Data for analysis
pacman::p_load(readxl, irr, psych, rmarkdown)
dat <- read_excel("~/Desktop/karina.xlsx")
paged_table(dat)
JSTR_FLEX
JSTR_FLEX <- subset(dat, select = c("JSTR_FLEX1", "JSTR_FLEX2"))
paged_table(JSTR_FLEX)
Descriptive
describe(JSTR_FLEX)
## vars n mean sd median trimmed mad min max range skew
## JSTR_FLEX1 1 20 202.65 76.97 179.0 197.06 71.16 93 351 258 0.47
## JSTR_FLEX2 2 20 201.90 68.78 197.5 199.00 77.10 87 327 240 0.29
## kurtosis se
## JSTR_FLEX1 -1.14 17.21
## JSTR_FLEX2 -1.03 15.38
ICC by session
icc(JSTR_FLEX, "twoway", "agreement")
## Single Score Intraclass Correlation
##
## Model: twoway
## Type : agreement
##
## Subjects = 20
## Raters = 2
## ICC(A,1) = 0.863
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(19,19) = 13 , p = 3.51e-07
##
## 95%-Confidence Interval for ICC Population Values:
## 0.685 < ICC < 0.944
JSTR_Ext
JSTR_EXT <- subset(dat, select = c("JSTR_EXT1", "JSTR_EXT2"))
Descriptive
describe(JSTR_EXT)
## vars n mean sd median trimmed mad min max range skew kurtosis
## JSTR_EXT1 1 20 183.75 63.27 171 184.75 68.20 73 283 210 0.05 -1.16
## JSTR_EXT2 2 20 198.60 69.57 192 192.88 74.13 97 360 263 0.55 -0.54
## se
## JSTR_EXT1 14.15
## JSTR_EXT2 15.56
ICC by session
icc(JSTR_EXT, "twoway", "agreement")
## Single Score Intraclass Correlation
##
## Model: twoway
## Type : agreement
##
## Subjects = 20
## Raters = 2
## ICC(A,1) = 0.782
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(19,19) = 8.7 , p = 8.7e-06
##
## 95%-Confidence Interval for ICC Population Values:
## 0.533 < ICC < 0.907
JSTR_ABD
JSTR_ABD <- subset(dat, select = c("JSTR_ABD1", "JSTR_ABD2"))
Descriptive
describe(JSTR_ABD)
## vars n mean sd median trimmed mad min max range skew kurtosis
## JSTR_ABD1 1 20 174.15 55.37 168.5 173.75 68.94 88 263 175 0.09 -1.44
## JSTR_ABD2 2 20 179.35 52.82 167.5 178.62 68.94 106 264 158 0.12 -1.67
## se
## JSTR_ABD1 12.38
## JSTR_ABD2 11.81
ICC
icc(JSTR_ABD, "twoway", "agreement")
## Single Score Intraclass Correlation
##
## Model: twoway
## Type : agreement
##
## Subjects = 20
## Raters = 2
## ICC(A,1) = 0.894
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(19,20) = 17.7 , p = 1.33e-08
##
## 95%-Confidence Interval for ICC Population Values:
## 0.755 < ICC < 0.956
JSTR_ADD
JSTR_ADD <- subset(dat, select = c("JSTR_ADD1", "JSTR_ADD2"))
paged_table(JSTR_ADD)
Descriptive
describe(JSTR_ADD)
## vars n mean sd median trimmed mad min max range skew kurtosis
## JSTR_ADD1 1 20 194.70 68.21 185.0 188.56 43.74 96 342 246 0.79 -0.13
## JSTR_ADD2 2 20 202.75 58.95 187.5 200.50 25.20 75 316 241 0.38 -0.13
## se
## JSTR_ADD1 15.25
## JSTR_ADD2 13.18
ICC
icc(JSTR_ADD, "twoway", "agreement")
## Single Score Intraclass Correlation
##
## Model: twoway
## Type : agreement
##
## Subjects = 20
## Raters = 2
## ICC(A,1) = 0.881
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(19,19.9) = 16.1 , p = 3.04e-08
##
## 95%-Confidence Interval for ICC Population Values:
## 0.73 < ICC < 0.951
JT_GRIP
JT_GRIP <- subset(dat, select = c("JT_GRIP1", "JT_GRIP2"))
paged_table(JT_GRIP)
Descriptive
describe(JT_GRIP)
## vars n mean sd median trimmed mad min max range skew
## JT_GRIP1 1 20 445.10 133.96 428.0 441.69 155.67 248 665 417 0.16
## JT_GRIP2 2 20 435.35 134.48 422.5 431.00 155.67 234 671 437 0.17
## kurtosis se
## JT_GRIP1 -1.39 29.95
## JT_GRIP2 -1.30 30.07
ICC
icc(JT_GRIP, "twoway", "agreement")
## Single Score Intraclass Correlation
##
## Model: twoway
## Type : agreement
##
## Subjects = 20
## Raters = 2
## ICC(A,1) = 0.967
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(19,19.6) = 62.2 , p = 1.44e-13
##
## 95%-Confidence Interval for ICC Population Values:
## 0.92 < ICC < 0.987