Compute and plot the estimated mean and confidence intervals of the non-parametric component of a `APLMS` object fited by `aplms()`.
Usage
# S3 method for class 'aplms'
plot(x, len = 100, plot = TRUE, level = 0.95, ...)Arguments
- x
an object with the result of fitting additive partial linear models with symmetric errors.
- len
The desired length of the sequence of covariates to compute the non parametric component functions.
- plot
a logical value to return plots. Default value is
TRUE.- level
Confidence level.
- ...
other arguments.
Value
Return a list of all non parametric component functions with their confidence intervals. If plot=TRUE, the estimated nonparametric component functions are plotted.
Examples
data(temperature)
temperature.df = data.frame(temperature,time=1:length(temperature))
model<-aplms::aplms(temperature ~ 1,
npc=c("time"), basis=c("cr"),Knot=c(60),
data=temperature.df,family=Powerexp(k=0.3),p=1,
control = list(tol = 0.001,
algorithm1 = c("P-GAM"),
algorithm2 = c("BFGS"),
Maxiter1 = 20,
Maxiter2 = 25),
lam=c(10))
plot(model)
#> [[1]]
#> cov fmean fmean_ls fmean_li
#> 1 1.000000 -0.213215062 -0.38051702 -0.045913101
#> 2 2.424242 -0.157657300 -0.25860288 -0.056711725
#> 3 3.848485 -0.212090507 -0.32001771 -0.104163305
#> 4 5.272727 -0.357959506 -0.46898184 -0.246937174
#> 5 6.696970 -0.389381087 -0.48749987 -0.291262301
#> 6 8.121212 -0.309883662 -0.42979400 -0.189973326
#> 7 9.545455 -0.256507180 -0.35408994 -0.158924423
#> 8 10.969697 -0.268972025 -0.38269634 -0.155247707
#> 9 12.393939 -0.328028685 -0.43751976 -0.218537614
#> 10 13.818182 -0.362119686 -0.46252234 -0.261717030
#> 11 15.242424 -0.329739301 -0.44963766 -0.209840943
#> 12 16.666667 -0.236456854 -0.33345593 -0.139457776
#> 13 18.090909 -0.187783201 -0.30292439 -0.072642015
#> 14 19.515152 -0.226748006 -0.33476704 -0.118728976
#> 15 20.939394 -0.229175092 -0.33080619 -0.127543993
#> 16 22.363636 -0.240824195 -0.36025925 -0.121389135
#> 17 23.787879 -0.396329451 -0.49288877 -0.299770132
#> 18 25.212121 -0.472299770 -0.58853860 -0.356060937
#> 19 26.636364 -0.346859356 -0.45344989 -0.240268819
#> 20 28.060606 -0.352051750 -0.45504127 -0.249062233
#> 21 29.484848 -0.509917727 -0.62863393 -0.391201528
#> 22 30.909091 -0.553447782 -0.64976105 -0.457134516
#> 23 32.333333 -0.458669461 -0.57595336 -0.341385561
#> 24 33.757576 -0.319163400 -0.42430868 -0.214018119
#> 25 35.181818 -0.274962044 -0.37937956 -0.170544531
#> 26 36.606061 -0.343014587 -0.46081001 -0.225219168
#> 27 38.030303 -0.415401411 -0.51166773 -0.319135092
#> 28 39.454545 -0.397422379 -0.51569997 -0.279144793
#> 29 40.878788 -0.285716394 -0.38941287 -0.182019919
#> 30 42.303030 -0.281039407 -0.38691070 -0.175168109
#> 31 43.727273 -0.364210514 -0.48097224 -0.247448788
#> 32 45.151515 -0.295850803 -0.39226072 -0.199440886
#> 33 46.575758 -0.202393200 -0.32149651 -0.083289890
#> 34 48.000000 -0.258064413 -0.36036400 -0.155764823
#> 35 49.424242 -0.294762948 -0.40206745 -0.187458442
#> 36 50.848485 -0.243743271 -0.35944671 -0.128039829
#> 37 52.272727 -0.212056003 -0.30881412 -0.115297886
#> 38 53.696970 -0.223624912 -0.34332922 -0.103920599
#> 39 55.121212 -0.245821845 -0.34682625 -0.144817439
#> 40 56.545455 -0.216226869 -0.32497022 -0.107483516
#> 41 57.969697 -0.122563126 -0.23710176 -0.008024492
#> 42 59.393939 -0.022064966 -0.11936732 0.075237389
#> 43 60.818182 0.043246936 -0.07680916 0.163303035
#> 44 62.242424 0.058536017 -0.04133763 0.158409663
#> 45 63.666667 0.064177908 -0.04607577 0.174431583
#> 46 65.090909 0.064875182 -0.04834389 0.178094259
#> 47 66.515152 0.005296521 -0.09272732 0.103320363
#> 48 67.939394 -0.105963819 -0.22614647 0.014218833
#> 49 69.363636 -0.206727445 -0.30561597 -0.107838917
#> 50 70.787879 -0.189575482 -0.30134938 -0.077801580
#> 51 72.212121 -0.076046167 -0.18782007 0.035727734
#> 52 73.636364 -0.079103697 -0.17799222 0.019784831
#> 53 75.060606 -0.162040947 -0.28222360 -0.041858296
#> 54 76.484848 -0.159214768 -0.25723861 -0.061190926
#> 55 77.909091 -0.096978598 -0.21019767 0.016240479
#> 56 79.333333 -0.034892264 -0.14514594 0.075361411
#> 57 80.757576 -0.001654978 -0.10152862 0.098218668
#> 58 82.181818 -0.016382605 -0.13643870 0.103673494
#> 59 83.606061 -0.082110591 -0.17941295 0.015191763
#> 60 85.030303 -0.149307173 -0.26384581 -0.034768540
#> 61 86.454545 -0.164889347 -0.27363270 -0.056145994
#> 62 87.878788 -0.103394785 -0.20439919 -0.002390379
#> 63 89.303030 -0.035126676 -0.15483099 0.084577636
#> 64 90.727273 -0.062581265 -0.15933938 0.034176852
#> 65 92.151515 -0.079086931 -0.19479037 0.036616512
#> 66 93.575758 -0.021958536 -0.12926304 0.085345970
#> 67 95.000000 -0.042156899 -0.14445649 0.060142691
#> 68 96.424242 -0.106105596 -0.22520891 0.012997714
#> 69 97.848485 -0.041483460 -0.13789338 0.054926457
#> 70 99.272727 0.086897127 -0.02986460 0.203658853
#> 71 100.696970 0.174316765 0.06844547 0.280188063
#> 72 102.121212 0.217565731 0.11386926 0.321262206
#> 73 103.545455 0.202671643 0.08439406 0.320949227
#> 74 104.969697 0.115659933 0.01939361 0.211926256
#> 75 106.393939 0.087320078 -0.03047534 0.205115498
#> 76 107.818182 0.208210642 0.10379313 0.312628152
#> 77 109.242424 0.335094675 0.22994939 0.440239956
#> 78 110.666667 0.360344269 0.24306036 0.477628175
#> 79 112.090909 0.268794480 0.17248123 0.365107728
#> 80 113.515152 0.189627094 0.07091091 0.308343273
#> 81 114.939394 0.240639673 0.13765014 0.343629210
#> 82 116.363636 0.346623508 0.24003297 0.453214045
#> 83 117.787879 0.413779514 0.29754069 0.530018341
#> 84 119.212121 0.384577682 0.28801824 0.481137123
#> 85 120.636364 0.369334466 0.24989923 0.488769704
#> 86 122.060606 0.473909701 0.37227863 0.575540771
#> 87 123.484848 0.560667717 0.45264868 0.668686751
#> 88 124.909091 0.568661309 0.45351981 0.683802810
#> 89 126.333333 0.576904017 0.47990550 0.673902532
#> 90 127.757576 0.592069051 0.47217142 0.711966684
#> 91 129.181818 0.589034350 0.48862971 0.689438988
#> 92 130.606061 0.577041729 0.46755078 0.686532679
#> 93 132.030303 0.575296888 0.46156731 0.689026471
#> 94 133.454545 0.606978315 0.50938891 0.704567721
#> 95 134.878788 0.696587825 0.57665900 0.816516648
#> 96 136.303030 0.838493153 0.74033856 0.936647746
#> 97 137.727273 0.917933747 0.80690816 1.028959338
#> 98 139.151515 0.894514412 0.78638554 1.002643279
#> 99 140.575758 0.865726591 0.76474733 0.966705850
#> 100 142.000000 0.859592517 0.68831338 1.030871657
#>