This document provides the analysis of the experiment conducted at UCP/CITAR comparing the movement of three devices - Mouse, Touchpad, and Leap Motion (via Touchless app) - for pointing tasks.
We use Maczenzie's accuracy metrics to compare the devices.
PRODUCE_BULK_CHARTS = FALSE
PRODUCE_BULK_HIST_CHARTS = TRUE
PRODUCE_BULK_CHARTS_DEVICE_PLUS_USERID = FALSE
#install.packages(c("ggplot2", "doBy", "psych", "car", "xtable"))
require(ggplot2)
require(doBy)
require(psych)
require(car)
require(xtable)
source("functions.R")
# load metrics data
data <- read.csv(file="data/measures-test.txt", head=TRUE, sep="")
# Users 0 and 5 did not complete the experiment
data <- data[data$UserId != 0 & data$UserId != 5,]
# convert the column to factor and name the levels
data$Device <- as.factor(data$Device)
# Set the levels
# LeapMotion=c(0), Mouse=c(1), Touchpad=c(2), LeapMotionTouchless=c(4)
# LeapMotion does not exist in the dataset from the UCP experiment
levels(data$Device) <- list( Mouse=c(1), Touchpad=c(2), LeapMotionTouchless=c(4))
# convert the block column to factor
data$Block <- factor(data$Block)
# In this analysis we use ErrorRateBinary, so we drop the ErrorRate column and change the column name ErrorRateBinary to ErrorRate
drops <- c("ErrorRate")
data<-data[,!(names(data) %in% drops)]
names(data)[names(data) == "ErrorRateBinary"] <- "ErrorRate"
# Compute the aggregate means for each variable.
# We agregate all values for the various circles
IVars <- c("Device", "Block", "Sequence", "UserId")
DVars <- c("Throughput", "ErrorRate", "MovementTime", "TRE", "TAC", "ODC", "MDC", "MV", "ME", "MO")
aggData <-aggregate(data[ ,DVars], data[ ,IVars], mean)
aggData$Block <- factor(aggData$Block)
describe.MT.aggData <- describeBy(aggData$MovementTime, aggData[,c("Block")])
describe.MT.aggData <-do.call("rbind", describe.MT.aggData)
describe.MT.aggData <- cbind(Block=rownames(describe.MT.aggData), describe.MT.aggData)
describe.MT.aggData$Block <- factor(describe.MT.aggData$Block)
p <- ggplot(describe.MT.aggData, aes(x=Block, y=mean )) +
geom_point(stat="identity") +
geom_line(aes(group=vars)) +
ylab("Movement time (seconds) with 95% conf int") +
#xlab("Sequence") +
#ggtitle("Throughput") +
geom_errorbar(aes(ymin=mean-1.96*se, ymax=mean+1.96*se), colour="Black",
width=.2 # Width of the error bars
) +
theme(legend.position="none") +
theme()
p
p <- ggplot(aggData, aes_string(x="Block", y="MovementTime", group="Device", colour="Device" )) +
stat_summary(fun.y="mean", geom="line") +
stat_summary(fun.y="mean", geom="point", aes(shape=Device)) +
theme(legend.direction = "horizontal", legend.position = "top") +
#ylab("Percentage (%)") +
#xlab("Sequence") +
#ggtitle(var) +
scale_fill_brewer(palette="Set1") +
scale_colour_brewer(palette="Set1") +
theme() #noop
p
ggsave(file = paste("charts/","all-movementtime-block-lineplot.pdf", sep=""),
width=13/2.54, height=7/2.54, dpi=100)
describe.T.aggData <- describeBy(aggData$Throughput, aggData[,c("Block")])
describe.T.aggData <-do.call("rbind", describe.T.aggData)
describe.T.aggData <- cbind(Block=rownames(describe.T.aggData), describe.T.aggData)
describe.T.aggData$Block <- factor(describe.T.aggData$Block)
p <- ggplot(describe.T.aggData, aes(x=Block, y=mean )) +
geom_point(stat="identity") +
geom_line(aes(group=vars)) +
ylab("Throughput (bps) with 95% conf int") +
#xlab("Sequence") +
#ggtitle("Throughput") +
geom_errorbar(aes(ymin=mean-1.96*se, ymax=mean+1.96*se), colour="Black",
width=.2 # Width of the error bars
) +
theme(legend.position="none") +
theme()
p
p <- ggplot(aggData, aes_string(x="Block", y="Throughput", group="Device", colour="Device" )) +
stat_summary(fun.y="mean", geom="line") +
stat_summary(fun.y="mean", geom="point", aes(shape=Device)) +
theme(legend.direction = "horizontal", legend.position = "top") +
#ylab("Percentage (%)") +
#xlab("Sequence") +
#ggtitle(var) +
theme() #noop
p
ggsave(file = paste("charts/","all-throughput-block-lineplot.pdf", sep=""),
width=20/2.54, height=16/2.54, dpi=100)
p <- ggplot(aggData, aes_string(x="Block", y="ErrorRate", group="Device", colour="Device" )) +
stat_summary(fun.y="mean", geom="line") +
stat_summary(fun.y="mean", geom="point", aes(shape=Device)) +
theme(legend.direction = "horizontal", legend.position = "top") +
#ylab("Percentage (%)") +
#xlab("Sequence") +
#ggtitle(var) +
theme() #noop
p
ggsave(file = paste("charts/","all-throughput-block-lineplot.pdf", sep=""),
width=20/2.54, height=16/2.54, dpi=100)
To estimate the learning effect, we ran pairwise t-tests for average throughput per block (considering all devices) with a significance level of 5%:
#describeBy(data$MovementTime, data$Block)
# pairwise t-tests to determine learning effect blocks
p.t.test <- pairwise.t.test(aggData$Throughput, aggData$Block, paired=T, p.adjust.method="none")
diag(p.t.test$p.value)
## [1] 0.0001677 0.0112456 0.0215020 0.4091830 0.2499649 0.0011833 0.7604690
diag(p.t.test$p.value) < 0.05
## [1] TRUE TRUE TRUE FALSE FALSE TRUE FALSE
# We will consider only blocks 4 to 8 in the rest of the analysis
filenameprefix <- "blocks4-8"
aggData.noLearn <- aggData[as.numeric(aggData$Block) > 3,]
# Drop unused block levels
aggData.noLearn$Block <- factor(aggData.noLearn$Block)
The results indicate a clear learning effect in blocks 1 to 3, but also indicate a significant different between blocks 6 and 7, suggesting that participants were still learning after block 6. However, in our following analysis we discard only blocks 1 to 3, since those represent the most significant learning effect.
options(width = 200)
# describe the main variables and store in file
s<-describeBy(aggData.noLearn[, DVars], aggData.noLearn$Device, mat=FALSE, digits=2)
s <- do.call("rbind", s)
print(xtable(s), type = "html")
| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mouse.Throughput | 1 | 250.00 | 4.85 | 0.43 | 4.81 | 4.82 | 0.33 | 3.64 | 6.35 | 2.70 | 0.72 | 1.35 | 0.03 |
| Mouse.ErrorRate | 2 | 250.00 | 0.02 | 0.04 | 0.00 | 0.01 | 0.00 | 0.00 | 0.27 | 0.27 | 2.55 | 8.76 | 0.00 |
| Mouse.MovementTime | 3 | 250.00 | 0.87 | 0.09 | 0.86 | 0.86 | 0.07 | 0.64 | 1.54 | 0.90 | 1.68 | 11.74 | 0.01 |
| Mouse.TRE | 4 | 250.00 | 0.10 | 0.09 | 0.07 | 0.09 | 0.10 | 0.00 | 0.47 | 0.47 | 0.95 | 0.74 | 0.01 |
| Mouse.TAC | 5 | 250.00 | 1.66 | 0.37 | 1.60 | 1.65 | 0.40 | 0.80 | 2.73 | 1.93 | 0.33 | -0.23 | 0.02 |
| Mouse.ODC | 6 | 250.00 | 1.19 | 0.59 | 1.07 | 1.14 | 0.59 | 0.13 | 3.00 | 2.87 | 0.71 | -0.13 | 0.04 |
| Mouse.MDC | 7 | 250.00 | 4.86 | 0.96 | 4.73 | 4.80 | 0.94 | 2.80 | 7.93 | 5.13 | 0.51 | -0.17 | 0.06 |
| Mouse.MV | 8 | 250.00 | 20.52 | 6.88 | 19.00 | 19.98 | 6.90 | 9.33 | 50.07 | 40.74 | 0.85 | 0.87 | 0.44 |
| Mouse.ME | 9 | 250.00 | 18.41 | 5.27 | 18.07 | 18.14 | 5.25 | 7.96 | 38.13 | 30.17 | 0.61 | 0.52 | 0.33 |
| Mouse.MO | 10 | 250.00 | -1.68 | 5.93 | -0.90 | -1.50 | 4.96 | -19.18 | 14.65 | 33.82 | -0.33 | 0.31 | 0.37 |
| Touchpad.Throughput | 1 | 250.00 | 3.21 | 0.42 | 3.24 | 3.20 | 0.46 | 2.24 | 4.28 | 2.04 | 0.16 | -0.51 | 0.03 |
| Touchpad.ErrorRate | 2 | 250.00 | 0.02 | 0.05 | 0.00 | 0.01 | 0.00 | 0.00 | 0.27 | 0.27 | 2.36 | 5.86 | 0.00 |
| Touchpad.MovementTime | 3 | 250.00 | 1.31 | 0.20 | 1.27 | 1.30 | 0.21 | 0.94 | 2.00 | 1.06 | 0.69 | 0.46 | 0.01 |
| Touchpad.TRE | 4 | 250.00 | 0.14 | 0.12 | 0.13 | 0.13 | 0.10 | 0.00 | 0.67 | 0.67 | 1.11 | 1.49 | 0.01 |
| Touchpad.TAC | 5 | 250.00 | 1.26 | 0.32 | 1.23 | 1.25 | 0.35 | 0.47 | 2.27 | 1.80 | 0.31 | -0.03 | 0.02 |
| Touchpad.ODC | 6 | 250.00 | 1.04 | 0.55 | 0.97 | 1.01 | 0.64 | 0.07 | 2.80 | 2.73 | 0.54 | -0.27 | 0.03 |
| Touchpad.MDC | 7 | 250.00 | 4.48 | 1.15 | 4.33 | 4.36 | 0.99 | 2.47 | 8.27 | 5.80 | 0.97 | 1.02 | 0.07 |
| Touchpad.MV | 8 | 250.00 | 20.05 | 6.81 | 20.22 | 19.93 | 6.65 | 6.90 | 41.93 | 35.03 | 0.18 | -0.26 | 0.43 |
| Touchpad.ME | 9 | 250.00 | 16.55 | 5.24 | 16.52 | 16.35 | 4.38 | 6.25 | 34.05 | 27.80 | 0.45 | 0.48 | 0.33 |
| Touchpad.MO | 10 | 250.00 | -1.58 | 6.36 | -1.37 | -1.40 | 6.13 | -23.41 | 17.47 | 40.88 | -0.25 | 0.16 | 0.40 |
| LeapMotionTouchless.Throughput | 1 | 250.00 | 2.16 | 0.39 | 2.19 | 2.17 | 0.49 | 1.18 | 3.22 | 2.04 | -0.04 | -0.77 | 0.02 |
| LeapMotionTouchless.ErrorRate | 2 | 250.00 | 0.07 | 0.08 | 0.07 | 0.06 | 0.10 | 0.00 | 0.40 | 0.40 | 1.21 | 1.51 | 0.00 |
| LeapMotionTouchless.MovementTime | 3 | 250.00 | 2.11 | 0.52 | 2.01 | 2.04 | 0.47 | 1.27 | 4.53 | 3.26 | 1.28 | 2.26 | 0.03 |
| LeapMotionTouchless.TRE | 4 | 250.00 | 0.35 | 0.21 | 0.33 | 0.34 | 0.20 | 0.00 | 1.27 | 1.27 | 0.73 | 0.73 | 0.01 |
| LeapMotionTouchless.TAC | 5 | 250.00 | 2.35 | 0.62 | 2.33 | 2.33 | 0.59 | 1.00 | 4.60 | 3.60 | 0.56 | 0.71 | 0.04 |
| LeapMotionTouchless.ODC | 6 | 250.00 | 4.50 | 2.18 | 4.20 | 4.25 | 1.88 | 0.87 | 12.67 | 11.80 | 1.10 | 1.28 | 0.14 |
| LeapMotionTouchless.MDC | 7 | 250.00 | 8.78 | 2.43 | 8.47 | 8.63 | 2.27 | 4.00 | 16.67 | 12.67 | 0.57 | -0.10 | 0.15 |
| LeapMotionTouchless.MV | 8 | 250.00 | 20.47 | 6.24 | 19.00 | 19.73 | 4.48 | 10.54 | 54.93 | 44.39 | 1.66 | 4.54 | 0.39 |
| LeapMotionTouchless.ME | 9 | 250.00 | 16.27 | 5.33 | 15.20 | 15.44 | 3.56 | 8.57 | 42.57 | 34.01 | 2.13 | 6.29 | 0.34 |
| LeapMotionTouchless.MO | 10 | 250.00 | -1.49 | 4.59 | -1.26 | -1.35 | 3.92 | -33.56 | 10.46 | 44.03 | -1.41 | 8.97 | 0.29 |
s <- cbind(Device=rownames(s), s)
write.table(s, file = paste("tables/", filenameprefix,"-measures-device-describeby.csv", sep=""), sep=",", row.names=FALSE)
remove(s)
# descriptive stats for device*userid
s1<-describeBy(aggData.noLearn[,DVars],
list(aggData.noLearn$Device,aggData.noLearn$UserId), mat=FALSE, digits=2)
s1 <- do.call("rbind", s1)
s1 <- cbind(Device=rownames(s1), s1)
write.table(s1, file = paste("tables/", filenameprefix,"-measures-device+userid-describeby.csv", sep=""), sep=",", row.names=FALSE)
remove(s1)
#s1
s <- summaryBy(ErrorRate+TRE+TAC+MDC+ODC+MV+ME+MO+MovementTime+Throughput~Device,
data=aggData.noLearn,
FUN=c(mean,sd))
# plot the movement offset over the TRE for each device
p <- ggplot(s, aes(x=TRE.mean, y=abs(MO.mean), group=Device, colour=Device )) +
geom_point(size=5, aes(shape=Device)) +
#coord_cartesian(xlim = c(0, 0.4), ylim=c(0, 8)) +
ylab("Movement offset (pixels)") +
xlab("Target Re-Entry") +
theme(legend.position=c(0.55,0.9), legend.direction="horizontal") +
theme()
p
ggsave(file = paste("charts/",filenameprefix,"-MO-TRE.pdf", sep=""), width=14/2.54, height=7/2.54, dpi=100)
# plot the throughput and ErrorRate
describe <- describeBy(aggData.noLearn$Throughput, list(aggData.noLearn$Device))
describe<-do.call("rbind",describe)
describe <- cbind(Device=rownames(describe), describe)
levels(describe$Device) <- list( Mouse=c("Mouse"), Touchpad=c("Touchpad"), LeapMotionTouchless=c("LeapMotionTouchless"))
p1 <- ggplot(describe, aes(x=Device, y=mean, colour=Device, fill=Device )) +
geom_bar(stat="identity") +
ylab("BPS") +
xlab("Throughput") +
#ggtitle("Throughput") +
geom_errorbar(aes(ymin=mean-1.96*se, ymax=mean+1.96*se), colour="Black",
width=.2, # Width of the error bars
position=position_dodge(.9)) +
theme(legend.position="none",
#axis.text.x = element_text(angle = 20, hjust = 1),
axis.text.x=element_blank()) +
scale_fill_brewer(palette="Set1") +
scale_colour_brewer(palette="Set1") +
theme()
#ggsave(file = paste("charts/",filenameprefix,"-Throughput.pdf",sep=""), width=20/2.54, height=16/2.54, dpi=100)
describe <- describeBy(aggData.noLearn$ErrorRate, list(aggData.noLearn$Device))
describe<-do.call("rbind",describe)
describe <- cbind(Device=rownames(describe), describe)
levels(describe$Device) <- list( Mouse=c("Mouse"), Touchpad=c("Touchpad"), LeapMotionTouchless=c("LeapMotionTouchless"))
p2 <- ggplot(describe, aes(x=Device, y=mean*100, colour=Device, fill=Device )) +
geom_bar(stat="identity") +
ylab("Percent.(%)") +
xlab("ErrorRate") +
#ggtitle("ErrorRate") +
geom_errorbar(aes(ymin=100*(mean-1.96*se), ymax=100*(mean+1.96*se)), colour="Black",
width=.2, # Width of the error bars
position=position_dodge(.9)) +
theme(legend.position=c(0,0.9), legend.direction="horizontal",
#axis.text.x = element_text(angle = 20, hjust = 1),
axis.text.x=element_blank()) +
scale_fill_brewer(palette="Set1") +
scale_colour_brewer(palette="Set1") +
theme()
#pdf(file = paste("charts/",filenameprefix,"-Throughput+ErrorRate.pdf",sep=""), width=20/2.54, height=16/2.54)
pdf(file = paste("charts/",filenameprefix,"-Throughput+ErrorRate-small.pdf",sep=""), width=16/2.54, height=7/2.54)
multiplot(p1, p2, cols=2)
dev.off()
## pdf
## 2
p1
p2
describeMetrics <- describeBy(aggData.noLearn[, c("TRE","TAC","MDC","ODC","MV","ME","MO")], list(aggData.noLearn$Device))
a=cbind(describeMetrics$LeapMotionTouchless, metric=rownames(describeMetrics$LeapMotionTouchless))
a=cbind(a, Device="LeapMotionTouchless")
b=cbind(describeMetrics$Mouse, metric=rownames(describeMetrics$Mouse))
b=cbind(b, Device="Mouse")
c=cbind(describeMetrics$Touchpad, metric=rownames(describeMetrics$Touchpad))
c=cbind(c, Device="Touchpad")
describeMetrics = rbind(a, b, c)
describeMetrics$metric <- factor(describeMetrics$metric, levels=c("TRE", "TAC", "MDC", "ODC", "MV", "ME", "MO"))
levels(describeMetrics$Device) <- list( Mouse=c("Mouse"), Touchpad=c("Touchpad"), LeapMotionTouchless=c("LeapMotionTouchless"))
ggplot(describeMetrics, aes(x=Device, y=abs(mean), group=Device, colour=Device, fill=Device)) +
#stat_summary(fun.y="mean", geom="bar") +
geom_bar(stat="identity", width=.5, position = position_dodge(width=0.5)) +
#geom_bar() +
#coord_flip() +
ylab("Mean") +
xlab("") +
geom_errorbar(aes(ymin=abs(mean)-1.96*abs(se), ymax=abs(mean)+1.96*abs(se)), colour="Black",
width=.2, # Width of the error bars
size = .1,
position=position_dodge(.5)) +
facet_wrap( ~ metric,nrow=1, scales="free") +
theme(legend.position=c(.5,-0.1), legend.direction="horizontal",
axis.text.x = element_blank()) +
scale_fill_brewer(palette="Set1") +
scale_colour_brewer(palette="Set1")
ggsave(file = paste("charts/",filenameprefix,"-metrics.pdf", sep=""), width=30/2.54, height=6/2.54, dpi=100)
vars <- c("Throughput", "TRE", "TAC", "ODC", "MDC", "MV", "ME", "MO")
print(xtable(round(cor(aggData.noLearn[, vars]), 2)), type = "html")
| Throughput | TRE | TAC | ODC | MDC | MV | ME | MO | |
|---|---|---|---|---|---|---|---|---|
| Throughput | 1.00 | -0.57 | -0.40 | -0.65 | -0.61 | -0.10 | 0.09 | 0.05 |
| TRE | -0.57 | 1.00 | 0.63 | 0.73 | 0.60 | 0.16 | 0.01 | -0.02 |
| TAC | -0.40 | 0.63 | 1.00 | 0.72 | 0.77 | 0.01 | -0.11 | 0.00 |
| ODC | -0.65 | 0.73 | 0.72 | 1.00 | 0.76 | 0.21 | 0.06 | -0.04 |
| MDC | -0.61 | 0.60 | 0.77 | 0.76 | 1.00 | -0.12 | -0.21 | -0.01 |
| MV | -0.10 | 0.16 | 0.01 | 0.21 | -0.12 | 1.00 | 0.93 | -0.28 |
| ME | 0.09 | 0.01 | -0.11 | 0.06 | -0.21 | 0.93 | 1.00 | -0.28 |
| MO | 0.05 | -0.02 | 0.00 | -0.04 | -0.01 | -0.28 | -0.28 | 1.00 |
Analysis of variance of the throughput for the three devices showed significant differences
#ggplot(aggData.noLearn, aes(x=1:nrow(aggData.noLearn), y=MO, group=Device, colour=Device)) + geom_point()
#ggplot(aggData.noLearn, aes(x=Throughput, y=MO, colour=Device)) + geom_point()
leveneTest(Throughput ~ Device,data=aggData.noLearn, center="mean")
## Levene's Test for Homogeneity of Variance (center = "mean")
## Df F value Pr(>F)
## group 2 0.85 0.43
## 747
leveneTest(ErrorRate ~ Device,data=aggData.noLearn, center="mean")
## Levene's Test for Homogeneity of Variance (center = "mean")
## Df F value Pr(>F)
## group 2 50.4 <2e-16 ***
## 747
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
leveneTest(TAC ~ Device,data=aggData.noLearn, center="mean")
## Levene's Test for Homogeneity of Variance (center = "mean")
## Df F value Pr(>F)
## group 2 48.8 <2e-16 ***
## 747
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
leveneTest(TRE ~ Device,data=aggData.noLearn, center="mean")
## Levene's Test for Homogeneity of Variance (center = "mean")
## Df F value Pr(>F)
## group 2 80.8 <2e-16 ***
## 747
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
leveneTest(MDC ~ Device,data=aggData.noLearn, center="mean")
## Levene's Test for Homogeneity of Variance (center = "mean")
## Df F value Pr(>F)
## group 2 112 <2e-16 ***
## 747
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
leveneTest(ODC ~ Device,data=aggData.noLearn, center="mean")
## Levene's Test for Homogeneity of Variance (center = "mean")
## Df F value Pr(>F)
## group 2 159 <2e-16 ***
## 747
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
leveneTest(ME ~ Device,data=aggData.noLearn, center="mean")
## Levene's Test for Homogeneity of Variance (center = "mean")
## Df F value Pr(>F)
## group 2 1.65 0.19
## 747
leveneTest(MV ~ Device,data=aggData.noLearn, center="mean")
## Levene's Test for Homogeneity of Variance (center = "mean")
## Df F value Pr(>F)
## group 2 4.85 0.008 **
## 747
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
leveneTest(MO ~ Device,data=aggData.noLearn, center="mean")
## Levene's Test for Homogeneity of Variance (center = "mean")
## Df F value Pr(>F)
## group 2 17.6 3.3e-08 ***
## 747
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
for ( var in DVars ) {
print(paste("Variances for ", var))
varMouse = var(aggData.noLearn[aggData.noLearn$Device=="Mouse", var])
varTouch = var(aggData.noLearn[aggData.noLearn$Device=="Touchpad", var])
varLeap = var(aggData.noLearn[aggData.noLearn$Device=="LeapMotionTouchless", var])
all = c(varMouse, varTouch, varLeap)
print (paste("Mouse: ", varMouse, " Touchpad: ", varTouch, " LeapMotionTouchless: ", varLeap, " Max/Min: ", max(all)/min(all)))
}
## [1] "Variances for Throughput"
## [1] "Mouse: 0.18621480358176 Touchpad: 0.177971275843047 LeapMotionTouchless: 0.15599852012554 Max/Min: 1.19369596219185"
## [1] "Variances for ErrorRate"
## [1] "Mouse: 0.00147077197679607 Touchpad: 0.00204194556001785 LeapMotionTouchless: 0.00585424364123159 Max/Min: 3.98038834951456"
## [1] "Variances for MovementTime"
## [1] "Mouse: 0.0081315631999286 Touchpad: 0.0399067797518965 LeapMotionTouchless: 0.266733437566515 Max/Min: 32.8022338397194"
## [1] "Variances for TRE"
## [1] "Mouse: 0.00774340026773762 Touchpad: 0.0137959125390451 LeapMotionTouchless: 0.0434466755912539 Max/Min: 5.61080069336874"
## [1] "Variances for TAC"
## [1] "Mouse: 0.13786245426149 Touchpad: 0.100161070950469 LeapMotionTouchless: 0.388749379741187 Max/Min: 3.88124224364005"
## [1] "Variances for ODC"
## [1] "Mouse: 0.343373922356091 Touchpad: 0.30481599286033 LeapMotionTouchless: 4.77398225792057 Max/Min: 15.6618496723958"
## [1] "Variances for MDC"
## [1] "Mouse: 0.914690298973672 Touchpad: 1.32112628290942 LeapMotionTouchless: 5.92468390896921 Max/Min: 6.47725674538912"
## [1] "Variances for MV"
## [1] "Mouse: 47.392669960312 Touchpad: 46.3843224190398 LeapMotionTouchless: 38.9132533226816 Max/Min: 1.21790562118557"
## [1] "Variances for ME"
## [1] "Mouse: 27.8230268678934 Touchpad: 27.4997402012203 LeapMotionTouchless: 28.3782501489403 Max/Min: 1.031946118083"
## [1] "Variances for MO"
## [1] "Mouse: 35.1266232997426 Touchpad: 40.387150865384 LeapMotionTouchless: 21.05782841732 Max/Min: 1.9179162288247"
# Anova for Throughput
aov.Throughput <- aov(Throughput~Device+Error(factor(UserId) / Device), data=aggData.noLearn)
#aov.Throughput <- aov(Throughput~Device, data=aggData.noLearn)
#aov.Throughput
summary(aov.Throughput)
##
## Error: factor(UserId)
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 9 48.6 5.4
##
## Error: factor(UserId):Device
## Df Sum Sq Mean Sq F value Pr(>F)
## Device 2 916 458 271 3.6e-14 ***
## Residuals 18 30 2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 720 50.6 0.0702
# pairwise comparisons, adjusting p-value
# all devices are significantly different from each other, in terms of throughput
TukeyHSD(aov.Throughput)
## Error: no applicable method for 'TukeyHSD' applied to an object of class "c('aovlist', 'listof')"
# Anova for TAC
aov.TAC <- aov(TAC~Device, data=aggData.noLearn)
#aov.TAC
summary(aov.TAC)
## Df Sum Sq Mean Sq F value Pr(>F)
## Device 2 153 76.7 367 <2e-16 ***
## Residuals 747 156 0.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov.TAC)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = TAC ~ Device, data = aggData.noLearn)
##
## $Device
## diff lwr upr p adj
## Touchpad-Mouse -0.4077 -0.5037 -0.3117 0
## LeapMotionTouchless-Mouse 0.6883 0.5923 0.7843 0
## LeapMotionTouchless-Touchpad 1.0960 1.0000 1.1920 0
# Anova for TRE
aov.TRE <- aov(TRE~Device, data=aggData.noLearn)
#aov.TRE
summary(aov.TRE)
## Df Sum Sq Mean Sq F value Pr(>F)
## Device 2 8.8 4.40 203 <2e-16 ***
## Residuals 747 16.2 0.02
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov.TRE)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = TRE ~ Device, data = aggData.noLearn)
##
## $Device
## diff lwr upr p adj
## Touchpad-Mouse 0.04027 0.009352 0.07118 0.0065
## LeapMotionTouchless-Mouse 0.24720 0.216285 0.27811 0.0000
## LeapMotionTouchless-Touchpad 0.20693 0.176019 0.23785 0.0000
# Anova for MDC
aov.MDC <- aov(MDC~Device, data=aggData.noLearn)
#aov.MDC
summary(aov.MDC)
## Df Sum Sq Mean Sq F value Pr(>F)
## Device 2 2834 1417 521 <2e-16 ***
## Residuals 747 2032 3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov.MDC)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = MDC ~ Device, data = aggData.noLearn)
##
## $Device
## diff lwr upr p adj
## Touchpad-Mouse -0.3808 -0.7272 -0.03437 0.0271
## LeapMotionTouchless-Mouse 3.9203 3.5738 4.26669 0.0000
## LeapMotionTouchless-Touchpad 4.3011 3.9546 4.64749 0.0000
# Anova for ODC
aov.ODC <- aov(ODC~Device, data=aggData.noLearn)
#aov.ODC
summary(aov.ODC)
## Df Sum Sq Mean Sq F value Pr(>F)
## Device 2 1909 954 528 <2e-16 ***
## Residuals 747 1350 2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov.ODC)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = ODC ~ Device, data = aggData.noLearn)
##
## $Device
## diff lwr upr p adj
## Touchpad-Mouse -0.1472 -0.4296 0.1352 0.4392
## LeapMotionTouchless-Mouse 3.3083 3.0259 3.5907 0.0000
## LeapMotionTouchless-Touchpad 3.4555 3.1731 3.7379 0.0000
# pairwise.t.test(aggData.noLearn$ODC, aggData.noLearn$Device, paired=F, pool=F)
# t.test(aggData.noLearn$ODC[aggData.noLearn$Device == "Mouse"], aggData.noLearn$ODC[aggData.noLearn$Device == "Touchpad"], paired=F, var.equal=T)
# Anova for MV
aov.MV <- aov(MV~Device, data=aggData.noLearn)
#aov.MV
summary(aov.MV)
## Df Sum Sq Mean Sq F value Pr(>F)
## Device 2 34 16.9 0.38 0.68
## Residuals 747 33040 44.2
TukeyHSD(aov.MV)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = MV ~ Device, data = aggData.noLearn)
##
## $Device
## diff lwr upr p adj
## Touchpad-Mouse -0.4764 -1.8734 0.9205 0.7026
## LeapMotionTouchless-Mouse -0.0569 -1.4538 1.3400 0.9950
## LeapMotionTouchless-Touchpad 0.4195 -0.9774 1.8165 0.7605
# Anova for ME
aov.ME <- aov(ME~Device, data=aggData.noLearn)
#aov.ME
summary(aov.ME)
## Df Sum Sq Mean Sq F value Pr(>F)
## Device 2 676 338 12.1 6.6e-06 ***
## Residuals 747 20842 28
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(aov.ME)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = ME ~ Device, data = aggData.noLearn)
##
## $Device
## diff lwr upr p adj
## Touchpad-Mouse -1.8605 -2.970 -0.7510 0.0003
## LeapMotionTouchless-Mouse -2.1387 -3.248 -1.0292 0.0000
## LeapMotionTouchless-Touchpad -0.2782 -1.388 0.8313 0.8262
# Anova for MO
aov.MO <- aov(MO~Device, data=aggData.noLearn)
#aov.MO
summary(aov.MO)
## Df Sum Sq Mean Sq F value Pr(>F)
## Device 2 4 2.1 0.07 0.94
## Residuals 747 24046 32.2
TukeyHSD(aov.MO)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = MO ~ Device, data = aggData.noLearn)
##
## $Device
## diff lwr upr p adj
## Touchpad-Mouse 0.09373 -1.098 1.285 0.9814
## LeapMotionTouchless-Mouse 0.18481 -1.007 1.377 0.9295
## LeapMotionTouchless-Touchpad 0.09108 -1.101 1.283 0.9824
model.tre <- lm(Throughput~TRE, data=aggData.noLearn)
summary(model.tre)
##
## Call:
## lm(formula = Throughput ~ TRE, data = aggData.noLearn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.265 -0.752 -0.123 0.819 2.529
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.1437 0.0525 78.9 <2e-16 ***
## TRE -3.6906 0.1943 -19.0 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.971 on 748 degrees of freedom
## Multiple R-squared: 0.325, Adjusted R-squared: 0.325
## F-statistic: 361 on 1 and 748 DF, p-value: <2e-16
model.odc <- lm(Throughput~ODC, data=aggData.noLearn)
summary(model.odc)
##
## Call:
## lm(formula = Throughput ~ ODC, data = aggData.noLearn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.750 -0.743 -0.223 0.828 2.391
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.2336 0.0482 87.8 <2e-16 ***
## ODC -0.3679 0.0157 -23.4 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.899 on 748 degrees of freedom
## Multiple R-squared: 0.422, Adjusted R-squared: 0.421
## F-statistic: 546 on 1 and 748 DF, p-value: <2e-16
model.mdc <- lm(Throughput~MDC, data=aggData.noLearn)
summary(model.mdc)
##
## Call:
## lm(formula = Throughput ~ MDC, data = aggData.noLearn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.841 -0.737 -0.168 0.788 2.589
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.1143 0.0880 58.1 <2e-16 ***
## MDC -0.2826 0.0134 -21.0 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.937 on 748 degrees of freedom
## Multiple R-squared: 0.372, Adjusted R-squared: 0.371
## F-statistic: 443 on 1 and 748 DF, p-value: <2e-16
model.odcmdc <- lm(Throughput~ODC+MDC, data=aggData.noLearn)
summary(model.odcmdc)
##
## Call:
## lm(formula = Throughput ~ ODC + MDC, data = aggData.noLearn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.732 -0.715 -0.175 0.800 2.388
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.7354 0.0896 52.83 < 2e-16 ***
## ODC -0.2496 0.0236 -10.56 < 2e-16 ***
## MDC -0.1271 0.0193 -6.57 9.4e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.875 on 747 degrees of freedom
## Multiple R-squared: 0.453, Adjusted R-squared: 0.452
## F-statistic: 310 on 2 and 747 DF, p-value: <2e-16
model.treodcmdc <- lm(MovementTime~TRE+ODC+MDC, data=aggData.noLearn)
summary(model.treodcmdc)
##
## Call:
## lm(formula = MovementTime ~ TRE + ODC + MDC, data = aggData.noLearn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5779 -0.2558 -0.0556 0.2159 1.1965
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.55883 0.03267 17.11 < 2e-16 ***
## TRE 0.31053 0.09379 3.31 0.00097 ***
## ODC 0.14358 0.01013 14.17 < 2e-16 ***
## MDC 0.08045 0.00706 11.39 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.318 on 746 degrees of freedom
## Multiple R-squared: 0.727, Adjusted R-squared: 0.725
## F-statistic: 661 on 3 and 746 DF, p-value: <2e-16
model.tretac <- lm(Throughput~TRE+TAC, data=aggData.noLearn)
summary(model.tretac)
##
## Call:
## lm(formula = Throughput ~ TRE + TAC, data = aggData.noLearn)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.288 -0.749 -0.154 0.819 2.502
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.3095 0.1072 40.20 <2e-16 ***
## TRE -3.4095 0.2505 -13.61 <2e-16 ***
## TAC -0.1262 0.0712 -1.77 0.077 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.97 on 747 degrees of freedom
## Multiple R-squared: 0.328, Adjusted R-squared: 0.326
## F-statistic: 183 on 2 and 747 DF, p-value: <2e-16
s <- summaryBy(ErrorRate+TRE+TAC+MDC+ODC+MV+ME+MO+MovementTime+Throughput~Device,
data=aggData.noLearn,
FUN=c(mean,sd))
# plot the movement offset over the TRE for each device
p <- ggplot(s, aes(x=MDC.mean, y=abs(ODC.mean), group=Device, colour=Device )) +
geom_point(size=5, aes(shape=Device)) +
#coord_cartesian(xlim = c(0, 0.4), ylim=c(0, 8)) +
ylab("Orthogonal direction change (pixels)") +
xlab("Movement direction change (pixels)") +
theme(legend.position=c(0.55,0.9), legend.direction="horizontal") +
theme()
p
ggsave(file = paste("charts/",filenameprefix,"-MDC-ODC.pdf", sep=""), width=14/2.54, height=7/2.54, dpi=100)