> qqplot(spear,fastrankweight)
> qqplot(spear,fastrankweight,main="title")
> qqplot(spear,fastrankweight,main="distribution relation between spearman and fastrank")
> cor(spear,fastrankweight,method="pearson")
[1] 0.3001109
> cor(spear,fastrankweight,method="spearman")
[1] -0.01457876
> cor(spear,fastrankweight,method="kendall")
[1] -0.002459491
data2=read.table("D:\WorkSpace\TestData\20170929\feats2.tsv",header=T)
Call:
lm(formula = y ~ x, data = data1)
Residuals:
Min 1Q Median 3Q Max
-188.34 -54.64 -3.18 24.25 1447.13
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -71.35 29.76 -2.397 0.0183 *
x 612.67 123.80 4.949 2.96e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 171.1 on 102 degrees of freedom
Multiple R-squared: 0.1936, Adjusted R-squared: 0.1857
F-statistic: 24.49 on 1 and 102 DF, p-value: 2.957e-06
> coefficient<-lm.data1$coefficients
> coefficient[1]
(Intercept)
-71.34905
> coefficient[2]
x
612.669
> ARsquared<-summary(lm.data1)$adj.r.squared
> ks.test(lm.data1$residuals,"pnorm",0)
One-sample Kolmogorov-Smirnov test
data: lm.data1$residuals
D = 0.51524, p-value < 2.2e-16
alternative hypothesis: two-sided
> ab<-round(lm.data1$coefficients[1],3)
> bb<-round(lm.data1$coefficients[2],3)
> plot(data1$x,data1$y,xlab="x",ylab = "y",col="red",pch="*")
> abline(lm.data1,col="blue")
> text(mean(data1$x),max(data1$y),paste("y = ",bb,"x+(",ab,")",sep = ""))
>