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Contents

1 蟯覿(Correlation Analysis)
2 一危
3 螻給(Covariance)
4 殊伎 蟯螻
5 ろ殊企 蟯螻
6 貅 蟯螻
7 data frame 蟯螻 る蠍
8 螳1
9 螳2
10 sql襦 蟯螻 蟲蠍
11 襦覯ろ 蟯 覿
12 ク蟯覿
13 谿瑚襭


1 蟯覿(Correlation Analysis) #

  • 襯 覲 蟯 襯 覿 (語 )
  • 螳螻 讌覃 煙 讌螳?
  • 螻給 螳 豌伎 覩碁慨る 覿瑚 譴 覩碁ゼ 螳讌 (螻給一 覦レ焔 )
  • 殊伎 蟯螻 覦 覦 2螳 覩 ( 覲螳 覈 蠏覿伎伎..)
  • ろ殊企, 貅 蟯螻 覿襯 覈襯朱 (貅 蟯螻螳 覈讌 豢豺企襦 碁り )
  • 蟯覿
    • 一(scatter plot)
    • 蟯螻(correlation coefficient)

2 一危 #

襴(girth) 覈碁願(weigth)
tmp <- textConnection( 
"girth  weight
35.00  3.45
32.00	3.20
30.00	3.00
31.50	3.20
32.70	3.30
30.00	3.20
36.00	3.85
30.50	3.15
34.70	3.65
30.50	3.40
33.00	3.50
35.00	4.00
31.80	3.10
38.00	4.20
33.00	3.45") 
x <- read.table(tmp, header=TRUE) 
close.connection(tmp)
plot(x)


1.png

3 螻給(Covariance) #

> cov(x)
           girth    weight
girth  5.7183810 0.7461667
weight 0.7461667 0.1210238
螻給一 企襦 蟯蟯螻襯 螳讌

4 殊伎 蟯螻 #

> cor.test(girth, weight, data=x, method="pearson") #default: pearson

	Pearson's product-moment correlation

data:  girth and weight 
t = 7.3142, df = 13, p-value = 5.881e-06
alternative hypothesis: true correlation is not equal to 0 
95 percent confidence interval:
 0.7116680 0.9655589 
sample estimates:
     cor 
0.896941 
  • 殊伎 蟯螻螳 0.896941襦 螳 蟯蟯螻螳 り 覲
  • [http]
    • 1.0 ~ 0.7: 螳
    • 0.7 ~ 0.3: 譴
    • 0.3 ~ 0.1:
    • 0.1 ~ 0.0: 覓伎

5 ろ殊企 蟯螻 #

> cor.test(girth, weight, data=x, method = "spearman")

	Spearman's rank correlation rho

data:  girth and weight 
S = 70.0628, p-value = 1.963e-05
alternative hypothesis: true rho is not equal to 0 
sample estimates:
      rho 
0.8748878 

Warning message:
In cor.test.default(girth, weight, data = x, method = "spearman") :
  Cannot compute exact p-values with ties
  • 襯 螳讌螻 蟆企襦 螳 螳 warning 覲伎.
  • 蟯螻螳 0.8748878襦 螳 蟯蟯螻

谿瑚:

6 貅 蟯螻 #

> cor.test(girth, weight, data=x, method = "kendall")

	Kendall's rank correlation tau

data:  girth and weight 
z = 3.7508, p-value = 0.0001762
alternative hypothesis: true tau is not equal to 0 
sample estimates:
      tau 
0.7425743 

Warning message:
In cor.test.default(girth, weight, data = x, method = "kendall") :
  ties 覓語  p螳 螻壱 螳 給
  • 蟯螻螳 0.7425743襦 螳 蟯蟯螻 .

7 data frame 蟯螻 る蠍 #

install.packages("Hmisc")
library("Hmisc")
rcorr(as.matrix(x), type="pearson")
rcorr(as.matrix(x), type="spearman")

8 螳1 #

#朱 一
pairs(x1)

install.packages("GGally")
library("GGally")

#覲願 襷 一
ggpairs(x1)

9 螳2 #

豢豌: R Graphics Cookbook
panel.cor <- function(x, y, digits=2, prefix="", cex.cor, ...) {
    usr <- par("usr")
    on.exit(par(usr))
    par(usr = c(0, 1, 0, 1))
    r <- abs(cor(x, y, use="complete.obs"))
    txt <- format(c(r, 0.123456789), digits=digits)[1]
    txt <- paste(prefix, txt, sep="")
    if(missing(cex.cor)) cex.cor <- 0.8/strwidth(txt)
    text(0.5, 0.5, txt, cex =  cex.cor * (1 + r) / 2)
}
    
panel.hist <- function(x, ...) {
  usr <- par("usr")
  on.exit(par(usr))
  par(usr = c(usr[1:2], 0, 1.5) )
  opar=par(ps=30) #font-size
  h <- hist(x, plot = FALSE)
  breaks <- h$breaks
  nB <- length(breaks)
  y <- h$counts
  y <- y/max(y)
  rect(breaks[-nB], 0, breaks[-1], y, col="white", ...)
}

panel.lm <- function (x, y, col = par("col"), bg = NA, pch = par("pch"),
                            cex = 1, col.smooth = "black", ...) {
    points(x, y, pch = pch, col = col, bg = bg, cex = cex)
    abline(stats::lm(y ~ x),  col = col.smooth, ...)

    #襦覯ろ蟇
    #abline(MASS::rlm(y ~ x),  col = col.smooth, ...)
}

pairs(c2009[,2:5], pch=".",
                   upper.panel = panel.cor,
                   diag.panel  = panel.hist,
                   lower.panel = panel.lm)

10 sql襦 蟯螻 蟲蠍 #

select 
    (count(*) * sum(x * y) - sum(x) * sum(y)) / 
    (sqrt(count(*) * sum(x * x) - sum(x) * sum(x)) * 
    sqrt(count(*) * sum(y * y) - sum(y) * sum(y))) r
from (select x,y from data) t

11 襦覯ろ 蟯 覿 #

library("robust")
covRob(stackloss) #Robust Estimate of Covariance
covRob(stackloss, corr = TRUE) #Robust Estimate of Correlation

蟆郁骸
> covRob(stackloss, estim = "mcd")
Call:
covRob(data = stackloss, estim = "mcd")

Robust Estimate of Covariance: 
           Air.Flow Water.Temp Acid.Conc. stack.loss
Air.Flow      77.32      24.02      54.91      67.30
Water.Temp    24.02      18.28      18.40      25.16
Acid.Conc.    54.91      18.40      99.03      48.99
stack.loss    67.30      25.16      48.99      60.93

Robust Estimate of Location: 
  Air.Flow Water.Temp Acid.Conc. stack.loss 
     56.15      20.23      85.38      13.15 
> covRob(stackloss, corr = TRUE)
Call:
covRob(data = stackloss, corr = TRUE)

Robust Estimate of Correlation: 
           Air.Flow Water.Temp Acid.Conc. stack.loss
Air.Flow     1.0000     0.6677     0.6174     0.9514
Water.Temp   0.6677     1.0000     0.4960     0.7868
Acid.Conc.   0.6174     0.4960     1.0000     0.5389
stack.loss   0.9514     0.7868     0.5389     1.0000

Robust Estimate of Location: 
  Air.Flow Water.Temp Acid.Conc. stack.loss 
     56.92      20.43      86.29      13.73 

12 ク蟯覿 #

覲螳 蟯蟯螻 レ 譴 3 覲螳 譟伎 朱襦 企ゼ 旧螻 蟯覿

library(ggm)
height <- c(160, 155, 170, 160, 180, 177)
weight <- c(55, 50, 56, 70,80,73)
gender <- c(0,0,0,1,1,1)
mydata <- data.frame(height, weight, gender)
pcor(c("height", "weight", "gender"), var(mydata))
# partial corr between [height] and [weight] controlling for [gender]
cor(height, weight)

蟆郁骸
> pcor(c("height", "weight", "gender"), var(mydata))
[1] 0.8267963
> cor(height, weight)
[1] 0.7598541
> 

13 谿瑚襭 #

  • 譴蟯覿 --> 襴暑蟲郁骸 譬覲蟲一 蟯蟯螻


譬れ -- sion 2018-09-12 20:55:04
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