#Library files for courses provided by: Highland Statistics Ltd. #To cite these functions, use: #Mixed effects models and extensions in ecology with R. (2009). #Zuur, AF, Ieno, EN, Walker, N, Saveliev, AA, and Smith, GM. Springer. #Copyright Highland Statistics LTD. ##################################################################### #VIF FUNCTION. #To use: corvif(YourDataFile) corvif <- function(dataz) { dataz <- as.data.frame(dataz) #vif part form <- formula(paste("fooy ~ ",paste(strsplit(names(dataz)," "),collapse=" + "))) dataz <- data.frame(fooy=1 + rnorm(nrow(dataz)) ,dataz) lm_mod <- lm(form,dataz) cat("\n\nVariance inflation factors\n\n") print(myvif(lm_mod)) } #Support function for corvif. Will not be called by the user myvif <- function(mod) { v <- vcov(mod) assign <- attributes(model.matrix(mod))\$assign if (names(coefficients(mod)[1]) == "(Intercept)") { v <- v[-1, -1] assign <- assign[-1] } else warning("No intercept: vifs may not be sensible.") terms <- labels(terms(mod)) n.terms <- length(terms) if (n.terms < 2) stop("The model contains fewer than 2 terms") if (length(assign) > dim(v)[1] ) { diag(tmp_cor)<-0 if (any(tmp_cor==1.0)){ return("Sample size is too small, 100% collinearity is present") } else { return("Sample size is too small") } } R <- cov2cor(v) detR <- det(R) result <- matrix(0, n.terms, 3) rownames(result) <- terms colnames(result) <- c("GVIF", "Df", "GVIF^(1/2Df)") for (term in 1:n.terms) { subs <- which(assign == term) result[term, 1] <- det(as.matrix(R[subs, subs])) * det(as.matrix(R[-subs, -subs])) / detR result[term, 2] <- length(subs) } if (all(result[, 2] == 1)) { result <- data.frame(GVIF=result[, 1]) } else { result[, 3] <- result[, 1]^(1/(2 * result[, 2])) } invisible(result) } #END VIF FUNCTIONS ################################################################## ################################################################## #Here are some functions that we took from the pairs help file and #modified, or wrote ourselves. To cite these, use the r citation: citation() panel.cor <- function(x, y, digits=1, prefix="", cex.cor = 6) { usr <- par("usr"); on.exit(par(usr)) par(usr = c(0, 1, 0, 1)) r1=cor(x,y,use="pairwise.complete.obs") r <- abs(cor(x, y,use="pairwise.complete.obs")) txt <- format(c(r1, 0.123456789), digits=digits)[1] txt <- paste(prefix, txt, sep="") if(missing(cex.cor)) { cex <- 0.9/strwidth(txt) } else { cex = cex.cor} text(0.5, 0.5, txt, cex = cex * r) } ################################################################## panel.smooth2=function (x, y, col = par("col"), bg = NA, pch = par("pch"), cex = 1, col.smooth = "black", span = 2/3, iter = 3, ...) { points(x, y, pch = pch, col = col, bg = bg, cex = cex) ok <- is.finite(x) & is.finite(y) if (any(ok)) lines(stats::lowess(x[ok], y[ok], f = span, iter = iter), col = 1, ...) } ################################################################## panel.lines2=function (x, y, col = par("col"), bg = NA, pch = par("pch"), cex = 1, ...) { points(x, y, pch = pch, col = col, bg = bg, cex = cex) ok <- is.finite(x) & is.finite(y) if (any(ok)){ tmp=lm(y[ok]~x[ok]) abline(tmp)} } ################################################################## panel.hist <- function(x, ...) { usr <- par("usr"); on.exit(par(usr)) par(usr = c(usr[1:2], 0, 1.5) ) h <- hist(x, plot = FALSE) breaks <- h\$breaks; nB <- length(breaks) y <- h\$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col="white", ...) } ################################################################## ################################################################## ################################################################## ################################################################## #Functions for variograms #To cite these functions, use: #Mixed effects models and extensions in ecology with R. (2009). #Zuur, AF, Ieno, EN, Walker, N, Saveliev, AA, and Smith, GM. Springer. #Make a variogram for one variable #To use, type: MyVariogram(XUTM, YUTM, E , MyDistance=10) # XUTM is x coordinates # XUTM is y coordinates # E is variable used in sample variogram # MyDistance is the cutoff value for the distances MyVariogram <- function(x,y,z, MyDistance) { library(gstat) mydata <- data.frame(z, x, y) coordinates(mydata) <- c("x", "y") Var <- variogram(z ~ 1, mydata, cutoff = MyDistance) data.frame(Var\$np, Var\$dist, Var\$gamma) } #Function for making multiple variograms in an xyplot #To use, type: MultiVariogram(Z, MyVar,XUTM, YUTM, MyDistance=10) # Z is a data frame with all the data # Character string with variable names that will be used in the xyplot # XUTM is x coordinates # XUTM is y coordinates # MyDistance is the cutoff value for the distances MultiVariogram <- function(Z, MyVar, x, y, MyDistance) { #Z is the data frame with data #MyVar is a list of variables for for which variograms are calculated #x, y: spatial coordinates #MyDistance: limit for distances in the variogram library(lattice) VarAll<- c(NA,NA,NA,NA) for (i in MyVar){ vi <- MyVariogram(x,y,Z[,i], MyDistance) vii <- cbind(vi, i) VarAll <- rbind(VarAll,vii) } VarAll <- VarAll[-1,] P <- xyplot(Var.gamma ~ Var.dist | factor(i), col = 1, type = "p", pch = 16, data = VarAll, xlab = "Distance", ylab = "Semi-variogram", strip = function(bg='white', ...) strip.default(bg='white', ...), scales = list(alternating = T, x = list(relation = "same"), y = list(relation = "same")) ) print(P) } #End variogram code ########################################################## #Function for multi-panel Cleveland dotplot. #The input file must contain no categorical variables Mydotplot <- function(DataSelected){ P <- dotplot(as.matrix(as.matrix(DataSelected)), groups=FALSE, strip = strip.custom(bg = 'white', par.strip.text = list(cex = 1.2)), scales = list(x = list(relation = "free", draw = TRUE), y = list(relation = "free", draw = FALSE)), col=1, cex = 0.5, pch = 16, xlab = list(label = "Value of the variable", cex = 1.5), ylab = list(label = "Order of the data from text file", cex = 1.5)) print(P) } #Add more code here: Mybwplot <- function(Z, MyVar, TargetVar){ #Multipanel boxplots #Z: data set #MyVar: character string #TargetVar: variable for the x-axis..must be a factor AllY <- as.vector(as.matrix(Z[,MyVar])) AllX <- rep(Z[,TargetVar], length(MyVar)) ID <- rep(MyVar, each = nrow(Z)) P <- bwplot(AllY ~ factor(AllX) | ID, horizontal = FALSE, ylab = "", xlab = "", scales = list(alternating = T,cex.lab = 1.5, x = list(relation = "same",rot =90, abbreviate = TRUE, cex = 1.5), y = list(relation = "free", draw = FALSE)), strip = strip.custom(bg = 'white', par.strip.text = list(cex = 1.2)), cex = .5, par.settings = list( box.rectangle = list(col = 1), box.umbrella = list(col = 1), plot.symbol = list(cex = .5, col = 1))) print(P) } ####################################################### MyxyplotBin <- function(Z, MyV, NameY1) { AllX <- as.vector(as.matrix(Z[,MyV])) AllY <- rep(Z[,NameY1] , length(MyV)) AllID <- rep(MyV, each = nrow(Z)) library(mgcv) library(lattice) P <- xyplot(AllY ~ AllX | factor(AllID), col = 1, strip = function(bg='white', ...) strip.default(bg='white', ...), scales = list(alternating = T, x = list(relation = "free"), y = list(relation = "same")), xlab = "Covariate", ylab = "Probability of presence", panel=function(x,y){ panel.grid(h=-1, v= 2) panel.points(x,y,col=1) tmp<-gam(y~s(x, k = 4), family = binomial) MyData <- data.frame(x = seq(min(x), max(x), length = 25)) p1 <- predict(tmp, newdata = MyData, type ="response") panel.lines(MyData\$x,p1, col = 1, lwd = 3) }) print(P) } ####################################################### ####################################################### Myxyplot <- function(Z, MyV, NameY1, MyXlab = "", MyYlab="") { AllX <- as.vector(as.matrix(Z[,MyV])) AllY <- rep(Z[,NameY1] , length(MyV)) AllID <- rep(MyV, each = nrow(Z)) library(mgcv) library(lattice) P <- xyplot(AllY ~ AllX|factor(AllID), col = 1, xlab = list(MyXlab, cex = 1.5), #ylab = list("Response variable", cex = 1.5), #ylab = list("Pearson residuals", cex = 1.5), ylab = list(MyYlab, cex = 1.5), #layout = c(2,2), #Modify strip = function(bg='white', ...) strip.default(bg='white', ...), scales = list(alternating = T, x = list(relation = "free"), y = list(relation = "same")), panel=function(x, y){ panel.grid(h=-1, v= 2) panel.points(x, y, col = 1) panel.loess(x, y, span = 0.8,col = 1, lwd = 2) }) print(P) } ####################################################### MyxyplotPolygon <- function(Z, MyV, NameY1) { AllX <- as.vector(as.matrix(Z[,MyV])) AllY <- rep(Z[,NameY1] , length(MyV)) AllID <- rep(MyV, each = nrow(Z)) library(mgcv) library(lattice) Z <- xyplot(AllY ~ AllX|factor(AllID), col = 1, xlab = list(label = "Explanatory variables", cex = 1.5), ylab = "", strip = function(bg='white',cex.lab = 1.5,...) strip.default(bg='white', ...), scales = list(alternating = T, x = list(relation = "free"), y = list(relation = "same")), panel=function(x, y){ t1 <- gam(y~s(x)) MD1 <- data.frame(x=seq(from = min(x, na.rm = TRUE), to = max(x, na.rm = TRUE), length = 100)) P1 <- predict(t1, se.fit = TRUE) I1 <- order(x) xs <- sort(x) panel.lines(xs, P1\$fit[I1], col = 1) panel.polygon(c(xs, rev(xs)), c(P1\$fit[I1]-2*P1\$se.fit[I1], rev(P1\$fit[I1]+2*P1\$se.fit[I1])), col = gray(0.7), density = 10 ) panel.grid(h=-1, v= 2) panel.abline(0,0) panel.points(x, y, col = 1) }) #Because the xyplot is inside a function you need to print #construction below print(Z) } ################################################ #Mypairs #Make fancy pair plots Mypairs <- function(Z) { MyVarx <- colnames(Z) pairs(Z, labels = MyVarx, cex.labels = 2, lower.panel = function(x, y, digits=2, prefix="", cex.cor = 7) { panel.cor(x, y, digits, prefix, cex.cor)}, upper.panel = function(x, y) points(x, y, pch = 16, cex = 0.8, col = gray(0.1))) #print(P) } ########################################################### #Show double zeros in a row-by-column matrix #To run: ShowZeros(Species) #Better use short names for the row labels ShowZeros <- function(XX) { #To run this function type: # ShowZeros(Species) #Load packages library(ggplot2) library(reshape2) library(zoo) N <- ncol(XX) X <- matrix(nrow = N, ncol = N) for (i in 1:N) { for (j in i:N){ X[i,j] <- sum(XX[,i] == 0 & XX[,j] == 0, na.rm = TRUE) X[j,i] <- X[i,j] } } Zeros <- X / nrow(XX) colnames(Zeros) <- colnames(XX) rownames(Zeros) <- colnames(XX) #Taken from: http://www.r-bloggers.com/controlling-heatmap-colors-with-ggplot2/ #Set color representation for specific values of the data distribution BreakPoints <- c(0, 0.25, 0.5, 0.75, 1) ColourQuantiles <- quantile(Zeros, probs = BreakPoints) ## use http://colorbrewer2.org/ to find optimal divergent color palette (or set own) color_palette <- colorRampPalette(c("#edf8b1", "#7fcdbb", "#2c7fb8"))(length(ColourQuantiles) - 1) #Or change to: #color_palette[4] <- "red" #color_palette[3] <- "orange" #color_palette[2] <- "yellow" #color_palette[1] <- "white" # prepare label text (use two adjacent values for range text) label_text <- rollapply(round(ColourQuantiles, 2), width = 2, by = 1, FUN = function(i) paste(i, collapse = " - ")) # discretize matrix; this is the most important step, # where for each value we find category of predefined # ranges (modify probs argument of quantile to detail the colors) mod_mat <- matrix(findInterval(Zeros, ColourQuantiles, all.inside = TRUE), nrow = nrow(Zeros)) #Prepare the graph Zeros.m <- melt(Zeros) Zeros.m\$NewValue <- as.vector(mod_mat) p <- ggplot(Zeros.m, aes(x = Var1, y = Var2, fill = factor(NewValue))) p <- p + geom_tile(color = "black") p <- p + scale_fill_manual(values = color_palette, name = "", labels = label_text) p <- p + scale_x_discrete(expand = c(0, 0)) p <- p + scale_y_discrete(expand = c(0, 0)) p <- p + theme(axis.text.x = element_text(angle=45, hjust = 1, size = 5)) p <- p + xlab("") + ylab("") p <- p + labs(title = "Percentage zeros in common") print(p) } ################################################################################ MyDotplot.ggp2 <- function(Z, varx) { library(ggplot2) K <- length(varx) MyData <- data.frame(Y = rep(1:nrow(Z), K), X = as.vector(as.matrix(Z[, MyVar])), Var = rep(MyVar, each = nrow(Z))) p <- ggplot(MyData, aes(y = Y, x = X)) p <- p + geom_point() + ylab("Order of the data") + xlab("Range of the data") p <- p + theme(text = element_text(size=15)) p <- p + facet_wrap(~ Var, scales = "free_x") print(p) } Myfapply <- function(Zfactor, INDEX) { INDEX <- factor(INDEX) AllLevels <- levels(INDEX) MyNewFactor <- NULL for (i in AllLevels) { ThisLevel <- as.character(Zfactor[INDEX == i] [1]) MyNewFactor <- c(MyNewFactor, ThisLevel) } MyNewFactor <- factor(MyNewFactor) MyNewFactor }