Very preliminary - just getting around to entering the data and just doing some explorations… I did do a quick ICC (intraclass correlation) analysis of Inter-Rater Agreement, not too bad overall - will likely be better when I control for within subject variation

df <- read.csv("NHE_April2022.csv")
df$Weight_Categories <- cut(df$Weight, breaks = 3)
ggplot(df, aes(Force, color = Weight_Categories)) +
  geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(df, aes(Force, color = Leg)) +
  geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(df, aes(Weight, Force, color = ID)) +
  geom_point() +
  geom_smooth(method = lm) +
  labs(x = "Body Weight", y = "Force", title = "Relationship between Body Weight and Force")
## `geom_smooth()` using formula 'y ~ x'

r1 <- filter(df, Rater == 1)
r1 <- select(r1, Force)
r1 <- rename(r1, Force1 = Force)
r2 <- filter(df, Rater == 2)
r2 <- select(r2, Force)
r2 <- rename(r2, Force2 = Force)
r12 <- cbind(r1, r2)

Scatterplot of force recorded from each rater paired by subject and leg

ggplot(r12, aes(Force1, Force2)) +
  geom_point() +
  geom_smooth(method = lm) +
  labs(x = "Rater 1", y = "Rater 2", title = "Comparison between Rater 1 and Rater 2")
## `geom_smooth()` using formula 'y ~ x'

The variability in this scatter plot does not only represent between rater variability; but within subject variability as well - both within subjects between tests and within subjects between legs

Inter-Rater Reliability

Not a final analysis - just preliminary - need to do a bit more analysis of within and between subject variation with some ANOVAs

icc(r12, model = "twoway",
  type = "agreement", unit = "single")
##  Single Score Intraclass Correlation
## 
##    Model: twoway 
##    Type : agreement 
## 
##    Subjects = 68 
##      Raters = 2 
##    ICC(A,1) = 0.65
## 
##  F-Test, H0: r0 = 0 ; H1: r0 > 0 
##  F(67,67.5) = 4.68 , p = 8.12e-10 
## 
##  95%-Confidence Interval for ICC Population Values:
##   0.488 < ICC < 0.769

Assymetry - preliminary

Right now simply the absolute difference between the right and left leg. This does not control for variation due to raters, or due to different tests.

right <- filter(df, Leg == "Right")
right <- select(right, Force)
right <- rename(right, Right_Force = Force)
left <- filter(df, Leg == "Left")
left <- select(left, Force)
left <- rename(left, Left_Force = Force)
RL_df<- cbind(right, left)
RL_df <- mutate(RL_df, LE_Difference = abs(Right_Force - Left_Force))
ggplot(RL_df, aes(LE_Difference)) + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(RL_df, aes(LE_Difference)) + geom_boxplot()

Summary stats of LE Difference

summary(RL_df$LE_Difference)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.30   13.10   26.25   30.57   46.42  126.30