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Marcos Matabuena
17/01/2016
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setwd("~/Descargas/datos_funcionales/2015/Carpeta sin tÃtulo")
library("lubridate")
library("dplyr")##
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## intersect, setdiff, setequal, unionlibrary("data.table")##
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## hour, mday, month, quarter, wday, week, yday, yearlibrary("data.table")
library("fda.usc")## Loading required package: fda
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## Loading required package: rpartlibrary("Funclustering")
library("ff")## Loading required package: bit
## Attaching package bit
## package:bit (c) 2008-2012 Jens Oehlschlaegel (GPL-2)
## creators: bit bitwhich
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## Attaching package: 'ff'
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## is.factor, is.orderedlibrary(PerformanceAnalytics)## Loading required package: xts
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## legendlibrary(ppcor)
library(dplyr)
datos= read.csv("gr2.csv",sep=";")
attach(datos)
gr=split(datos,list(DIA,ANO))
#write.csv(gr[[1]],"result.csv")
#var1t= gr[[1]]$BEN
nombres= names(datos)[4:12]
datos2= group_by(datos,MES)
meses=unique(MES)
MESES=split(datos,MES)
for(i in meses){
aux= i
D= as.data.frame(MESES[i])
A=D[,4:12]
B=A[,sapply(A,is.numeric)]
print(cor(B))
pairs(B)
}## enero.BEN enero.CO enero.NO enero.NO2 enero.NOX
## enero.BEN 1.000000000 0.003564786 0.14817511 0.2175585 0.2048618
## enero.CO 0.003564786 1.000000000 -0.01145604 0.2437868 0.1515270
## enero.NO 0.148175106 -0.011456036 1.00000000 0.6755519 0.8766904
## enero.NO2 0.217558505 0.243786817 0.67555188 1.0000000 0.9458731
## enero.NOX 0.204861847 0.151527049 0.87669040 0.9458731 1.0000000
## enero.O3 -0.130206604 -0.244453502 -0.49690208 -0.7703506 -0.7207622
## enero.PM10 0.105888138 0.056252755 0.11226119 0.1613924 0.1547436
## enero.SO2 -0.109678512 0.084590138 0.13422916 0.1308873 0.1447532
## enero.O3 enero.PM10 enero.SO2
## enero.BEN -0.13020660 0.10588814 -0.10967851
## enero.CO -0.24445350 0.05625276 0.08459014
## enero.NO -0.49690208 0.11226119 0.13422916
## enero.NO2 -0.77035058 0.16139239 0.13088731
## enero.NOX -0.72076224 0.15474358 0.14475321
## enero.O3 1.00000000 -0.16120787 -0.08248539
## enero.PM10 -0.16120787 1.00000000 0.07237464
## enero.SO2 -0.08248539 0.07237464 1.00000000
## febrero.BEN febrero.CO febrero.NO febrero.NO2 febrero.NOX
## febrero.BEN 1.00000000 0.035171358 -0.08558170 -0.2161152 -0.16691589
## febrero.CO 0.03517136 1.000000000 0.01641083 0.0401187 0.03191026
## febrero.NO -0.08558170 0.016410825 1.00000000 0.5146123 0.88091518
## febrero.NO2 -0.21611521 0.040118698 0.51461225 1.0000000 0.85804455
## febrero.NOX -0.16691589 0.031910256 0.88091518 0.8580445 1.00000000
## febrero.O3 0.31704908 -0.040304705 -0.47083247 -0.7816303 -0.71295705
## febrero.PM10 -0.06835295 -0.019541749 0.44241531 0.3705409 0.46861924
## febrero.SO2 -0.10724983 0.001622946 0.53839941 0.2760433 0.47227377
## febrero.O3 febrero.PM10 febrero.SO2
## febrero.BEN 0.31704908 -0.06835295 -0.107249828
## febrero.CO -0.04030471 -0.01954175 0.001622946
## febrero.NO -0.47083247 0.44241531 0.538399415
## febrero.NO2 -0.78163029 0.37054090 0.276043284
## febrero.NOX -0.71295705 0.46861924 0.472273767
## febrero.O3 1.00000000 -0.35919138 -0.328679489
## febrero.PM10 -0.35919138 1.00000000 0.432005302
## febrero.SO2 -0.32867949 0.43200530 1.000000000
## marzo.BEN marzo.CO marzo.NO marzo.NO2 marzo.NOX
## marzo.BEN 1.00000000 -0.62479090 -0.0179300 -0.1359128 -0.07341856
## marzo.CO -0.62479090 1.00000000 0.2074151 0.4297425 0.33425700
## marzo.NO -0.01793000 0.20741512 1.0000000 0.5653780 0.90267172
## marzo.NO2 -0.13591279 0.42974247 0.5653780 1.0000000 0.86406631
## marzo.NOX -0.07341856 0.33425700 0.9026717 0.8640663 1.00000000
## marzo.O3 0.11263899 -0.22061640 -0.5168328 -0.6288240 -0.64499158
## marzo.PM10 0.07646485 0.09125953 0.3460231 0.4314426 0.43409630
## marzo.SO2 0.01144181 0.11875198 0.5080204 0.3975965 0.51606647
## marzo.O3 marzo.PM10 marzo.SO2
## marzo.BEN 0.1126390 0.07646485 0.01144181
## marzo.CO -0.2206164 0.09125953 0.11875198
## marzo.NO -0.5168328 0.34602311 0.50802041
## marzo.NO2 -0.6288240 0.43144261 0.39759650
## marzo.NOX -0.6449916 0.43409630 0.51606647
## marzo.O3 1.0000000 -0.15425471 -0.24975023
## marzo.PM10 -0.1542547 1.00000000 0.22963077
## marzo.SO2 -0.2497502 0.22963077 1.00000000
## abril.BEN abril.CO abril.NO abril.NO2 abril.NOX
## abril.BEN 1.000000000 0.07254874 -0.01795525 0.02516962 -0.008173364
## abril.CO 0.072548736 1.00000000 0.28429243 0.55171589 0.460454197
## abril.NO -0.017955254 0.28429243 1.00000000 0.48364965 0.874545239
## abril.NO2 0.025169620 0.55171589 0.48364965 1.00000000 0.845480852
## abril.NOX -0.008173364 0.46045420 0.87454524 0.84548085 1.000000000
## abril.O3 0.174729182 -0.22276575 -0.51253880 -0.59690072 -0.648096250
## abril.PM10 -0.091965322 0.08731386 0.10288812 0.11706513 0.128315673
## abril.SO2 -0.222505125 0.15782742 0.51324281 0.27617481 0.467372177
## abril.O3 abril.PM10 abril.SO2
## abril.BEN 0.1747292 -0.09196532 -0.22250513
## abril.CO -0.2227658 0.08731386 0.15782742
## abril.NO -0.5125388 0.10288812 0.51324281
## abril.NO2 -0.5969007 0.11706513 0.27617481
## abril.NOX -0.6480962 0.12831567 0.46737218
## abril.O3 1.0000000 -0.17996465 -0.28830433
## abril.PM10 -0.1799646 1.00000000 0.04789049
## abril.SO2 -0.2883043 0.04789049 1.00000000
## mayo.BEN mayo.CO mayo.NO mayo.NO2 mayo.NOX
## mayo.BEN 1.000000000 -0.83276298 0.01383344 -0.005598342 -0.0184532
## mayo.CO -0.832762978 1.00000000 0.06074169 0.217347754 0.1762886
## mayo.NO 0.013833445 0.06074169 1.00000000 0.461695909 0.8606020
## mayo.NO2 -0.005598342 0.21734775 0.46169591 1.000000000 0.8461667
## mayo.NOX -0.018453198 0.17628861 0.86060196 0.846166728 1.0000000
## mayo.O3 0.168504613 -0.13285390 -0.35187888 -0.403151849 -0.4461447
## mayo.PM10 -0.147261304 0.13815059 0.18827094 0.268450295 0.2735849
## mayo.SO2 -0.044689283 0.13640799 0.40113654 0.439242467 0.4927534
## mayo.O3 mayo.PM10 mayo.SO2
## mayo.BEN 0.1685046 -0.1472613 -0.04468928
## mayo.CO -0.1328539 0.1381506 0.13640799
## mayo.NO -0.3518789 0.1882709 0.40113654
## mayo.NO2 -0.4031518 0.2684503 0.43924247
## mayo.NOX -0.4461447 0.2735849 0.49275344
## mayo.O3 1.0000000 0.1810593 -0.18257472
## mayo.PM10 0.1810593 1.0000000 0.31680914
## mayo.SO2 -0.1825747 0.3168091 1.00000000
## junio.BEN junio.CO junio.NO junio.NO2 junio.NOX
## junio.BEN 1.000000000 -0.4043345 0.02021857 0.03780410 0.007257126
## junio.CO -0.404334470 1.0000000 0.17821645 0.36732111 0.303350693
## junio.NO 0.020218574 0.1782164 1.00000000 0.62197302 0.902601986
## junio.NO2 0.037804098 0.3673211 0.62197302 1.00000000 0.894396112
## junio.NOX 0.007257126 0.3033507 0.90260199 0.89439611 1.000000000
## junio.O3 0.282035639 0.1286955 -0.21405375 -0.07354955 -0.184899706
## junio.PM10 -0.015512539 0.1644439 0.08244267 0.15906465 0.127543963
## junio.SO2 -0.198830195 0.3393280 0.38099724 0.36051838 0.411394426
## junio.O3 junio.PM10 junio.SO2
## junio.BEN 0.282035639 -0.01551254 -0.198830195
## junio.CO 0.128695475 0.16444385 0.339328010
## junio.NO -0.214053747 0.08244267 0.380997244
## junio.NO2 -0.073549550 0.15906465 0.360518380
## junio.NOX -0.184899706 0.12754396 0.411394426
## junio.O3 1.000000000 0.08163913 0.005677349
## junio.PM10 0.081639130 1.00000000 0.106358952
## junio.SO2 0.005677349 0.10635895 1.000000000
## julio.BEN julio.CO julio.NO julio.NO2 julio.NOX
## julio.BEN 1 NA NA NA NA
## julio.CO NA 1.0000000000 0.06186285 0.2082866 0.1272137
## julio.NO NA 0.0618628471 1.00000000 0.4781413 0.9343078
## julio.NO2 NA 0.2082866288 0.47814134 1.0000000 0.7533301
## julio.NOX NA 0.1272137137 0.93430781 0.7533301 1.0000000
## julio.O3 NA -0.0356742156 -0.26456043 -0.2926332 -0.3114290
## julio.PM10 NA -0.0009847099 0.17488151 0.3229123 0.2479385
## julio.SO2 NA 0.0245437210 0.47439380 0.2606128 0.4698783
## julio.O3 julio.PM10 julio.SO2
## julio.BEN NA NA NA
## julio.CO -0.03567422 -0.0009847099 0.02454372
## julio.NO -0.26456043 0.1748815115 0.47439380
## julio.NO2 -0.29263321 0.3229123285 0.26061275
## julio.NOX -0.31142902 0.2479385096 0.46987834
## julio.O3 1.00000000 0.2869778435 0.01277999
## julio.PM10 0.28697784 1.0000000000 0.04740928
## julio.SO2 0.01277999 0.0474092810 1.00000000
## agosto.BEN agosto.CO agosto.NO agosto.NO2 agosto.NOX
## agosto.BEN 1 NA NA NA NA
## agosto.CO NA 1.00000000 0.04695599 0.26932110 0.13312865
## agosto.NO NA 0.04695599 1.00000000 0.59618433 0.92306420
## agosto.NO2 NA 0.26932110 0.59618433 1.00000000 0.85282130
## agosto.NOX NA 0.13312865 0.92306420 0.85282130 1.00000000
## agosto.O3 NA -0.01375621 -0.28874399 -0.39423868 -0.37554644
## agosto.PM10 NA 0.11627844 0.03570739 0.08964097 0.06337994
## agosto.SO2 NA -0.31717603 0.30758951 0.14824307 0.28831639
## agosto.O3 agosto.PM10 agosto.SO2
## agosto.BEN NA NA NA
## agosto.CO -0.01375621 0.11627844 -0.31717603
## agosto.NO -0.28874399 0.03570739 0.30758951
## agosto.NO2 -0.39423868 0.08964097 0.14824307
## agosto.NOX -0.37554644 0.06337994 0.28831639
## agosto.O3 1.00000000 0.05516677 -0.01527351
## agosto.PM10 0.05516677 1.00000000 -0.02005492
## agosto.SO2 -0.01527351 -0.02005492 1.00000000
## septiembre.BEN septiembre.CO septiembre.NO septiembre.NO2
## septiembre.BEN 1.00000000 2.898775e-01 0.07298541 0.09905835
## septiembre.CO 0.28987745 1.000000e+00 0.16617304 0.28319022
## septiembre.NO 0.07298541 1.661730e-01 1.00000000 0.53548061
## septiembre.NO2 0.09905835 2.831902e-01 0.53548061 1.00000000
## septiembre.NOX 0.09217343 2.240235e-01 0.93492339 0.79145995
## septiembre.O3 -0.03646090 -7.014371e-05 -0.42657311 -0.53445356
## septiembre.PM10 0.11261086 2.552928e-01 0.35417230 0.45895007
## septiembre.SO2 -0.04450792 2.911481e-02 0.57793245 0.41426434
## septiembre.NOX septiembre.O3 septiembre.PM10
## septiembre.BEN 0.09217343 -3.646090e-02 0.1126109
## septiembre.CO 0.22402354 -7.014371e-05 0.2552928
## septiembre.NO 0.93492339 -4.265731e-01 0.3541723
## septiembre.NO2 0.79145995 -5.344536e-01 0.4589501
## septiembre.NOX 1.00000000 -5.282350e-01 0.4382387
## septiembre.O3 -0.52823502 1.000000e+00 -0.0888225
## septiembre.PM10 0.43823870 -8.882250e-02 1.0000000
## septiembre.SO2 0.58561354 -2.565390e-01 0.1871523
## septiembre.SO2
## septiembre.BEN -0.04450792
## septiembre.CO 0.02911481
## septiembre.NO 0.57793245
## septiembre.NO2 0.41426434
## septiembre.NOX 0.58561354
## septiembre.O3 -0.25653905
## septiembre.PM10 0.18715225
## septiembre.SO2 1.00000000
## octubre.BEN octubre.CO octubre.NO octubre.NO2 octubre.NOX
## octubre.BEN 1.00000000 -0.34669401 -0.1482721 -0.1391773 -0.1545025
## octubre.CO -0.34669401 1.00000000 0.3737684 0.3304701 0.3834230
## octubre.NO -0.14827214 0.37376838 1.0000000 0.6246271 0.9594488
## octubre.NO2 -0.13917730 0.33047012 0.6246271 1.0000000 0.8186374
## octubre.NOX -0.15450255 0.38342301 0.9594488 0.8186374 1.0000000
## octubre.O3 0.26527885 -0.33488440 -0.5792386 -0.6498744 -0.6594232
## octubre.PM10 0.02003915 -0.07237582 0.1432681 0.1946252 0.1780313
## octubre.SO2 -0.22097253 0.43838260 0.3731990 0.2885364 0.3738489
## octubre.O3 octubre.PM10 octubre.SO2
## octubre.BEN 0.26527885 0.02003915 -0.22097253
## octubre.CO -0.33488440 -0.07237582 0.43838260
## octubre.NO -0.57923857 0.14326806 0.37319896
## octubre.NO2 -0.64987439 0.19462518 0.28853644
## octubre.NOX -0.65942322 0.17803134 0.37384893
## octubre.O3 1.00000000 -0.07555244 -0.31928873
## octubre.PM10 -0.07555244 1.00000000 0.05006079
## octubre.SO2 -0.31928873 0.05006079 1.00000000
## noviembre.BEN noviembre.CO noviembre.NO noviembre.NO2
## noviembre.BEN 1.00000000 -0.62554443 0.27301030 0.35687418
## noviembre.CO -0.62554443 1.00000000 -0.04937098 -0.02051894
## noviembre.NO 0.27301030 -0.04937098 1.00000000 0.68536423
## noviembre.NO2 0.35687418 -0.02051894 0.68536423 1.00000000
## noviembre.NOX 0.33823781 -0.05075140 0.95408932 0.87098476
## noviembre.O3 -0.01413069 0.24130226 -0.40329833 -0.33445377
## noviembre.PM10 0.61416678 -0.07369578 0.35595305 0.48335865
## noviembre.SO2 -0.09533213 0.05178661 0.05769359 0.14261704
## noviembre.NOX noviembre.O3 noviembre.PM10 noviembre.SO2
## noviembre.BEN 0.33823781 -0.01413069 0.61416678 -0.09533213
## noviembre.CO -0.05075140 0.24130226 -0.07369578 0.05178661
## noviembre.NO 0.95408932 -0.40329833 0.35595305 0.05769359
## noviembre.NO2 0.87098476 -0.33445377 0.48335865 0.14261704
## noviembre.NOX 1.00000000 -0.41565523 0.43901211 0.09212373
## noviembre.O3 -0.41565523 1.00000000 0.01564140 0.22122201
## noviembre.PM10 0.43901211 0.01564140 1.00000000 -0.01786316
## noviembre.SO2 0.09212373 0.22122201 -0.01786316 1.00000000
## diciembre.BEN diciembre.CO diciembre.NO diciembre.NO2
## diciembre.BEN 1.00000000 -0.8058006 -0.08955084 -0.17759289
## diciembre.CO -0.80580056 1.0000000 0.29933606 0.36113105
## diciembre.NO -0.08955084 0.2993361 1.00000000 0.67630538
## diciembre.NO2 -0.17759289 0.3611310 0.67630538 1.00000000
## diciembre.NOX -0.11774332 0.3360714 0.96673765 0.84148602
## diciembre.O3 0.23092578 -0.2650876 -0.57303653 -0.76829920
## diciembre.PM10 0.38890850 -0.1811584 0.27642871 0.26995300
## diciembre.SO2 0.37730987 -0.2888514 0.03532505 -0.02405469
## diciembre.NOX diciembre.O3 diciembre.PM10 diciembre.SO2
## diciembre.BEN -0.11774332 0.23092578 0.3889085 0.37730987
## diciembre.CO 0.33607140 -0.26508761 -0.1811584 -0.28885136
## diciembre.NO 0.96673765 -0.57303653 0.2764287 0.03532505
## diciembre.NO2 0.84148602 -0.76829920 0.2699530 -0.02405469
## diciembre.NOX 1.00000000 -0.68588725 0.3003533 0.02171755
## diciembre.O3 -0.68588725 1.00000000 -0.2145425 0.05128073
## diciembre.PM10 0.30035327 -0.21454254 1.0000000 0.14562769
## diciembre.SO2 0.02171755 0.05128073 0.1456277 1.00000000
datos2=summarize(group_by(datos,DIA,ANO),mean(NO),mean(NO2),mean(O3))
datos3=summarize(group_by(datos,DIA,ANO),median(NO),median(NO2),median(O3))
datos4= summarize(group_by(datos,DIA,ANO),max(NO),max(NO2),max(O3))
datos5= summarize(group_by(datos,DIA,ANO),quantile(NO,probs=0.85),quantile(NO2,probs=0.85),quantile(O3,probs=0.85))
datos6= summarize(group_by(datos,DIA,ANO),min(NO),min(NO2),min(O3))
datos7= summarize(group_by(datos,DIA,ANO),sum(NO),sum(NO2),sum(O3))
datos8= summarize(group_by(datos,DIA,ANO),max(NO)+min(NO),min(NO2)+max(NO2),min(O3)+max(O3))
meanNO= (datos2[,3])[[1]]
meanNO2= datos2[,4][[1]]
meanO3= datos2[,5][[1]]
medianNO= datos3[,3][[1]]
medianNO2= datos3[,4][[1]]
medianO3= datos3[,5][[1]]
maxNO= datos4[,3][[1]]
maxNO2= datos4[,4][[1]]
maxO3= datos4[,5][[1]]
minNO= datos6[,3][[1]]
minNO2= datos6[,4][[1]]
minO3= datos6[,5][[1]]
cuantilNO= datos5[,3][[1]]
cuantilNO2= datos5[,4][[1]]
cuantilO3= datos5[,5][[1]]
sumaNO=datos7[,3][[1]]
sumaNO2=datos7[,4][[1]]
sumaO3=datos7[,5][[1]]
maxminNO= datos8[,3][[1]]
maxminNO2= datos8[,4][[1]]
maxminO3= datos8[,5][[1]]
var1= as.data.frame(gr[1])[,4]
var2= as.data.frame(gr[1])[,4]
var3= as.data.frame(gr[1])[,5]
var4= as.data.frame(gr[1])[,6]
var5= as.data.frame(gr[1])[,7]
var6= as.data.frame(gr[1])[,8]
var7= as.data.frame(gr[1])[,9]
var8= as.data.frame(gr[1])[,10]
var9= as.data.frame(gr[1])[,11]
for(i in 2:730){
aux=as.data.frame(gr[i])
var1= rbind.data.frame(var1,aux[,4])
var2= rbind.data.frame(var2,aux[,5])
var3= rbind.data.frame(var3,aux[,6])
var4= rbind.data.frame(var4,aux[,7])
var5= rbind.data.frame(var5,aux[,8])
var6= rbind.data.frame(var6,aux[,9])
var7= rbind.data.frame(var7,aux[,10])
var8= rbind.data.frame(var8,aux[,11])
var9= rbind.data.frame(var9,aux[,12])
}
varf1= fdata(var1)
varf2= fdata(var2)
varf3= fdata(var3)
varf4= fdata(var4)
varf5= fdata(var5)
varf6= fdata(var6)
varf7= fdata(var7)
varf8= fdata(var8)
varf9= fdata(var9)
NO2f= varf4
NOf= varf3
O3f= varf6
NOx= varf5
regbasis1=fregre.basis(NO2f,meanNO2)
regbasis2=fregre.basis(NOf,meanNO)
regbasis3=fregre.basis(O3f,meanO3)
summary(regbasis1)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = NO2f, y = meanNO2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.480 -6.725 -2.352 5.293 38.962
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.502959 0.370759 49.906 <2e-16 ***
## NO2f.bspl4.1 -0.009219 0.026991 -0.342 0.733
## NO2f.bspl4.2 0.011902 0.035141 0.339 0.735
## NO2f.bspl4.3 -0.015804 0.050145 -0.315 0.753
## NO2f.bspl4.4 -0.024296 0.036379 -0.668 0.504
## NO2f.bspl4.5 0.008487 0.027205 0.312 0.755
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.02 on 724 degrees of freedom
## Multiple R-squared: 0.0297, Adjusted R-squared: 0.023
## F-statistic: 4.432 on 5 and 724 DF, p-value: 0.0005535
##
## -Names of possible atypical curves: 84 132 210 591 658 670 729
## -Names of possible influence curves: 22 66 71 74 75 85 105 276 296 299
## It prints only the 10 most influence curves
summary(regbasis2)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = NOf, y = meanNO)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.715 -4.329 -3.223 0.808 63.391
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.454381 0.309309 20.867 <2e-16 ***
## NOf.bspl4.1 0.008208 0.029317 0.280 0.780
## NOf.bspl4.2 -0.010661 0.029824 -0.357 0.721
## NOf.bspl4.3 0.011690 0.045010 0.260 0.795
## NOf.bspl4.4 -0.012297 0.037974 -0.324 0.746
## NOf.bspl4.5 0.019475 0.037502 0.519 0.604
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.357 on 724 degrees of freedom
## Multiple R-squared: 0.00116, Adjusted R-squared: -0.005738
## F-statistic: 0.1681 on 5 and 724 DF, p-value: 0.9743
##
## -Names of possible atypical curves: 84 100 545 587 589 591 592 593 595 599
## It prints only the 10 most atypical curves.
## -Names of possible influence curves: 22 76 202 270 273 283 293 294 295 296
## It prints only the 10 most influence curves
summary(regbasis3)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = O3f, y = meanO3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -53.946 -8.826 1.580 10.617 55.374
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 52.983257 0.630623 84.017 <2e-16 ***
## O3f.bspl4.1 0.036480 0.038659 0.944 0.346
## O3f.bspl4.2 -0.023155 0.047131 -0.491 0.623
## O3f.bspl4.3 0.005973 0.065358 0.091 0.927
## O3f.bspl4.4 -0.001076 0.050727 -0.021 0.983
## O3f.bspl4.5 -0.003801 0.037916 -0.100 0.920
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.04 on 724 degrees of freedom
## Multiple R-squared: 0.002377, Adjusted R-squared: -0.004513
## F-statistic: 0.3449 on 5 and 724 DF, p-value: 0.8856
##
## -Names of possible atypical curves: 315 317 319 321 322 637 639 641 643
## -Names of possible influence curves: 65 66 71 469 497 520 537 545 640 669
regbasis1_2=fregre.basis(NO2f,medianNO2)
regbasis2_2=fregre.basis(NOf,medianNO)
regbasis3_2=fregre.basis(O3f,medianO3)
summary(regbasis1_2)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = NO2f, y = medianNO2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -19.542 -6.762 -2.824 4.696 40.850
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16.660274 0.382155 43.596 <2e-16 ***
## NO2f.bspl4.1 -0.006236 0.027820 -0.224 0.823
## NO2f.bspl4.2 0.002046 0.036221 0.056 0.955
## NO2f.bspl4.3 -0.004849 0.051686 -0.094 0.925
## NO2f.bspl4.4 -0.030881 0.037497 -0.824 0.410
## NO2f.bspl4.5 0.018585 0.028041 0.663 0.508
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.33 on 724 degrees of freedom
## Multiple R-squared: 0.02522, Adjusted R-squared: 0.01849
## F-statistic: 3.746 on 5 and 724 DF, p-value: 0.002349
##
## -Names of possible atypical curves: 84 100 132 200 210 591 658 670 687 729
## -Names of possible influence curves: 22 66 71 74 75 85 105 276 296 299
## It prints only the 10 most influence curves
summary(regbasis2_2)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = NOf, y = medianNO)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.068 -1.725 -1.505 -0.399 56.836
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.730822 0.194350 19.196 <2e-16 ***
## NOf.bspl4.1 0.011478 0.018421 0.623 0.533
## NOf.bspl4.2 -0.014699 0.018739 -0.784 0.433
## NOf.bspl4.3 0.019391 0.028281 0.686 0.493
## NOf.bspl4.4 -0.009581 0.023860 -0.402 0.688
## NOf.bspl4.5 0.012944 0.023564 0.549 0.583
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.251 on 724 degrees of freedom
## Multiple R-squared: 0.002832, Adjusted R-squared: -0.004055
## F-statistic: 0.4112 on 5 and 724 DF, p-value: 0.8412
##
## -Names of possible atypical curves: 589 591 593 595 597 632 636 658 668 670
## It prints only the 10 most atypical curves.
## -Names of possible influence curves: 22 76 202 270 273 283 293 294 295 296
## It prints only the 10 most influence curves
summary(regbasis3_2)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = O3f, y = medianO3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -54.314 -9.140 1.785 11.640 53.155
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 53.740411 0.686518 78.280 <2e-16 ***
## O3f.bspl4.1 0.026407 0.042086 0.627 0.531
## O3f.bspl4.2 -0.019395 0.051308 -0.378 0.706
## O3f.bspl4.3 0.001139 0.071151 0.016 0.987
## O3f.bspl4.4 0.002377 0.055223 0.043 0.966
## O3f.bspl4.5 0.002248 0.041277 0.054 0.957
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18.55 on 724 degrees of freedom
## Multiple R-squared: 0.001099, Adjusted R-squared: -0.0058
## F-statistic: 0.1593 on 5 and 724 DF, p-value: 0.9772
##
## -Names of possible atypical curves: No atypical curves
## -Names of possible influence curves: 65 66 71 469 497 520 537 545 640 669
regbasis1_3=fregre.basis(NO2f,maxNO2)
regbasis2_3=fregre.basis(NOf,maxNO)
regbasis3_3=fregre.basis(O3f,maxO3)
summary(regbasis1_3)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = NO2f, y = maxNO2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -48.69 -16.58 -1.71 14.36 74.02
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 42.861644 0.777981 55.093 <2e-16 ***
## NO2f.bspl4.1 -0.035814 0.056636 -0.632 0.527
## NO2f.bspl4.2 0.062418 0.073738 0.846 0.398
## NO2f.bspl4.3 -0.064206 0.105222 -0.610 0.542
## NO2f.bspl4.4 -0.038914 0.076336 -0.510 0.610
## NO2f.bspl4.5 0.001013 0.057086 0.018 0.986
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21.02 on 724 degrees of freedom
## Multiple R-squared: 0.03122, Adjusted R-squared: 0.02453
## F-statistic: 4.667 on 5 and 724 DF, p-value: 0.0003356
##
## -Names of possible atypical curves: 564 605 670 711
## -Names of possible influence curves: 22 66 71 74 75 85 105 276 296 299
## It prints only the 10 most influence curves
summary(regbasis2_3)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = NOf, y = maxNO)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.361 -23.853 -17.682 8.177 295.838
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.50548 1.46247 19.491 <2e-16 ***
## NOf.bspl4.1 -0.04207 0.13862 -0.304 0.762
## NOf.bspl4.2 0.06849 0.14101 0.486 0.627
## NOf.bspl4.3 -0.11053 0.21281 -0.519 0.604
## NOf.bspl4.4 0.01756 0.17955 0.098 0.922
## NOf.bspl4.5 0.03422 0.17732 0.193 0.847
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 39.51 on 724 degrees of freedom
## Multiple R-squared: 0.001141, Adjusted R-squared: -0.005757
## F-statistic: 0.1654 on 5 and 724 DF, p-value: 0.9752
##
## -Names of possible atypical curves: 84 100 141 184 403 545 550 587 592 593
## It prints only the 10 most atypical curves.
## -Names of possible influence curves: 22 76 202 270 273 283 293 294 295 296
## It prints only the 10 most influence curves
summary(regbasis3_3)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = O3f, y = maxO3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -78.246 -11.749 -1.223 11.055 96.156
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.748e+01 7.836e-01 98.877 <2e-16 ***
## O3f.bspl4.1 -9.603e-05 4.804e-02 -0.002 0.998
## O3f.bspl4.2 5.983e-03 5.856e-02 0.102 0.919
## O3f.bspl4.3 -1.748e-02 8.121e-02 -0.215 0.830
## O3f.bspl4.4 1.962e-02 6.303e-02 0.311 0.756
## O3f.bspl4.5 -1.550e-03 4.711e-02 -0.033 0.974
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21.17 on 724 degrees of freedom
## Multiple R-squared: 0.001945, Adjusted R-squared: -0.004947
## F-statistic: 0.2822 on 5 and 724 DF, p-value: 0.9229
##
## -Names of possible atypical curves: 264 315 317 319 344 360 380 637 639 641
## It prints only the 10 most atypical curves.
## -Names of possible influence curves: 65 66 71 469 497 520 537 545 640 669
regbasis1_4=fregre.basis(NO2f,minNO2)
regbasis2_4=fregre.basis(NOf,minNO)
regbasis3_4=fregre.basis(O3f,minO3)
summary(regbasis1_4)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = NO2f, y = minNO2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1942 -2.5349 -0.7819 1.2606 29.3336
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.697260 0.152309 30.840 <2e-16 ***
## NO2f.bspl4.1 0.002868 0.011088 0.259 0.796
## NO2f.bspl4.2 -0.005011 0.014436 -0.347 0.729
## NO2f.bspl4.3 0.008055 0.020600 0.391 0.696
## NO2f.bspl4.4 -0.013046 0.014945 -0.873 0.383
## NO2f.bspl4.5 0.008906 0.011176 0.797 0.426
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.115 on 724 degrees of freedom
## Multiple R-squared: 0.005952, Adjusted R-squared: -0.0009129
## F-statistic: 0.867 on 5 and 724 DF, p-value: 0.5028
##
## -Names of possible atypical curves: 47 48 200 591 593 656 670 690 713 729
## -Names of possible influence curves: 22 66 71 74 75 85 105 276 296 299
## It prints only the 10 most influence curves
summary(regbasis2_4)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = NOf, y = minNO)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.1711 -0.1037 -0.0971 -0.0817 12.9031
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.0917808 0.0286451 38.114 <2e-16 ***
## NOf.bspl4.1 -0.0013582 0.0027150 -0.500 0.617
## NOf.bspl4.2 0.0001801 0.0027620 0.065 0.948
## NOf.bspl4.3 0.0010146 0.0041683 0.243 0.808
## NOf.bspl4.4 -0.0024553 0.0035168 -0.698 0.485
## NOf.bspl4.5 0.0017179 0.0034731 0.495 0.621
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7739 on 724 degrees of freedom
## Multiple R-squared: 0.002708, Adjusted R-squared: -0.00418
## F-statistic: 0.3931 on 5 and 724 DF, p-value: 0.8537
##
## -Names of possible atypical curves: 591 593 628 632 634 690
## -Names of possible influence curves: 22 76 202 270 273 283 293 294 295 296
## It prints only the 10 most influence curves
summary(regbasis3_4)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = O3f, y = minO3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.775 -14.876 -1.486 11.969 50.035
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.591781 0.655846 37.496 <2e-16 ***
## O3f.bspl4.1 0.084518 0.040205 2.102 0.0359 *
## O3f.bspl4.2 -0.056366 0.049016 -1.150 0.2505
## O3f.bspl4.3 0.051815 0.067972 0.762 0.4461
## O3f.bspl4.4 -0.048675 0.052756 -0.923 0.3565
## O3f.bspl4.5 0.007467 0.039433 0.189 0.8499
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.72 on 724 degrees of freedom
## Multiple R-squared: 0.02447, Adjusted R-squared: 0.01773
## F-statistic: 3.632 on 5 and 724 DF, p-value: 0.002981
##
## -Names of possible atypical curves: No atypical curves
## -Names of possible influence curves: 65 66 71 469 497 520 537 545 640 669
regbasis1_5=fregre.basis(NO2f,cuantilNO2)
regbasis2_5=fregre.basis(NOf,cuantilNO)
regbasis3_5=fregre.basis(O3f,cuantilO3)
summary(regbasis1_5)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = NO2f, y = cuantilNO2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.429 -11.634 -3.458 10.325 53.040
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 29.486027 0.590832 49.906 <2e-16 ***
## NO2f.bspl4.1 -0.025280 0.043012 -0.588 0.557
## NO2f.bspl4.2 0.029178 0.056000 0.521 0.602
## NO2f.bspl4.3 -0.030305 0.079910 -0.379 0.705
## NO2f.bspl4.4 -0.038449 0.057973 -0.663 0.507
## NO2f.bspl4.5 0.007674 0.043354 0.177 0.860
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.96 on 724 degrees of freedom
## Multiple R-squared: 0.0333, Adjusted R-squared: 0.02663
## F-statistic: 4.988 on 5 and 724 DF, p-value: 0.0001684
##
## -Names of possible atypical curves: 636 670 675
## -Names of possible influence curves: 22 66 71 74 75 85 105 276 296 299
## It prints only the 10 most influence curves
summary(regbasis2_5)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = NOf, y = cuantilNO)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.485 -8.706 -6.724 0.197 136.134
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.50390 0.64543 17.824 <2e-16 ***
## NOf.bspl4.1 0.03197 0.06118 0.523 0.601
## NOf.bspl4.2 -0.04146 0.06223 -0.666 0.505
## NOf.bspl4.3 0.04903 0.09392 0.522 0.602
## NOf.bspl4.4 -0.04128 0.07924 -0.521 0.603
## NOf.bspl4.5 0.04949 0.07826 0.632 0.527
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.44 on 724 degrees of freedom
## Multiple R-squared: 0.00186, Adjusted R-squared: -0.005033
## F-statistic: 0.2699 on 5 and 724 DF, p-value: 0.9296
##
## -Names of possible atypical curves: 84 100 204 585 587 591 592 593 632 636
## It prints only the 10 most atypical curves.
## -Names of possible influence curves: 22 76 202 270 273 283 293 294 295 296
## It prints only the 10 most influence curves
summary(regbasis3_5)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = O3f, y = cuantilO3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -70.717 -10.833 0.289 11.964 70.715
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 69.6487671 0.7511076 92.728 <2e-16 ***
## O3f.bspl4.1 0.0135329 0.0460453 0.294 0.769
## O3f.bspl4.2 -0.0167696 0.0561357 -0.299 0.765
## O3f.bspl4.3 0.0136243 0.0778451 0.175 0.861
## O3f.bspl4.4 0.0001273 0.0604187 0.002 0.998
## O3f.bspl4.5 0.0056464 0.0451606 0.125 0.901
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.29 on 724 degrees of freedom
## Multiple R-squared: 0.002073, Adjusted R-squared: -0.004819
## F-statistic: 0.3008 on 5 and 724 DF, p-value: 0.9124
##
## -Names of possible atypical curves: 264 315 317 319 321 344 360 637 639 641
## It prints only the 10 most atypical curves.
## -Names of possible influence curves: 65 66 71 469 497 520 537 545 640 669
regbasis1_6=fregre.basis(NO2f,sumaNO2)
regbasis2_6=fregre.basis(NOf,sumaNO)
regbasis3_6=fregre.basis(O3f,sumaO3)
summary(regbasis1_6)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = NO2f, y = sumaNO2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -515.6 -162.3 -56.4 126.9 935.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 444.1219 8.9030 49.884 <2e-16 ***
## NO2f.bspl4.1 -0.2198 0.6481 -0.339 0.735
## NO2f.bspl4.2 0.2837 0.8438 0.336 0.737
## NO2f.bspl4.3 -0.3741 1.2041 -0.311 0.756
## NO2f.bspl4.4 -0.5888 0.8736 -0.674 0.501
## NO2f.bspl4.5 0.2070 0.6533 0.317 0.751
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 240.5 on 724 degrees of freedom
## Multiple R-squared: 0.02974, Adjusted R-squared: 0.02303
## F-statistic: 4.438 on 5 and 724 DF, p-value: 0.000547
##
## -Names of possible atypical curves: 84 132 210 591 658 670 729
## -Names of possible influence curves: 22 66 71 74 75 85 105 276 296 299
## It prints only the 10 most influence curves
summary(regbasis2_6)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = NOf, y = sumaNO)
##
## Residuals:
## Min 1Q Median 3Q Max
## -161.19 -103.94 -77.38 19.35 1521.38
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 154.9479 7.4283 20.859 <2e-16 ***
## NOf.bspl4.1 0.1968 0.7041 0.279 0.780
## NOf.bspl4.2 -0.2560 0.7162 -0.357 0.721
## NOf.bspl4.3 0.2810 1.0809 0.260 0.795
## NOf.bspl4.4 -0.2957 0.9120 -0.324 0.746
## NOf.bspl4.5 0.4675 0.9007 0.519 0.604
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 200.7 on 724 degrees of freedom
## Multiple R-squared: 0.001157, Adjusted R-squared: -0.005741
## F-statistic: 0.1677 on 5 and 724 DF, p-value: 0.9744
##
## -Names of possible atypical curves: 84 100 545 587 589 591 592 593 595 597
## It prints only the 10 most atypical curves.
## -Names of possible influence curves: 22 76 202 270 273 283 293 294 295 296
## It prints only the 10 most influence curves
summary(regbasis3_6)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = O3f, y = sumaO3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1294.29 -211.79 38.26 254.70 1328.86
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1271.51096 15.12539 84.065 <2e-16 ***
## O3f.bspl4.1 0.87083 0.92724 0.939 0.348
## O3f.bspl4.2 -0.54937 1.13043 -0.486 0.627
## O3f.bspl4.3 0.12826 1.56760 0.082 0.935
## O3f.bspl4.4 -0.01224 1.21668 -0.010 0.992
## O3f.bspl4.5 -0.09792 0.90942 -0.108 0.914
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 408.7 on 724 degrees of freedom
## Multiple R-squared: 0.002364, Adjusted R-squared: -0.004526
## F-statistic: 0.3431 on 5 and 724 DF, p-value: 0.8867
##
## -Names of possible atypical curves: 315 317 319 321 322 637 639 641 643
## -Names of possible influence curves: 65 66 71 469 497 520 537 545 640 669
regbasis1_7=fregre.basis(NO2f,maxminNO2)
regbasis2_7=fregre.basis(NOf,maxminNO)
regbasis3_7=fregre.basis(O3f,maxminO3)
summary(regbasis1_7)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = NO2f, y = maxminNO2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -53.883 -16.968 -2.412 14.690 102.235
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 47.558904 0.850326 55.930 <2e-16 ***
## NO2f.bspl4.1 -0.032946 0.061903 -0.532 0.595
## NO2f.bspl4.2 0.057407 0.080595 0.712 0.477
## NO2f.bspl4.3 -0.056151 0.115006 -0.488 0.626
## NO2f.bspl4.4 -0.051960 0.083434 -0.623 0.534
## NO2f.bspl4.5 0.009919 0.062394 0.159 0.874
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.97 on 724 degrees of freedom
## Multiple R-squared: 0.03029, Adjusted R-squared: 0.02359
## F-statistic: 4.523 on 5 and 724 DF, p-value: 0.0004559
##
## -Names of possible atypical curves: 564 670 711 729
## -Names of possible influence curves: 22 66 71 74 75 85 105 276 296 299
## It prints only the 10 most influence curves
summary(regbasis2_7)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = NOf, y = maxminNO)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.484 -23.983 -17.274 8.238 296.705
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 29.59726 1.47053 20.127 <2e-16 ***
## NOf.bspl4.1 -0.04343 0.13938 -0.312 0.755
## NOf.bspl4.2 0.06867 0.14179 0.484 0.628
## NOf.bspl4.3 -0.10952 0.21399 -0.512 0.609
## NOf.bspl4.4 0.01510 0.18054 0.084 0.933
## NOf.bspl4.5 0.03593 0.17830 0.202 0.840
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 39.73 on 724 degrees of freedom
## Multiple R-squared: 0.001141, Adjusted R-squared: -0.005757
## F-statistic: 0.1655 on 5 and 724 DF, p-value: 0.9752
##
## -Names of possible atypical curves: 84 100 141 184 403 545 550 587 591 592
## It prints only the 10 most atypical curves.
## -Names of possible influence curves: 22 76 202 270 273 283 293 294 295 296
## It prints only the 10 most influence curves
summary(regbasis3_7)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = O3f, y = maxminO3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -105.382 -19.240 0.684 19.202 100.316
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102.071233 1.138476 89.656 <2e-16 ***
## O3f.bspl4.1 0.084422 0.069792 1.210 0.227
## O3f.bspl4.2 -0.050383 0.085087 -0.592 0.554
## O3f.bspl4.3 0.034336 0.117992 0.291 0.771
## O3f.bspl4.4 -0.029056 0.091578 -0.317 0.751
## O3f.bspl4.5 0.005917 0.068451 0.086 0.931
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 30.76 on 724 degrees of freedom
## Multiple R-squared: 0.005489, Adjusted R-squared: -0.001379
## F-statistic: 0.7992 on 5 and 724 DF, p-value: 0.5504
##
## -Names of possible atypical curves: 315 317 319 322 637 639 641 643
## -Names of possible influence curves: 65 66 71 469 497 520 537 545 640 669
regbasis2_1=fregre.basis(NO2f,medianNO)
regbasis2_2=fregre.basis(NOf,medianNO2)
regbasis2_3=fregre.basis(O3f,medianNO)
summary(regbasis2_1)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = NO2f, y = medianNO)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.522 -1.783 -1.480 -0.429 56.770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.730822 0.194399 19.192 <2e-16 ***
## NO2f.bspl4.1 -0.004248 0.014152 -0.300 0.764
## NO2f.bspl4.2 0.009323 0.018425 0.506 0.613
## NO2f.bspl4.3 -0.016178 0.026292 -0.615 0.539
## NO2f.bspl4.4 0.006328 0.019075 0.332 0.740
## NO2f.bspl4.5 0.003646 0.014264 0.256 0.798
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.252 on 724 degrees of freedom
## Multiple R-squared: 0.002322, Adjusted R-squared: -0.004568
## F-statistic: 0.337 on 5 and 724 DF, p-value: 0.8906
##
## -Names of possible atypical curves: 589 591 593 595 597 632 636 658 668 670
## It prints only the 10 most atypical curves.
## -Names of possible influence curves: 22 66 71 74 75 85 105 276 296 299
## It prints only the 10 most influence curves
summary(regbasis2_2)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = NOf, y = medianNO2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.402 -7.110 -3.037 5.357 42.207
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16.6602740 0.3853022 43.240 <2e-16 ***
## NOf.bspl4.1 -0.0227331 0.0365197 -0.622 0.534
## NOf.bspl4.2 -0.0003154 0.0371510 -0.008 0.993
## NOf.bspl4.3 -0.0106300 0.0560679 -0.190 0.850
## NOf.bspl4.4 0.0059913 0.0473038 0.127 0.899
## NOf.bspl4.5 -0.0121040 0.0467164 -0.259 0.796
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.41 on 724 degrees of freedom
## Multiple R-squared: 0.009097, Adjusted R-squared: 0.002254
## F-statistic: 1.329 on 5 and 724 DF, p-value: 0.2496
##
## -Names of possible atypical curves: 84 100 130 132 200 210 591 658 670 729
## -Names of possible influence curves: 22 76 202 270 273 283 293 294 295 296
## It prints only the 10 most influence curves
summary(regbasis2_3)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = O3f, y = medianNO)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.689 -1.983 -1.225 -0.169 56.066
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.730822 0.193483 19.282 <2e-16 ***
## O3f.bspl4.1 0.002001 0.011861 0.169 0.866
## O3f.bspl4.2 -0.006489 0.014460 -0.449 0.654
## O3f.bspl4.3 0.009925 0.020053 0.495 0.621
## O3f.bspl4.4 -0.002316 0.015564 -0.149 0.882
## O3f.bspl4.5 -0.012396 0.011633 -1.066 0.287
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.228 on 724 degrees of freedom
## Multiple R-squared: 0.0117, Adjusted R-squared: 0.004879
## F-statistic: 1.715 on 5 and 724 DF, p-value: 0.1288
##
## -Names of possible atypical curves: 589 591 593 595 597 632 636 658 668 670
## It prints only the 10 most atypical curves.
## -Names of possible influence curves: 65 66 71 469 497 520 537 545 640 669
reg_basis3_2= fregre.basis(NOx,medianNO)
summary(regbasis3_3)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = O3f, y = maxO3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -78.246 -11.749 -1.223 11.055 96.156
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.748e+01 7.836e-01 98.877 <2e-16 ***
## O3f.bspl4.1 -9.603e-05 4.804e-02 -0.002 0.998
## O3f.bspl4.2 5.983e-03 5.856e-02 0.102 0.919
## O3f.bspl4.3 -1.748e-02 8.121e-02 -0.215 0.830
## O3f.bspl4.4 1.962e-02 6.303e-02 0.311 0.756
## O3f.bspl4.5 -1.550e-03 4.711e-02 -0.033 0.974
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21.17 on 724 degrees of freedom
## Multiple R-squared: 0.001945, Adjusted R-squared: -0.004947
## F-statistic: 0.2822 on 5 and 724 DF, p-value: 0.9229
##
## -Names of possible atypical curves: 264 315 317 319 344 360 380 637 639 641
## It prints only the 10 most atypical curves.
## -Names of possible influence curves: 65 66 71 469 497 520 537 545 640 669basis1= create.fourier.basis(rangeval(NOx),nbasis=7)
reg_basis3_2= fregre.basis(NOx,medianNO,basis.x = basis1)
summary(regbasis3_3)## *** Summary Functional Data Regression with representation in Basis ***
##
## Call:
## fregre.basis(fdataobj = O3f, y = maxO3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -78.246 -11.749 -1.223 11.055 96.156
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.748e+01 7.836e-01 98.877 <2e-16 ***
## O3f.bspl4.1 -9.603e-05 4.804e-02 -0.002 0.998
## O3f.bspl4.2 5.983e-03 5.856e-02 0.102 0.919
## O3f.bspl4.3 -1.748e-02 8.121e-02 -0.215 0.830
## O3f.bspl4.4 1.962e-02 6.303e-02 0.311 0.756
## O3f.bspl4.5 -1.550e-03 4.711e-02 -0.033 0.974
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21.17 on 724 degrees of freedom
## Multiple R-squared: 0.001945, Adjusted R-squared: -0.004947
## F-statistic: 0.2822 on 5 and 724 DF, p-value: 0.9229
##
## -Names of possible atypical curves: 264 315 317 319 344 360 380 637 639 641
## It prints only the 10 most atypical curves.
## -Names of possible influence curves: 65 66 71 469 497 520 537 545 640 669
You can also embed plots, for example:
## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## Length Class Mode
## fregre.pc 19 fregre.fd list
## pc.opt 1 -none- numeric
## lambda.opt 1 -none- numeric
## PC.order 8 -none- numeric
## MSC.order 8 -none- numeric## $fregre.pc
##
## -Call: fregre.pc(fdataobj = fdataobj, y = y, l = drop(pc.opt3), lambda = rn.opt, P = P, weights = weights)
##
## -Coefficients:
## (Intercept) PC2
## 3.730822 0.002863
##
## -R squared: 0.00124658
## -Residual variance: 27.46552
##
## $pc.opt
## PC2
## 2
##
## $lambda.opt
## [1] 0
##
## $PC.order
## PC(1) PC(2) PC(3) PC(4) PC(5) PC(6) PC(7) PC(8)
## lambda=0 2 7 8 4 3 1 6 5
##
## $MSC.order
## PC(1) PC(2) PC(3) PC(4) PC(5) PC(6) PC(7)
## lambda=0 3.328251 3.336394 3.344602 3.353036 3.361606 3.370413 3.379444
## PC(8)
## lambda=0 3.388474Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.