install.packages("agricolae")library(agricolae)PBIB.test(block,trt,replication,y,k, method=c("REML","ML","VC"),
test = c("lsd","tukey"), alpha=0.05, console=TRUE, group=TRUE)For example,
my data name is ALdata, REP is the replication, BLOCK is block, GEN is trt (treatment)
ALdata<-read.csv(file='https://gist.githubusercontent.com/ikmalmal/1819fc03dd0fb9219c3f42d62693be35/raw/e3f9120f2629d28d68fcb9a600eae29a46a1d288/ALdata.csv')Check structure of data
str(ALdata)## 'data.frame': 182 obs. of 7 variables:
## $ SAMPLE : int 112 112 113 113 114 114 116 116 117 117 ...
## $ Genotype : chr "GEN112" "GEN112" "GEN113" "GEN113" ...
## $ BLOCK : int 1 1 1 1 1 1 1 1 1 1 ...
## $ QTLclasses: chr "B" "B" "E" "E" ...
## $ ENV : chr "NS" "NS" "NS" "NS" ...
## $ REP : int 1 2 1 2 1 2 1 2 1 2 ...
## $ GY : num 8214 8763 8771 10725 12199 ...ALdata$REP<-as.factor(ALdata$REP)ALdata$BLOCK<-as.factor(ALdata$BLOCK)ALdata$Genotype<-as.factor(ALdata$Genotype)AL1<-PBIB.test(ALdata$BLOCK,ALdata$Genotype,ALdata$REP,ALdata$GY,7,method="REML", test = "lsd", alpha=0.05,console=TRUE, group=TRUE)##
## ANALYSIS PBIB: ALdata$GY
##
## Class level information
## ALdata$BLOCK : 26
## ALdata$Genotype : 91
##
## Number of observations: 182
##
## Estimation Method: Residual (restricted) maximum likelihood
##
## Parameter Estimates
## Variance
## ALdata$BLOCK:ALdata$REP 4.346409e-04
## ALdata$REP 1.447415e-03
## Residual 7.604985e+05
##
## Fit Statistics
## AIC 1741.6206
## BIC 1977.6414
## -2 Res Log Likelihood -776.8103
##
## Analysis of Variance Table
##
## Response: ALdata$GY
## Df Sum Sq Mean Sq F value Pr(>F)
## ALdata$Genotype 90 977038280 10855981 14.275 < 2.2e-16 ***
## Residuals 84 63881874 760498
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Coefficient of variation: 10.9 %
## ALdata$GY Means: 7967.263
##
## Parameters PBIB
## .
## ALdata$Genotype 91
## ALdata$BLOCK size 7
## ALdata$BLOCK/ALdata$REP 13
## ALdata$REP 2
##
## Efficiency factor 0.7894737
##
## Comparison test lsd
##
## Treatments with the same letter are not significantly different.
##
## ALdata$GY.adj groups
## GEN206 13892.800 a
## GEN249 13190.615 ab
## GEN230 13015.465 abc
## GEN234 12910.130 abc
## GEN191 12732.800 abcd
## GEN114 12163.520 abcde
## GEN269 12059.415 bcde
## GEN157 11958.080 bcde
## GEN190 11442.775 cdef
## GEN124 11155.255 defg
## GEN154 11101.120 defg
## GEN239 11083.575 defg
## GEN128 11061.705 defg
## GEN266 10747.625 efg
## GEN192 10607.470 efgh
## GEN213 10201.070 fghi
## GEN251 10113.280 fghij
## GEN229 9761.065 fghijk
## NMR152 9756.000 fghijk
## GEN113 9747.735 fghijk
## UKM5 9625.600 ghijkl
## GEN159 9617.035 ghijkl
## GEN210 9545.865 ghijklm
## GEN219 8996.335 hijklmn
## GEN176 8948.160 hijklmno
## GEN207 8861.335 ijklmno
## GEN150 8657.070 ijklmnop
## GEN133 8645.200 ijklmnop
## GEN163 8630.745 ijklmnop
## GEN188 8579.465 ijklmnop
## GEN263 8540.845 ijklmnop
## GEN132 8509.120 ijklmnop
## GEN112 8488.215 ijklmnop
## GEN250 8442.135 jklmnop
## GEN168 8436.615 jklmnop
## GEN118 8381.440 jklmnopq
## GEN122 8361.865 klmnopq
## GEN262 8182.880 klmnopqr
## GEN247 8131.520 klmnopqr
## GEN172 8077.760 klmnopqr
## GEN268 8059.155 klmnopqr
## GEN261 7956.960 lmnopqrs
## GEN164 7926.745 lmnopqrs
## GEN240 7869.760 mnopqrst
## GEN231 7849.200 mnopqrst
## GEN197 7747.945 nopqrstu
## GEN165 7720.935 nopqrstu
## GEN218 7709.065 nopqrstu
## GEN193 7617.335 nopqrstuv
## GEN140 7582.200 nopqrstuv
## GEN147 7565.385 nopqrstuv
## GEN158 7495.725 nopqrstuvw
## GEN216 7384.265 nopqrstuvwx
## GEN227 7219.945 opqrstuvwxy
## GEN228 7104.400 pqrstuvwxyz
## GEN167 7102.135 pqrstuvwxyz
## GEN152 7073.065 pqrstuvwxyzA
## GEN155 7061.515 pqrstuvwxyzA
## GEN243 7028.800 pqrstuvwxyzA
## GEN116 7005.865 pqrstuvwxyzAB
## IR64-Sub1 6938.880 pqrstuvwxyzABC
## GEN134 6682.400 qrstuvwxyzABCD
## GEN238 6675.520 qrstuvwxyzABCD
## GEN209 6658.135 qrstuvwxyzABCD
## GEN236 6517.600 rstuvwxyzABCDE
## GEN267 6275.200 stuvwxyzABCDE
## GEN258 6153.335 tuvwxyzABCDEF
## GEN166 6136.410 tuvwxyzABCDEF
## GEN170 6037.120 uvwxyzABCDEFG
## MR219 5959.815 vwxyzABCDEFG
## GEN223 5946.560 vwxyzABCDEFG
## GEN160 5817.095 wxyzABCDEFG
## GEN148 5781.920 wxyzABCDEFG
## GEN264 5778.745 wxyzABCDEFG
## GEN146 5686.775 xyzABCDEFG
## GEN204 5544.535 yzABCDEFG
## GEN127 5521.580 yzABCDEFG
## GEN222 5509.865 yzABCDEFG
## GEN187 5451.465 zABCDEFG
## GEN270 5355.015 ABCDEFG
## GEN125 5294.080 BCDEFGH
## GEN226 5232.000 CDEFGH
## GEN138 5219.095 CDEFGH
## GEN242 5060.960 DEFGH
## GEN117 5005.280 DEFGH
## GEN220 4865.600 EFGH
## GEN129 4535.305 FGH
## IR84984 4519.040 FGH
## GEN185 4457.600 FGH
## IR81896 4346.400 GH
## GEN215 3616.535 H
##
## <<< to see the objects: means, comparison and groups. >>>References
De Mendiburu, Felipe (2009). Una herramienta de analisis estadistico para la investigacion agricola. Tesis. Universidad Nacional de Ingenieria (UNI-PERU).
Mohd Ikmal, A.; Noraziyah, A.A.S.;Wickneswari, R. Incorporating Drought and Submergence Tolerance QTL in Rice (Oryza sativa L.)βThe Effects under Reproductive Stage Drought and Vegetative Stage Submergence Stresses. Plants 2021, 10, 225. https://doi.org/10.3390/plants10020225