In omarbenites/fbanalysis: Statistical Analysis for Breeding Data in HiDAP

r i = {{i}}

{{i+1}}. Analysis for trait `r traits[i]`

nt <- nlevels(as.factor(data[, geno]))
model <- aov(data[, traits[i]] ~ data[, geno])
model$terms[[2]] <- traits[i]
at <- anova(model)
rownames(at)[1] <- geno

{{i+1}}.1. ANOVA

You have fitted a linear model for a CRD. The ANOVA table for your model is:

at

The coefficient of variation for this experiment is r format(agricolae::cv.model(model), digits = 4)%. The p-value for genotypes is r format(at[1, 5], digits = 4) r if(at[1, 5] < 0.05) {"which is significant at the 5% level."} else {"which is not significant at the 5% level."}

{{i+1}}.2. Lineal Discriminant Analysis Plotting

Plot for linear discriminant analysis

temp1 <- data.frame(read_excel("D:\\HIDAP_DOCUMENTATION_AND_EXAMPLES\\Drought Report\\PTDrought022216_ICA (1).xlsx", na = "NA"))
#temp1 <- data.frame(read_excel("PTDrought022217_ICA.xlsx", 1, na = "NA"))
#dallpc <- na.omit(dallpc) #Omitir NAs#
dallpc <- na.omit(temp1) #Omitir NAs#

#6.2. Generating colors and Categorical order
# http://research.stowers.org/mcm/efg/R/Color/Chart/index.htm #see this link to choose colors
myColors <- colors()[c(258, 144, 75)]
dallpc$FACTOR <- factor(dallpc$FACTOR, levels=c("NI", "REC", "TD"))
names(myColors) <- levels(dallpc$FACTOR)
colScale <- scale_colour_manual(name = "Treat",values = myColors)
#Treatment contraction names must be the same used in Module 8.1 – Abiotic Stress protocol.

# 6.3. Generate a Train and Test data set
set.seed(1)
intrain <- sample(nrow(temp2), round(0.50*nrow(temp2)))
train <- temp2[intrain, ]
test <- temp2[-intrain, ]

library(gridExtra)
library(ggplot2)
# 6.4. Plotting the discrimination coefficients in a bi-dimensional graph. Note that what is written in red must be replaced for the respective selected traits
#lda2train <- lda(as.factor(TREAT)~ DSI_a + SD_Slp + ChCI_Slp + CR_Slp, data=dallpc, CV = F)
lda2train <- lda(as.factor(FACTOR)~ DSI + SD_Slp + ChISPAD_Slp + CR_Slp, data=dallpc, CV = F)
prop.lda =  lda2train$svd^2/sum(lda2train$svd^2)
plda <- predict(object = lda2train, newdata = dallpc)
dataset = data.frame(Stress = dallpc[,"FACTOR"], lda = plda$x)
p1.1 <- ggplot(dataset) + geom_point(aes(lda.LD1, lda.LD2, colour = Stress), size = 2) + xlim(-4, 4) + ylim(-5, 1) +
  labs(x = paste("LD1 (",  ")", sep=""),
       y = paste("LD2 (",  ")", sep="")) +
  #ggtitle('Linear Discriminant analysis\nTraits: DSI, SD_Slp, ChCI_Slp, CR_Slp') +
  ggtitle('Linear Discriminant analysis\nTraits: DSI, SD_Slp, ChISPAD_Slp, CR_Slp') +
  theme(plot.title = element_text(hjust = 0.5, size = 10))
grid.arrange(p1.1 + colScale)

Any trend in the residuals in the left plot would violate the assumption of independence while a trend in the variability of the residuals --for instance a funnel shape-- suggests heterogeneity of variances. Departures from the theoretical normal line on the right plot are symptoms of lack of normality.

{{i+1}}.3. Genotype means

r if(at[1, 5] < 0.05) {"Below are the sorted means for each genotype with letters indicating if there are significant differences using the multiple comparisons method of Tukey at the 5% level."} else {"The means of your genotypes are:"}

if (at[1, 5] < 0.05)
  agricolae::HSD.test(data[, traits[i]], data[, geno], at[2, 1], at[2, 3])$groups else
    tapply(data[, traits[i]], data[, geno], mean, na.rm = TRUE)

r if(nt < 10) {"It is always good to have some visualization of the data. Because the number of genotypes in your experiment is not so big, we can plot the data for each genotypes:"}

if (nt < 10) msdplot(traits[i], geno, data, conf = 1)

{{i+1}}.4. Variance components

Below are the variance components for this model, under the assumption that genotypes are random. Here the model is fitted using REML.

y <- data[, traits[i]]
fg <- data[, geno]
ff <- as.formula(y ~ (1|fg))
model <- lme4::lmer(ff)
vc <- data.frame(lme4::VarCorr(model))
vc[1, 1] <- geno
rownames(vc) <- vc[, 1]
vc <- vc[, c(4, 5)]
colnames(vc) <- c("Variance", "Std.Dev.")
vc