smithdanielle

occlusion2.data$qqplot.comparison.resid <- ddply(.data = occlusion2.data$qqplot.comparison, .variables = .(condition, transformed), function(dat){
  q <- qqnorm(dat$threshold, plot = FALSE)
  dat$xq <- q$x
  dat
}
)

occlusion2.qqplot <- ggplot(data = occlusion2.data$qqplot.comparison.resid, aes(x = xq, y = threshold)) +
  geom_smooth(method = "lm", se = FALSE, size = 1) +
  geom_point() +

smithdanielle

require(plyr)
require(dplyr)

data.outliersRemoved<-ddply(data, .(withinIV1, withinIV2), function(d){
  limits.outliers = median(d$DV) + 2.5*c(-1, 1)*mad(d$DV))
  d$DV[which(((d$DV - limits.outliers[1])*(limits.outliers[2] - d$DV)) <= 0)]<-NA
  return(d)
})

smithdanielle

ggplot(NULL, aes(x = x, y = NULL))+
  geom_point(data = data, 
             aes(y = y, fill = z, color = z),
             position = position_jitterdodge(jitter.width = .2, jitter.height = .2, dodge.width = .9),
             alpha = .15, size = 3)+
  geom_crossbar(data = data.frame(summary(lsmeans(mixed.model$full.model, ~ z|x)),
                aes(y = lsmean, ymax = upper.CL, ymin = lower.CL, color = view),
                size = 1, position = "dodge", fatten = 2)

smithdanielle

ssw2014.data$behavioural$resid.log.crossedTNO<-residuals(lm(log.crossedTNO~log.uncrossedTNO, data = ssw2014.data$behavioural))
ssw2014.data$behavioural$resid.log.uncrossedTNO<-residuals(lm(log.uncrossedTNO~log.crossedTNO, data = ssw2014.data$behavioural))
ssw2014.data$behavioural$c.resid.log.crossedTNO<-scale(ssw2014.data$behavioural$resid.log.crossedTNO, center = T, scale = F)
ssw2014.data$behavioural$c.resid.log.uncrossedTNO<-scale(ssw2014.data$behavioural$resid.log.uncrossedTNO, center = T, scale = F)
ssw2014.data$behavioural$principal.component.TNO<-prcomp(ssw2014.data$behavioural[,c("log.crossedTNO", "log.uncrossedTNO")], center = T, scale. = T)$x[,"PC1"]
ssw2014.data$behavioural$c.principal.component.TNO<-scale(ssw2014.data$behavioural$principal.component.TNO, center = T, scale = F)

ssw2014.mixed.all.residUncrossed<-mixed(formula = z.score ~ view*activity*c.log.crossedTNO*c.age + view*activity*c.resid.log.uncrossedTNO*c.age + (1+activity+view|ID), data = ssw2014.data$behavioural)
ssw2014.mixed.all.resi

smithdanielle

> sessionInfo()
R version 3.2.2 (2015-08-14)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.10.5 (Yosemite)

locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

smithdanielle

lm.beta.lmer <- function(mod) {
    b <- fixef(mod)[-1]
    sd.x <- apply(getME(mod,"X")[,-1],2,sd)
    sd.y <- sd(getME(mod,"y"))
    b*sd.x/sd.y
}

smithdanielle

R version 3.1.0 (2014-04-10)
Platform: x86_64-apple-darwin13.1.0 (64-bit)

locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages:
[1] splines   grid      stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:

smithdanielle

runNum = input('run number: ');

% Load rocket image
endRocket.load = imread('rocket2.png');
endRocket.texture = Screen('MakeTexture', windowPtr, endRocket.load);
% Get the size of the image
[s1, s2, s3] = size(endRocket.load);
% Get the aspect ratio of the image. We need this to maintain the aspect
% ratio of the image when we draw it different sizes. Otherwise, if we
% don't match the aspect ratio the image will appear warped / stretched

smithdanielle

close all
clear all;

screenParams.Res = [1024 768];
visAngPxl = 0.0018; % visual angle subtended by a single pixel

% Find center of screen:
xcen = screenParams.Res(1)/2;
ycen = screenParams.Res(2)/2;

smithdanielle

% gaborDotsMinimal.m
% An example of how to draw dots using PTB3's `CreateProceduralGabor`

sca;
try
% PTB-3 correctly installed and functional? Abort otherwise.
AssertOpenGL;

% Select screen with maximum id for output window:
screenid = max(Screen('Screens'));