UsingRNotes < BETA

<verbatim>

# read data from file:
> rtd <- read.table("uf100-0239-ws55-rtd.dat")

> median(rtd$V2)

> summary(rtd)
       V1               V2              V3           
 Min.   :0.0010   Min.   :   95   Min.   :0.0001115  
 1st Qu.:0.2507   1st Qu.: 3276   1st Qu.:0.0038440  
 Median :0.5005   Median : 8318   Median :0.0097611  
 Mean   :0.5005   Mean   :12995   Mean   :0.0152500  
 3rd Qu.:0.7502   3rd Qu.:18308   3rd Qu.:0.0214859  
 Max.   :1.0000   Max.   :91660   Max.   :0.1075688  

# produce histogram of column V2:
> hist(rtd$V2)

# plot cdf:
> library(stepfun)
> plot(ecdf(rtd$V2))

# qq plot against std normal:
> qqnorm(rtd$V2); qqline(rtd$V2)

# wilcoxon rank sum test (compare rtds):
> library(ctest)
> wilcox.test(rtd$V2,rtd40$V2)

        Wilcoxon rank sum test with continuity correction

data:  rtd$V2 and rtd40$V2 
W = 440056, p-value = 3.45e-06
alternative hypothesis: true mu is not equal to 0 

# -> reject null hyp (med are equal)


# kolmogorov-smirnoff test:
> ks.test(rtd$V2,rtd50$V2)

        Two-sample Kolmogorov-Smirnov test

data:  rtd$V2 and rtd50$V2 
D = 0.029, p-value = 0.7944
alternative hypothesis: two.sided 

Warning message: 
cannot compute correct p-values with ties in: ks.test(rtd$V2, rtd50$V2) 

# -> do not reject null hyp (distr are equal)
 

# kendall's tau test:

> corr <- read.table("flat100-corr-nov+.dat") # xxx
> cor.test(corr$V1,corr$V2, method="kendall")

        Kendall's rank correlation tau

data:  corr$V1 and corr$V2 
z.tau = 12.9965, p-value = < 2.2e-16
alternative hypothesis: true tau is not equal to 0 
sample estimates:
      tau 
0.8816162 

# -> reject null hyp (no correlation between data)


# spearman's rank order test (alt to above):
> cor.test(corr$V1,corr$V2, method="spear")


# wilcoxon matched pairs signed-rank test:
> wilcox.test(corr$V1,corr$V2, paired=TRUE)

        Wilcoxon signed rank test with continuity correction

data:  corr$V1 and corr$V2 
V = 3919, p-value = 1.657e-06
alternative hypothesis: true mu is not equal to 0 

# -> reject null hyp (no sign perf diff)


#kolmogorov-smirnov test against exp distr 
> ks.test(rtd$V2, pexp, 1/mean(rtd$V2))

# note: chisq.test is _not_ the goodness of fit test!


# qqplot of rtd vs. simple exp approx:
> qqplot(rtd$V2,qexp(rtd$V1,1/mean(rtd$V2)))
> qqplot(rtd$V2,qexp(rtd$V1,1/mean(rtd$V2)),log="xy")


# combine columns into table (array):
> qq <- cbind(rtd$V2,qexp(rtd$V1,1/mean(rtd$V2)))

# write 2-dim table (array) to file:
> write (t(qq), file="qq.dat", ncolumns=2)


# count number of inst for which alg A > alg B:
> table(corr$V1 > corr$V2)

</verbatim>

-- Main.DaveTompkins - 15 Jan 2006
This topic: BETA > TipsAndTricks > UsingRNotes
Topic revision: r1 - 2006-01-15 - DaveTompkins