##########################################################
#############PERMUTATION TESTS##############################


> library(survival, pos=21)

> library(coin, pos=21)

> summary (ToothGrowth)
      len        supp     dose   
 Min.   : 4.20   OJ:30   0.5:20  
 1st Qu.:13.07   VC:30   1  :20  
 Median :19.25           2  :20  
 Mean   :18.81                   
 3rd Qu.:25.27                   
 Max.   :33.90                   

> oneway_test(len ~ supp, data = ToothGrowth, distribution = "approximate")

	Approximative Two-Sample Fisher-Pitman Permutation Test

data:  len by supp (OJ, VC)
Z = 1.8734, p-value = 0.0609
alternative hypothesis: true mu is not equal to 0


> oneway_test(len ~ dose, data = ToothGrowth, distribution = "approximate")

	Approximative K-Sample Fisher-Pitman Permutation Test

data:  len by dose (0.5, 1, 2)
chi-squared = 41.469, p-value < 0.0001


> summary (trees)
     Girth           Height       Volume     
 Min.   : 8.30   Min.   :63   Min.   :10.20  
 1st Qu.:11.05   1st Qu.:72   1st Qu.:19.40  
 Median :12.90   Median :76   Median :24.20  
 Mean   :13.25   Mean   :76   Mean   :30.17  
 3rd Qu.:15.25   3rd Qu.:80   3rd Qu.:37.30  
 Max.   :20.60   Max.   :87   Max.   :77.00  

> spearman_test(Volume ~ Height, data = trees, distribution = "approximate")

	Approximative Spearman Correlation Test

data:  Volume by Height
Z = 3.1697, p-value = 0.0014
alternative hypothesis: true rho is not equal to 0

######################LINEAR MODELS###############
> library(lmPerm, pos=23)

> summary (trees)
     Girth           Height       Volume     
 Min.   : 8.30   Min.   :63   Min.   :10.20  
 1st Qu.:11.05   1st Qu.:72   1st Qu.:19.40  
 Median :12.90   Median :76   Median :24.20  
 Mean   :13.25   Mean   :76   Mean   :30.17  
 3rd Qu.:15.25   3rd Qu.:80   3rd Qu.:37.30  
 Max.   :20.60   Max.   :87   Max.   :77.00  

> tree1 <- lmp (Volume ~ Girth + Height, data = trees, perm = "Prob")
[1] "Settings:  unique SS : numeric variables centered"

> summary (tree1)

Call:
lmp(formula = Volume ~ Girth + Height, data = trees, perm = "Prob")

Residuals:
    Min      1Q  Median      3Q     Max 
-6.4065 -2.6493 -0.2876  2.2003  8.4847 

Coefficients:
       Estimate Iter Pr(Prob)    
Girth    4.7082 5000   <2e-16 ***
Height   0.3393 4549   0.0215 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 3.882 on 28 degrees of freedom
Multiple R-Squared: 0.948,	Adjusted R-squared: 0.9442 
F-statistic:   255 on 2 and 28 DF,  p-value: < 2.2e-16 


> data(ToothGrowth, package="datasets")

> ToothGrowth <- within(ToothGrowth, {
+   dose <- as.factor(dose)
+ })

> summary (ToothGrowth)
      len        supp     dose   
 Min.   : 4.20   OJ:30   0.5:20  
 1st Qu.:13.07   VC:30   1  :20  
 Median :19.25           2  :20  
 Mean   :18.81                   
 3rd Qu.:25.27                   
 Max.   :33.90                   

> gp1 <- aovp (len ~ dose, data = ToothGrowth, perm = "Prob")
[1] "Settings:  unique SS "

> summary (gp1)
Component 1 :
            Df R Sum Sq R Mean Sq Iter  Pr(Prob)    
dose         2   2426.4    1213.2 5000 < 2.2e-16 ***
Residuals   57   1025.8      18.0                   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1




> library(rcompanion, pos=25)

> pairwisePermutationTest (len ~ dose, data = ToothGrowth, method = "fdr")
   Comparison   Stat      p.value     p.adjust
1 0.5 - 1 = 0 -4.524   0.00000608 0.0000091200
2 0.5 - 2 = 0 -5.535 0.0000000311 0.0000000933
3   1 - 2 = 0 -3.886    0.0001018 0.0001018000


###############################################
###SCRIPT

##########################################################
#############PERMUTATION TESTS##############################


library(survival, pos=21)
library(coin, pos=21)
summary (ToothGrowth)
oneway_test(len ~ supp, data = ToothGrowth, distribution = "approximate")
oneway_test(len ~ dose, data = ToothGrowth, distribution = "approximate")

summary (trees)
spearman_test(Volume ~ Height, data = trees, distribution = "approximate")

######################LINEAR MODELS###############
library(lmPerm, pos=23)

summary (trees)

tree1 <- lmp (Volume ~ Girth + Height, data = trees, perm = "Prob")
summary (tree1)

ToothGrowth <- within(ToothGrowth, {
  dose <- as.factor(dose)
})
summary (ToothGrowth)
gp1 <- aovp (len ~ dose, data = ToothGrowth, perm = "Prob")
summary (gp1)

###########POST HOC
library(rcompanion, pos=25)

pairwisePermutationTest (len ~ dose, data = ToothGrowth, method = "fdr")