r i = {{i}}
r traits[i]
nt <- nlevels(as.factor(data[, geno])) model <- aov(data[, traits[i]] ~ data[, geno]) model$terms[[2]] <- traits[i] at <- anova(model) rownames(at)[1] <- geno
You have fitted a linear model for a CRD. The ANOVA table for your model is:
at
The coefficient of variation for this experiment is r format(agricolae::cv.model(model), digits = 4)
%.
The p-value for genotypes is r format(at[1, 5], digits = 4)
r if(at[1, 5] < 0.05) {"which is significant at the 5% level."} else {"which is not significant at the 5% level."}
Plot for linear discriminant analysis
temp1 <- data.frame(read_excel("D:\\HIDAP_DOCUMENTATION_AND_EXAMPLES\\Drought Report\\PTDrought022216_ICA (1).xlsx", na = "NA")) #temp1 <- data.frame(read_excel("PTDrought022217_ICA.xlsx", 1, na = "NA")) #dallpc <- na.omit(dallpc) #Omitir NAs# dallpc <- na.omit(temp1) #Omitir NAs# #6.2. Generating colors and Categorical order # http://research.stowers.org/mcm/efg/R/Color/Chart/index.htm #see this link to choose colors myColors <- colors()[c(258, 144, 75)] dallpc$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$x) p1.1 <- ggplot(dataset) + geom_point(aes(lda.LD1, lda.LD2, colour = Stress), size = 2) + xlim(-4, 4) + ylim(-5, 1) + labs(x = paste("LD1 (", ")", sep=""), y = paste("LD2 (", ")", sep="")) + #ggtitle('Linear Discriminant analysis\nTraits: DSI, SD_Slp, ChCI_Slp, CR_Slp') + ggtitle('Linear Discriminant analysis\nTraits: DSI, SD_Slp, ChISPAD_Slp, CR_Slp') + theme(plot.title = element_text(hjust = 0.5, size = 10)) grid.arrange(p1.1 + colScale)
Any trend in the residuals in the left plot would violate the assumption of independence while a trend in the variability of the residuals --for instance a funnel shape-- suggests heterogeneity of variances. Departures from the theoretical normal line on the right plot are symptoms of lack of normality.
r if(at[1, 5] < 0.05) {"Below are the sorted means for each genotype with letters indicating if there are significant differences using the multiple comparisons method of Tukey at the 5% level."} else {"The means of your genotypes are:"}
if (at[1, 5] < 0.05) agricolae::HSD.test(data[, traits[i]], data[, geno], at[2, 1], at[2, 3])$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)
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
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