These are some photos comparing different macOS retina scaling options. All use a non-vector test pattern displayed at 100% (AAPM TG18). All photos are taken at roughly the same distance, using the same crop. Forgive the slight hand-held motion blur, and focus on these variables:
| # $Id: textplot.R 1213 2007-11-01 20:20:10Z warnes $ | |
| textplot <- function(object, halign="center", valign="center", cex, ... ) | |
| UseMethod('textplot') | |
| textplot.default <- function(object, | |
| halign=c("center","left","right"), | |
| valign=c("center","top","bottom"), | |
| cex, ... ) |
| Pf1 Pf2 Pf3 Pf4 | |
| 0.4 0.3 0.1 0.6 | |
| 0.1 0.7 0.7 1 | |
| 0.4 0.9 0.5 0.7 | |
| 0.3 0.4 0.4 0.7 | |
| 1 0.8 0.4 0.2 | |
| 0.4 0 0.3 | |
| 0.4 0.8 0.5 | |
| 0.1 0.2 | |
| 0.2 0.2 |
| original = expand.grid(ID1=c('a', 'b', 'c'), ID2 = c('red', 'green', 'blue')) | |
| original$value = 0 | |
| replacement = data.frame(ID1='b', ID2='red', value=1) | |
| rownames(original) = paste(original$ID1, original$ID2) | |
| rownames(replacement) = paste(replacement$ID1, replacement$ID2) | |
| original[rownames(replacement), ] = replacement |
| require(plyr) | |
| set.seed(12345) | |
| n = 10 | |
| n.sum = 2 | |
| # Simulate data | |
| a = sample.int(10, n, replace=T) | |
| df = data.frame(n=1:n, a) |
| # Download example data from: https://github.com/johncolby/SVM-RFE/zipball/master | |
| setwd('/path/to/SVM-RFE/') # Change this to your setup | |
| load('demo/input.Rdata') | |
| data = input[, 2:3] | |
| input = input[, -(1:3)] | |
| # Function to residualize a vector x for the covariates in data | |
| residualize <- function(x, fun=x~., data) { |
| -1.22920245520088 1.26297072626596 1.83337525320795 | |
| 1.20936180458861 0.611215979963239 -1.30133702133403 | |
| 0.522987838117479 -0.451731971345027 -0.58388185591968 | |
| 0.101340272969554 -0.435726497850704 0.569398963635685 | |
| 0.426031516466784 0.151080475310323 0.956491447694583 | |
| -0.252059713648723 -0.175471660877733 -0.980883888395024 | |
| 0.662052189667485 1.33212600996006 -1.47268894052709 | |
| 1.48817331808508 -0.0473711069814109 -0.492789669961388 | |
| -0.503441036994115 -0.294926138113795 0.366179342735957 | |
| -2.12920560829524 1.02332212331524 -1.58266122316052 |
| library(plyr) | |
| data = arrange(data.frame(ID=1:10, FA=runif(50, 0.2, 0.8)), ID) | |
| ddply(data, 'ID', mean) # using plyr library | |
| aggregate(data, list(ID=data$ID), mean) # using base |
| library(plyr) | |
| library(ggplot2) | |
| # Simulate data | |
| numSubs = 40 | |
| x = runif(numSubs, 0, 30) | |
| x = c(x, x + abs(rnorm(numSubs, 2))) | |
| y = 2 + x - 0.02*x^2 + rnorm(2*numSubs, 0, 1) |
| # Function to plot color bar | |
| color.bar <- function(lut, min, max=-min, nticks=11, ticks=seq(min, max, len=nticks), title='') { | |
| scale = (length(lut)-1)/(max-min) | |
| dev.new(width=1.75, height=5) | |
| plot(c(0,10), c(min,max), type='n', bty='n', xaxt='n', xlab='', yaxt='n', ylab='', main=title) | |
| axis(2, ticks, las=1) | |
| for (i in 1:(length(lut)-1)) { | |
| y = (i-1)/scale + min | |
| rect(0,y,10,y+1/scale, col=lut[i], border=NA) |