R包mgcv的廣義加模型函數(shù)gam

1、R包mgcv的廣義加模型函數(shù)gam()library(mgcv)set.seed(0)#simulatesomedata.#模擬一些數(shù)據(jù).dat<-gamSim(1,n=400,dist="normal",scale=2)b<-gam(ys(x0)+s(x1)+s(x2)+s(x3),data=dat)summary(b)plot(b,pages=1,residuals=TRUE)#showpartialresiduals#顯示部分殘差plot(b,pages=1,seWithMean=TRUE)#'withintercept'CIs#攔截證明書(shū)

2、#samefitintwoparts.#同樣適合兩部分G<-gam(ys(x0)+s(x1)+s(x2)+s(x3),fit=FALSE,data=dat)b<-gam(G=G)print(b)#changethesmoothnessselectionmethodtoREML#REML法改變平滑的選擇方法b0<-gam(ys(x0)+s(x1)+s(x2)+s(x3),data=dat,method="REML")plot(b0,pages=1,scheme=1)# #Wouldasmoothinteractionofx0andx1bebetter?#X0

3、和X1的流暢交互更好？# #Usetensorproductsmoothofx0andx1,basis#使用張量積X0和X1順禾U,基礎(chǔ)# #dimension49(see?tefordetails,also?t2).#尺寸49(見(jiàn)詳情T(mén)E,也T2)。bt<-gam(yte(x0,x1,k=7)+s(x2)+s(x3),data=dat,method="REML")plot(bt,pages=1)plot(bt,pages=1,scheme=2)#alternativevisualization#替代的可視化AIC(b0,bt)#interactionworsetha

4、nadditive#交互比添加劑差# #Ifitisbelievedthatx0andx1arenaturallyon#如果它被認(rèn)為X0和X1自然# #thesamescale,andshouldbetreatedisotropically#相同的規(guī)模，應(yīng)被視為各向同性# #thencouldtry.#然后可以嘗試.bs<-gam(ys(x0,x1,k=50)+s(x2)+s(x3),data=dat,method="REML")plot(bs,pages=1)AIC(b0,bt,bs)#additivestillbetter.#添加劑更好。#Nowdoautomat

5、ictermsselectionaswell#現(xiàn)在做選擇自動(dòng)條款，以及bl<-gam(ys(x0)+s(x1)+s(x2)+s(x3),data=dat,method="REML",select=TRUE)#設(shè)置的第一任期內(nèi),plot(b1,pages=1)#setthesmoothingparameterforthefirstterm,estimaterest.估計(jì)截?cái)嗟钠交瑓?shù).bp<-gam(ys(x0)+s(x1)+s(x2)+s(x3),sp=c(0.01,-1,-1,-1),data=dat)plot(bp,pages=1,scheme=1)# #a

6、lternatively.1#或者.bp<-gam(ys(x0,sp=.01)+s(x1)+s(x2)+s(x3),data=dat)# setlowerboundsonsmoothingparameters.平滑參數(shù)設(shè)定的下限.bp<-gam(ys(x0)+s(x1)+s(x2)+s(x3),min.sp=c(0.001,0.01,0,10),data=dat)|print(b);print(bp)# samewithREML同與REML法bp<-gam(ys(x0)+s(x1)+s(x2)+s(x3),min.sp=c(0.1,0.1,0,10),data=dat,met

7、hod="REML")print(b0);print(bp)# #nowaGAMwith3dfregressionsplineterm&amp;2penalizedterms3現(xiàn)在與的3DF回歸樣條任期和2的GAM處罰條款b0<-gam(ys(x0,k=4,fx=TRUE,bs="tp")+s(x1,k=12)+s(x2,k=15),data=dat)plot(b0,pages=1)# #nowsimulatepoissondata.#現(xiàn)在模擬泊松資料的.dat<-gamSim(1,n=4000,dist="poisson&

8、quot;,scale=.1)# #use"cr"basistosavetime,with4000data.#使用“C耐基礎(chǔ)上，與4000數(shù)據(jù)保存時(shí)間，.b2<-gam(y-s(x0,bs="cr")+s(x1,bs="cr")+s(x2,bs="cr")+s(x3,bs="cr"),family=poisson,data=dat,method="REML")plot(b2,pages=1)# #dropx3,butinitializesp'sfromprevi

9、ousfit,to#下降X3,但初始化SP,從以前的契合，# #savemoretime.#節(jié)省更多的時(shí)間b2a<-gam(ys(x0,bs="cr")+s(x1,bs="cr")+s(x2,bs="cr"),family=poisson,data=dat,method="REML",in.out=list(sp=b2$sp1:3,scale=1)par(mfrow=c(2,2)plot(b2a)par(mfrow=c(1,1)# #similarexampleusingperformanceiteratio

10、n#類(lèi)似的例子，使用性能迭代dat<-gamSim(1,n=400,dist="poisson",scale=.25)b3<-gam(ys(x0)+s(x1)+s(x2)+s(x3),family=poisson,data=dat,optimizer="perf")plot(b3,pages=1)# #repeatusingGACVasinWood2008.#重復(fù)使用GACV作為2008年在木材.b4<-gam(ys(x0)+s(x1)+s(x2)+s(x3),family=poisson,data=dat,method="G

11、ACV.Cp",scale=-1)plot(b4,pages=1)# #repeatusingREMLasinWood2011.#重復(fù)使用REML法作為2011年在木材.b5<-gam(ys(x0)+s(x1)+s(x2)+s(x3),family=poisson,data=dat,method="REML")plot(b5,pages=1)# #abinaryexample(seelaterforlargedatasetversion).#二進(jìn)制的例子(見(jiàn)稍后為大型數(shù)據(jù)集版本).dat<-gamSim(1,n=400,dist="binar

12、y",scale=.33)lr.fit<-gam(ys(x0)+s(x1)+s(x2)+s(x3),family=binomial,data=dat,method="REML")# #plotmodelcomponentswithtruthoverlaidinred覆蓋在紅色的圖與真理的模型組件op<-par(mfrow=c(2,2)fn<-c("f0","f1","f2","f3");xn<-c("x0","x1",&q

13、uot;x2","x3")for(kin1:4)plot(lr.fit,residuals=TRUE,select=k)ff<-datfnk;xx<-datxnkind<-(xx,index.return=TRUE)$ixlines(xxind,(ff-mean(ff)ind*.33,col=2)par(op)anova(lr.fit)lr.fit1<-gam(ys(x0)+s(x1)+s(x2),family=binomial,data=dat,method="REML")lr.fit2<-gam

14、(ys(x1)+s(x2),family=binomial,data=dat,method="REML")AIC(lr.fit,lr.fit1,lr.fit2)#AGammaexample,bymodify'gamSim'output.#伽瑪例如，通過(guò)修改gamSim的輸出.dat<-gamSim(1,n=400,dist="normal",scale=1)dat$f&lt;-dat$f/4#truelinearpredictor#真正的線(xiàn)性預(yù)測(cè)Ey&lt;-exp(dat$f);scale&lt;-.5#m

15、eanandGLMscaleparameter#的意思，GLM尺度參數(shù)# #Notethat'shape'and'scale'in'rgamma'arealmost#注意形和規(guī)模rgamma幾乎# #oppositeterminologytothatusedwithGLM/GAM.#對(duì)面的GLM/自由亞齊運(yùn)動(dòng)所使用的術(shù)語(yǔ)來(lái).dat$y<-rgamma(Ey*0,shape=1/scale,scale=Ey*scale)bg<-gam(ys(x0)+s(x1)+s(x2)+s(x3),family=Gamma(link=log),dat

16、a=dat,method="REML")plot(bg,pages=1,scheme=1)# #ForinverseGaussian,see?rig#逆高斯，看至U了什么？鉆機(jī)# #nowa2Dsmoothingexample.#現(xiàn)在的二維平滑的例子.eg<-gamSim(2,n=500,scale=.1)attach(eg)op<-par(mfrow=c(2,2),mar=c(4,4,1,1)contour(truth$x,truth$z,truth$f)#contourtruth#輪廓真相b4&lt;-gam(ys(x,z),data=data)#f

17、itmodel#適合模型fitl<-matrix(predict.gam(b4,pr,se=FALSE),40,40)contour(truth$x,truth$z,fit1)#contourfit#輪廓適合persp(truth$x,truth$z,truth$f)#persptruth#persp真相vis.gam(b4)#perspfit#persp適合detach(eg)par(op)# #-# #largishdatasetexamplewithuserdefinedknots#稍大例如與用戶(hù)定義的節(jié)集# #薪#一#par(mfrow=c(2,2)eg<-gamSim(2

18、,n=10000,scale=.5)attach(eg)ind<-sample(1:10000,1000,replace=FALSE)b5<-gam(ys(x,z,k=50),data=data,knots=list(x=data$xind,z=data$zind)# #variousvisualizations#各種可視化vis.gam(b5,theta=30,phi=30)plot(b5)plot(b5,scheme=1,theta=50,phi=20)plot(b5,scheme=2)par(mfrow=c(1,1)# #andapure"knotbased&quo

19、t;splineofthesamedata#和一個(gè)純粹的結(jié)基于“相同的數(shù)據(jù)樣條b6<-gam(y-s(x,z,k=100),data=data,knots=list(x=rep(1:10-0.5)/10,10),|z=rep(1:10-0.5)/10,rep(10,10)vis.gam(b6,color="heat",theta=30,phi=30)|# #varyingthedefaultlargedatasetbehaviourvia'xt'#不同的默認(rèn)的大型數(shù)據(jù)集的行為，通過(guò)XTb7<-gam(ys(x,z,k=50,xt=list(max

20、.knots=1000,seed=2),data=data)vis.gam(b7,theta=30,phi=30)detach(eg)，TTffuTTTTifiiTTTTTnTTTTTTnTTTTTTnTTTTTtiuTTTTtiuTTTTtt11TTTTTnTTTTTTnTTTTTTnTTTTTtiuTTTTtttiiiTTTTiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii# #Approximatelargedataset

21、logisticregressionforrareevents#近似的大型數(shù)據(jù)集稀有事件logistic回歸# #basedonsubsamplingthezeroes,andaddinganoffsetto#基于欠采樣的零，并力入一個(gè)偏移量# #approximatelyallowforthis.#約允許。# #Doingthesamething,butupweightingthesampledzeroes#做同樣的事情，但upweighting采樣零# #leadstoproblemswithsmoothnessselection,andCIs.#平滑的選擇，與獨(dú)聯(lián)體的問(wèn)題。fffffff

22、fffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffTTffuTTTTifiiTTTTTnTTTTTTnTTTTTTnTTTTTtiuTTTTtiuTTTTtt11TTTTTnTTTTTTnTTTTTTnTTTTTtiuTTTTtttiiiTTTTiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii# #n&lt;-100000#simulatendata#模擬n個(gè)數(shù)據(jù)dat<-gamSim(1,n=n,dist="binary",scale=.33)p&lt;-binomial()$linkinv(dat$f-6)#make1'srare#1的罕見(jiàn)dat$y&lt;-rbinom(p,1,p)#re-simulaterareresponse#重新模擬罕見(jiàn)的反應(yīng)# #Nowsampleallthe1'sbutonlyproportionSofthe0's#