Dataset corresponding to serum glucose (measurements of glucose concentration in blood used to control diabetes) testing. Eight laboratories conducted tests to five different blood samples tagged with different references, ranging them from low sugar content to very high. Three replicates were obtained for each sample. It is retrieved from ASTM E 691 standard.
Format
A data frame with 120 observations composed of the following 4 variables:
- Glucose
Glucose content in Serum.
- Replicate
Number of glucose measurement corresponding to each material.
- Material
Level of glucose, ranging from low content of sugar to very high level of glucose in blood.
- Laboratory
Laboratories conducted tests.
References
ASTM E 691 (1999). Standard practice for conducting an interlaboratory study to determine the precision of a test method. American Society for Testing and Materials. West Conshohocken, PA, USA.
Examples
library(ILS)
data(Glucose)
summary(Glucose)
#> Glucose Replicate Material Laboratory
#> Min. : 39.02 Min. :1 Length:120 Length:120
#> 1st Qu.: 78.45 1st Qu.:1 Class :character Class :character
#> Median :135.03 Median :2 Mode :character Mode :character
#> Mean :149.09 Mean :2
#> 3rd Qu.:196.66 3rd Qu.:3
#> Max. :309.40 Max. :3
attach(Glucose)
#> The following object is masked _by_ .GlobalEnv:
#>
#> Glucose
#> The following object is masked from package:ILS:
#>
#> Glucose
#> The following object is masked from package:nlme:
#>
#> Glucose
str(Glucose)
#> 'data.frame': 120 obs. of 4 variables:
#> $ Glucose : num 41 41.5 41.4 41.2 42 ...
#> $ Replicate : num 1 2 3 1 2 3 1 2 3 1 ...
#> $ Material : chr "A" "A" "A" "A" ...
#> $ Laboratory: chr "Lab1" "Lab1" "Lab1" "Lab2" ...
table(Replicate,Material,Laboratory)
#> , , Laboratory = Lab1
#>
#> Material
#> Replicate A B C D E
#> 1 1 1 1 1 1
#> 2 1 1 1 1 1
#> 3 1 1 1 1 1
#>
#> , , Laboratory = Lab2
#>
#> Material
#> Replicate A B C D E
#> 1 1 1 1 1 1
#> 2 1 1 1 1 1
#> 3 1 1 1 1 1
#>
#> , , Laboratory = Lab3
#>
#> Material
#> Replicate A B C D E
#> 1 1 1 1 1 1
#> 2 1 1 1 1 1
#> 3 1 1 1 1 1
#>
#> , , Laboratory = Lab4
#>
#> Material
#> Replicate A B C D E
#> 1 1 1 1 1 1
#> 2 1 1 1 1 1
#> 3 1 1 1 1 1
#>
#> , , Laboratory = Lab5
#>
#> Material
#> Replicate A B C D E
#> 1 1 1 1 1 1
#> 2 1 1 1 1 1
#> 3 1 1 1 1 1
#>
#> , , Laboratory = Lab6
#>
#> Material
#> Replicate A B C D E
#> 1 1 1 1 1 1
#> 2 1 1 1 1 1
#> 3 1 1 1 1 1
#>
#> , , Laboratory = Lab7
#>
#> Material
#> Replicate A B C D E
#> 1 1 1 1 1 1
#> 2 1 1 1 1 1
#> 3 1 1 1 1 1
#>
#> , , Laboratory = Lab8
#>
#> Material
#> Replicate A B C D E
#> 1 1 1 1 1 1
#> 2 1 1 1 1 1
#> 3 1 1 1 1 1
#>
table(Laboratory,Material)
#> Material
#> Laboratory A B C D E
#> Lab1 3 3 3 3 3
#> Lab2 3 3 3 3 3
#> Lab3 3 3 3 3 3
#> Lab4 3 3 3 3 3
#> Lab5 3 3 3 3 3
#> Lab6 3 3 3 3 3
#> Lab7 3 3 3 3 3
#> Lab8 3 3 3 3 3
st <- with(Glucose, tapply(Glucose, list(Material,Laboratory), mean))
st
#> Lab1 Lab2 Lab3 Lab4 Lab5 Lab6 Lab7
#> A 41.28333 41.44000 41.45000 41.45667 41.46333 42.02000 40.45667
#> B 78.31667 79.23333 79.90333 80.96333 78.69000 79.89333 79.51667
#> C 133.19667 135.40667 134.59000 140.83000 133.26667 136.61667 132.49333
#> D 193.65000 195.10667 192.09000 197.21333 193.05000 197.24333 191.26000
#> E 293.25333 298.91667 292.67000 295.82000 293.56333 294.95667 290.13667
#> Lab8
#> A 42.57667
#> B 80.34667
#> C 134.71000
#> D 198.12333
#> E 296.62000