Class-specific marginal predictions in the latent process scale for
lcmm, Jointlcmm and multlcmm objects
Source: R/predictL.lcmm.R
predictL.RdThis function provides a matrix containing the class-specific predicted
trajectories computed in the latent process scale, that is the latent
process underlying the curvilinear outcome(s), for a profile of covariates
specified by the user. This function applies only to lcmm and
multlcmm objects. The function plot.predict provides directly
the plot of these class-specific predicted trajectories. The function
predictY provides the class-specific predicted trajectories computed
in the natural scale of the outcome(s).
Arguments
- x
an object inheriting from class
lcmm,multlcmmorJointlcmmrepresenting a (joint) (latent class) mixed model involving a latent process and estimated link function(s).- newdata
data frame containing the data from which predictions are computed. The data frame should include at least all the covariates listed in x$Xnames2. Names in the data frame should be exactly x$Xnames2 that are the names of covariates specified in
lcmmormultlcmmcalls.- var.time
A character string containing the name of the variable that corresponds to time in the data frame (x axis in the plot).
- na.action
Integer indicating how NAs are managed. The default is 1 for 'na.omit'. The alternative is 2 for 'na.fail'. Other options such as 'na.pass' or 'na.exclude' are not implemented in the current version.
- confint
logical indicating if confidence should be provided. Default to FALSE.
- ...
further arguments to be passed to or from other methods. They are ignored in this function.
Value
An object of class predictL with values :
- pred : a matrix containing the class-specific predicted values in
the latent process scale, the lower and the upper limits of the confidence
intervals (if calculated).
- times : the var.time variable from newdata
Examples
#### Prediction from a 2-class model with a Splines link function
# \dontrun{
## fitted model
m<-lcmm(Ydep2~Time*X1,mixture=~Time,random=~Time,classmb=~X2+X3,
subject='ID',ng=2,data=data_lcmm,link="splines",B=c(
-0.175, -0.191, 0.654, -0.443,
-0.345, -1.780, 0.913, 0.016,
0.389, 0.028, 0.083, -7.349,
0.722, 0.770, 1.376, 1.653,
1.640, 1.285))
summary(m)
#> General latent class mixed model
#> fitted by maximum likelihood method
#>
#> lcmm(fixed = Ydep2 ~ Time * X1, mixture = ~Time, random = ~Time,
#> subject = "ID", classmb = ~X2 + X3, ng = 2, link = "splines",
#> data = data_lcmm)
#>
#> Statistical Model:
#> Dataset: data_lcmm
#> Number of subjects: 300
#> Number of observations: 1678
#> Number of latent classes: 2
#> Number of parameters: 18
#> Link function: Quadratic I-splines with nodes
#> 0 7.5 15 22.5 30
#>
#> Iteration process:
#> Convergence criteria satisfied
#> Number of iterations: 2
#> Convergence criteria: parameters= 2.9e-07
#> : likelihood= 1.8e-06
#> : second derivatives= 1.1e-09
#>
#> Goodness-of-fit statistics:
#> maximum log-likelihood: -3960.43
#> AIC: 7956.86
#> BIC: 8023.52
#>
#> Discrete posterior log-likelihood: -3946.06
#> Discrete AIC: 7928.12
#>
#> Mean discrete AIC per subject: 13.2135
#> Mean UACV per subject: 13.2152
#> Mean discrete LL per subject: -13.1535
#>
#> Maximum Likelihood Estimates:
#>
#> Fixed effects in the class-membership model:
#> (the class of reference is the last class)
#>
#> coef Se Wald p-value
#> intercept class1 -0.17512 0.23496 -0.745 0.45610
#> X2 class1 -0.19156 0.35836 -0.535 0.59295
#> X3 class1 0.65455 0.19492 3.358 0.00079
#>
#> Fixed effects in the longitudinal model:
#>
#> coef Se Wald p-value
#> intercept class1 (not estimated) 0
#> intercept class2 -0.44319 0.19768 -2.242 0.02496
#> Time class1 -0.34568 0.11917 -2.901 0.00372
#> Time class2 -1.78053 0.14188 -12.550 0.00000
#> X1 0.91348 0.14896 6.133 0.00000
#> Time:X1 0.01635 0.14302 0.114 0.90899
#>
#>
#> Variance-covariance matrix of the random-effects:
#> intercept Time
#> intercept 0.38917
#> Time 0.02830 0.08307
#>
#> Residual standard error (not estimated) = 1
#>
#> Parameters of the link function:
#>
#> coef Se Wald p-value
#> I-splines1 -7.34981 0.56642 -12.976 0.00000
#> I-splines2 0.72200 0.26698 2.704 0.00684
#> I-splines3 0.77031 0.40833 1.886 0.05923
#> I-splines4 1.37681 0.13169 10.455 0.00000
#> I-splines5 1.65381 0.06419 25.763 0.00000
#> I-splines6 1.64064 0.04902 33.467 0.00000
#> I-splines7 1.28585 0.05117 25.130 0.00000
#>
## predictions for times from 0 to 5 for X1=0
newdata<-data.frame(Time=seq(0,5,length=100),
X1=rep(0,100),X2=rep(0,100),X3=rep(0,100))
predictL(m,newdata,var.time="Time")
#> $pred
#> pred_class1 pred_class2
#> [1,] 0.00000000 -0.4431914
#> [2,] -0.01745865 -0.5331172
#> [3,] -0.03491730 -0.6230430
#> [4,] -0.05237596 -0.7129689
#> [5,] -0.06983461 -0.8028947
#> [6,] -0.08729326 -0.8928205
#> [7,] -0.10475191 -0.9827463
#> [8,] -0.12221056 -1.0726721
#> [9,] -0.13966922 -1.1625979
#> [10,] -0.15712787 -1.2525237
#> [11,] -0.17458652 -1.3424496
#> [12,] -0.19204517 -1.4323754
#> [13,] -0.20950382 -1.5223012
#> [14,] -0.22696247 -1.6122270
#> [15,] -0.24442113 -1.7021528
#> [16,] -0.26187978 -1.7920786
#> [17,] -0.27933843 -1.8820044
#> [18,] -0.29679708 -1.9719303
#> [19,] -0.31425573 -2.0618561
#> [20,] -0.33171439 -2.1517819
#> [21,] -0.34917304 -2.2417077
#> [22,] -0.36663169 -2.3316335
#> [23,] -0.38409034 -2.4215593
#> [24,] -0.40154899 -2.5114851
#> [25,] -0.41900765 -2.6014110
#> [26,] -0.43646630 -2.6913368
#> [27,] -0.45392495 -2.7812626
#> [28,] -0.47138360 -2.8711884
#> [29,] -0.48884225 -2.9611142
#> [30,] -0.50630090 -3.0510400
#> [31,] -0.52375956 -3.1409658
#> [32,] -0.54121821 -3.2308917
#> [33,] -0.55867686 -3.3208175
#> [34,] -0.57613551 -3.4107433
#> [35,] -0.59359416 -3.5006691
#> [36,] -0.61105282 -3.5905949
#> [37,] -0.62851147 -3.6805207
#> [38,] -0.64597012 -3.7704465
#> [39,] -0.66342877 -3.8603724
#> [40,] -0.68088742 -3.9502982
#> [41,] -0.69834608 -4.0402240
#> [42,] -0.71580473 -4.1301498
#> [43,] -0.73326338 -4.2200756
#> [44,] -0.75072203 -4.3100014
#> [45,] -0.76818068 -4.3999272
#> [46,] -0.78563933 -4.4898531
#> [47,] -0.80309799 -4.5797789
#> [48,] -0.82055664 -4.6697047
#> [49,] -0.83801529 -4.7596305
#> [50,] -0.85547394 -4.8495563
#> [51,] -0.87293259 -4.9394821
#> [52,] -0.89039125 -5.0294079
#> [53,] -0.90784990 -5.1193338
#> [54,] -0.92530855 -5.2092596
#> [55,] -0.94276720 -5.2991854
#> [56,] -0.96022585 -5.3891112
#> [57,] -0.97768451 -5.4790370
#> [58,] -0.99514316 -5.5689628
#> [59,] -1.01260181 -5.6588886
#> [60,] -1.03006046 -5.7488145
#> [61,] -1.04751911 -5.8387403
#> [62,] -1.06497777 -5.9286661
#> [63,] -1.08243642 -6.0185919
#> [64,] -1.09989507 -6.1085177
#> [65,] -1.11735372 -6.1984435
#> [66,] -1.13481237 -6.2883693
#> [67,] -1.15227102 -6.3782952
#> [68,] -1.16972968 -6.4682210
#> [69,] -1.18718833 -6.5581468
#> [70,] -1.20464698 -6.6480726
#> [71,] -1.22210563 -6.7379984
#> [72,] -1.23956428 -6.8279242
#> [73,] -1.25702294 -6.9178500
#> [74,] -1.27448159 -7.0077759
#> [75,] -1.29194024 -7.0977017
#> [76,] -1.30939889 -7.1876275
#> [77,] -1.32685754 -7.2775533
#> [78,] -1.34431620 -7.3674791
#> [79,] -1.36177485 -7.4574049
#> [80,] -1.37923350 -7.5473307
#> [81,] -1.39669215 -7.6372566
#> [82,] -1.41415080 -7.7271824
#> [83,] -1.43160945 -7.8171082
#> [84,] -1.44906811 -7.9070340
#> [85,] -1.46652676 -7.9969598
#> [86,] -1.48398541 -8.0868856
#> [87,] -1.50144406 -8.1768114
#> [88,] -1.51890271 -8.2667373
#> [89,] -1.53636137 -8.3566631
#> [90,] -1.55382002 -8.4465889
#> [91,] -1.57127867 -8.5365147
#> [92,] -1.58873732 -8.6264405
#> [93,] -1.60619597 -8.7163663
#> [94,] -1.62365463 -8.8062921
#> [95,] -1.64111328 -8.8962180
#> [96,] -1.65857193 -8.9861438
#> [97,] -1.67603058 -9.0760696
#> [98,] -1.69348923 -9.1659954
#> [99,] -1.71094788 -9.2559212
#> [100,] -1.72840654 -9.3458470
#>
#> $times
#> Time
#> 1 0.00000000
#> 2 0.05050505
#> 3 0.10101010
#> 4 0.15151515
#> 5 0.20202020
#> 6 0.25252525
#> 7 0.30303030
#> 8 0.35353535
#> 9 0.40404040
#> 10 0.45454545
#> 11 0.50505051
#> 12 0.55555556
#> 13 0.60606061
#> 14 0.65656566
#> 15 0.70707071
#> 16 0.75757576
#> 17 0.80808081
#> 18 0.85858586
#> 19 0.90909091
#> 20 0.95959596
#> 21 1.01010101
#> 22 1.06060606
#> 23 1.11111111
#> 24 1.16161616
#> 25 1.21212121
#> 26 1.26262626
#> 27 1.31313131
#> 28 1.36363636
#> 29 1.41414141
#> 30 1.46464646
#> 31 1.51515152
#> 32 1.56565657
#> 33 1.61616162
#> 34 1.66666667
#> 35 1.71717172
#> 36 1.76767677
#> 37 1.81818182
#> 38 1.86868687
#> 39 1.91919192
#> 40 1.96969697
#> 41 2.02020202
#> 42 2.07070707
#> 43 2.12121212
#> 44 2.17171717
#> 45 2.22222222
#> 46 2.27272727
#> 47 2.32323232
#> 48 2.37373737
#> 49 2.42424242
#> 50 2.47474747
#> 51 2.52525253
#> 52 2.57575758
#> 53 2.62626263
#> 54 2.67676768
#> 55 2.72727273
#> 56 2.77777778
#> 57 2.82828283
#> 58 2.87878788
#> 59 2.92929293
#> 60 2.97979798
#> 61 3.03030303
#> 62 3.08080808
#> 63 3.13131313
#> 64 3.18181818
#> 65 3.23232323
#> 66 3.28282828
#> 67 3.33333333
#> 68 3.38383838
#> 69 3.43434343
#> 70 3.48484848
#> 71 3.53535354
#> 72 3.58585859
#> 73 3.63636364
#> 74 3.68686869
#> 75 3.73737374
#> 76 3.78787879
#> 77 3.83838384
#> 78 3.88888889
#> 79 3.93939394
#> 80 3.98989899
#> 81 4.04040404
#> 82 4.09090909
#> 83 4.14141414
#> 84 4.19191919
#> 85 4.24242424
#> 86 4.29292929
#> 87 4.34343434
#> 88 4.39393939
#> 89 4.44444444
#> 90 4.49494949
#> 91 4.54545455
#> 92 4.59595960
#> 93 4.64646465
#> 94 4.69696970
#> 95 4.74747475
#> 96 4.79797980
#> 97 4.84848485
#> 98 4.89898990
#> 99 4.94949495
#> 100 5.00000000
#>
#> attr(,"class")
#> [1] "predictL"
## predictions for times from 0 to 5 for X1=1
newdata$X1 <- 1
predictY(m,newdata,var.time="Time")
#> $pred
#> Ypred_class1 Ypred_class2
#> [1,] 26.86654 26.093656
#> [2,] 26.83833 25.929600
#> [3,] 26.80998 25.764062
#> [4,] 26.78153 25.597010
#> [5,] 26.75295 25.428430
#> [6,] 26.72425 25.258264
#> [7,] 26.69543 25.086491
#> [8,] 26.66650 24.913101
#> [9,] 26.63745 24.736599
#> [10,] 26.60825 24.558394
#> [11,] 26.57897 24.378220
#> [12,] 26.54954 24.196194
#> [13,] 26.51999 24.012331
#> [14,] 26.49032 23.826628
#> [15,] 26.46053 23.639031
#> [16,] 26.43063 23.449497
#> [17,] 26.40058 23.257993
#> [18,] 26.37042 23.064460
#> [19,] 26.34014 22.868868
#> [20,] 26.30973 22.671032
#> [21,] 26.27551 22.471003
#> [22,] 26.24444 22.268730
#> [23,] 26.21323 22.064193
#> [24,] 26.18186 21.857318
#> [25,] 26.15038 21.648047
#> [26,] 26.11876 21.436368
#> [27,] 26.08700 21.222151
#> [28,] 26.05513 21.005367
#> [29,] 26.02313 20.785921
#> [30,] 25.99100 20.563747
#> [31,] 25.95874 20.338764
#> [32,] 25.92635 20.110921
#> [33,] 25.89387 19.880139
#> [34,] 25.86126 19.646256
#> [35,] 25.82852 19.409219
#> [36,] 25.79566 19.168915
#> [37,] 25.76270 18.925199
#> [38,] 25.72960 18.677957
#> [39,] 25.69642 18.426995
#> [40,] 25.66308 18.172234
#> [41,] 25.62965 17.914179
#> [42,] 25.59609 17.652444
#> [43,] 25.56243 17.386596
#> [44,] 25.52862 17.116584
#> [45,] 25.49473 16.842272
#> [46,] 25.46072 16.563483
#> [47,] 25.42657 16.279932
#> [48,] 25.39230 15.991300
#> [49,] 25.35791 15.697466
#> [50,] 25.32342 15.402078
#> [51,] 25.28878 15.104793
#> [52,] 25.25404 14.802168
#> [53,] 25.21918 14.494476
#> [54,] 25.18418 14.181951
#> [55,] 25.14908 13.864671
#> [56,] 25.11383 13.542504
#> [57,] 25.07848 13.215046
#> [58,] 25.04298 12.881765
#> [59,] 25.00736 12.542549
#> [60,] 24.97163 12.220935
#> [61,] 24.93576 11.899501
#> [62,] 24.89974 11.571886
#> [63,] 24.86361 11.239108
#> [64,] 24.82736 10.902179
#> [65,] 24.79094 10.561970
#> [66,] 24.75442 10.219008
#> [67,] 24.71774 9.873385
#> [68,] 24.68095 9.524742
#> [69,] 24.64400 9.172386
#> [70,] 24.60692 8.822232
#> [71,] 24.56971 8.536943
#> [72,] 24.53236 8.244459
#> [73,] 24.49487 7.945917
#> [74,] 24.45721 7.642777
#> [75,] 24.41943 7.336671
#> [76,] 24.38151 7.029192
#> [77,] 24.34343 6.721653
#> [78,] 24.30520 6.414984
#> [79,] 24.26683 6.109628
#> [80,] 24.22829 5.805566
#> [81,] 24.18962 5.502358
#> [82,] 24.15077 5.199329
#> [83,] 24.11177 5.008255
#> [84,] 24.07264 4.812659
#> [85,] 24.03335 4.611975
#> [86,] 23.99390 4.407093
#> [87,] 23.95426 4.199179
#> [88,] 23.91446 3.989567
#> [89,] 23.87450 3.779634
#> [90,] 23.83437 3.570649
#> [91,] 23.79404 3.363655
#> [92,] 23.75355 3.159381
#> [93,] 23.71290 2.958247
#> [94,] 23.67207 2.760384
#> [95,] 23.63106 2.565667
#> [96,] 23.58989 2.373786
#> [97,] 23.54854 2.274609
#> [98,] 23.50702 2.186959
#> [99,] 23.46532 2.096579
#> [100,] 23.42345 2.003659
#>
#> $times
#> Time
#> 1 0.00000000
#> 2 0.05050505
#> 3 0.10101010
#> 4 0.15151515
#> 5 0.20202020
#> 6 0.25252525
#> 7 0.30303030
#> 8 0.35353535
#> 9 0.40404040
#> 10 0.45454545
#> 11 0.50505051
#> 12 0.55555556
#> 13 0.60606061
#> 14 0.65656566
#> 15 0.70707071
#> 16 0.75757576
#> 17 0.80808081
#> 18 0.85858586
#> 19 0.90909091
#> 20 0.95959596
#> 21 1.01010101
#> 22 1.06060606
#> 23 1.11111111
#> 24 1.16161616
#> 25 1.21212121
#> 26 1.26262626
#> 27 1.31313131
#> 28 1.36363636
#> 29 1.41414141
#> 30 1.46464646
#> 31 1.51515152
#> 32 1.56565657
#> 33 1.61616162
#> 34 1.66666667
#> 35 1.71717172
#> 36 1.76767677
#> 37 1.81818182
#> 38 1.86868687
#> 39 1.91919192
#> 40 1.96969697
#> 41 2.02020202
#> 42 2.07070707
#> 43 2.12121212
#> 44 2.17171717
#> 45 2.22222222
#> 46 2.27272727
#> 47 2.32323232
#> 48 2.37373737
#> 49 2.42424242
#> 50 2.47474747
#> 51 2.52525253
#> 52 2.57575758
#> 53 2.62626263
#> 54 2.67676768
#> 55 2.72727273
#> 56 2.77777778
#> 57 2.82828283
#> 58 2.87878788
#> 59 2.92929293
#> 60 2.97979798
#> 61 3.03030303
#> 62 3.08080808
#> 63 3.13131313
#> 64 3.18181818
#> 65 3.23232323
#> 66 3.28282828
#> 67 3.33333333
#> 68 3.38383838
#> 69 3.43434343
#> 70 3.48484848
#> 71 3.53535354
#> 72 3.58585859
#> 73 3.63636364
#> 74 3.68686869
#> 75 3.73737374
#> 76 3.78787879
#> 77 3.83838384
#> 78 3.88888889
#> 79 3.93939394
#> 80 3.98989899
#> 81 4.04040404
#> 82 4.09090909
#> 83 4.14141414
#> 84 4.19191919
#> 85 4.24242424
#> 86 4.29292929
#> 87 4.34343434
#> 88 4.39393939
#> 89 4.44444444
#> 90 4.49494949
#> 91 4.54545455
#> 92 4.59595960
#> 93 4.64646465
#> 94 4.69696970
#> 95 4.74747475
#> 96 4.79797980
#> 97 4.84848485
#> 98 4.89898990
#> 99 4.94949495
#> 100 5.00000000
#>
#> attr(,"class")
#> [1] "predictY"
# }