Rでの作業200605

[R][source][data]alldata Inbox

  
take yamakita  to yamakita3+sour. 
 More options   May 13 (7 days ago) 

alldata <- matrix(c(

1975, 1560307 ,  15.45, 77.68,  1600, 471,1522   , 3.1, 6.0 ,   15  ,2011.8  ,104

),ncol=12,byrow=T) colnames(alldata ) <- c("yr","Seagrass.area","sunspot","sealevel.mera","sandbar.near"

alldata.ts <- ts(alldata[,c(2,4:12)], start=c(1975), frequency = 1) plot(alldata.ts)

acf(alldata[,c(2,4,5,6,8,10)],lag=20)

par(mfrow=c(2,2),pty="a") biplot(princomp(alldata[,c(2,4,5,6,8,10)])) biplot(princomp(alldata[1:9,c(2,4,5,6,8,10)])) biplot(princomp(alldata[10:19,c(2,4,5,6,8,10)])) biplot(princomp(alldata[20:27,c(2,4,5,6,8,10)]))

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take yamakita  to yamakita3+sour. 
 More options   May 14 (6 days ago) 

###値更新と面積時間変化見積もり（CV）

##########

alldata <- matrix(c(

1975, 1560307 ,  15.45, 77.68,  1600, 471,1522   , 3.1, 6.0 ,   15,2011.8  ,104  ,679,874,981

15.9,2089.7 ,141 ,597,728,957

15.3,2020.3 ,163 ,592,714,894

16.3,2112.7 ,138 ,585,702,906

16.3,1550.4 ,112 ,590,705,901

16.6,1958.0 ,175 ,588,665,842

16.2,1986.1 ,146 ,574,675,835

15.8,2006.3 ,180 ,569,606,860

16.1,1991.4 ,179 ,562,671,856

15.5,1699.6 ,125 ,569,666,867 ),ncol=15,byrow=T)

colnames(alldata ) <- c("yr","Seagrass.area","sunspot","sealevel.mera","sandbar.near"

x <- data.frame(alldata) plot(x) alldata.ts <- ts(alldata[,c(2,4:5,7:10,13:15)], start=c(1975), frequency = 1) plot(alldata.ts)

########################面積変化の時間変化の見積もり

x <- data.frame(alldata)

plot(x)

sd(alldata[,2])/mean(alldata[,2])

hist(alldata[,2])

http://www.sci.kagoshima-u.ac.jp/~itls/Japanese/index.html

Rweb:> summary(alldata[,2])

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
389500  755500  937800  977400 1228000 1560000

Rweb:> Rweb:> sd(alldata[,2])/mean(alldata[,2]) [1] 0.2994279 Rweb:> Rweb:> sd(alldata[1:10,2])/mean(alldata[1:10,2]) [1] 0.3144541 Rweb:> sd(alldata[11:20,2])/mean(alldata[11:20,2]) [1] 0.1881615 Rweb:> sd(alldata[21:29,2])/mean(alldata[21:29,2]) [1] 0.1841874 Rweb:> Rweb:> Rweb:> shapiro.test(alldata[,2])

      Shapiro-Wilk normality test

data: alldata[, 2] W = 0.9516, p-value = 0.2011

Rweb:> Rweb:> hist(alldata[,2]) Rweb:> Rweb:> hist(alldata[1:10,2]) Rweb:> hist(alldata[11:20,2]) Rweb:> hist(alldata[21:29,2])

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---

  
take yamakita  to yamakita3+sour. 
 More options   May 16 (4 days ago) 

pairs(alldata)

panel.lsfit <- function(x,y,...)

1. {
2. f <- lsfit(x,y)$coef
3. xx<- c ( min (x) , max(x) )
4. yy <- f["X"] * xx+ f["Intercept"]
5. lines(xx,yy,col="red")
6. points(x,y)
7. }

   pairs(alldata, panel = panel.lsfit, pch = ".")

   alldata.lim<- x[,c(2,4:5,7:10,13:15)]

   my.cor <- function(x)

   1. {
   2. x <- x[complete.cases(x),]
   3. r <- cor(x)
   4. i <- solve(r)
   5. d <- diag(i)
   6. p <- -i/sqrt(outer(d, d))
   7. r[lower.tri(r)] <- p[lower.tri(p)]
   8. diag(r) <- sqrt(1-1/d)
   9. rownames(r) <- colnames(r) <- paste("Var", 1:ncol(x))
   10. r
   11. }

######バッファの設定 buffer1 <- matrix(0,ncol=ncol(x),nrow=ncol(x)) buffer2 <- matrix(0,ncol=ncol(x),nrow=ncol(x))

#もっとスマートにできそうな気がするが・・・Forループで for(i in 1:ncol(x)){ for(j in 1:ncol(x)){ y <- cor.test(x[,i],x[,j],method="pearson") buffer1[i,j] <- y$p.value#リストで提供されているので、これでpだけだせる buffer2[i,j] <-y$estimate }} cor.p.p <- buffer1 cor.p.2 <- buffer2 rm(buffer1,buffer2)

###########ここまで

#####ぜんぶで重回帰

str(alldata.lim) `data.frame': 29 obs. of 10 variables:

$ Seagrass.area: num  1560307 1540192 1028715  685566 1038911 ...
$ sealevel.mera: num  77.7 78.0 76.7 79.9 79.9 ...
$ sandbar.near : num  1600 2000 2085 1837 2085 ...
$ sandbar.arch : num  1522 1733 2177 1416 1739 ...
$ cod75        : num  3.1 2.7 2.3 3.7 3.4 3.6 3.5 3.2 3.7 3.3 ...
$ trapa        : num  6 6 5.93 5.32 4.68 4.76 4.85 4.2 5 4.48 ...
$ mean_temp    : num  15 14.6 15.2 15.4 16.2 14.9 14.3 15 14.7 13.8 ...
$ border.west  : num  679 661 623 609 629 547 631 647 641 617 ...
$ border.middle: num  874 874 816 768 829 612 818 830 819 816 ...
$ border.east  : num  981 981 954 931 964 872 918 972 958 914 ...

All.possible.subset.selection <- function(df, adjusted=TRUE, limit=10)

1. {
2. df <- df[complete.cases(df),]
3. if ((nv <- ncol(df)-1) > limit) {
4. stop("Too many independent variables.")
5. }
6. name <- names(df)
7. n <- 2^nv-1
8. str <- character(nv)
9. result1 <- result2 <- numeric(n)
10. result3 <- character(n)
11. for (i in 1:n) {
12. k <- i
13. m <- 0
14. for (j in 1:nv) {
15. if (k%%2) {
16. m <- m+1
17. str[m] <- name[j]
18. }
19. k <- k %/% 2
20. }
21. form <- reformulate(str[1:m], name[nv+1])
22. result1[i] <- (result <- summary(lm(form, df)))$r.square
23. result2[i] <- result$adj.r.square
24. result3[i] <- paste(c(name[nv+1], "~", paste(str[1:m], collapse=" + ")), collapse=" ")
25. }
26. o <- rev(order(if (adjusted) result2 else result1))
27. cbind("R square"=result1[o], "Adjusted R square"=result2[o], "formula"=result3[o])
28. }

[63,] "0.0564075796090868" "-0.0568235108378228" "Seagrass.area ~sandbar.arch + cod75 + trapa"

All.possible.subset.selection(alldata.lim[,c(2,3,4,5,6,7,9)])

    R square             Adjusted R square     formula
[1,] "0.536408623115185"  "0.5007477479702"     "border.middle ~sealevel.mera + sandbar.near"
[2,] "0.549677257268671"  "0.495638528140911"   "border.middle ~sealevel.mera + sandbar.near + trapa"
[3,] "0.56329705250355"   "0.490513227920808"   "border.middle ~sealevel.mera + sandbar.near + sandbar.arch + trapa"
[4,] "0.54444947293636"   "0.489783409688723"   "border.middle ~sealevel.mera + sandbar.near + sandbar.arch"
[5,] "0.537786555474024"  "0.482320942130907"   "border.middle ~sealevel.mera + sandbar.near + mean_temp"
[6,] "0.536686726148349"  "0.48108913328615"    "border.middle ~sealevel.mera + sandbar.near + cod75"
[7,] "0.553765052516519"  "0.479392561269272"   "border.middle ~sealevel.mera + sandbar.near + trapa + mean_temp"
[8,] "0.56929814593773"   "0.475667308098106"   "border.middle ~sealevel.mera + sandbar.near + sandbar.arch + trapa + mean_temp"
[9,] "0.549922256166305"  "0.47490929886069"    "border.middle ~sealevel.mera + sandbar.near + cod75 + trapa"

[10,] "0.546263593868003" "0.47064085951267" "border.middle ~sealevel.mera + sandbar.near + sandbar.arch + mean_temp" [11,] "0.564069616936863" "0.46930214235792" "border.middle ~sealevel.mera + sandbar.near + sandbar.arch + cod75 + trapa" [12,] "0.544625170134171" "0.468729365156532" "border.middle ~sealevel.mera + sandbar.near + sandbar.arch + cod75" [13,] "0.520440540662363" "0.462893405541847" "border.middle ~sealevel.mera + trapa + mean_temp" [14,] "0.538245224569898" "0.461286095331547" "border.middle ~sealevel.mera + sandbar.near + cod75 + mean_temp" [15,] "0.553894789553263" "0.456915395977886" "border.middle ~sealevel.mera + sandbar.near + cod75 + trapa + mean_temp" [16,] "0.492948621723698" "0.453944669548597" "border.middle ~sealevel.mera + mean_temp" [17,] "0.512139255325284" "0.453595965964318" "border.middle ~sealevel.mera + cod75 + trapa" [18,] "0.569849301054795" "0.452535474069739" "border.middle ~sealevel.mera + sandbar.near + sandbar.arch + cod75 + trapa + mean_temp" [19,] "0.530347523696011" "0.452072110978679" "border.middle ~sealevel.mera + cod75 + trapa + mean_temp" [20,] "0.489017039203764" "0.449710657604053" "border.middle ~sealevel.mera + trapa" [21,] "0.46885855655358" "0.449186651240750" "border.middle ~sealevel.mera" [22,] "0.546608015796905" "0.448044540970145" "border.middle ~sealevel.mera + sandbar.near + sandbar.arch + cod75 + mean_temp" [23,] "0.484450377306719" "0.444792714022621" "border.middle ~sealevel.mera + cod75" [24,] "0.520451300759197" "0.440526517552396" "border.middle ~sealevel.mera + sandbar.arch + trapa + mean_temp" [25,] "0.498746403517721" "0.438595971939847" "border.middle ~sealevel.mera + cod75 + mean_temp" [26,] "0.478196000768511" "0.438057231596858" "border.middle ~sealevel.mera + sandbar.arch" [27,] "0.495105672151291" "0.434518352809446" "border.middle ~sealevel.mera + sandbar.arch + mean_temp" [28,] "0.493347109582709" "0.432548762732634" "border.middle ~sealevel.mera + sandbar.arch + trapa" [29,] "0.512141596791402" "0.430831862923303" "border.middle ~sealevel.mera + sandbar.arch + cod75 + trapa" [30,] "0.531911214207366" "0.430152782513315" "border.middle ~sealevel.mera + sandbar.arch + cod75 + trapa + mean_temp" [31,] "0.487447763134614" "0.425941494710768" "border.middle ~sealevel.mera + sandbar.arch + cod75" [32,] "0.499425800626429" "0.415996767397501" "border.middle ~sealevel.mera + sandbar.arch + cod75 + mean_temp" [33,] "0.388988959473913" "0.36635892093591" "border.middle ~sandbar.near" [34,] "0.411432382185642" "0.366157950046076" "border.middle ~sandbar.near + mean_temp" [35,] "0.429373729920097" "0.360898577510509" "border.middle ~sandbar.near + trapa + mean_temp" [36,] "0.451158926345797" "0.359685414070097" "border.middle ~sandbar.near + sandbar.arch + trapa + mean_temp" [37,] "0.424193564954951" "0.355096792749545" "border.middle ~sandbar.near + sandbar.arch + mean_temp" [38,] "0.400264674746562" "0.354131188188606" "border.middle ~sandbar.near + sandbar.arch" [39,] "0.398430352799792" "0.352155764553623" "border.middle ~sandbar.near + trapa" [40,] "0.415305104592163" "0.345141717143222" "border.middle ~sandbar.near + sandbar.arch + trapa" [41,] "0.413407255651950" "0.343016126330183" "border.middle ~sandbar.near + cod75 + mean_temp" [42,] "0.389764561260057" "0.342823373664676" "border.middle ~sandbar.near + cod75" [43,] "0.429480286499517" "0.334393667582769" "border.middle ~sandbar.near + cod75 + trapa + mean_temp" [44,] "0.451178506121523" "0.331869485713159" "border.middle ~sandbar.near + sandbar.arch + cod75 + trapa + mean_temp" [45,] "0.425835915405731" "0.330141901306686" "border.middle ~sandbar.near + sandbar.arch + cod75 + mean_temp" [46,] "0.400825270610972" "0.328924303084289" "border.middle ~sandbar.near + sandbar.arch + cod75" [47,] "0.39843207404519" "0.326243922930613" "border.middle ~sandbar.near + cod75 + trapa" [48,] "0.415455890710462" "0.318031872495539" "border.middle ~sandbar.near + sandbar.arch + cod75 + trapa" [49,] "0.318739429604424" "0.266334770343226" "border.middle ~trapa + mean_temp" [50,] "0.343642340330201" "0.264879421169825" "border.middle ~cod75 + trapa + mean_temp" [51,] "0.273566960904541" "0.246662033530635" "border.middle ~mean_temp" [52,] "0.323028709254195" "0.241792154364698" "border.middle ~sandbar.arch + trapa + mean_temp" [53,] "0.290652390640683" "0.236087189920736" "border.middle ~cod75 + mean_temp" [54,] "0.290024170078692" "0.23541064470013" "border.middle ~sandbar.arch + mean_temp" [55,] "0.343749057328952" "0.234373900217111" "border.middle
sandbar.arch + cod75 + trapa + mean_temp"

[56,] "0.299452853827167" "0.215387196286427" "border.middle ~sandbar.arch + cod75 + mean_temp" [57,] "0.182293133479288" "0.119392605285387" "border.middle ~sandbar.arch + cod75" [58,] "0.176512347069658" "0.113167142998093" "border.middle ~cod75 + trapa" [59,] "0.204458733390888" "0.108993781397794" "border.middle ~sandbar.arch + cod75 + trapa" [60,] "0.135842602568604" "0.103836773034108" "border.middle ~sandbar.arch" [61,] "0.129804920467801" "0.0975754730777196" "border.middle ~cod75" [62,] "0.142056294938659" "0.0760606253185554" "border.middle ~sandbar.arch + trapa" [63,] "0.0248042654631487" "-0.0113140950752531" "border.middle ~trapa"

All.possible.subset.selection(alldata.lim[,c(2,3,4,5,6,7,1)])

    R square              Adjusted R square       formula
[1,] "0.334287851302361"   "0.254402393458644"     "Seagrass.area ~sealevel.mera + sandbar.near + cod75"
[2,] "0.352275924983303"   "0.24432191248052"      "Seagrass.area ~sealevel.mera + sandbar.near + cod75 + mean_temp"
[3,] "0.34966319630933"    "0.241273729027552"     "Seagrass.area ~sealevel.mera + sandbar.near + sandbar.arch + cod75"
[4,] "0.340514786747555"   "0.230600584538814"     "Seagrass.area ~sealevel.mera + sandbar.near + cod75 + trapa"
[5,] "0.365890367151682"   "0.228040446967265"     "Seagrass.area ~sealevel.mera + sandbar.near + sandbar.arch + cod75 + mean_temp"
[6,] "0.361895643374762"   "0.223177304977971"     "Seagrass.area ~sealevel.mera + sandbar.near + sandbar.arch + cod75 + trapa"
[7,] "0.355179259037182"   "0.215000837088743"     "Seagrass.area ~sealevel.mera + sandbar.near + cod75 + trapa + mean_temp"
[8,] "0.263071415835513"   "0.206384601669014"     "Seagrass.area ~sealevel.mera + cod75"
[9,] "0.373182258553375"   "0.202231965431568"     "Seagrass.area ~sealevel.mera + sandbar.near + sandbar.arch + cod75 + trapa +

mean_temp" [10,] "0.282807060635939" "0.196743907912252" "Seagrass.area ~sealevel.mera + cod75 + trapa" [11,] "0.249397263815296" "0.191658591801088" "Seagrass.area ~sandbar.near + cod75" [12,] "0.244868678941114" "0.186781654244276" "Seagrass.area ~sealevel.mera + trapa" [13,] "0.213431527550556" "0.184299361904280" "Seagrass.area ~sealevel.mera" [14,] "0.267937929262112" "0.180090480773566" "Seagrass.area ~sandbar.near + sandbar.arch + cod75" [15,] "0.265319344753992" "0.177157666124471" "Seagrass.area ~sealevel.mera + sandbar.arch + cod75" [16,] "0.263876521604600" "0.175541704197152" "Seagrass.area ~sealevel.mera + cod75 + mean_temp" [17,] "0.254993600490370" "0.165592832549214" "Seagrass.area ~sealevel.mera + sandbar.near + mean_temp" [18,] "0.224751696095274" "0.165117211179526" "Seagrass.area ~sealevel.mera + sandbar.near" [19,] "0.224092252499255" "0.164407041153043" "Seagrass.area ~sealevel.mera + mean_temp" [20,] "0.283589678427582" "0.164187958165512" "Seagrass.area ~sealevel.mera + sandbar.near + sandbar.arch + trapa" [21,] "0.253271421805902" "0.163663992422611" "Seagrass.area ~sandbar.near + cod75 + trapa" [22,] "0.283060299661249" "0.163570349604790" "Seagrass.area ~sealevel.mera + cod75 + trapa + mean_temp" [23,] "0.282813557485697" "0.163282483733313" "Seagrass.area ~sealevel.mera + sandbar.arch + cod75 + trapa" [24,] "0.252643185420906" "0.162960367671415" "Seagrass.area ~sealevel.mera + sandbar.near + trapa" [25,] "0.251301501414757" "0.161457681584527" "Seagrass.area ~sealevel.mera + sandbar.arch + trapa" [26,] "0.251073879665806" "0.161202745225703" "Seagrass.area ~sealevel.mera + trapa + mean_temp" [27,] "0.250960195182875" "0.161075418604819" "Seagrass.area ~sandbar.near + cod75 + mean_temp" [28,] "0.277280638542265" "0.156827411632642" "Seagrass.area ~sandbar.near + sandbar.arch + cod75 + trapa" [29,] "0.214828348795799" "0.154430529472399" "Seagrass.area ~sealevel.mera + sandbar.arch" [30,] "0.243403081135259" "0.152611450871490" "Seagrass.area ~sealevel.mera + sandbar.near + sandbar.arch" [31,] "0.272440950802405" "0.151181109269473" "Seagrass.area ~sealevel.mera + sandbar.near + trapa + mean_temp" [32,] "0.27095451469847" "0.149446933814881" "Seagrass.area ~sealevel.mera + sandbar.near + sandbar.arch + mean_temp" [33,] "0.269097422386906" "0.147280326118057" "Seagrass.area ~sandbar.near + sandbar.arch + cod75 + mean_temp" [34,] "0.298425819224427" "0.145909692968867" "Seagrass.area ~sealevel.mera + sandbar.near + sandbar.arch + trapa + mean_temp" [35,] "0.267028729442833" "0.144866851016639" "Seagrass.area ~sealevel.mera + sandbar.arch + cod75 + mean_temp" [36,] "0.224097402260158" "0.130989090531377" "Seagrass.area ~sealevel.mera + sandbar.arch + mean_temp" [37,] "0.254029116705867" "0.129700636156845" "Seagrass.area ~sandbar.near + cod75 + trapa + mean_temp" [38,] "0.253973092799286" "0.129635274932500" "Seagrass.area ~sealevel.mera + sandbar.arch + trapa + mean_temp" [39,] "0.283116802544491" "0.127272629184598" "Seagrass.area ~sealevel.mera + sandbar.arch + cod75 + trapa + mean_temp" [40,] "0.277458428293634" "0.120384173574859" "Seagrass.area ~sandbar.near + sandbar.arch + cod75 + trapa + mean_temp" [41,] "0.134075523522723" "0.102004246616157" "Seagrass.area ~sandbar.near" [42,] "0.192142916632654" "0.0952000666285728" "Seagrass.area ~sandbar.near + sandbar.arch + trapa" [43,] "0.157420101436040" "0.0926062630849666" "Seagrass.area ~sandbar.near + trapa" [44,] "0.1564142737927" "0.0915230640844461" "Seagrass.area ~sandbar.near + sandbar.arch" [45,] "0.139897891173332" "0.0737361904943581" "Seagrass.area ~sandbar.near + mean_temp" [46,] "0.161275532759622" "0.0606285966907761" "Seagrass.area ~sandbar.near + sandbar.arch + mean_temp" [47,] "0.159297183782346" "0.0584128458362269" "Seagrass.area ~sandbar.near + trapa + mean_temp" [48,] "0.192789695151248" "0.0582546443431221" "Seagrass.area ~sandbar.near + sandbar.arch + trapa + mean_temp" [49,] "0.0767735587125819" "0.00575614015201131" "Seagrass.area ~trapa + mean_temp" [50,] "0.0352503927669171" "-0.000481074167641538" "Seagrass.area ~trapa" [51,] "0.0336114939348971" "-0.00218067295640312" "Seagrass.area ~mean_temp" [52,] "0.0240550028136193" "-0.0120911081932837" "Seagrass.area ~sandbar.arch" [53,] "0.0917343233745986" "-0.0172575578204497" "Seagrass.area ~cod75 + trapa + mean_temp" [54,] "0.0551428595339605" "-0.0175384589634271" "Seagrass.area ~cod75 + mean_temp" [55,] "0.0489111578331544" "-0.0242495223335260" "Seagrass.area ~sandbar.arch + trapa" [56,] "0.040737912202852" "-0.0330514791661594" "Seagrass.area ~sandbar.arch + cod75" [57,] "0.0768877307739583" "-0.0338857415331668" "Seagrass.area ~sandbar.arch + trapa + mean_temp" [58,] "0.039192250171151" "-0.034716038277222" "Seagrass.area ~sandbar.arch + mean_temp" [59,] "0.00192542145794812" "-0.0350403036732390" "Seagrass.area ~cod75" [60,] "0.0354717604261594" "-0.0387227195410591" "Seagrass.area ~cod75 + trapa" [61,] "0.07043418842694" "-0.0411137089618274" "Seagrass.area ~sandbar.arch + cod75 + mean_temp" [62,] "0.0950624253232855" "-0.0557605037895004" "Seagrass.area ~sandbar.arch + cod75 + trapa + mean_temp"

[63,] "0.0564075796090868" "-0.0568235108378228" "Seagrass.area ~sandbar.arch + cod75 + trapa"

All.possible.subset.selection(alldata.lim[,c(2,3,4,5,6,7,9)])

###########ぜんぶで重回帰終了

cor.p.p

            [,1]         [,2]      [,3]         [,4]         [,5]
   [,6]         [,7]         [,8]      [,9]        [,10]      [,11]
  [,12]        [,13]        [,14]        [,15]
[1,] 0.000000e+00 5.750594e-03 0.8978212 8.562393e-07 3.855333e-11

3.888081e-10 5.514383e-04 0.0004044766 0.3681946 0.0006689744 0.21210831 0.23075637 5.686207e-04 1.277952e-06 5.990190e-04

[2,] 5.750594e-03 0.000000e+00 0.9023232 1.163583e-02 5.075266e-02

3.089698e-02 4.217660e-01 0.8211909395 0.3294090 0.3411173685 0.27694724 0.67451211 5.023565e-06 5.217284e-06 1.182586e-03

[3,] 8.978212e-01 9.023232e-01 0.0000000 3.203051e-01 7.498137e-01

7.205319e-01 5.473434e-01 0.3186574254 0.4942245 0.2170354014 0.16637184 0.52939446 3.988155e-01 2.730867e-01 1.788440e-01

[4,] 8.562393e-07 1.163583e-02 0.3203051 0.000000e+00 4.411207e-04

4.682189e-07 2.737656e-02 0.0582349438 0.9069168 0.0009976178 0.26582946 0.15147609 8.358116e-04 4.182238e-05 1.994177e-04

[5,] 3.855333e-11 5.075266e-02 0.7498137 4.411207e-04 0.000000e+00

2.286133e-05 1.558108e-05 0.0004077303 0.6149705 0.0001058117 0.24901670 0.52481121 1.162476e-02 3.004858e-04 2.687034e-02

[6,] 3.888081e-10 3.089698e-02 0.7205319 4.682189e-07 2.286133e-05

0.000000e+00 1.948681e-02 0.0031832472 0.2897322 0.0627396372 0.28076632 0.32539729 2.027865e-03 8.773953e-05 1.262674e-03

[7,] 5.514383e-04 4.217660e-01 0.5473434 2.737656e-02 1.558108e-05

1.948681e-02 0.000000e+00 0.0125674737 0.2545681 0.0068389852 0.17423858 0.43207521 9.236367e-02 4.913940e-02 7.167176e-01

[8,] 4.044766e-04 8.211909e-01 0.3186574 5.823494e-02 4.077303e-04

3.183247e-03 1.256747e-02 0.0000000000 0.4207587 0.0104641973 0.01503811 0.19788276 1.939047e-01 5.487227e-02 3.018875e-01

[9,] 3.681946e-01 3.294090e-01 0.4942245 9.069168e-01 6.149705e-01

2.897322e-01 2.545681e-01 0.4207586791 0.0000000 0.5946675348 0.86071635 0.65285407 3.522639e-01 4.145383e-01 5.851163e-01 [10,] 6.689744e-04 3.411174e-01 0.2170354 9.976178e-04 1.058117e-04 6.273964e-02 6.838985e-03 0.0104641973 0.5946675 0.0000000000 0.27851889 0.02871191 2.135890e-02 3.599830e-03 5.126950e-02 [11,] 2.121083e-01 2.769472e-01 0.1663718 2.658295e-01 2.490167e-01 2.807663e-01 1.742386e-01 0.0150381106 0.8607163 0.2785188895 0.00000000 0.54239850 8.311113e-01 3.365861e-01 9.019558e-01 [12,] 2.307564e-01 6.745121e-01 0.5293945 1.514761e-01 5.248112e-01 3.253973e-01 4.320752e-01 0.1978827580 0.6528541 0.0287119118 0.54239850 0.00000000 1.210917e-01 4.090799e-02 4.444436e-01 [13,] 5.686207e-04 5.023565e-06 0.3988155 8.358116e-04 1.162476e-02 2.027865e-03 9.236367e-02 0.1939046762 0.3522639 0.0213588993 0.83111132 0.12109175 0.000000e+00 2.205580e-10 4.043878e-05 [14,] 1.277952e-06 5.217284e-06 0.2730867 4.182238e-05 3.004858e-04 8.773953e-05 4.913940e-02 0.0548722664 0.4145383 0.0035998298 0.33658605 0.04090799 2.205580e-10 0.000000e+00 1.168809e-05 [15,] 5.990190e-04 1.182586e-03 0.1788440 1.994177e-04 2.687034e-02 1.262674e-03 7.167176e-01 0.3018874987 0.5851163 0.0512694964 0.90195575 0.44444355 4.043878e-05 1.168809e-05 0.000000e+00

cor.p.2

           [,1]        [,2]        [,3]        [,4]        [,5]
[,6]        [,7]        [,8]        [,9]      [,10]       [,11]
[,12]       [,13]      [,14]       [,15]
[1,]  1.00000000 -0.49995669  0.02493926  0.77368446 -0.89822718

0.87811905 -0.60192580 -0.61323461 -0.17345815 0.5946558 -0.23884349 0.22965201 -0.60078282 -0.7660123 -0.59883293

[2,] -0.49995669  1.00000000 -0.02383510 -0.46198650  0.36616325

• 0.40143079 0.15509675 -0.04387962 0.18775088 -0.1833344 -0.20883828
• 0.08143773 0.73744277 0.7365976 0.57216512

  [3,]  0.02493926 -0.02383510  1.00000000  0.19125163  0.06188066

• 0.06940487 0.11648373 0.19189172 -0.13219844 0.2363635 -0.26402092

  0.12170763 -0.16278996 -0.2104868 -0.25671938
  [4,]  0.77368446 -0.46198650  0.19125163  1.00000000 -0.61011292

  0.78471599 -0.40952094 -0.35573804 -0.02270922 0.5790512 -0.21362689 0.27326948 -0.58605852 -0.6847325 -0.63752582

  [5,] -0.89822718  0.36616325  0.06188066 -0.61011292  1.00000000

• 0.70094622 0.71071935 0.61294758 0.09746754 -0.6576709 0.22112249
• 0.12305503 0.46204071 0.6236898 0.41075286

  [6,]  0.87811905 -0.40143079 -0.06940487  0.78471599 -0.70094622

  1.00000000 -0.43131260 -0.52884113 -0.20348189 0.3499593 -0.20722197 0.18928609 -0.54929061 -0.6633549 -0.56947155

  [7,] -0.60192580  0.15509675  0.11648373 -0.40952094  0.71071935

• 0.43131260 1.00000000 0.45757223 0.21861184 -0.4910005 0.25937241

  0.15171680  0.31834850  0.3685683  0.07039206
  [8,] -0.61323461 -0.04387962  0.19189172 -0.35573804  0.61294758

• 0.52884113 0.45757223 1.00000000 -0.15542943 -0.4679786 0.44706417
• 0.24623229 0.24836364 0.3602845 0.19852678

  [9,] -0.17345815  0.18775088 -0.13219844 -0.02270922  0.09746754

• 0.20348189 0.21861184 -0.15542943 1.00000000 0.1030758 -0.03407186

  0.08720228  0.17921412  0.1574937 -0.10574242

  [10,] 0.59465580 -0.18333438 0.23636346 0.57905119 -0.65767092 0.34995926 -0.49100049 -0.46797855 0.10307578 1.0000000 -0.20817141 0.40635795 -0.42556364 -0.5230363 -0.36540552 [11,] -0.23884349 -0.20883828 -0.26402092 -0.21362689 0.22112249
• 0.20722197 0.25937241 0.44706417 -0.03407186 -0.2081714 1.00000000
• 0.11791472 0.04140898 0.1850325 -0.02392520 [12,] 0.22965201 -0.08143773 0.12170763 0.27326948 -0.12305503 0.18928609 0.15171680 -0.24623229 0.08720228 0.4063580 -0.11791472 1.00000000 -0.29439395 -0.3819183 -0.14771933 [13,] -0.60078282 0.73744277 -0.16278996 -0.58605852 0.46204071
• 0.54929061 0.31834850 0.24836364 0.17921412 -0.4255636 0.04140898
• 0.29439395 1.00000000 0.8834124 0.68566312 [14,] -0.76601226 0.73659757 -0.21048678 -0.68473247 0.62368979
• 0.66335494 0.36856831 0.36028450 0.15749370 -0.5230363 0.18503254
• 0.38191835 0.88341237 1.0000000 0.71779896 [15,] -0.59883293 0.57216512 -0.25671938 -0.63752582 0.41075286
• 0.56947155 0.07039206 0.19852678 -0.10574242 -0.3654055 -0.02392520
• 0.14771933 0.68566312 0.7177990 1.00000000

mycor(alldata)

            Var 1       Var 2       Var 3      Var 4         Var 5
  Var 6       Var 7       Var 8       Var 9      Var 10      Var 11
  Var 12      Var 13     Var 14      Var 15

Var 1 0.984484434 -0.49995669 0.02493926 0.7736845 -0.8982271763 0.87811905 -0.60192580 -0.61323461 -0.17345815 0.59465580 -0.23884349

0.22965201 -0.60078282 -0.7660123 -0.59883293

Var 2 -0.488904203 0.92402655 -0.02383510 -0.4619865 0.3661632471

• 0.40143079 0.15509675 -0.04387962 0.18775088 -0.18333438
• 0.20883828 -0.08143773 0.73744277 0.7365976 0.57216512 Var 3 0.250694357 0.35766851 0.76080065 0.1912516 0.0618806578
• 0.06940487 0.11648373 0.19189172 -0.13219844 0.23636346
• 0.26402092 0.12170763 -0.16278996 -0.2104868 -0.25671938 Var 4 -0.003777920 -0.28355980 0.22058771 0.9137841 -0.6101129215 0.78471599 -0.40952094 -0.35573804 -0.02270922 0.57905119 -0.21362689

  0.27326948 -0.58605852 -0.6847325 -0.63752582

  Var 5 -0.638289794 -0.12384739 0.15519558 0.2146111 0.9522649389
• 0.70094622 0.71071935 0.61294758 0.09746754 -0.65767092 0.22112249 -0.12305503 0.46204071 0.6236898 0.41075286 Var 6 0.602592739 0.50255887 -0.38764191 0.5884441 0.1104826277 0.95951765 -0.43131260 -0.52884113 -0.20348189 0.34995926 -0.20722197

  0.18928609 -0.54929061 -0.6633549 -0.56947155

  Var 7 -0.325722345 -0.41087855 0.29891360 -0.2772799 0.1110049447 0.34357158 0.88471905 0.45757223 0.21861184 -0.49100049 0.25937241

  0.15171680  0.31834850  0.3685683  0.07039206

  Var 8 -0.412390979 -0.38234725 0.33013926 0.2272006 -0.1378238745 0.03608861 -0.10761716 0.85957605 -0.15542943 -0.46797855 0.44706417
• 0.24623229 0.24836364 0.3602845 0.19852678 Var 9 -0.278503078 -0.08045739 -0.26575255 0.2775706 -0.2153896658
• 0.16530210 0.08190718 -0.33647009 0.71628191 0.10307578
• 0.03407186 0.08720228 0.17921412 0.1574937 -0.10574242 Var 10 0.068793696 0.22315323 0.16961516 0.4438271 -0.3328103421
• 0.43239081 -0.10763737 -0.13724943 0.05594580 0.87772970
• 0.20817141 0.40635795 -0.42556364 -0.5230363 -0.36540552 Var 11 -0.050678396 -0.27261970 -0.31506004 -0.1711610 -0.0608587997 0.07066328 0.01547520 0.29226345 -0.12784163 0.19429257 0.71625111
• 0.11791472 0.04140898 0.1850325 -0.02392520 Var 12 0.330197931 0.43107964 -0.21179768 0.1443322 0.2567855871
• 0.20428014 0.59206957 0.07561324 0.10669924 0.27871725 0.14520766 0.80770349 -0.29439395 -0.3819183 -0.14771933 Var 13 0.397943094 0.37983416 -0.07565256 0.0740756 0.0005940823
• 0.27037391 0.36017502 0.26110589 0.20997650 -0.13281538
• 0.07006321 -0.18395803 0.92945279 0.8834124 0.68566312 Var 14 0.009742214 0.47458554 -0.20055494 0.1514962 0.1128999763
• 0.13894203 0.17580276 0.03926337 0.04512658 -0.02962396 0.34448869 -0.43463533 0.41628418 0.9607109 0.71779896 Var 15 -0.415961959 -0.32382530 -0.08987679 -0.1428757 -0.2160781895 0.12647981 -0.47812819 -0.16998783 -0.43489921 -0.01987584 -0.28984557

  0.51051657  0.35702393  0.3015191  0.90435975

5 attachments — Download all attachments View all images

 alldata_pairs2_lim_lsfit.emf

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---

  
take yamakita  to kyamada, yamakita3+sour. 
 More options   May 16 (4 days ago) 

##重回帰の結果

x <- alldata[,c(4,5,8,10)]

y <- alldata[,2]

fit1 <- lsfit(x,y)

print(fit1$coef)

  Intercept sealevel.mera  sandbar.near         cod75     mean_temp
2678733.6043   -30260.6656      204.2512  -243944.3366    75929.1377

fit2 <- lm(y ~ ., data.frame(x,y))

print(fit2$coef)

(Intercept) sealevel.mera  sandbar.near         cod75     mean_temp
2678733.6043   -30260.6656      204.2512  -243944.3366    75929.1377

ls.print(fit1) Residual Standard Error=254406.6 R-Square=0.3523 F-statistic (df=4, 24)=3.2632 p-value=0.0285

                Estimate      Std.Err t-value Pr(>|t|)

Intercept 2678733.6043 1730060.2534 1.5483 0.1346 sealevel.mera -30260.6656 15618.1445 -1.9375 0.0645 sandbar.near 204.2512 112.8572 1.8098 0.0829 cod75 -243944.3366 128488.1776 -1.8986 0.0697 mean_temp 75929.1377 93004.8374 0.8164 0.4223

print(summary(fit2))

Call: lm(formula = y ~ ., data = data.frame(x, y))

Residuals:

  Min      1Q  Median      3Q     Max

• 428368 -155148 -49293 159028 522710

Coefficients:

             Estimate Std. Error t value Pr(>|t|)

(Intercept) 2678733.6 1730060.3 1.548 0.1346 sealevel.mera -30260.7 15618.1 -1.938 0.0645 . sandbar.near 204.3 112.9 1.810 0.0829 . cod75 -243944.3 128488.2 -1.899 0.0697 . mean_temp 75929.1 93004.8 0.816 0.4223

• Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 254400 on 24 degrees of freedom Multiple R-Squared: 0.3523, Adjusted R-squared: 0.2443 F-statistic: 3.263 on 4 and 24 DF, p-value: 0.02851

 重回帰やりまくり.txt

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take yamakita  to yamakita3+sour. 
 More options   May 17 (3 days ago) 

 album200001.png

2159K View Download

---

[R][source][DATA]藻場の沖側境界線

1976年以前のCOD、透明度は東京湾のほかの場所との5年分の直線回帰の値を元に補間した。 また2003年以降の透明度は富津沖の値を元に補間した。 1978年の面積は東側の最大パッチサイズを元に推定した。

まだどうも9.0使いこなしてない Batch Define Coordinateのサンプルスクリプトがsampleのdata managementにある。超便利、全部に緯度経度をつけてくれる。てかこうやってコードをつかうのかとよくわかった。 phytonですか・・・ それより嗚呼苦カタログの操作性をなんとかしてほしい、せめてExplorlerみたく更新順で並べてほしい。

ArcView?のTOCでの操作マイナスで全部閉じる。スペースで全部選択・解除。普通にwinのショートカット知らないと苦労します。 こっちは名前順にすらならない。

olim自動処理の注意点 変な名前： 1990wolim 1999emlim50.ers

• >どちらも座標にのらない
• >ERでworldファイル書き出し。

offshore <- matrix(c(

#1967,NA,752,1041

#1970,652,813,1037

#1971,NA,782,1012

#1972,662,864,1029

#1973,666,864,988

#1974,669,NA,NA

1975,679,874,981.292#4補間

),ncol=4,byrow=T)

#########補間

lm(offshore[c(1:15,17:29),2]~offshore[c(1:15,17:29),3])

Call: lm(formula = offshore[c(1:15, 17:29), 2] ~ offshore[c(1:15, 17:29), 3])

Coefficients:

              (Intercept)  offshore[c(1:15, 17:29), 3]
                 280.0604                       0.4439

offshore[16,3]*0.4439+280.0604 [1] 613.8732

lm(offshore[6:29,4]~offshore[6:29,3])

Call: lm(formula = offshore[6:29, 4] ~ offshore[6:29, 3])

Coefficients:

    (Intercept)  offshore[6:29, 3]
       574.7072             0.4652

offshore[1:5,3]*0.4652+574.7072

[R][source][data]潮汐抽出

毎時潮位

setwd("G:/USER/yamakita13TB/バックアップ/OK yamakitata20050305_バックアップDVDにコピー済み/NEW@作業中/航空写真、JDOSSデータ")

data <- read.csv("Futtsu_airphoto_date.csv") buffer <- matrix(0,nrow=nrow(data),ncol=30) rownames(buffer)<-data[,2] bufferdf <- as.dataframe(buffer) rownames(bufferdf)<-data[,2]

setwd("G:/USER/yamakita13TB/バックアップ/OK yamakitata20050305_バックアップDVDにコピー済み/NEW@作業中/航空写真、JDOSSデータ/潮位")

for(i in 1:nrow(data)){ yyyy <- data[i,2] mm <- formatC(data[i,4],width=2,flag="0")#２ケタ表示 dd <- formatC(data[i,5],width=2,flag="0")

x <- read.csv(paste("ftp_HD08_",yyyy,"_00.csv.txt",sep=""),header=F)

if(yyyy<=1999){ key <- paste(yyyy-1900,"/",mm,"/",dd, sep="") } if(yyyy >= 2000){ key <- paste(formatC(yyyy-2000,width=2,flag="0"),"/",mm,"/",dd, sep="") }

buffer[i,1:26] <- as.numeric(x[x[,2]== key,])

}

#"ID","Year","Place","Month","Date","Number","Date","Time","Height","Focus"

#1,1967,"W",3,19,"C34-3",38795,13:07,2270,152.18

########### =HLOOKUP((M2);1:74;A2+1) =HOUR(H2)+ROUND(MINUTE(H2)/60)

#excelでこれ

## データ等を使用した論文等成果物には、「日本海洋データセンター」の資料を使用した旨を記し、成果物1部の提出をお願いいたします。 これらの成果物は日本海洋データセンターに保管され、二次、三次の利用に供されます。mail@jodc.go.jp

##1982の横須賀HD8でエラーあり、 こんな値で補正する。

xx <- replace(x,x==9999,NA)

yy <- replace(y,y==9999,NA)

plot(xx,yy)

plot(xx[,3],yy[,3])

cor.test(xx[,3],yy[,3])

      Pearson's product-moment correlation

data: xx[, 3] and yy[, 3] t = 171.3429, df = 344, p-value < 2.2e-16 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval:

0.9928283 0.9952976

sample estimates:

    cor

0.9941924

cor(xx[,3],yy[,3]) 以下にエラーcor(xx[, 3], yy[, 3]) : cov/cor 関数に欠損した観測値があります

lm(xx[,3]~yy[,3])

Call: lm(formula = xx[, 3] ~ yy[, 3])

Coefficients: (Intercept) yy[, 3]

  -15.242        0.884

[R][source][Data]千葉気象データ

http://www.data.kishou.go.jp/index45.htm 千葉(千葉県) 緯度：北緯35度36.1分／経度：東経140度06.2分

setwd("C:/Documents and Settings/yamakita/デスクトップ/bib_tokyobay_060509")

met1 <- read.csv("s_kisyoucyou_chiba_1.csv") met2 <- read.csv("s_kisyoucyou_chiba_2.csv") plot(met1.ts[1:9],xlim=c(1975,2003)) plot(met2.ts,xlim=c(1975,2003)) met1.ts <- ts(met1,start=1970,frequency=1) met1 <- read.csv("s_kisyoucyou_chiba_1.csv") met2 <- read.csv("s_kisyoucyou_chiba_2.csv") met1.ts <- ts(met1,start=1970,frequency=1) met2.ts <- ts(met2,start=1970,frequency=1) plot(met1.ts,xlim=c(1975,2003)) plot(met2.ts,xlim=c(1975,2003))

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 東京湾の長期環境変化を指標する海草藻場動態と砂州の変動との関連性90x210cm.doc

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[R][source][Data]砂州の変動

Reply | Reply to all | Forward | Print | Add sender to Contacts list | Delete this message | Report phishing | Show original | Message text garbled? sandbar <- matrix(c(

# 1967,2425, 403,2185 1970,1545, 553,2173

),ncol=4,byrow=T)

colnames(sandbar) <- c("year","near","fer","arch") sandbar.ts <- ts(sandbar[,2], start=c(1970), frequency = 1) plot(sandbar.ts,xlim=c(1975,2003))

[R][source][Data]水質COD富津

Reply | Reply to all | Forward | Print | Add sender to Contacts list | Delete this message | Report phishing | Show original | Message text garbled? 「国立環境研究所環境データベース」公共用水域水質年間値データファイル

cod75 <- matrix(c(

1971,2.0,NA ,1.5,0.7,1.1,1.3,2.3,NA ,2.3,1.3

),ncol=11,byrow=T)

colnames(cod75) <- c("西暦","東京湾１２","10001","東京湾６","東京湾９","東京湾１１","東京湾１７","東京湾８","東京湾１０","東京湾１５","東京湾１８") cod75.ts <- ts(cod75[,2:11],start=c(1971),frequency=1) plot(cod75.ts,xlim=c(1975,2003))

#########古いもの

cod18 <- matrix(c(

#No 1230224 12 609 3 0

1978,2.6             ,4.8

#以下千葉県ソース

),ncol=3,byrow=T)

colnames(cod18) <- c("西暦","COD_日間平均_平均","COD_日間平均_最大") cod18.ts <- ts(cod18[,2],start=c(1978),frequency=1) plot(cod18.ts,xlim=c(1975,2003)) par(new=T) cod18max.ts <- ts(cod18[,3],start=c(1978),frequency=1) plot(cod18max.ts,xlim=c(1975,2003),col="red")

 cod75_ts.emf

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[R][source][Data]各地の平均海面変動＆[soft]シンプルデジタイザー画像計測

Reply | Reply to all | Forward | Print | Add sender to Contacts list | Delete this message | Report phishing | Show original | Message text garbled? list.tide.deff <- list(0)

#こんなんができます。

#> str(list.tide.deff)

#List of 8

# $ : num 0

# $ Kushiro : num [1:34, 1:2] 1970 1971 1972 1973 1974 ...

# $ O-funato : num [1:34, 1:2] 1970 1971 1972 1973 1974 ...

# $ Mera : num [1:34, 1:2] 1970 1971 1972 1973 1974 ...

# $ Chichijima: num [1:28, 1:2] 1976 1977 1978 1979 1980 ...

# $ Kushimoto : num [1:34, 1:2] 1970 1971 1972 1973 1974 ...

# $ aburatsu : num [1:34, 1:2] 1970 1971 1972 1973 1974 ...

# $ Naha : num [1:34, 1:2] 1970 1971 1972 1973 1974 ...

list.tide.deff[["Kushiro"]] <- matrix(c(

1970,95.855

),ncol=2,byrow=T)

list.tide.deff[["O-funato"]] <- matrix(c(

1970,81.759

),ncol=2,byrow=T)

list.tide.deff[["Mera"]] <- matrix(c(

1970,72.988

),ncol=2,byrow=T)

list.tide.deff[["Chichijima"]] <- matrix(c(

1976,63.277

),ncol=2,byrow=T) list.tide.deff[["Kushimoto"]] <- matrix(c(

1970,56.072

),ncol=2,byrow=T)

list.tide.deff[["aburatsu"]] <- matrix(c(

1970,47.614

),ncol=2,byrow=T)

list.tide.deff[["Naha"]] <- matrix(c(

1970,33.518

),ncol=2,byrow=T)

plot(ts(list.tide.deff$Mera[,2], start=c(1970), frequency = 1),xlim=c(1975,2003)) tide.deff.ts <- ts(list.tide.deff4?[,2], start=c(1970), frequency = 1) acf(tide.deff.ts) adf.test(tide.deff.ts)

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[R][source]平均海面・太陽黒点数の推移

Reply | Reply to all | Forward | Print | Add sender to Contacts list | Delete this message | Report phishing | Show original | Message text garbled? ポスター横 90 cm x 縦 210 cm

##############平均海面データ

##http://www.data.kishou.go.jp/kaiyou/shindan/a_1/sl_trend/sl_trend.html

tide.deff <- matrix(c( 1960,-1.0

),ncol=2,byrow=T)

colnames(tide.deff) <- c("yr","deff") tide.deff.ts <- ts(tide.deff[,2], start=c(1975), frequency = 1) plot(tide.deff.ts,xlim=c(1975,2003)) acf(tide.deff.ts,lag.max=25) adf.test(tide.deff.ts)

#######################

#太陽黒点数

http://www.astroarts.co.jp/news/2004/11/02solar_cycle/index-j.shtml 太陽活動のサイクルは正確な11年周期ではなく、もっとも短いときで9年ほど、最長14年間程度とばらつきがあると。。中略。。次の活動極大期は2006年の極小期からちょうど4年後の2010年と計算されている。

http://science.msfc.nasa.gov/ssl/pad/solar/sunspots.htm

The NOAA sunspot number is compiled by the US National Oceanic and Atmospheric Administration. The numbers tabulated in spot_num.txt are the monthly averages (SSN) and standard deviation (DEV) derived from the International Sunspot Numbers)

sunspot.noaa <- matrix(c( 1960,1,146.3,32.9, 1960,2,106.0,39.6, 1960,3,102.2,28.2, 1960,4,122.0,23.6, 1960,5,119.6,19.5, 1960,6,110.2,32.0, 1960,7,121.7,31.2, 1960,8,134.1,76.3, 1960,9,127.2,36.4, 1960,10,82.8,31.4, 1960,11,89.6,29.8, 1960,12,85.6,22.7, 1961,1,57.9,29.0, 1961,2,46.1,14.7, 1961,3,53.0,22.5, 1961,4,61.4,17.8, 1961,5,51.0,18.5, 1961,6,77.4,32.0, 1961,7,70.2,21.3, 1961,8,55.8,30.5, 1961,9,63.6,20.1, 1961,10,37.7,18.6, 1961,11,32.6,21.7, 1961,12,39.9,29.7, 1962,1,38.7,29.5, 1962,2,50.3,35.0, 1962,3,45.6,26.4, 1962,4,46.4,24.0, 1962,5,43.7,13.4, 1962,6,42.0,11.8, 1962,7,21.8,11.7, 1962,8,21.8,16.7, 1962,9,51.3,16.9, 1962,10,39.5,15.5, 1962,11,26.9,20.3, 1962,12,23.2,14.8, 1963,1,19.8,11.4, 1963,2,24.4,13.5, 1963,3,17.1,8.8, 1963,4,29.3,21.8, 1963,5,43.0,19.2, 1963,6,35.9,23.4, 1963,7,19.6,13.0, 1963,8,33.2,18.8, 1963,9,38.8,27.8, 1963,10,35.3,17.0, 1963,11,23.4,13.0, 1963,12,14.9,10.4, 1964,1,15.3,6.3, 1964,2,17.7,16.1, 1964,3,16.5,10.4, 1964,4,8.6,6.2, 1964,5,9.5,5.3, 1964,6,9.1,8.4, 1964,7,3.1,4.4, 1964,8,9.3,10.2, 1964,9,4.7,6.6, 1964,10,6.1,6.6, 1964,11,7.4,6.6, 1964,12,15.1,10.5, 1965,1,17.5,8.9, 1965,2,14.3,7.8, 1965,3,11.7,7.0, 1965,4,6.8,7.3, 1965,5,24.1,25.4, 1965,6,15.9,11.2, 1965,7,11.9,11.4, 1965,8,8.9,7.6, 1965,9,16.8,10.3, 1965,10,20.1,19.1, 1965,11,15.8,14.5, 1965,12,17.0,16.0, 1966,1,28.2,20.7, 1966,2,24.4,13.6, 1966,3,25.3,17.9, 1966,4,48.7,15.2, 1966,5,45.3,20.9, 1966,6,47.7,16.6, 1966,7,56.7,10.7, 1966,8,51.2,26.6, 1966,9,50.2,19.9, 1966,10,57.2,17.8, 1966,11,57.2,16.1, 1966,12,70.4,30.3, 1967,1,110.9,28.3, 1967,2,93.6,30.5, 1967,3,111.8,40.3, 1967,4,69.5,17.9, 1967,5,86.5,57.0, 1967,6,67.3,32.0, 1967,7,91.5,31.6, 1967,8,107.2,19.9, 1967,9,76.8,28.4, 1967,10,88.2,29.0, 1967,11,94.3,32.1, 1967,12,126.4,25.0, 1968,1,121.8,47.5, 1968,2,111.9,42.7, 1968,3,92.2,32.8, 1968,4,81.2,20.9, 1968,5,127.2,20.3, 1968,6,110.3,13.1, 1968,7,96.1,36.3, 1968,8,109.3,33.7, 1968,9,117.2,32.7, 1968,10,107.7,22.3, 1968,11,86.0,10.2, 1968,12,109.8,21.3, 1969,1,104.4,26.5, 1969,2,120.5,49.3, 1969,3,135.8,35.0, 1969,4,106.8,30.6, 1969,5,120.0,44.7, 1969,6,106.0,58.3, 1969,7,96.8,34.5, 1969,8,98.0,49.5, 1969,9,91.3,22.5, 1969,10,95.7,31.6, 1969,11,93.5,19.8, 1969,12,97.9,34.5, 1970,1,111.5,40.3, 1970,2,127.8,28.7, 1970,3,102.9,29.2, 1970,4,109.5,35.6, 1970,5,127.5,22.9, 1970,6,106.8,33.7, 1970,7,112.5,34.3, 1970,8,93.0,19.6, 1970,9,99.5,21.4, 1970,10,86.6,25.3, 1970,11,95.2,23.0, 1970,12,83.5,17.3, 1971,1,91.3,22.2, 1971,2,79.0,17.0, 1971,3,60.7,14.3, 1971,4,71.8,26.7, 1971,5,57.5,16.9, 1971,6,49.8,27.7, 1971,7,81.0,18.7, 1971,8,61.4,22.4, 1971,9,50.2,20.7, 1971,10,51.7,19.4, 1971,11,63.2,22.1, 1971,12,82.2,19.1, 1972,1,61.5,26.9, 1972,2,88.4,41.3, 1972,3,80.1,25.9, 1972,4,63.2,17.1, 1972,5,80.5,33.3, 1972,6,88.0,19.3, 1972,7,76.5,16.5, 1972,8,76.8,29.3, 1972,9,64.0,21.6, 1972,10,61.3,30.8, 1972,11,41.6,21.9, 1972,12,45.3,17.2, 1973,1,43.4,21.5, 1973,2,42.9,20.1, 1973,3,46.0,19.7, 1973,4,57.7,21.1, 1973,5,42.4,21.3, 1973,6,39.5,18.0, 1973,7,23.1,15.4, 1973,8,25.6,20.7, 1973,9,59.3,36.6, 1973,10,30.7,22.8, 1973,11,23.9,21.1, 1973,12,23.3,20.3, 1974,1,27.6,24.2, 1974,2,26.0,15.4, 1974,3,21.3,9.0, 1974,4,40.3,24.1, 1974,5,39.5,36.4, 1974,6,36.0,19.8, 1974,7,55.8,20.8, 1974,8,33.6,18.7, 1974,9,40.2,26.0, 1974,10,47.1,35.4, 1974,11,25.0,11.7, 1974,12,20.5,14.1, 1975,1,18.9,10.1, 1975,2,11.5,10.7, 1975,3,11.5,9.2, 1975,4,5.1,7.5, 1975,5,9.0,11.1, 1975,6,11.4,11.2, 1975,7,28.2,7.9, 1975,8,39.7,31.3, 1975,9,13.9,9.7, 1975,10,9.1,7.4, 1975,11,19.4,12.7, 1975,12,7.8,8.3, 1976,1,8.1,10.0, 1976,2,4.3,6.1, 1976,3,21.9,16.0, 1976,4,18.8,8.4, 1976,5,12.4,8.9, 1976,6,12.2,9.4, 1976,7,1.9,3.7, 1976,8,16.4,7.0, 1976,9,13.5,7.4, 1976,10,20.6,9.6, 1976,11,5.2,5.9, 1976,12,15.3,10.7, 1977,1,16.4,10.8, 1977,2,23.1,18.4, 1977,3,8.7,6.3, 1977,4,12.9,9.2, 1977,5,18.6,10.2, 1977,6,38.5,18.6, 1977,7,21.4,15.6, 1977,8,30.1,8.2, 1977,9,44.0,14.2, 1977,10,43.8,9.2, 1977,11,29.1,12.7, 1977,12,43.2,16.3, 1978,1,51.9,33.3, 1978,2,93.6,26.7, 1978,3,76.5,16.0, 1978,4,99.7,18.3, 1978,5,82.7,13.2, 1978,6,95.1,41.9, 1978,7,70.4,32.7, 1978,8,58.1,17.4, 1978,9,138.2,29.2, 1978,10,125.1,27.3, 1978,11,97.9,21.5, 1978,12,122.7,35.0, 1979,1,166.6,19.3, 1979,2,137.5,25.0, 1979,3,138.0,15.1, 1979,4,101.5,24.7, 1979,5,134.4,30.5, 1979,6,149.5,43.2, 1979,7,159.4,37.2, 1979,8,142.2,42.8, 1979,9,188.4,36.4, 1979,10,186.2,24.8, 1979,11,183.3,57.4, 1979,12,176.3,57.3, 1980,1,159.6,41.0, 1980,2,155.0,34.1, 1980,3,126.2,51.5, 1980,4,164.1,42.3, 1980,5,179.9,39.8, 1980,6,157.3,25.0, 1980,7,136.3,51.6, 1980,8,135.4,49.4, 1980,9,155.0,43.9, 1980,10,164.7,43.4, 1980,11,147.9,37.6, 1980,12,174.4,32.1, 1981,1,114.0,25.3, 1981,2,141.3,20.1, 1981,3,135.5,20.8, 1981,4,156.4,45.2, 1981,5,127.5,33.7, 1981,6,90.9,31.8, 1981,7,143.8,41.1, 1981,8,158.7,43.4, 1981,9,167.3,27.8, 1981,10,162.4,42.7, 1981,11,137.5,54.1, 1981,12,150.1,72.6, 1982,1,111.2,46.6, 1982,2,163.6,51.2, 1982,3,153.8,19.0, 1982,4,122.0,22.6, 1982,5,82.2,25.1, 1982,6,110.4,35.0, 1982,7,106.1,90.2, 1982,8,107.6,29.9, 1982,9,118.8,24.9, 1982,10,94.7,33.9, 1982,11,98.1,19.4, 1982,12,127.0,37.2, 1983,1,84.3,17.2, 1983,2,51.0,29.6, 1983,3,66.5,23.8, 1983,4,80.7,30.5, 1983,5,99.2,16.5, 1983,6,91.1,22.2, 1983,7,82.2,18.6, 1983,8,71.8,25.4, 1983,9,50.3,15.3, 1983,10,55.8,37.4, 1983,11,33.4,26.1, 1983,12,33.4,21.3, 1984,1,57.0,29.1, 1984,2,85.4,23.5, 1984,3,83.5,20.2, 1984,4,69.7,32.4, 1984,5,76.4,21.0, 1984,6,46.1,14.4, 1984,7,37.4,22.2, 1984,8,25.5,9.4, 1984,9,15.7,18.7, 1984,10,12.0,6.9, 1984,11,22.8,13.9, 1984,12,18.7,6.2, 1985,1,16.5,17.4, 1985,2,15.9,6.1, 1985,3,17.2,11.1, 1985,4,16.2,12.1, 1985,5,27.5,13.5, 1985,6,24.2,17.7, 1985,7,30.7,23.0, 1985,8,11.1,8.8, 1985,9,3.9,4.3, 1985,10,18.6,23.0, 1985,11,16.2,15.6, 1985,12,17.3,18.9, 1986,1,2.5,5.0, 1986,2,23.2,19.2, 1986,3,15.1,11.5, 1986,4,18.5,14.5, 1986,5,13.7,10.5, 1986,6,1.1,2.8, 1986,7,18.1,11.0, 1986,8,7.4,4.6, 1986,9,3.8,4.8, 1986,10,35.5,21.8, 1986,11,14.7,12.6, 1986,12,6.7,8.3, 1987,1,10.4,6.6, 1987,2,2.4,3.6, 1987,3,14.8,6.2, 1987,4,39.3,24.7, 1987,5,30.6,13.4, 1987,6,17.5,14.1, 1987,7,33.0,35.4, 1987,8,38.6,11.7, 1987,9,33.5,15.3, 1987,10,60.5,22.7, 1987,11,39.9,17.1, 1987,12,27.1,9.8, 1988,1,59.0,18.8, 1988,2,40.0,18.2, 1988,3,76.2,23.4, 1988,4,88.0,37.7, 1988,5,60.1,22.1, 1988,6,101.8,30.0, 1988,7,113.8,22.9, 1988,8,111.6,45.6, 1988,9,120.1,34.1, 1988,10,125.1,14.0, 1988,11,125.1,31.1, 1988,12,179.2,40.8, 1989,1,161.3,25.3, 1989,2,165.1,28.9, 1989,3,131.4,31.5, 1989,4,130.6,25.0, 1989,5,138.5,32.8, 1989,6,196.2,40.0, 1989,7,126.9,28.5, 1989,8,168.9,53.6, 1989,9,176.7,66.0, 1989,10,159.4,29.3, 1989,11,173.0,33.8, 1989,12,165.5,47.9, 1990,1,177.3,27.9, 1990,2,130.5,62.5, 1990,3,140.3,47.8, 1990,4,140.3,42.2, 1990,5,132.2,37.2, 1990,6,105.4,39.6, 1990,7,149.4,59.6, 1990,8,200.3,57.0, 1990,9,125.2,21.8, 1990,10,145.5,38.1, 1990,11,131.4,35.6, 1990,12,129.7,32.6, 1991,1,136.9,47.9, 1991,2,167.5,32.8, 1991,3,141.9,36.1, 1991,4,140.0,52.5, 1991,5,121.3,20.0, 1991,6,169.7,34.1, 1991,7,173.7,42.0, 1991,8,176.3,70.0, 1991,9,125.3,20.4, 1991,10,144.1,42.8, 1991,11,108.2,29.1, 1991,12,144.4,31.4, 1992,1,150.0,42.6, 1992,2,161.1,36.3, 1992,3,106.7,21.3, 1992,4,99.8,39.7, 1992,5,73.8,22.4, 1992,6,65.2,14.7, 1992,7,85.7,36.7, 1992,8,64.5,26.4, 1992,9,63.9,13.7, 1992,10,88.7,27.8, 1992,11,91.8,16.8, 1992,12,82.6,30.1, 1993,1,59.3,22.3, 1993,2,91.0,24.7, 1993,3,69.8,16.6, 1993,4,62.2,26.5, 1993,5,61.3,31.5, 1993,6,49.8,30.1, 1993,7,57.9,13.7, 1993,8,42.2,12.9, 1993,9,22.4,13.4, 1993,10,56.4,20.4, 1993,11,35.6,15.0, 1993,12,48.9,22.2, 1994,1,57.8,24.7, 1994,2,35.5,7.4, 1994,3,31.7,15.5, 1994,4,16.1,12.1, 1994,5,17.8,12.1, 1994,6,28.0,20.1, 1994,7,35.1,18.9, 1994,8,22.5,12.4, 1994,9,25.7,19.2, 1994,10,43.8,13.6, 1994,11,18.0,10.8, 1994,12,26.7,13.9, 1995,1,24.2,16.6, 1995,2,29.9,11.1, 1995,3,31.1,17.4, 1995,4,14.0,16.2, 1995,5,14.5,13.4, 1995,6,15.6,8.4, 1995,7,14.5,9.4, 1995,8,14.3,10.0, 1995,9,11.8,9.9, 1995,10,21.1,17.7, 1995,11,9.0,7.6, 1995,12,10.0,6.1, 1996,1,11.5,14.4, 1996,2,4.4,5.0, 1996,3,9.2,7.3, 1996,4,4.8,6.0, 1996,5,5.5,6.4, 1996,6,11.8,7.3, 1996,7,8.2,9.1, 1996,8,14.4,4.5, 1996,9,1.6,3.8, 1996,10,0.9,3.8, 1996,11,17.9,18.5, 1996,12,13.3,11.4, 1997,1,5.7,5.3, 1997,2,7.6,9.5, 1997,3,8.7,7.8, 1997,4,15.5,9.5, 1997,5,18.5,13.4, 1997,6,12.7,5.2, 1997,7,10.4,11.3, 1997,8,24.4,15.6, 1997,9,51.3,24.5, 1997,10,22.8,9.4, 1997,11,39.0,10.8, 1997,12,41.2,13.3, 1998,1,31.9,21.0, 1998,2,40.3,15.9, 1998,3,54.8,13.7, 1998,4,53.4,29.9, 1998,5,56.3,19.1, 1998,6,70.6,18.1, 1998,7,66.2,20.6, 1998,8,91.7,16.7, 1998,9,92.9,24.8, 1998,10,55.6,24.8, 1998,11,73.6,25.0, 1998,12,81.6,24.5, 1999,1,62.0,29.7, 1999,2,66.3,41.7, 1999,3,68.8,24.9, 1999,4,63.7,17.8, 1999,5,106.4,20.0, 1999,6,137.6,33.1, 1999,7,113.5,26.0, 1999,8,93.8,42.7, 1999,9,71.4,24.0, 1999,10,116.7,28.1, 1999,11,133.2,36.6, 1999,12,84.6,19.3, 2000,1,90.1,35.0, 2000,2,112.9,23.0, 2000,3,138.5,25.7, 2000,4,125.5,29.5, 2000,5,121.6,43.3, 2000,6,124.9,25.0, 2000,7,169.1,43.1, 2000,8,130.5,36.8, 2000,9,109.9,39.5, 2000,10,99.4,25.4, 2000,11,106.8,23.3, 2000,12,104.3,28.4, 2001,1,95.6,19.0, 2001,2,80.6,19.8, 2001,3,113.5,63.2, 2001,4,107.7,33.6, 2001,5,96.6,21.2, 2001,6,134.0,36.7, 2001,7,81.8,24.9, 2001,8,106.4,14.6, 2001,9,150.7,27.7, 2001,10,125.7,23.2, 2001,11,106.5,24.0, 2001,12,132.2,19.0, 2002,1,114.1,16.9, 2002,2,107.3,21.6, 2002,3,98.3,13.5, 2002,4,120.7,24.3, 2002,5,120.8,25.0, 2002,6,84.3,29.3, 2002,7,99.6,41.8, 2002,8,116.4,36.3, 2002,9,109.5,19.2, 2002,10,97.5,22.1, 2002,11,95.0,26.1, 2002,12,81.6,35.8, 2003,1,79.7,21.8, 2003,2,46.0,21.6, 2003,3,61.1,25.1, 2003,4,60.0,29.1, 2003,5,54.6,21.6, 2003,6,77.4,22.7, 2003,7,83.3,33.4, 2003,8,72.7,10.6, 2003,9,48.7,16.8, 2003,10,65.5,46.1, 2003,11,67.3,42.7, 2003,12,46.5,22.4, 2004,1,37.3,14.8, 2004,2,45.6,14.1, 2004,3,49.1,16.8, 2004,4,39.3,14.8, 2004,5,41.5,15.9, 2004,6,43.2,19.1, 2004,7,51.0,30.1, 2004,8,40.9,18.6, 2004,9,27.7,13.2, 2004,10,48.4,34.8, 2004,11,43.7,14.2, 2004,12,17.9,7.0, 2005,1,31.3,16.4, 2005,2,29.1,16.0, 2005,3,24.8,12.6, 2005,4,24.4,8.2, 2005,5,42.6,17.2, 2005,6,39.6,20.7, 2005,7,39.9,34.1, 2005,8,36.4,11.9, 2005,9,22.1,11.8, ),ncol=4,byrow=T)

colnames(sunspot.noaa) <- c("YEAR","MON","SSN","DEV") sunspot.ssn.ts <- ts(sunspot.noaa[,3], start=c(1960), frequency = 12 )

plot(sunspot.ssn.ts,xlim=c(1975,2003))

---

  
take yamakita  to yamakita3+sour. 
 More options   May 13 

sunspot <-matrix(c(

1960, 112.27500

),ncol=2,byrow=T) sunspot.ts <- ts(sunspot[,2], start=c(1965),frequency=1) plot(trapa.ts, xlim=c(1975,1993))

[R][source][Data]水質透明度富津

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#全値の平均 trapa <-matrix(c(

1975,6.0#NAだが代用

),ncol=2,byrow=T) trapa.ts <- ts(trapa[,2], start=c(1975),frequency=1) plot(trapa.ts, xlim=c(1975,2003))

## summaryの結果

$"1977"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
3.000   4.500   6.000   5.939   6.875  10.000   4.000

$"1978"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
2.000   4.000   5.000   5.322   6.000  11.000  14.000

$"1979"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
2.000   4.000   5.000   4.689   5.250   8.000   8.000

$"1980"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
2.500   4.000   5.000   4.766   5.000   6.000   5.000

$"1981"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
2.500   4.000   5.000   4.851   5.000   8.000   5.000

$"1982"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
2.000   3.500   4.000   4.205   5.000   9.000   4.000

$"1983"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
    2       4       5       5       6      10      10

$"1984"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
2.000   4.000   4.000   4.486   5.000  11.000   7.000

$"1985"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
 2.50    4.50    5.00    5.36    6.00   10.00    7.00

$"1986"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
 2.50    4.00    5.00    5.16    6.00   12.00    6.00

$"1987"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
 1.50    4.00    5.00    4.84    5.50    9.00    2.00

$"1988"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
2.000   4.000   5.000   5.099   6.000  10.000

$"1989"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
2.000   4.500   5.000   5.464   6.000  10.500   1.000

$"1990"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
1.500   4.000   5.000   4.717   5.500   8.000

$"1991"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
1.000   4.000   5.000   4.631   6.000   7.500

$"1992"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
 2.00    4.00    5.00    4.75    5.50    8.00

$"1993"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
2.500   4.000   4.000   4.391   5.000   7.000

$"1994"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
2.500   4.000   4.500   4.614   5.000   6.500   1.000

$"1995"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
2.500   4.000   5.000   4.889   5.500   8.000

$"1996"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
2.000   4.000   5.000   5.007   6.000   9.000   1.000

$"1997"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
2.000   3.500   4.500   4.471   5.000   8.000   6.000

$"1998"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
3.000   5.000   6.000   5.859   6.625  10.000   3.000

$"1999"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
3.500   5.500   6.750   6.541   7.000  11.000   6.000

$"2000"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
3.000   5.000   6.500   6.654   8.000  12.000   6.000

$"2001"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
2.000   3.500   5.000   4.656   5.000  10.000   5.000

$"2002"

 Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
 2.00    3.00    4.00    4.24    5.00    7.00    9.00

###########

#補間に試用したデータ

#（千葉県公共水域調査より） chiba<- matrix(c(

#千葉県公共

1998,4.3,1.9,7.0 , 5.859375

#,2003,3.8,2.0,5.3

#,2004,3.5,2.0,5.5 ),ncol=5,byrow=T) chiba.ta <- data.frame(chiba) plot(chiba.ta) cor.test(chiba.ta) lm(chiba.ta[,2]~chiba.ta[,5])

####################### 石井さんからの元データ

#,"dtm","dblTomei","Year","Month","Day"

trance.p <- matrix(c(

28414,3,1977,10,16

),ncol=5,byrow=T) colnames(trance.p) <- c("dtm","dblTomei","Year","Month","Day")

trance.p.yr <-　tapply(trance.p[,2],trance.p[,3],mean,na.rm=T) as.matrix(trance.p.yr) trance.p.yrsum <- tapply(trance.p[,2],trance.p[,3],summary,na.rm=T) trance.p.yrsum

##DUMPの結果

"trance.p.yrsum" <- structure(list("1977" = structure(c(3, 4.5, 6, 5.939, 6.875, 10, 4), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max.", "NA's"), class = "table"), "1978" = structure(c(2, 4, 5, 5.322, 6, 11, 14), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max.", "NA's"), class = "table"), "1979" = structure(c(2, 4, 5, 4.689, 5.25, 8, 8), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max.", "NA's"), class = "table"), "1980" = structure(c(2.5, 4, 5, 4.766, 5, 6, 5), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max.", "NA's"), class = "table"), "1981" = structure(c(2.5, 4, 5, 4.851, 5, 8, 5), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max.", "NA's"), class = "table"), "1982" = structure(c(2, 3.5, 4, 4.205, 5, 9, 4), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max.", "NA's"), class = "table"), "1983" = structure(c(2, 4, 5, 5, 6, 10, 10), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max.", "NA's"), class = "table"), "1984" = structure(c(2, 4, 4, 4.486, 5, 11, 7), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max.", "NA's"), class = "table"), "1985" = structure(c(2.5, 4.5, 5, 5.36, 6, 10, 7), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max.", "NA's"), class = "table"), "1986" = structure(c(2.5, 4, 5, 5.16, 6, 12, 6), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max.", "NA's"), class = "table"), "1987" = structure(c(1.5, 4, 5, 4.84, 5.5, 9, 2), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max.", "NA's"), class = "table"), "1988" = structure(c(2, 4, 5, 5.099, 6, 10), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max."), class = "table"), "1989" = structure(c(2, 4.5, 5, 5.464, 6, 10.5, 1), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max.", "NA's"), class = "table"), "1990" = structure(c(1.5, 4, 5, 4.717, 5.5, 8), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max."), class = "table"), "1991" = structure(c(1, 4, 5, 4.631, 6, 7.5), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max."), class = "table"), "1992" = structure(c(2, 4, 5, 4.75, 5.5, 8), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max."), class = "table"), "1993" = structure(c(2.5, 4, 4, 4.391, 5, 7), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max."), class = "table"), "1994" = structure(c(2.5, 4, 4.5, 4.614, 5, 6.5, 1), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max.", "NA's"), class = "table"), "1995" = structure(c(2.5, 4, 5, 4.889, 5.5, 8), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max."), class = "table"), "1996" = structure(c(2, 4, 5, 5.007, 6, 9, 1), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max.", "NA's"), class = "table"), "1997" = structure(c(2, 3.5, 4.5, 4.471, 5, 8, 6), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max.", "NA's"), class = "table"), "1998" = structure(c(3, 5, 6, 5.859, 6.625, 10, 3), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max.", "NA's"), class = "table"), "1999" = structure(c(3.5, 5.5, 6.75, 6.541, 7, 11, 6), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max.", "NA's"), class = "table"), "2000" = structure(c(3, 5, 6.5, 6.654, 8, 12, 6), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max.", "NA's"), class = "table"), "2001" = structure(c(2, 3.5, 5, 4.656, 5, 10, 5), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max.", "NA's"), class = "table"), "2002" = structure(c(2, 3, 4, 4.24, 5, 7, 9), .Names = c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max.", "NA's"), class = "table")), .Dim = as.integer(26), .Dimnames = list(

  c("1977", "1978", "1979", "1980", "1981", "1982", "1983",
  "1984", "1985", "1986", "1987", "1988", "1989", "1990", "1991",
  "1992", "1993", "1994", "1995", "1996", "1997", "1998", "1999",
  "2000", "2001", "2002")))

[R][source]all DBF data sum

Reply | Reply to all | Forward | Print | Add sender to Contacts list | Delete this message | Report phishing | Show original | Message text garbled? shapefileのDBFから面積を取り出して、合計値の時系列作成：all DBF data sum

setwd("I:/USER/yamakita13G/Airphoto3/o_lim_ew_shp_dbf") buffer <- matrix(0,nrow=45,ncol=8) alldata <- c(0) yr <- c(1972:1989) for(yyyy in yr){

      x <- read.dbf(paste("rp_re",yyyy,"ewsg.dbf",sep=""))#rp_re1972ewsg.dbf#1973NA
      Sum <- sum(x$Area, na.rm=T)
      summary <- summary(x$Area)
      summary <- as.matrix(summary)
      if (nrow(summary)==6){
              summary <- rbind(summary,0)
              }
      tsummary <- t(summary)
      data1 <- cbind(Sum,tsummary)
      rownames(data1) <- yyyy
      alldata <- rbind(alldata,data1)
      }
            Sum    Min. 1st Qu.  Median   Mean 3rd Qu.    Max.

alldata 0.0 0.00000 0.00000 0.00000 0.0000 0.0000 0 0 1972 647424.5 0.08497 0.16990 0.33990 1.7590 0.4249 233200 0 1973 0.0 NA NA NA NaN NA NA 283765 1974 0.0 NA NA NA NaN NA NA 497336 1975 1560307.5 0.09109 0.09109 0.18220 3.9670 0.3644 1075000 0 1976 1540192.8 0.25000 0.25000 0.25000 6.7470 0.5000 1247000 0 1977 1028715.1 0.25000 0.25000 0.25000 5.4300 0.5000 778700 0 1978 416807.8 0.25000 0.25000 0.25000 3.1850 0.7500 228400 0 1979 1038911.5 0.25000 0.25000 0.25000 2.4700 0.7500 262500 0 1980 593961.0 0.25000 0.25000 0.25000 1.2360 0.5000 171500 0 1981 937826.6 0.25000 0.25000 0.25000 3.0430 0.5000 571900 0 1982 1232264.0 0.25000 0.25000 0.25000 2.7930 0.5000 819200 0 1983 284228.2 0.06968 0.06968 0.06968 0.3697 0.2090 26460 6 1984 773651.8 0.06821 0.06821 0.06821 1.5720 0.2728 390400 0 1985 1000727.8 0.06838 0.06838 0.06838 0.7255 0.1368 563200 0 1986 1096935.1 0.25000 0.25000 0.25000 3.5390 0.5000 742800 0 1987 1271709.1 0.25000 0.25000 0.25000 3.5170 0.5000 955000 0 1988 1242230.3 0.25000 0.25000 0.25000 5.9620 0.5000 929600 0 1989 1228247.3 0.25000 0.25000 0.25000 4.6380 0.5000 913900 0

---

  
take yamakita  to yamakita3+sour. 
 More options   May 1 

setwd("G:/USER/yamakita03G/o_lim_ew_sh_dbf")

buffer <- matrix(0,nrow=45,ncol=8)

alldata <- c(0)

yr <- c(1978:1981,1983:2003)

for(yyyy in yr){

1. x <- read.dbf(paste("rp_re",yyyy,"ewsg.dbf",sep=""))#rp_re1972ewsg.dbf#1973NA
2. Sum <- sum(x$Area, na.rm=T)
3. summary <- summary(x$Area)
4. summary <- as.matrix(summary)
5. if (nrow(summary)==6){
6. summary <- rbind(summary,0)
7. }
8. tsummary <- t(summary)
9. data1 <- cbind(Sum,tsummary)
10. rownames(data1) <- yyyy
11. alldata <- rbind(alldata,data1)

1. }

   alldata

               Sum Min. 1st Qu. Median   Mean 3rd Qu.    Max.    NA's

   alldata 0.0 0.00 0.00 0.00 0.000 0.0 0 0 1978 0.0 NA NA NA NaN NA NA 130878 1979 0.0 NA NA NA NaN NA NA 420687 1980 0.0 NA NA NA NaN NA NA 480437 1981 0.0 NA NA NA NaN NA NA 308220 1983 0.0 NA NA NA NaN NA NA 768858 1984 0.0 NA NA NA NaN NA NA 492182 1985 0.0 NA NA NA NaN NA NA 1379355 1986 0.0 NA NA NA NaN NA NA 309982 1987 0.0 NA NA NA NaN NA NA 361586 1988 0.0 NA NA NA NaN NA NA 208372 1989 1228247.3 0.25 0.25 0.25 4.638 0.5 913900 0 1990 1235831.3 0.25 0.25 0.25 4.405 0.5 875500 0 1991 1528538.5 0.25 0.25 0.25 7.641 0.5 1092000 0 1992 1147004.1 0.25 0.25 0.25 2.392 0.5 661600 0 1993 839680.8 0.25 0.25 0.25 2.254 0.5 397400 0 1994 812670.8 0.25 0.25 0.25 3.349 0.5 321200 0 1995 733135.3 0.25 0.25 0.25 5.212 0.5 543100 0 1996 798676.3 0.25 0.25 0.25 1.312 0.5 265300 0 1997 755542.1 0.25 0.25 0.25 2.031 0.5 285500 0 1998 1873750.5 0.25 0.25 0.25 10.880 0.5 1407000 0 1999 746708.8 0.25 0.25 0.25 1.894 0.5 439200 0 2000 863073.8 0.25 0.25 0.25 3.212 0.5 617800 0 2001 389452.8 0.25 0.25 0.25 1.517 0.5 56860 0 2002 711007.3 0.25 0.25 0.25 1.709 0.5 341200 0 2003 785308.7 0.25 0.25 0.25 5.073 0.5 328600 0

---

  
take yamakita  to yamakita3+sour. 
 More options   May 2 

     Sum    Min. 1st Qu.  Median   Mean 3rd Qu.    Max.

1972 647424.5 0.08497 0.16990 0.33990 1.7590 0.4249 233200 0

1974 0.0 NA NA NA NaN NA NA 497336 1975 1560307.5 0.09109 0.09109 0.18220 3.9670 0.3644 1075000 0 1976 1540192.8 0.25000 0.25000 0.25000 6.7470 0.5000 1247000 0 1977 1028715.1 0.25000 0.25000 0.25000 5.4300 0.5000 778700 0 1978 416807.8 0.25000 0.25000 0.25000 3.1850 0.7500 228400 0 1979 1038911.5 0.25000 0.25000 0.25000 2.4700 0.7500 262500 0 1980 593961.0 0.25000 0.25000 0.25000 1.2360 0.5000 171500 0 1981 937826.6 0.25000 0.25000 0.25000 3.0430 0.5000 571900 0 1982 1232264.0 0.25000 0.25000 0.25000 2.7930 0.5000 819200 0 1983 284228.2 0.06968 0.06968 0.06968 0.3697 0.2090 26460 6 1984 773651.8 0.06821 0.06821 0.06821 1.5720 0.2728 390400 0 1985 1000727.8 0.06838 0.06838 0.06838 0.7255 0.1368 563200 0 1986 1096935.1 0.25000 0.25000 0.25000 3.5390 0.5000 742800 0 1987 1271709.1 0.25000 0.25000 0.25000 3.5170 0.5000 955000 0 1988 1242230.3 0.25000 0.25000 0.25000 5.9620 0.5000 929600 0

1989 1228247.3 0.25000 0.25000 0.25000 4.6380 0.5000 913900 0

1990 1235831.3 0.25 0.25 0.25 4.405 0.5 875500 0 1991 1528538.5 0.25 0.25 0.25 7.641 0.5 1092000 0 1992 1147004.1 0.25 0.25 0.25 2.392 0.5 661600 0 1993 839680.8 0.25 0.25 0.25 2.254 0.5 397400 0 1994 812670.8 0.25 0.25 0.25 3.349 0.5 321200 0 1995 733135.3 0.25 0.25 0.25 5.212 0.5 543100 0 1996 798676.3 0.25 0.25 0.25 1.312 0.5 265300 0 1997 755542.1 0.25 0.25 0.25 2.031 0.5 285500 0 1998 1873750.5 0.25 0.25 0.25 10.880 0.5 1407000 0 1999 746708.8 0.25 0.25 0.25 1.894 0.5 439200 0 2000 863073.8 0.25 0.25 0.25 3.212 0.5 617800 0 2001 389452.8 0.25 0.25 0.25 1.517 0.5 56860 0 2002 711007.3 0.25 0.25 0.25 1.709 0.5 341200 0 2003 785308.7 0.25 0.25 0.25 5.073 0.5 328600 0

カンマ区切り

1972,647424.5,0.08497,0.16990,0.33990,1.7590,0.4249,233200,0

1974,,0.0,NA,NA,NA,NaN,NA,NA,497336

1975,1560307.5,0.09109,0.09109,0.18220,3.9670,0.3644,1075000,0 1976,1540192.8,0.25000,0.25000,0.25000,6.7470,0.5000,1247000,0 1977,1028715.1,0.25000,0.25000,0.25000,5.4300,0.5000,778700,0 1978,416807.8,0.25000,0.25000,0.25000,3.1850,0.7500,228400,0 1979,1038911.5,0.25000,0.25000,0.25000,2.4700,0.7500,262500,0 1980,593961.0,0.25000,0.25000,0.25000,1.2360,0.5000,171500,0 1981,937826.6,0.25000,0.25000,0.25000,3.0430,0.5000,571900,0 1982,1232264.0,0.25000,0.25000,0.25000,2.7930,0.5000,819200,0 1983,284228.2,0.06968,0.06968,0.06968,0.3697,0.2090,26460,6 1984,773651.8,0.06821,0.06821,0.06821,1.5720,0.2728,390400,0 1985,1000727.8,0.06838,0.06838,0.06838,0.7255,0.1368,563200,0 1986,1096935.1,0.25000,0.25000,0.25000,3.5390,0.5000,742800,0 1987,1271709.1,0.25000,0.25000,0.25000,3.5170,0.5000,955000,0 1988,1242230.3,0.25000,0.25000,0.25000,5.9620,0.5000,929600,0

1989,1228247.3,0.25000,0.25000,0.25000,4.6380,0.5000,913900,0

• Show quoted text - 1990,1235831.3,0.25,0.25,0.25,4.405,0.5,875500,0 1991,1528538.5,0.25,0.25,0.25,7.641,0.5,1092000,0 1992,1147004.1,0.25,0.25,0.25,2.392,0.5,661600,0 1993,839680.8,0.25,0.25,0.25,2.254,0.5,397400,0 1994,812670.8,0.25,0.25,0.25,3.349,0.5,321200,0 1995,733135.3,0.25,0.25,0.25,5.212,0.5,543100,0 1996,798676.3,0.25,0.25,0.25,1.312,0.5,265300,0 1997,755542.1,0.25,0.25,0.25,2.031,0.5,285500,0 1998,1873750.5,0.25,0.25,0.25,10.880,0.5,1407000,0 1999,746708.8,0.25,0.25,0.25,1.894,0.5,439200,0 2000,863073.8,0.25,0.25,0.25,3.212,0.5,617800,0 2001,389452.8,0.25,0.25,0.25,1.517,0.5,56860,0 2002,711007.3,0.25,0.25,0.25,1.709,0.5,341200,0 2003,785308.7,0.25,0.25,0.25,5.073,0.5,328600,0

---

  
take yamakita  to yamakita3+sour. 
 More options   May 10 

seagrass.area <- matrix(c(

• Show quoted text - 1975,1560307.5,0.09109,0.09109,0.18220,3.9670,0.3644,1075000,0

  1976	1540192.8	0.25000	0.25000	0.25000	6.7470	0.5000	1247000	0
  1977	1028715.1	0.25000	0.25000	0.25000	5.4300	0.5000	778700	0
  1978	416807.8	0.25000	0.25000	0.25000	3.1850	0.7500	228400	0
  1979	1038911.5	0.25000	0.25000	0.25000	2.4700	0.7500	262500	0
  1980	593961.0	0.25000	0.25000	0.25000	1.2360	0.5000	171500	0
  1981	937826.6	0.25000	0.25000	0.25000	3.0430	0.5000	571900	0
  1982	1232264.0	0.25000	0.25000	0.25000	2.7930	0.5000	819200	0
  1983	284228.2	0.06968	0.06968	0.06968	0.3697	0.2090	26460	6
  1984	773651.8	0.06821	0.06821	0.06821	1.5720	0.2728	390400	0
  1985	1000727.8	0.06838	0.06838	0.06838	0.7255	0.1368	563200	0
  1986	1096935.1	0.25000	0.25000	0.25000	3.5390	0.5000	742800	0
  1987	1271709.1	0.25000	0.25000	0.25000	3.5170	0.5000	955000	0
  1988	1242230.3	0.25000	0.25000	0.25000	5.9620	0.5000	929600	0
  1989	1228247.3	0.25000	0.25000	0.25000	4.6380	0.5000	913900	0
  1990	1235831.3	0.25	0.25	0.25	4.405	0.5	875500	0
  1991	1528538.5	0.25	0.25	0.25	7.641	0.5	1092000	0
  1992	1147004.1	0.25	0.25	0.25	2.392	0.5	661600	0
  1993	839680.8	0.25	0.25	0.25	2.254	0.5	397400	0
  1994	812670.8	0.25	0.25	0.25	3.349	0.5	321200	0
  1995	733135.3	0.25	0.25	0.25	5.212	0.5	543100	0
  1996	798676.3	0.25	0.25	0.25	1.312	0.5	265300	0
  1997	755542.1	0.25	0.25	0.25	2.031	0.5	285500	0
  1998	1873750.5	0.25	0.25	0.25	10.880	0.5	1407000	0
  1999	746708.8	0.25	0.25	0.25	1.894	0.5	439200	0
  2000	863073.8	0.25	0.25	0.25	3.212	0.5	617800	0
  2001	389452.8	0.25	0.25	0.25	1.517	0.5	56860	0
  2002	711007.3	0.25	0.25	0.25	1.709	0.5	341200	0
  2003	785308.7	0.25	0.25	0.25	5.073	0.5	328600	0

),ncol=9,byrow=T) colnames(seagrass.area ) <- c("yr","Sum","Min.","1st_Qu.","Median","Mean","3rd_Qu.","Max.","NA") seagrass.area.ts <- ts(seagrass.area [,2], start=c(1975), frequency = 1) plot(seagrass.area.ts)

acf(seagrass.area.ts) ##　　　acf(seagrass.area)

#としてtable名を指定すると、全部と相互作用まで書き出してくれる。

adf.test(seagrass.area.ts)

#

# y(t) = αy(t-1)+ε(t)

#

# Augmented Dickey-Fuller Test

#

#data: seagrass.area.ts

#Dickey-Fuller = -2.3882, Lag order = 3, p-value = 0.4238

#alternative hypothesis: stationary

#

##??? n <- end(seagrass.area.ts) - start(seagrass.area.ts) seagrass.area3 <- c(0)

for(i in 1:n[1]-1){
  seagrass.area3 [i] <- c(mean(seagrass.area.ts[i:i+2]))
}

seagrass.area3.ts <- ts(seagrass.area3, start=c(1976), frequency = 1) plot(seagrass.area3.ts, col="red") par(new=T) plot(seagrass.area.ts)

---

  
take yamakita  to yamakita3+sour. 
 More options   May 13 

library("maptools")

setwd("G:/USER/yamakita03G/o_lim_ew_sh_dbf")

buffer <- matrix(0,nrow=45,ncol=8) alldata <- c(0)

yr <- c(1972:2003)

for(yyyy in yr){

     x <- read.dbf(paste("rp_re",yyyy,"ewsg.dbf",sep=""))#rp_re1972ewsg.dbf#1973NA
     Sum <- sum(x$Area, na.rm=T)
     summary <- summary(x$Area)
     summary <- as.matrix(summary)
     if (nrow(summary)==6){
             summary <- rbind(summary,0)
             }
     tsummary <- t(summary)
     data1 <- cbind(Sum,tsummary)
     rownames(data1) <- yyyy
     alldata <- rbind(alldata,data1)
     }

alldata

ts.all <- ts(alldata,start=1972,frequency=1)
plot(ts.all)

####################

            Sum    Min. 1st Qu.  Median   Mean 3rd Qu.    Max.

alldata 0.0 0.00000 0.00000 0.00000 0.0000 0.0000 0 0

1972 647424.5 0.08497 0.16990 0.33990 1.7590 0.4249 233200 0

1973 0.0 NA NA NA NaN NA NA 283765

1974 0.0 NA NA NA NaN NA NA 497336

1975 1560307.5 0.09109 0.09109 0.18220 3.9670 0.3644 1075000 0 1976 1540192.8 0.25000 0.25000 0.25000 6.7470 0.5000 1247000 0 1977 1028715.1 0.25000 0.25000 0.25000 5.4300 0.5000 778700 0

1978 685566.7 0.25000 0.25000 0.25000 3.1850 0.7500 228400 0 #area hokan

1979 1038911.5 0.25000 0.25000 0.25000 2.4700 0.7500 262500 0 1980 593961.0 0.25000 0.25000 0.25000 1.2360 0.5000 171500 0 1981 937826.6 0.25000 0.25000 0.25000 3.0430 0.5000 571900 0 1982 1232264.0 0.25000 0.25000 0.25000 2.7930 0.5000 819200 0

1983 1018633.2 0.06968 0.06968 0.06968 1.3250 0.2090 734400 0

1984 773651.8 0.06821 0.06821 0.06821 1.5720 0.2728 390400 0 1985 1000727.8 0.06838 0.06838 0.06838 0.7255 0.1368 563200 0 1986 1096935.1 0.25000 0.25000 0.25000 3.5390 0.5000 742800 0 1987 1271709.1 0.25000 0.25000 0.25000 3.5170 0.5000 955000 0 1988 1242230.3 0.25000 0.25000 0.25000 5.9620 0.5000 929600 0 1989 1228247.3 0.25000 0.25000 0.25000 4.6380 0.5000 913900 0

1990 1235831.3 0.25000 0.25000 0.25000 4.4050 0.5000 875500 0 1991 1528538.5 0.25000 0.25000 0.25000 7.6410 0.5000 1092000 0 1992 1147004.1 0.25000 0.25000 0.25000 2.3920 0.5000 661600 0 1993 839680.8 0.25000 0.25000 0.25000 2.2540 0.5000 397400 0 1994 812670.8 0.25000 0.25000 0.25000 3.3490 0.5000 321200 0 1995 733135.3 0.25000 0.25000 0.25000 5.2120 0.5000 543100 0 1996 798676.3 0.25000 0.25000 0.25000 1.3120 0.5000 265300 0 1997 755542.1 0.25000 0.25000 0.25000 2.0310 0.5000 285500 0 1998 747800.3 0.25000 0.25000 0.25000 3.9910 0.5000 519100 0 1999 746708.8 0.25000 0.25000 0.25000 1.8940 0.5000 439200 0 2000 863073.8 0.25000 0.25000 0.25000 3.2120 0.5000 617800 0 2001 389452.8 0.25000 0.25000 0.25000 1.5170 0.5000 56860 0 2002 711007.3 0.25000 0.25000 0.25000 1.7090 0.5000 341200 0 2003 785308.7 0.25000 0.25000 0.25000 5.0730 0.5000 328600 0

###############

"alldata" <- structure(c(0, 647424.5044015, 0, 0, 1560307.5373775, 1540192.75, 1028715.1, 685566.7, 1038911.5, 593961, 937826.6, 1232264, 1018633.2356417, 773651.7837596, 1000727.79828100, 1096935.05, 1271709.05, 1242230.25, 1228247.3, 1235831.25, 1528538.5, 1147004.05, 839680.85, 812670.75, 733135.3, 798676.35, 755542.1, 747800.3, 746708.75, 863073.8, 389452.85, 711007.35, 785308.7, 0, 0.08497, NA, NA, 0.09109, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.06968, 0.06821, 0.06838, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0, 0.1699, NA, NA, 0.09109, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.06968, 0.06821, 0.06838, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0, 0.3399, NA, NA, 0.1822, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.06968, 0.06821, 0.06838, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0, 1.759, NaN, NaN, 3.967, 6.747, 5.43, 3.185, 2.47, 1.236, 3.043, 2.793, 1.325, 1.572, 0.7255, 3.539, 3.517, 5.962, 4.638, 4.405, 7.641, 2.392, 2.254, 3.349, 5.212, 1.312, 2.031, 3.991, 1.894, 3.212, 1.517, 1.709, 5.073, 0, 0.4249, NA, NA, 0.3644, 0.5, 0.5, 0.75, 0.75, 0.5, 0.5, 0.5, 0.209, 0.2728, 0.1368, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 233200, NA, NA, 1075000, 1247000, 778700, 228400, 262500, 171500, 571900, 819200, 734400, 390400, 563200, 742800, 955000, 929600, 913900, 875500, 1092000, 661600, 397400, 321200, 543100, 265300, 285500, 519100, 439200, 617800, 56860, 341200, 328600, 0, 0, 283765, 497336, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = as.integer(c(33, 8)), .Dimnames = list(c("alldata", "1972", "1973", "1974", "1975", "1976", "1977", "1978", "1979", "1980", "1981", "1982", "1983", "1984", "1985", "1986", "1987", "1988", "1989", "1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", "1998", "1999", "2000", "2001", "2002", "2003"), c("Sum", "Min.", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max.", "")))

##欠損1978の補間

cor.p <- cor(alldata[c(5:7,9:33),1],alldata[c(5:7,9:33),7])

cor.p [1] 0.9243706

lmdata <- lm(alldata[c(5:7,9:33),1]~alldata[c(5:7,9:33),7])

lmdata

Call: lm(formula = alldata[c(5:7, 9:33), 1] ~ alldata[c(5:7, 9:33), 7])

Coefficients:

           (Intercept)  alldata[c(5:7, 9:33), 7]
             4.581e+05                 8.777e-01

228400*(8.777e-01)+(4.851e+05) [1] 685566.7

 dump_alldata_area.emf

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