**R tutorial for Spatial Statistics**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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Statistical Methods for Bioscience (Wisconsin-Madison): http://www.stat.wisc.edu/courses/st572-larget/Spring2007/

https://cran.r-project.org/web/packages/agricolae/vignettes/tutorial.pdf

### Complete Randomized Design

` TR.Structure = expand.grid(rep=1:3, Treatment1=c("A","B"), Treatment2=c("A","B","C")) `

Data.CRD = TR.Structure[sample(1:nrow(TR.Structure),nrow(TR.Structure)),]

Data.CRD = cbind(PlotN=1:nrow(Data.CRD), Data.CRD[,-1])

write.csv(Data.CRD, "CompleteRandomDesign.csv", row.names=F)

` > TR.Structure `

rep Treatment1 Treatment2

1 1 A A

2 2 A A

3 3 A A

4 1 B A

5 2 B A

6 3 B A

7 1 A B

8 2 A B

9 3 A B

10 1 B B

11 2 B B

12 3 B B

13 1 A C

14 2 A C

15 3 A C

16 1 B C

17 2 B C

18 3 B C

The second line randomizes the whole data.frame to obtain a CRD, then we add with cbind a column at the beginning with an ID for the plot, while also eliminating the columns with rep.

### Add Control

` TR.Structure = expand.grid(rep=1:3, Treatment1=c("A","B"), Treatment2=c("A","B","C")) `

CR.Structure = expand.grid(rep=1:3, Treatment1=c("Control"), Treatment2=c("Control"))

Data.CCRD = rbind(TR.Structure, CR.Structure)

` > Data.CCRD `

rep Treatment1 Treatment2

1 1 A A

2 2 A A

3 3 A A

4 1 B A

5 2 B A

6 3 B A

7 1 A B

8 2 A B

9 3 A B

10 1 B B

11 2 B B

12 3 B B

13 1 A C

14 2 A C

15 3 A C

16 1 B C

17 2 B C

18 3 B C

19 1 Control Control

20 2 Control Control

21 3 Control Control

As you can see the control is totally excluded from the rest. Now we just need to randomize, again using the function sample:

` Data.CCRD = Data.CCRD[sample(1:nrow(Data.CCRD),nrow(Data.CCRD)),] `

Data.CCRD = cbind(PlotN=1:nrow(Data.CCRD), Data.CCRD[,-1])

write.csv(Data.CCRD, "CompleteRandomDesign_Control.csv", row.names=F)

### Block Design with Control

` TR.Structure = expand.grid(Treatment1=c("A","B"), Treatment2=c("A","B","C")) `

CR.Structure = expand.grid(Treatment1=c("Control"), Treatment2=c("Control"))

Data.CBD = rbind(TR.Structure, CR.Structure)

Block1 = Data.CBD[sample(1:nrow(Data.CBD),nrow(Data.CBD)),]

Block2 = Data.CBD[sample(1:nrow(Data.CBD),nrow(Data.CBD)),]

Block3 = Data.CBD[sample(1:nrow(Data.CBD),nrow(Data.CBD)),]

Data.CBD = rbind(Block1, Block2, Block3)

BlockID = rep(1:nrow(Block1),3)

Data.CBD = cbind(Block = BlockID, Data.CBD)

write.csv(Data.CBD, "BlockDesign_Control.csv", row.names=F)

### Other Designs with Agricolae

` install.packages("agricolae") `

library(agricolae)

` Trt1 = c("A","B","C") `

design.crd(trt=Trt1, r=3)

The result is the output below:

` > design.crd(trt=Trt1, r=3) `

$parameters

$parameters$design

[1] "crd"

$parameters$trt

[1] "A" "B" "C"

$parameters$r

[1] 3 3 3

$parameters$serie

[1] 2

$parameters$seed

[1] 1572684797

$parameters$kinds

[1] "Super-Duper"

$parameters[[7]]

[1] TRUE

$book

plots r Trt1

1 101 1 A

2 102 1 B

3 103 2 B

4 104 2 A

5 105 1 C

6 106 3 A

7 107 2 C

8 108 3 C

9 109 3 B

As you can see the function takes only one argument for treatments and another for replicates. Therefore, if we need to include a more complex treatment structure we first need to work on them:

` Trt1 = c("A","B","C") `

Trt2 = c("1","2")

Trt3 = c("+","-")

TRT.tmp = as.vector(sapply(Trt1, function(x){paste0(x,Trt2)}))

TRT = as.vector(sapply(TRT.tmp, function(x){paste0(x,Trt3)}))

TRT.Control = c(TRT, rep("Control", 3))

As you can see we have now three treatments, which are merged into unique strings within the function sapply:

` > TRT `

[1] "A1+" "A1-" "A2+" "A2-" "B1+" "B1-" "B2+" "B2-" "C1+" "C1-" "C2+" "C2-"

Then we need to include the control, and then we can use the object TRT.Control with the function design.crd, from which we can directly obtain the data.frame with $book:

` > design.crd(trt=TRT.Control, r=3)$book `

plots r TRT.Control

1 101 1 A2+

2 102 1 B1+

3 103 1 Control

4 104 1 B2+

5 105 1 A1+

6 106 1 C2+

7 107 2 A2+

8 108 1 C2-

9 109 2 Control

10 110 1 B2-

11 111 3 Control

12 112 1 Control

13 113 2 C2-

14 114 2 Control

15 115 1 C1+

16 116 2 C1+

17 117 2 B2-

18 118 1 C1-

19 119 2 C2+

20 120 3 C2-

21 121 1 A2-

22 122 2 C1-

23 123 2 A1+

24 124 3 C1+

25 125 1 B1-

26 126 3 Control

27 127 3 A1+

28 128 2 B1+

29 129 2 B2+

30 130 3 B2+

31 131 1 A1-

32 132 2 B1-

33 133 2 A2-

34 134 1 Control

35 135 3 C2+

36 136 2 Control

37 137 2 A1-

38 138 3 B1+

39 139 3 Control

40 140 3 A2-

41 141 3 A1-

42 142 3 A2+

43 143 3 B2-

44 144 3 C1-

45 145 3 B1-

Other possible designs are:

` #Random Block Design `

design.rcbd(trt=TRT.Control, r=3)$book

#Incomplete Block Design

design.bib(trt=TRT.Control, r=7, k=3)

#Split-Plot Design

design.split(Trt1, Trt2, r=3, design=c("crd"))

#Latin Square

design.lsd(trt=TRT.tmp)$sketch

Others not included above are: Alpha designs, Cyclic designs, Augmented block designs, Graeco – latin square designs, Lattice designs, Strip Plot Designs, Incomplete Latin Square Design

### Final Note

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**R tutorial for Spatial Statistics**.

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