We begin by importing the data and visualizing it
Data Frame Summary
df_sonos
Dimensions: 147 x 3
Duplicates: 80
-----------------------------------------------------------------------------------
No Variable Stats / Values Freqs (% of Valid) Valid Missing
---- ------------- ---------------------- -------------------- ---------- ---------
1 side 1. Right 147 (100.0%) 147 0
[character] (100.0%) (0.0%)
2 sono1 Mean (sd) : 1 (0.2) 14 distinct values 146 1
[numeric] min < med < max: (99.3%) (0.7%)
0.5 < 1 < 1.9
IQR (CV) : 0.3 (0.2)
3 sono2 Mean (sd) : 1 (0.2) 11 distinct values 146 1
[numeric] min < med < max: (99.3%) (0.7%)
0.5 < 1 < 1.5
IQR (CV) : 0.3 (0.2)
-----------------------------------------------------------------------------------Intraclass Correlation Coefficient
This is a measure of how the two measures correlate with the various accepted categorizations: - Less than 0.40 — Poor. - Between 0.40 and 0.59 — Fair. - Between 0.60 and 0.74 — Good. - Between 0.75 and 1.00 — Excellent.
Single Score Intraclass Correlation
Model: twoway
Type : agreement
Subjects = 146
Raters = 2
ICC(A,1) = 0.49
F-Test, H0: r0 = 0 ; H1: r0 > 0
F(145,144) = 2.96 , p = 1.08e-10
95%-Confidence Interval for ICC Population Values:
0.357 < ICC < 0.603ICC is rather low.
Bland-And_Altman’s plot
Next we draw a Bland-And-Altmans plot
