source("http://www.r-statistics.com/wp-content/uploads/2010/02/Friedman-Test-with-Post-Hoc.r.txt")

MAPE <- data.frame(
		Erro = c(
					9.94791898491264, 9.95128651698003, 11.71769500808290, 37.64590951416360, 80.03704803900400,
					9.14577365740354, 9.03211367686554, 11.02763318400050, 48.80097917481330, 38.93827725481760),
		Algoritmo = factor(rep(c("SACS", "CS", "HAP", "ACO", "PSO"), 2)),
		Base = factor(rep(1:2, rep(5, 2))))

with(MAPE , boxplot( Erro  ~ Algoritmo ))

friedman.test(Erro ~ Algoritmo | Base, data = MAPE)

friedman.test.with.post.hoc(Erro ~ Algoritmo | Base, data = MAPE)

		
					
MAPE <- data.frame(
		ErroTexas = c(
					9.79707617211012, 9.89605045456683, 10.6485447021387, 25.6233327172394, 353.146176881615, 
					9.90038424867809, 9.56567077560525, 11.409833586847, 30.4238674649772, 10.3961860451553, 
					10.3639538766719, 10.0056831749453, 9.81611565395256, 23.6028144322927, 10.3960218374498, 
					9.94988430996445, 10.2620584843356, 13.1655236093023, 67.3346129794838, 11.0004618503917, 
					9.91593458838744, 9.92517843859876, 14.161078301325, 34.1950006701578, 13.1675230293946, 
					9.90768975011309, 9.80067046683108, 12.8183876139402, 49.7334789629958, 353.146176929561, 
					9.97420335879778, 10.1056945105313, 11.6132550541715, 11.7828281393643, 12.764607532609,
					9.89496656975315, 10.15622978777, 10.7105736047081, 42.3178243456874, 13.0603714415202, 
					9.79958146801131, 9.79568106262065, 12.8040987174073, 12.5465942320189, 13.4192286214679, 
					9.97551550663907, 9.9999480139955, 10.0295392370364, 78.8987411974183, 9.873726220875
					),
		Algoritmo = factor(rep(c("SACS", "CS", "HAP", "ACO", "PSO"), 10)),
		Execucao = factor(rep(1:10, rep(5, 10))))

tapply(MAPE$ErroTexas, MAPE$Algoritmo, mean)
ajuste <- lm(MAPE$ErroTexas ~ MAPE$Algoritmo + MAPE$Execucao)
summary(ajuste)
anova(ajuste)

a1 <- aov(MAPE$ErroTexas ~ MAPE$Algoritmo + MAPE$Execucao)
> posthoc <- TukeyHSD(x=a1, 'MAPE$Algoritmo', conf.level=0.95)
> 
> posthoc
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = MAPE$ErroTexas ~ MAPE$Algoritmo + MAPE$Execucao)

$`MAPE$Algoritmo`
                  diff        lwr       upr     p adj
CS-ACO   -27.694622997 -111.35516  55.96591 0.8750687
HAP-ACO  -25.928214506 -109.58875  57.73232 0.8988311
PSO-ACO   42.391138525  -41.26939 126.05167 0.5975196
SACS-ACO -27.697990529 -111.35852  55.96254 0.8750209
HAP-CS     1.766408491  -81.89413  85.42694 0.9999968
PSO-CS    70.085761522  -13.57477 153.74630 0.1371992
SACS-CS   -0.003367532  -83.66390  83.65717 1.0000000
PSO-HAP   68.319353031  -15.34118 151.97989 0.1545886
SACS-HAP  -1.769776023  -85.43031  81.89076 0.9999968
SACS-PSO -70.089129054 -153.74966  13.57140 0.1371676

plot(a1)

library(agricolae)
> r <- HSD.test(ajuste, 'MAPE$Algoritmo')
> 
> r
$statistics
      Mean       CV  MSerror      HSD
  29.85997 218.2271 4246.157 83.66053

$parameters
  Df ntr StudentizedRange alpha  test         name.t
  36   5         4.059968  0.05 Tukey MAPE$Algoritmo

$means
     MAPE$ErroTexas         std  r       Min       Max
ACO       37.645910  22.2507016 10 11.782828  78.89874
CS         9.951287   0.2019571 10  9.565671  10.26206
HAP       11.717695   1.4601013 10  9.816116  14.16108
PSO       80.037048 143.9470506 10  9.873726 353.14618
SACS       9.947919   0.1588164 10  9.797076  10.36395

$comparison
NULL

$groups
   trt     means M
1 PSO  80.037048 a
2 ACO  37.645910 a
3 HAP  11.717695 a
4 CS    9.951287 a
5 SACS  9.947919 a

with(MAPE , boxplot( ErroTexas  ~ Algoritmo ))

friedman.test(ErroTexas ~ Algoritmo | Execucao, data = MAPE)

friedman.test.with.post.hoc(ErroTexas ~ Algoritmo | Execucao, data = MAPE)

> friedman.test.with.post.hoc(ErroTexas ~ Algoritmo | Execucao, data = MAPE)
$Friedman.Test

	Asymptotic General Symmetry Test

data:  ErroTexas by
	 Algoritmo (ACO, CS, HAP, PSO, SACS) 
	 stratified by Execucao
maxT = 4.1012, p-value = 0.0003905
alternative hypothesis: two.sided


$PostHoc.Test
                       
CS - ACO   0.0012656351
HAP - ACO  0.4357039442
PSO - ACO  0.8599849085
SACS - ACO 0.0003992331
HAP - CS   0.2108621166
PSO - CS   0.0377630601
SACS - CS  0.9986028200
PSO - HAP  0.9549493524
SACS - HAP 0.1140783643
SACS - PSO 0.0160060075

$Friedman.Test

	Asymptotic General Symmetry Test

data:  ErroTexas by
	 Algoritmo (ACO, CS, HAP, PSO, SACS) 
	 stratified by Execucao
maxT = 4.1012, p-value = 0.0003995
alternative hypothesis: two.sided


$PostHoc.Test
                       
CS - ACO   0.0012656351
HAP - ACO  0.4357039442
PSO - ACO  0.8599849085
SACS - ACO 0.0003992331
HAP - CS   0.2108621166
PSO - CS   0.0377630601
SACS - CS  0.9986028200
PSO - HAP  0.9549493524
SACS - HAP 0.1140783643
SACS - PSO 0.0160060075