Overblik over data
#Indlæs nødvendige pakker
library(foreign)
library(ggplot2)
library(ICC)
library(psych) # god til deskriptic statistik
Attaching package: 'psych'
The following objects are masked from 'package:ggplot2':
%+%, alpha
library(BlandAltmanLeh)
library(openxlsx) # god til at importere Excel filer
# indlæser modificeret excel fil (der er tilføjet kolonner i excel)
data <- read.xlsx(file.path("C:/Users/Vision/Desktop/STATISTIK/BIOSTATISTIK2/HeatPainThresholds.xlsx"))
head(data)
test.1 test.2 mean sum forskel sumxsum sumxsumhalv
1 45.93333 46.40000 46.16667 92.33333 -0.4666667 0.10888889 0.05444444
2 47.60000 44.83333 46.21667 92.43333 2.7666667 3.82722222 1.91361111
3 44.80333 44.03667 44.42000 88.84000 0.7666667 0.29388889 0.14694444
4 45.32333 45.76000 45.54167 91.08333 -0.4366667 0.09533889 0.04766944
5 45.21000 43.93667 44.57333 89.14667 1.2733333 0.81068889 0.40534444
6 45.46667 44.48000 44.97333 89.94667 0.9866667 0.48675556 0.24337778
sd
1 0.2333333
2 1.3833333
3 0.3833333
4 0.2183333
5 0.6366667
6 0.4933333
data <-data[,c(1,2,3,4,5,8)] # reducerer antallet af kolonner (fjerner unødvendige mellemregningskolonner fra Excel)
head(data)
test.1 test.2 mean sum forskel sd
1 45.93333 46.40000 46.16667 92.33333 -0.4666667 0.2333333
2 47.60000 44.83333 46.21667 92.43333 2.7666667 1.3833333
3 44.80333 44.03667 44.42000 88.84000 0.7666667 0.3833333
4 45.32333 45.76000 45.54167 91.08333 -0.4366667 0.2183333
5 45.21000 43.93667 44.57333 89.14667 1.2733333 0.6366667
6 45.46667 44.48000 44.97333 89.94667 0.9866667 0.4933333
data$test.1 <- as.numeric(data$test.1)
data$test.2 <- as.numeric(data$test.2)
describe.by(data)
vars n mean sd median trimmed mad min max range skew
test.1 1 18 45.69 0.72 45.58 45.65 0.54 44.53 47.60 3.07 0.80
test.2 2 18 45.03 0.86 45.10 45.09 0.62 42.77 46.40 3.63 -0.77
mean 3 18 45.36 0.63 45.41 45.37 0.66 44.12 46.48 2.37 -0.13
sum 4 18 90.72 1.26 90.81 90.74 1.33 88.23 92.97 4.73 -0.13
forskel 5 18 0.66 0.97 0.55 0.62 0.73 -0.77 2.77 3.53 0.66
sd 6 18 0.45 0.37 0.34 0.42 0.18 0.03 1.38 1.35 1.50
kurtosis se
test.1 0.60 0.17
test.2 0.48 0.20
mean -0.76 0.15
sum -0.76 0.30
forskel -0.13 0.23
sd 1.28 0.09
boxplot(data$test.1, data$test.2)
CV & ICC
mean<- mean(data$mean)
sdmean<-mean(data$sd)
CV<- sdmean/mean
CV*100 # i procent
[1] 0.990584
ICC <- ICCbare(data$test.1, data$test.2, data=data)
ICC
[1] -0.97779
Bland Altman Plot
bland.altman.plot(data$test.1, data$test.2, main="Bland Altman Plot", xlab="Means", ylab="Differences")
NULL
Sample size for parallel study (detect an effect of 2C)
Nparallel<- ((21*0.66^2)/2^2)
Nparallel
[1] 2.2869
Der skal altså bruges mindst 3 personer
Sample size for cross-over study (detect an effect of 2C)
NCrossOver<- ((21*(0.66^2))*(1-0.25))/(2^2)
NCrossOver
[1] 1.715175
Der skal altså bruges mindst 2 personer