problem is changing the order of the xyplots by idno.

The plotting order in lattice is determined by the levels of md\$idno. By default it was reading the levels from top to bottom of the dataset as unique(md\$idno) would do.

library(nlme); library(lattice)
md <- read.table('sample.txt', header=T)  md <- groupedData(md ~ month | idno, data=md)

levels( md\$id ) # old levels that were causing the problem [1] "TP_603" "TP_549" "TP_642" "NN_533" "NN_619" "NN_833" "SN_683"  "NN_577" "SN_594" "SN_616" "TP_842" "NN_675" "SN_673" "SN_828" "TP_855"

You see now that the original plotting order was determined by the above. We can create and set the following new.levels as follows

( new.levels <- sort( levels(md\$idno) ) ) # create new levels [1] "NN_533" "NN_577" "NN_619" "NN_675" "NN_833" "SN_594" "SN_616" "SN_673" "SN_683" "SN_828" "TP_549" "TP_603" "TP_642" "TP_842" "TP_855"

md\$idno <- factor( md\$idno, levels=new.levels ) # set the new levels

Now you proceed as you have done before.

trellis.device(theme=col.whitebg())
xyplot(md ~ month | idno, data=md, main="title",

xlab="x", ylab="y", layout=c(5, 3),
panel=function(x, y){
panel.xyplot(x, y)
panel.lmline(x, y, lty=2)

}
)

BTW, I tried to play around with the 'index.cond' and 'perm.cond' arguments in xyplot with no success.

Hope this helps.

On Tue, 2005-03-15 at 10:58 -0800, Zhongming Yang wrote:
> Dear All:
>
> In the attached file, I have 3 group patients, and there are 5 in each group (the groups are decided by the prefix of the idno). I want draw a repeat measurement comparison figure. My goal is to list 5 patients from same group on one horizontal line. But xyplot sounds pick them randomly (or I was confused?). Could you please help me modify the following code to accomplish this?
>
> Thanks
>
> Zhongming Yang
>
>
> library(nlme)
> library(lattice)
> md <- groupedData(md ~ month | idno, data=md)
> trellis.device(theme=col.whitebg())
> xyplot(md ~ month | idno, data=md, main="title",
> xlab="x", ylab="y", layout=c(5, 3),
> panel=function(x, y){
> panel.xyplot(x, y)
> panel.lmline(x, y, lty=2)
> }
> )
>
>
>
>
>
>
> ---------------------------------
>
> plain text document attachment (sample.txt), "sample.txt"
> md idno month
> -7.705 NN_533 0.000000
> -5.880 NN_533 3.254795
> -5.850 NN_533 6.049315
> -5.700 NN_533 8.876712
> -6.470 NN_533 11.967123
> -8.240 NN_533 14.958904
> -7.180 NN_533 18.673973
> -5.200 NN_533 21.501370
> -8.180 NN_533 25.380822
> -10.130 NN_533 28.303077
> -6.750 NN_533 31.057175
> -7.190 NN_533 33.647339
> -6.410 NN_533 36.630945
> -7.640 NN_533 39.616438
> -9.820 NN_533 42.772603
> -6.260 NN_533 45.534247
> -5.580 NN_533 48.131507
> -6.350 NN_533 51.747945
> -7.380 NN_533 57.534247
> -5.780 NN_533 60.723288
> -7.130 NN_533 64.931507
> -9.440 NN_533 70.389041
> -3.885 NN_577 0.000000
> -5.990 NN_577 3.221918
> -4.610 NN_577 5.983562
> -5.680 NN_577 9.205479
> -6.410 NN_577 11.736986
> -6.050 NN_577 16.338828
> -4.990 NN_577 19.092926
> -5.760 NN_577 21.617516
> -6.050 NN_577 24.601123
> -4.900 NN_577 27.584729
> -6.690 NN_577 30.575342
> -5.460 NN_577 33.567123
> -4.840 NN_577 36.558904
> -6.280 NN_577 39.550685
> -5.090 NN_577 42.542466
> -5.700 NN_577 46.684932
> -5.280 NN_577 52.208219
> -6.330 NN_577 54.739726
> -5.650 NN_577 57.271233
> -6.130 NN_577 61.347945
> -6.130 NN_577 65.027352
> -4.930 NN_577 69.945385
> -6.650 NN_577 73.355221
> -6.680 NN_577 76.339726
> -6.130 NN_577 79.561644
> -7.090 NN_577 82.093151
> -6.680 NN_577 86.268493
> -7.210 NN_577 92.876712
> -7.290 NN_577 96.723288
> -6.530 NN_619 0.000000
> -10.090 NN_619 3.452055
> -7.220 NN_619 6.274062
> -8.220 NN_619 9.815046
> -8.820 NN_619 12.634718
> -6.210 NN_619 15.552751
> -6.750 NN_619 18.772603
> -7.020 NN_619 21.304110
> -6.700 NN_619 23.901370
> -6.170 NN_619 26.663014
> -5.890 NN_619 29.884932
> -6.110 NN_619 32.876712
> -5.530 NN_619 36.493151
> -6.510 NN_619 39.254795
> -6.630 NN_619 42.082192
> -6.760 NN_619 44.843836
> -7.620 NN_619 48.000000
> -6.490 NN_619 50.991781
> -5.340 NN_619 54.437997
> -5.530 NN_619 57.880620
> -6.710 NN_619 60.470784
> -8.130 NN_619 62.995374
> -4.830 NN_619 66.838356
> -5.610 NN_619 69.600000
> -5.720 NN_619 72.131507
> -6.440 NN_619 75.419178
> -5.870 NN_619 78.378082
> -5.130 NN_619 81.830137
> -8.990 NN_619 84.821918
> -6.260 NN_619 90.345205
> -5.320 NN_619 96.789041
> -2.085 NN_675 0.000000
> -2.610 NN_675 2.761644
> -2.710 NN_675 6.904110
> -1.560 NN_675 9.205479
> -1.590 NN_675 12.197260
> -0.950 NN_675 14.958904
> -0.660 NN_675 19.791781
> -0.630 NN_675 23.967123
> -0.380 NN_675 26.695890
> -0.930 NN_675 31.528767
> -2.570 NN_675 34.323288
> -3.030 NN_675 36.621603
> -0.750 NN_675 39.801931
> 0.320 NN_675 43.244554
> 0.640 NN_675 45.769144
> 0.420 NN_675 48.986301
> 0.180 NN_675 51.978082
> 0.500 NN_675 54.969863
> 0.700 NN_675 57.961644
> 0.050 NN_675 60.460274
> -0.100 NN_675 63.024658
> -0.050 NN_675 69.665753
> -0.250 NN_675 72.887671
> -1.050 NN_675 80.646575
> -2.090 NN_675 86.260948
> -5.195 NN_833 0.000000
> -4.640 NN_833 3.180328
> -10.275 TP_549 0.000000
> -15.520 TP_549 3.484932
> -15.080 TP_549 6.443836
> -19.900 TP_549 9.468493
> -19.290 TP_549 14.301370
> -20.450 TP_549 19.134247
> -19.090 TP_549 22.356164
> -20.410 TP_549 24.885426
> -19.980 TP_549 28.098540
> -20.250 TP_549 33.606737
> -18.830 TP_549 36.361644
> -19.600 TP_549 39.945205
> -19.840 TP_549 43.200000
> -20.870 TP_549 46.257534
> -20.150 TP_549 49.249315
> -21.130 TP_549 52.241096
> -20.800 TP_549 54.871233
> -23.300 TP_549 57.534247
> -21.130 TP_549 60.526027
> -22.350 TP_549 64.898630
> -21.970 TP_549 69.501370
> -21.900 TP_549 72.721491
> -22.130 TP_549 76.360835
> -22.100 TP_549 79.803458
> -24.000 TP_549 83.934606
> -22.450 TP_549 88.306849
> -25.190 TP_549 97.019178
> -23.420 TP_549 99.583562
> -13.560 TP_603 0.000000
> -15.670 TP_603 3.452055
> -13.410 TP_603 6.904110
> -15.560 TP_603 9.857804
> -12.090 TP_603 13.103705
> -14.020 TP_603 15.628296
> -15.080 TP_603 18.382394
> -16.540 TP_603 21.139726
> -13.590 TP_603 24.328767
> -16.130 TP_603 28.043836
> -14.720 TP_603 31.167123
> -12.300 TP_603 33.567123
> -15.490 TP_603 36.361644
> -16.650 TP_603 40.241096
> -17.670 TP_603 43.002740
> -15.020 TP_603 46.454795
> -14.490 TP_603 49.446575
> -18.500 TP_603 54.542466
> -13.910 TP_603 56.939771
> -18.620 TP_603 61.169279
> -18.600 TP_603 64.087312
> -20.420 TP_603 67.300427
> -18.450 TP_603 70.389041
> -19.830 TP_603 72.920548
> -19.190 TP_603 78.871233
> -18.630 TP_603 84.821918
> -10.215 TP_642 0.000000
> -12.900 TP_642 2.983607
> -12.930 TP_642 5.967213
> -7.810 TP_642 9.409836
> -7.560 TP_642 11.706265
> -12.060 TP_642 15.585717
> -9.200 TP_642 19.300786
> -9.200 TP_642 22.193937
> -11.330 TP_642 24.824074
> -14.500 TP_642 28.045991
> -11.280 TP_642 30.807635
> -13.210 TP_642 34.029553
> -11.280 TP_642 37.021334
> -15.120 TP_642 39.782978
> -13.620 TP_642 43.235033
> -13.300 TP_642 45.799416
> -15.580 TP_642 48.327869
> -11.590 TP_642 50.590164
> -14.910 TP_642 54.950820
> -12.660 TP_642 57.704918
> -16.610 TP_642 60.922704
> -16.310 TP_642 63.684348
> -16.110 TP_642 66.610375
> -20.070 TP_642 69.898046
> -13.850 TP_642 72.889827
> -18.260 TP_642 75.914485
> -16.520 TP_642 79.070649
> -15.050 TP_642 85.054211
> -14.830 TP_642 92.122704
> -3.485 TP_842 0.000000
> -3.770 TP_842 3.214821
> -2.610 TP_842 5.779205
> -3.060 TP_842 9.231260
> -2.730 TP_842 11.960027
> -2.650 TP_842 15.280575
> -1.500 TP_842 17.713452
> -3.120 TP_842 20.770986
> -3.490 TP_842 24.157287
> -3.210 TP_842 27.379205
> -3.810 TP_842 30.370986
> -3.100 TP_842 32.277835
> -4.040 TP_842 35.894274
> -3.150 TP_842 40.262295
> -3.060 TP_842 42.163934
> -5.270 TP_842 44.196721
> -4.450 TP_842 48.098361
> -2.460 TP_842 51.642219
> -2.970 TP_842 53.812082
> -3.800 TP_842 55.981945
> -3.080 TP_842 59.828520
> -2.500 TP_842 63.247698
> -3.270 TP_842 67.390164
> -2.540 TP_842 70.447698
> -4.330 TP_842 72.058657
> -4.390 TP_842 75.444958
> -3.740 TP_842 79.127150
> -4.410 TP_842 81.987424
> -5.970 TP_842 85.176465
> 2.395 TP_855 0.000000
> 1.990 TP_855 2.728767
> 1.790 TP_855 5.950685
> 1.380 TP_855 9.402740
> 1.230 TP_855 11.901370
> 1.130 TP_855 15.123288
> 1.880 TP_855 18.180822
> 1.350 TP_855 20.679452
> 0.960 TP_855 23.868493
> 1.620 TP_855 26.695890
> 0.780 TP_855 29.720548
> 1.340 TP_855 32.679452
> 1.230 TP_855 35.141882
> 2.970 TP_855 39.076308
> 0.570 TP_855 41.830406
> 1.050 TP_855 45.502538
> 1.110 TP_855 48.263014
> 0.090 TP_855 50.169863
> 2.160 TP_855 54.246575
> 0.030 TP_855 56.745205
> 1.460 TP_855 60.230137
> 1.540 TP_855 62.531507
> 2.180 TP_855 65.983562
> 1.680 TP_855 68.153425
> 2.950 TP_855 71.736986
> 2.230 TP_855 78.871233
> 0.720 TP_855 84.682865
> -3.300 SN_594 0.000000
> -3.550 SN_594 3.221918
> -5.580 SN_594 5.753425
> -4.020 SN_594 9.501370
> -2.550 SN_594 12.192230
> -3.370 SN_594 15.470918
> -5.730 SN_594 18.388951
> -1.640 SN_594 21.438132
> -5.270 SN_594 24.427397
> -1.990 SN_616 0.000000
> -2.430 SN_616 4.043836
> -2.280 SN_616 6.340175
> -3.400 SN_616 9.881159
> -2.420 SN_616 13.094274
> -3.810 SN_616 15.618864
> -2.730 SN_616 18.378082
> -1.650 SN_616 21.830137
> -4.190 SN_616 25.282192
> -2.700 SN_616 28.043836
> -2.610 SN_616 31.035616
> -2.530 SN_616 33.336986
> -2.410 SN_616 37.249315
> -3.700 SN_616 40.010959
> -3.140 SN_616 42.772603
> -3.100 SN_616 45.304110
> -2.420 SN_616 49.841096
> -1.600 SN_616 53.389355
> -2.740 SN_616 56.799192
> -2.950 SN_616 60.930339
> -4.670 SN_616 64.143454
> -2.420 SN_616 66.673973
> -2.500 SN_616 69.698630
> -4.810 SN_616 73.347945
> -4.680 SN_616 75.879452
> -3.780 SN_616 79.101370
> -2.870 SN_616 82.060274
> -2.930 SN_616 86.663014
> -7.230 SN_616 89.884932
> -3.780 SN_616 97.643836
> -0.265 SN_673 0.000000
> -0.890 SN_673 3.876937
> 1.480 SN_673 5.915293
> 0.800 SN_673 9.202964
> 1.260 SN_673 11.931731
> 1.330 SN_673 14.726252
> 2.010 SN_673 17.027622
> 1.300 SN_673 21.400225
> 1.720 SN_673 23.931731
> 1.760 SN_673 26.660499
> 1.780 SN_673 29.685156
> 1.210 SN_673 32.907074
> 1.150 SN_673 35.898855
> 1.200 SN_673 39.540984
> 0.780 SN_673 41.868852
> 1.240 SN_673 45.475410
> 2.010 SN_673 48.983786
> 1.600 SN_673 52.402964
> 1.880 SN_673 53.750909
> 1.470 SN_673 57.564608
> 1.560 SN_673 59.865978
> 1.400 SN_673 63.482416
> 1.630 SN_673 65.718033
> 2.550 SN_673 70.846800
> 0.950 SN_673 78.671457
> -0.360 SN_673 84.194745
> -7.610 SN_683 0.000000
> -5.940 SN_683 3.419178
> -6.270 SN_683 6.641096
> -6.230 SN_683 10.093151
> -4.210 SN_683 12.854795
> -5.360 SN_683 15.649315
> -4.300 SN_683 18.871233
> -6.240 SN_683 22.290411
> -5.010 SN_683 25.084932
> -6.400 SN_683 28.306849
> -6.670 SN_683 31.068493
> -5.870 SN_683 34.060274
> -6.510 SN_683 36.586481
> -2.975 SN_828 0.000000
> -2.990 SN_828 2.983607
> -3.990 SN_828 6.885246
> -2.620 SN_828 9.184011
> -1.380 SN_828 11.945655
> -2.230 SN_828 14.510038
> -2.080 SN_828 17.699079
> -2.410 SN_828 21.611408
> -3.290 SN_828 24.175792
> -1.980 SN_828 27.134696
> -1.970 SN_828 29.929216
> -2.450 SN_828 32.263463
> -2.390 SN_828 36.603189
> -0.950 SN_828 39.134696
> -2.490 SN_828 41.896340
> 2.550 SN_828 44.950820
> -3.120 SN_828 48.163934
> -0.300 SN_828 52.295082
> -2.730 SN_828 54.262295
> -3.230 SN_828 57.808668
> -1.780 SN_828 60.110038
> -2.370 SN_828 63.430586
> -3.140 SN_828 65.863463
> -2.730 SN_828 69.315518
> -1.830 SN_828 71.715518
> -2.460 SN_828 75.167572
> -2.770 SN_828 77.830586
> -2.690 SN_828 84.142915
> -2.890 SN_828 90.126477
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