sommer
library(sommer)
data(DT_example)
head(DT_example)
## Name Env Loc Year Block Yield Weight
## 33 Manistee(MSL292-A) CA.2013 CA 2013 CA.2013.1 4 -1.904711
## 65 CO02024-9W CA.2013 CA 2013 CA.2013.1 5 -1.446958
## 66 Manistee(MSL292-A) CA.2013 CA 2013 CA.2013.2 5 -1.516271
## 67 MSL007-B CA.2011 CA 2011 CA.2011.2 5 -1.435510
## 68 MSR169-8Y CA.2013 CA 2013 CA.2013.1 5 -1.469051
## 103 AC05153-1W CA.2013 CA 2013 CA.2013.1 6 -1.307167
## 'data.frame': 185 obs. of 7 variables:
## $ Name : Factor w/ 41 levels "A01143-3C","AC00206-2W",..: 16 12 16 18 28 6 8 18 25 32 ...
## $ Env : Factor w/ 3 levels "CA.2011","CA.2012",..: 3 3 3 1 3 3 2 1 2 2 ...
## $ Loc : Factor w/ 1 level "CA": 1 1 1 1 1 1 1 1 1 1 ...
## $ Year : Factor w/ 3 levels "2011","2012",..: 3 3 3 1 3 3 2 1 2 2 ...
## $ Block : Factor w/ 6 levels "CA.2011.1","CA.2011.2",..: 5 5 6 2 5 5 4 1 3 4 ...
## $ Yield : int 4 5 5 5 5 6 6 6 6 6 ...
## $ Weight: num -1.9 -1.45 -1.52 -1.44 -1.47 ...
ans1 <- mmer(Yield~Env,
random= ~ Name + Env:Name,
rcov= ~ units,
data=DT, verbose = FALSE)
summary(ans1)
## ============================================================
## Multivariate Linear Mixed Model fit by REML
## ********************** sommer 4.1 **********************
## ============================================================
## logLik AIC BIC Method Converge
## Value -20.14538 46.29075 55.95182 NR TRUE
## ============================================================
## Variance-Covariance components:
## VarComp VarCompSE Zratio Constraint
## Name.Yield-Yield 3.682 1.691 2.177 Positive
## Env:Name.Yield-Yield 5.173 1.495 3.460 Positive
## units.Yield-Yield 4.366 0.647 6.748 Positive
## ============================================================
## Fixed effects:
## Trait Effect Estimate Std.Error t.value
## 1 Yield (Intercept) 16.496 0.6855 24.065
## 2 Yield EnvCA.2012 -5.777 0.7558 -7.643
## 3 Yield EnvCA.2013 -6.380 0.7960 -8.015
## ============================================================
## Groups and observations:
## Yield
## Name 41
## Env:Name 123
## ============================================================
## Use the '$' sign to access results and parameters
data(DT_example)
DT <- DT_example
ans2 <- mmer(Yield~Env,
random= ~Name + vs(ds(Env),Name),
rcov= ~ vs(ds(Env),units),
data=DT, verbose = FALSE)
summary(ans2)
## ============================================================
## Multivariate Linear Mixed Model fit by REML
## ********************** sommer 4.1 **********************
## ============================================================
## logLik AIC BIC Method Converge
## Value -15.42983 36.85965 46.52072 NR TRUE
## ============================================================
## Variance-Covariance components:
## VarComp VarCompSE Zratio Constraint
## Name.Yield-Yield 2.963 1.496 1.980 Positive
## CA.2011:Name.Yield-Yield 10.146 4.507 2.251 Positive
## CA.2012:Name.Yield-Yield 1.878 1.870 1.004 Positive
## CA.2013:Name.Yield-Yield 6.629 2.503 2.649 Positive
## CA.2011:units.Yield-Yield 4.942 1.525 3.242 Positive
## CA.2012:units.Yield-Yield 5.725 1.312 4.363 Positive
## CA.2013:units.Yield-Yield 2.560 0.640 4.000 Positive
## ============================================================
## Fixed effects:
## Trait Effect Estimate Std.Error t.value
## 1 Yield (Intercept) 16.508 0.8268 19.965
## 2 Yield EnvCA.2012 -5.817 0.8575 -6.783
## 3 Yield EnvCA.2013 -6.412 0.9356 -6.854
## ============================================================
## Groups and observations:
## Yield
## Name 41
## CA.2011:Name 41
## CA.2012:Name 41
## CA.2013:Name 41
## ============================================================
## Use the '$' sign to access results and parameters
data(DT_example)
DT <- DT_example
ans3 <- mmer(Yield~Env,
random=~ vs(us(Env),Name),
rcov=~vs(us(Env),units),
data=DT, verbose = FALSE)
summary(ans3)
## ===================================================================
## Multivariate Linear Mixed Model fit by REML
## ************************** sommer 4.1 **************************
## ===================================================================
## logLik AIC BIC Method Converge
## Value -11.49971 28.99943 38.66049 NR TRUE
## ===================================================================
## Variance-Covariance components:
## VarComp VarCompSE Zratio Constraint
## CA.2011:Name.Yield-Yield 15.665 5.4207 2.890 Positive
## CA.2012:CA.2011:Name.Yield-Yield 6.110 2.4851 2.459 Unconstr
## CA.2012:Name.Yield-Yield 4.530 1.8208 2.488 Positive
## CA.2013:CA.2011:Name.Yield-Yield 6.384 3.0659 2.082 Unconstr
## CA.2013:CA.2012:Name.Yield-Yield 0.393 1.5234 0.258 Unconstr
## CA.2013:Name.Yield-Yield 8.597 2.4838 3.461 Positive
## CA.2011:units.Yield-Yield 4.970 1.5323 3.243 Positive
## CA.2012:CA.2011:units.Yield-Yield 4.087 0.0000 Inf Unconstr
## CA.2012:units.Yield-Yield 5.673 1.3008 4.361 Positive
## CA.2013:CA.2011:units.Yield-Yield 4.087 0.0000 Inf Unconstr
## CA.2013:CA.2012:units.Yield-Yield 4.087 0.0000 Inf Unconstr
## CA.2013:units.Yield-Yield 2.557 0.6393 4.000 Positive
## ===================================================================
## Fixed effects:
## Trait Effect Estimate Std.Error t.value
## 1 Yield (Intercept) 16.331 0.8137 20.070
## 2 Yield EnvCA.2012 -5.696 0.7404 -7.693
## 3 Yield EnvCA.2013 -6.271 0.8191 -7.656
## ===================================================================
## Groups and observations:
## Yield
## CA.2011:Name 41
## CA.2012:CA.2011:Name 82
## CA.2012:Name 41
## CA.2013:CA.2011:Name 82
## CA.2013:CA.2012:Name 82
## CA.2013:Name 41
## ===================================================================
## Use the '$' sign to access results and parameters
data(DT_example)
DT <- DT_example
DT$EnvName <- paste(DT$Env,DT$Name)
ans4 <- mmer(cbind(Yield, Weight) ~ Env,
random= ~ vs(Name, Gtc=unsm(2)) + vs(EnvName, Gtc=unsm(2)),
rcov= ~ vs(units, Gtc=unsm(2)),
data=DT, verbose = FALSE)
summary(ans4)
## ============================================================
## Multivariate Linear Mixed Model fit by REML
## ********************** sommer 4.1 **********************
## ============================================================
## logLik AIC BIC Method Converge
## Value 167.0252 -322.0505 -298.5695 NR TRUE
## ============================================================
## Variance-Covariance components:
## VarComp VarCompSE Zratio Constraint
## u:Name.Yield-Yield 3.7089 1.68117 2.206 Positive
## u:Name.Yield-Weight 0.9071 0.37944 2.391 Unconstr
## u:Name.Weight-Weight 0.2243 0.08775 2.557 Positive
## u:EnvName.Yield-Yield 5.0921 1.47879 3.443 Positive
## u:EnvName.Yield-Weight 1.0269 0.30767 3.338 Unconstr
## u:EnvName.Weight-Weight 0.2101 0.06661 3.154 Positive
## u:units.Yield-Yield 4.3837 0.64941 6.750 Positive
## u:units.Yield-Weight 0.9077 0.14145 6.417 Unconstr
## u:units.Weight-Weight 0.2280 0.03377 6.751 Positive
## ============================================================
## Fixed effects:
## Trait Effect Estimate Std.Error t.value
## 1 Yield (Intercept) 16.4093 0.6783 24.191
## 2 Weight (Intercept) 0.9806 0.1497 6.550
## 3 Yield EnvCA.2012 -5.6844 0.7474 -7.606
## 4 Weight EnvCA.2012 -1.1846 0.1593 -7.439
## 5 Yield EnvCA.2013 -6.2952 0.7850 -8.019
## 6 Weight EnvCA.2013 -1.3559 0.1681 -8.065
## ============================================================
## Groups and observations:
## Yield Weight
## u:Name 41 41
## u:EnvName 94 94
## ============================================================
## Use the '$' sign to access results and parameters
data(DT_example)
DT <- DT_example
DT$EnvName <- paste(DT$Env,DT$Name)
ans5 <- mmer(cbind(Yield, Weight) ~ Env,
random= ~ vs(Name, Gtc=unsm(2)) + vs(ds(Env),Name, Gtc=unsm(2)),
rcov= ~ vs(ds(Env),units, Gtc=unsm(2)),
data=DT, verbose = FALSE)
summary(ans5)
## =============================================================
## Multivariate Linear Mixed Model fit by REML
## ********************** sommer 4.1 **********************
## =============================================================
## logLik AIC BIC Method Converge
## Value 177.8154 -343.6308 -320.1497 NR TRUE
## =============================================================
## Variance-Covariance components:
## VarComp VarCompSE Zratio Constraint
## u:Name.Yield-Yield 3.31936 1.45269 2.2850 Positive
## u:Name.Yield-Weight 0.79393 0.32621 2.4338 Unconstr
## u:Name.Weight-Weight 0.19085 0.07503 2.5438 Positive
## CA.2011:Name.Yield-Yield 8.70657 4.01470 2.1687 Positive
## CA.2011:Name.Yield-Weight 1.77892 0.83926 2.1196 Unconstr
## CA.2011:Name.Weight-Weight 0.35966 0.17903 2.0089 Positive
## CA.2012:Name.Yield-Yield 2.57109 1.94951 1.3188 Positive
## CA.2012:Name.Yield-Weight 0.33245 0.39840 0.8345 Unconstr
## CA.2012:Name.Weight-Weight 0.03842 0.08595 0.4470 Positive
## CA.2013:Name.Yield-Yield 5.46908 2.16307 2.5284 Positive
## CA.2013:Name.Yield-Weight 1.34713 0.50479 2.6687 Unconstr
## CA.2013:Name.Weight-Weight 0.32902 0.12208 2.6952 Positive
## CA.2011:units.Yield-Yield 4.93852 1.52318 3.2422 Positive
## CA.2011:units.Yield-Weight 0.99447 0.32150 3.0932 Unconstr
## CA.2011:units.Weight-Weight 0.23982 0.07394 3.2433 Positive
## CA.2012:units.Yield-Yield 5.73887 1.31533 4.3631 Positive
## CA.2012:units.Yield-Weight 1.28009 0.30157 4.2448 Unconstr
## CA.2012:units.Weight-Weight 0.31806 0.07286 4.3652 Positive
## CA.2013:units.Yield-Yield 2.56127 0.63993 4.0024 Positive
## CA.2013:units.Yield-Weight 0.44569 0.12645 3.5246 Unconstr
## CA.2013:units.Weight-Weight 0.12232 0.03057 4.0009 Positive
## =============================================================
## Fixed effects:
## Trait Effect Estimate Std.Error t.value
## 1 Yield (Intercept) 16.4243 0.7891 20.815
## 2 Weight (Intercept) 0.9866 0.1683 5.863
## 3 Yield EnvCA.2012 -5.7339 0.8266 -6.937
## 4 Weight EnvCA.2012 -1.1998 0.1698 -7.066
## 5 Yield EnvCA.2013 -6.3128 0.8757 -7.209
## 6 Weight EnvCA.2013 -1.3621 0.1915 -7.114
## =============================================================
## Groups and observations:
## Yield Weight
## u:Name 41 41
## CA.2011:Name 41 41
## CA.2012:Name 41 41
## CA.2013:Name 41 41
## =============================================================
## Use the '$' sign to access results and parameters
data(DT_example)
DT <- DT_example
DT$EnvName <- paste(DT$Env,DT$Name)
ans6 <- mmer(cbind(Yield, Weight) ~ Env,
random= ~ vs(us(Env),Name, Gtc=unsm(2)),
rcov= ~ vs(ds(Env),units, Gtc=unsm(2)),
data=DT, verbose = FALSE)
summary(ans6)
## ====================================================================
## Multivariate Linear Mixed Model fit by REML
## ************************** sommer 4.1 **************************
## ====================================================================
## logLik AIC BIC Method Converge
## Value 181.7945 -351.5889 -328.1079 NR TRUE
## ====================================================================
## Variance-Covariance components:
## VarComp VarCompSE Zratio Constraint
## CA.2011:Name.Yield-Yield 15.6451 5.35692 2.921 Positive
## CA.2011:Name.Yield-Weight 3.3586 1.14633 2.930 Unconstr
## CA.2011:Name.Weight-Weight 0.7182 0.24871 2.888 Positive
## CA.2012:CA.2011:Name.Yield-Yield 6.5289 2.48615 2.626 Positive
## CA.2012:CA.2011:Name.Yield-Weight 1.3505 0.52388 2.578 Unconstr
## CA.2012:CA.2011:Name.Weight-Weight 0.2842 0.11259 2.524 Positive
## CA.2012:Name.Yield-Yield 4.7893 1.86183 2.572 Positive
## CA.2012:Name.Yield-Weight 0.8640 0.38377 2.251 Unconstr
## CA.2012:Name.Weight-Weight 0.1693 0.08354 2.027 Positive
## CA.2013:CA.2011:Name.Yield-Yield 5.9934 2.93830 2.040 Positive
## CA.2013:CA.2011:Name.Yield-Weight 1.4232 0.64973 2.190 Unconstr
## CA.2013:CA.2011:Name.Weight-Weight 0.3379 0.14680 2.302 Positive
## CA.2013:CA.2012:Name.Yield-Yield 2.0987 1.44034 1.457 Positive
## CA.2013:CA.2012:Name.Yield-Weight 0.5240 0.32356 1.619 Unconstr
## CA.2013:CA.2012:Name.Weight-Weight 0.1342 0.07572 1.772 Positive
## CA.2013:Name.Yield-Yield 8.6257 2.47811 3.481 Positive
## CA.2013:Name.Yield-Weight 2.1048 0.58748 3.583 Unconstr
## CA.2013:Name.Weight-Weight 0.5125 0.14285 3.588 Positive
## CA.2011:units.Yield-Yield 4.9516 1.52694 3.243 Positive
## CA.2011:units.Yield-Weight 0.9993 0.32286 3.095 Unconstr
## CA.2011:units.Weight-Weight 0.2411 0.07432 3.244 Positive
## CA.2012:units.Yield-Yield 5.7790 1.32423 4.364 Positive
## CA.2012:units.Yield-Weight 1.2914 0.30408 4.247 Unconstr
## CA.2012:units.Weight-Weight 0.3212 0.07356 4.366 Positive
## CA.2013:units.Yield-Yield 2.5567 0.63883 4.002 Positive
## CA.2013:units.Yield-Weight 0.4452 0.12631 3.524 Unconstr
## CA.2013:units.Weight-Weight 0.1223 0.03056 4.001 Positive
## ====================================================================
## Fixed effects:
## Trait Effect Estimate Std.Error t.value
## 1 Yield (Intercept) 16.3342 0.8254 19.790
## 2 Weight (Intercept) 0.9677 0.1770 5.466
## 3 Yield EnvCA.2012 -5.6637 0.7449 -7.604
## 4 Weight EnvCA.2012 -1.1855 0.1604 -7.390
## 5 Yield EnvCA.2013 -6.2153 0.8340 -7.453
## 6 Weight EnvCA.2013 -1.3406 0.1806 -7.425
## ====================================================================
## Groups and observations:
## Yield Weight
## CA.2011:Name 41 41
## CA.2012:CA.2011:Name 82 82
## CA.2012:Name 41 41
## CA.2013:CA.2011:Name 82 82
## CA.2013:CA.2012:Name 82 82
## CA.2013:Name 41 41
## ====================================================================
## Use the '$' sign to access results and parameters