In CIP-RIU/hidap: High Interactive Data Analysis Platform for Plant breeding

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. Assumptions

Don't forget the assumptions of the model. It is supposed that the errors are independent with a normal distribution and with the same variance for all the genotypes. The following residuals plots must help you evaluate this:

par(mfrow = c(1, 2))
plot(model, which = 1)
plot(model, which = 2)

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