add leading zero

R/Densi.R

@@ -129,7 +129,7 @@ add_tiplabels <- function(xy, tip.label, direction, adj, font, srt = 0, cex = 1,
 #' @export
 densiTree <- function(x, type = "phylogram", ..., alpha = 1 / length(x),
                       consensus = NULL, direction = "rightwards", optim = FALSE,
-                      scaleX = FALSE, col = 1, width = 1, lty = 1, cex = .8,
+                      scaleX = FALSE, col = 1, width = 1, lty = 1, cex = 0.8,
                       font = 3, tip.color = 1, adj = 0, srt = 0,
                       underscore = FALSE, label.offset = 0, scale.bar = TRUE,
                       jitter = list(amount = 0, random = TRUE), tip.dates=NULL,
@@ -140,7 +140,7 @@ densiTree <- function(x, type = "phylogram", ..., alpha = 1 / length(x),
     consensus$edge.length <- rep(1.0, nrow(consensus$edge))
   }
   if (is.null(consensus)) {
-    consensus <- tryCatch(consensus(x, p = .5),
+    consensus <- tryCatch(consensus(x, p = 0.5),
                           error = function(e) unroot(midpoint(superTree(x))))
   }
   if (inherits(consensus, "multiPhylo")) consensus <- consensus[[1]]

R/clanistic.R

@@ -353,7 +353,7 @@ getDiv <- function(tree, x, native=NULL){
     clans <- getClans(tree)
     labels <- tree$tip.label
     x <- subset(x, labels)
-    LHG <- E_Intruder_2(clans, subset(x,,1))
+    LHG <- E_Intruder_2(clans, subset(x,select=1))
     if(!is.null(native)){
 	    ll <- match(native, attr(x, "allLevels"))
 	    ind <- (as.numeric(x) %in% ll)
@@ -367,7 +367,7 @@ getDiv <- function(tree, x, native=NULL){
     }
     else tree2 <- NULL
 	list(c(shannon(rowSums(LHG)),
-    summary(factor(attr(x, "allLevels"))[as.numeric(subset(x,,1))]),
+    summary(factor(attr(x, "allLevels"))[as.numeric(subset(x,select=1))]),
           parsimony(tree, x)), tree2 )
 }
 

R/discrete.gamma.R

@@ -162,7 +162,7 @@ plot_gamma_plus_inv <- function(shape=1, inv=0, k=4, discrete=TRUE, cdf=TRUE,
       if(!append) plot(g, w, xlim = xlim, #c(-.5, 1.25 * max(g)),
                        ylim=c(0, 1),
                        type="n", xlab=xlab, ylab=ylab, ...)
-      plot(function(x)cdf_fun(x, shape, inv),  xlim[1], -.001, add=TRUE, ...)
+      plot(function(x)cdf_fun(x, shape, inv),  xlim[1], -0.001, add=TRUE, ...)
       plot(function(x)cdf_fun(x, shape, inv),  0, xlim[2], add=TRUE, ...)
       points(0, inv, ...)
       if(verticals) segments(0, 0, 0, inv, ...)
@@ -231,7 +231,7 @@ plotRates <- function(obj, cdf.color="blue", main="cdf", ...){
   plot(ecdf_pscores, verticals = TRUE, do.points=FALSE, main=main, ...)
   rug(jitter(pscores)) # rug(sort(unique(pscores)))
   el <- obj$tree$edge.length
-  xlim <- c(-.25, 1.1 * max(pscores))
+  xlim <- c(-0.25, 1.1 * max(pscores))
   plot_gamma_plus_inv(k=obj$k, shape=obj$shape, inv=obj$inv, append=TRUE,
                       xlim = xlim,
                       edge.length=sum(el), verticals=TRUE, col=cdf.color,

R/lento.R

@@ -32,7 +32,7 @@
 #' @export lento
 lento <- function(obj, xlim = NULL, ylim = NULL, main = "Lento plot",
                   sub = NULL, xlab = NULL, ylab = NULL, bipart = TRUE,
-                  trivial = FALSE, col = rgb(0, 0, 0, .5), ...) {
+                  trivial = FALSE, col = rgb(0, 0, 0, 0.5), ...) {
   if (inherits(obj, "phylo")) {
     if (inherits(obj, "phylo", TRUE) == 1) obj <- as.splits(obj)[obj$edge[, 2]]
     obj <- as.splits(obj)
@@ -79,11 +79,11 @@ lento <- function(obj, xlim = NULL, ylim = NULL, main = "Lento plot",
   at <- min(ylim) + (1:l) * aty
   if (bipart) {
     Y <- rep(at, n)
-    X <- rep( (1:n) - .5, each = l)
+    X <- rep( (1:n) - 0.5, each = l)
     Circles <- matrix(1, l, n)
     for (i in 1:n) Circles[obj[[ord[i]]], i] <- 19
     col <- rep(col, each = l)
-    text(x = n + .1, y = at, labels, pos = 4, ...)
+    text(x = n + 0.1, y = at, labels, pos = 4, ...)
     points(X, Y, pch = as.numeric(Circles), col = col, ...)
   }
   invisible(list(support = cbind(support, conflict), splits = obj[ord]))

R/phylo.R

@@ -132,7 +132,7 @@ optimGamma <- function(tree, data, shape = 1, k = 4, ...) {
   fn <- function(shape, tree, data, k, ...) pml.fit(tree, data, shape = shape,
       k = k, ...)
   res <- optimize(f = fn, interval = c(0.1, 100), lower = 0.1, upper = 100,
-    maximum = TRUE,  tol = .001, tree = tree, data = data, k = k, ...)
+    maximum = TRUE,  tol = 0.001, tree = tree, data = data, k = k, ...)
   res
 }
 
@@ -145,12 +145,12 @@ optimInv <- function(tree, data, inv = 0.01, INV, ...) {
   fn <- function(inv, tree, data, ...) pml.fit(tree, data, inv = inv, INV = INV,
       ll.0 = NULL, ...)
   res <- optimize(f = fn, interval = c(0, max_inv), lower = 0, upper = max_inv,
-    maximum = TRUE, tol = .0001, tree = tree, data = data, ...)
+    maximum = TRUE, tol = 0.0001, tree = tree, data = data, ...)
   res
 }
 
 
-optimFreeRate <- function(tree, data, g = c(.25, .75, 1, 2), k=4, w=w, ...) {
+optimFreeRate <- function(tree, data, g = c(0.25, 0.75, 1, 2), k=4, w=w, ...) {
   g0 <- c(g[1], diff(g))
   g0[g0 < 1e-8] <- 1e-8 # required by constrOptim
   R <- matrix(0, k, k)
@@ -172,7 +172,7 @@ optimFreeRate <- function(tree, data, g = c(.25, .75, 1, 2), k=4, w=w, ...) {
 }
 
 
-optimWs <- function(tree, data, w = c(.25, .25, .25, .25), g=g, ...) {
+optimWs <- function(tree, data, w = c(0.25, 0.25, 0.25, 0.25), g=g, ...) {
   k <- length(w)
   nenner <- 1 / w[1]
   eta <- log(w * nenner)
@@ -207,7 +207,7 @@ optimRate <- function(tree, data, rate = 1, ...) {
 }
 
 
-optimBf <- function(tree, data, bf = c(.25, .25, .25, .25), trace = 0, ...) {
+optimBf <- function(tree, data, bf = c(0.25, 0.25, 0.25, 0.25), trace = 0, ...) {
   l <- length(bf)
   nenner <- 1 / bf[l]
   lbf <- log(bf * nenner)
@@ -228,7 +228,7 @@ optimBf <- function(tree, data, bf = c(.25, .25, .25, .25), trace = 0, ...) {
 
 
 # ML F3x4 model
-optimF3x4 <- function(tree, data, bf_codon = matrix(.25, 4, 3), trace = 0, ...){
+optimF3x4 <- function(tree, data, bf_codon = matrix(0.25, 4, 3), trace = 0, ...){
   l <- nrow(bf_codon)
   nenner <- 1 / bf_codon[l, ]
   lbf <- log(bf_codon * rep(nenner, each = 4))
@@ -1489,11 +1489,11 @@ optimRooted <- function(tree, data, bf, g, w, eig, ll.0,
       tree$edge.length <- tree$edge.length * t
       pml.fit4(tree, data, ...)
     }
-    min_scaler <- max(.25, tau / min(tree$edge.length) )
+    min_scaler <- max(0.25, tau / min(tree$edge.length) )
     min_scaler <- min(min_scaler, 1)
 #    if(min_scaler>1) browser()
     optimize(f = fn, interval = c(min_scaler, 4), tree = tree, data = data, ...,
-      maximum = TRUE, tol = .00001)
+      maximum = TRUE, tol = 0.00001)
   }
   # ensure that each edge is at least tau long
   # tips have the same height
@@ -1718,7 +1718,7 @@ rooted.nni <- function(tree, data, eig, w, g, bf, rate, ll.0, INV, RELL=NULL,
   parent <- tree$edge[, 1]
   ll <- pml.fit4(tree, data, bf = bf, eig = eig, ll.0 = ll.0, w = w, g = g, ...)
   llstart <- ll
-  eps <- .00001
+  eps <- 0.00001
   iter <- 1
   EL <- numeric(max(tree$edge))
   EL[tree$edge[, 2]] <- tree$edge.length
@@ -1810,15 +1810,15 @@ rooted.nni <- function(tree, data, eig, w, g, bf, rate, ll.0, INV, RELL=NULL,
           tree3$edge <- X3
           edge1 <- X1[, 2]
           edge1[4] <- dad
-          res1 <- optim(par = c(.1, .1), optRootU, gr = NULL, tree = tree1,
+          res1 <- optim(par = c(0.1, 0.1), optRootU, gr = NULL, tree = tree1,
             data = data, nh = nh[X1[, 2]], g = g, w = w, eig = eig, bf = bf,
             ll.0 = ll.0, ..., method = "L-BFGS-B",
             lower = 1e-8, upper = 5, control = list(fnscale = -1))
-          res2 <- optim(par = c(.1, .1), optRootU, gr = NULL, tree = tree2,
+          res2 <- optim(par = c(0.1, 0.1), optRootU, gr = NULL, tree = tree2,
             data = data, nh = nh[X2[, 2]], g = g, w = w, eig = eig, bf = bf,
             ll.0 = ll.0, ..., method = "L-BFGS-B",
             lower = 1e-8, upper = 5, control = list(fnscale = -1))
-          res3 <- optim(par = c(.1, .1), optRootU, gr = NULL, tree = tree3,
+          res3 <- optim(par = c(0.1, 0.1), optRootU, gr = NULL, tree = tree3,
             data = data,  nh = nh[X3[, 2]], g = g, w = w, eig = eig, bf = bf,
             ll.0 = ll.0, ..., method = "L-BFGS-B",
             lower = 1e-8, upper = 5, control = list(fnscale = -1))
@@ -1868,7 +1868,7 @@ rooted.nni <- function(tree, data, eig, w, g, bf, rate, ll.0, INV, RELL=NULL,
           tree1$edge <- X1
           tree2$edge <- X2
           tree3$edge <- X3
-          tt <- c(.3, .5)
+          tt <- c(0.3, 0.5)
 
           res1 <- optim(par = tt, optEdgeU, gr = NULL, tree = tree1, data,
             nh = nh[X1[, 2]], g = g, w = w, eig = eig, bf = bf, ll.0 = ll.0,
@@ -2701,7 +2701,7 @@ optimQuartet <- function(tree, data, eig, w, g, bf, rate, ll.0, nTips,
 
 
 # allow weight Matrix or return site likelihood
-pml.quartet <- function(tree, data, bf = rep(.25, 4), k = 1, rate = 1, g, w,
+pml.quartet <- function(tree, data, bf = rep(0.25, 4), k = 1, rate = 1, g, w,
                         eig, ll.0 = NULL, #ind.ll0 = NULL,
                         inv=0, llMix = NULL,
                         wMix = 0, nTips, weight, nr, nc, contrast, nco, ...,

R/plotAnc.R

@@ -77,7 +77,7 @@ getTransition <- function(scheme, levels){
 #' @rdname plot.ancestral
 #' @export
 plotAnc <- function(x, i = 1, type="phylogram", ..., col = NULL,
-                    cex.pie = .5, pos = "bottomright", scheme=NULL) {
+                    cex.pie = 0.5, pos = "bottomright", scheme=NULL) {
   stopifnot(inherits(x, "ancestral"))
   type <- match.arg(type, c("phylogram", "cladogram", "fan", "unrooted",
                             "radial", "tidy"))
@@ -153,7 +153,7 @@ my_ggseqlogo <-function (data, facet = "wrap", scales = "free_x", ncol = NULL,
           nrow = NULL, start=NULL, end=NULL, ...)
 {
   x <- ggseqlogo::geom_logo(data = data, ...)
-  x[[2]] <- ggplot2::scale_x_continuous(limits = c(start-0.5, end+.5) ,
+  x[[2]] <- ggplot2::scale_x_continuous(limits = c(start-0.5, end+0.5) ,
                                breaks=pretty(seq(start, end)))
   p <- ggplot2::ggplot() + x + ggseqlogo::theme_logo()
   if (!"list" %in% class(data)) return(p)

R/pmlMix.R

@@ -123,7 +123,7 @@ optimAllRate <- function(object, rate = 1, omega, ...) {
 
 
 
-optimMixBf <- function(object, bf = c(.25, .25, .25, .25), omega, ...) {
+optimMixBf <- function(object, bf = c(0.25, 0.25, 0.25, 0.25), omega, ...) {
   l <- length(bf)
   nenner <- 1 / bf[l]
   lbf <- log(bf * nenner)
@@ -157,7 +157,7 @@ optimMixInv <- function(object, inv = 0.01, omega, ...) {
     res
   }
   res <- optimize(f = fn, interval = c(0, 1), lower = 0, upper = 1,
-                  maximum = TRUE, tol = .0001, object, omega = omega, ...)
+                  maximum = TRUE, tol = 0.0001, object, omega = omega, ...)
   res[[1]]
 }
 
@@ -231,7 +231,7 @@ optimMixEdge <- function(object, omega, trace = 1, ...) {
   iter <- 0
   scalep <- 1
   if (trace > 0) cat(ll0)
-  while (abs(eps) > .0001 & iter < 10) {
+  while (abs(eps) > 0.0001 & iter < 10) {
     dl <- matrix(0, p, q)
     for (i in 1:n) dl <- dl + dl(object[[i]], TRUE) * omega[i]
     dl <- dl / lv1