HepatitisBvaccine: Hepatitis B vaccine
In pla: Parallel Line Assays
Description
Usage
Source
Examples
Description
Example 07 - Five-dose parallel line assay; completely randomized;
logarithmic transformation; explicit ratio notation,
from CombiStats - EDQM, Council of Europe (http://combistats.edqm.eu).
Usage
1
data("HepatitisBvaccine")
Source
EDQM Combistats:
http://combistats.edqm.eu/images/stories/Examples/Hepatitis%20B.pdf
Examples
1
2
3
4
5
6
7
8
9
Example <- "Example 7"
data(HepatitisBvaccine); Data <- HepatitisBvaccine
Data <- readAssayTable(paste(system.file(package = "pla"),
"vignettes/CombiStat/data/HepatitisBvaccine.txt",
sep = "/"), fun = log,
rows = "Dilutions & Samples", columns = "Replicates")
plaModel <- plaCRD(Data); plaModel
plots <- plot(plaModel)
Example output
Warning: Problem with label number 24 , S1/1000 : 1/2000 1/4000 1/8000
Warning: Problem with label number 27 , T1/1000 : 1/2000 1/4000 1/8000
Warning: Problem with label number 30 , U1/1000 : 1/2000 1/4000 1/8000
================================================================================
Project: CombiStats - EDQM, Council of Europe
Assay: Example 7: Hepatitis B vaccine
================================================================================
================================================================================
Data values:
--------------------------------------------------------------------------------
Standard_1:1:16000 Standard_1:2:8000 Standard_1:3:4000 Standard_1:4:2000
1 -3.147 -2.375 -1.839 -1.26
2 -3.101 -2.313 -1.871 -1.22
3 -2.976 -2.501 -1.796 -1.02
Mean -3.075 -2.396 -1.835 -1.17
SD 0.088 0.096 0.038 0.13
CV -2.874 -4.005 -2.052 -11.30
Standard_1:5:1000 Test_2:1:16000 Test_2:2:8000 Test_2:3:4000 Test_2:4:2000
1 -0.666 -2.333 -1.790 -1.118 -0.69
2 -0.633 -2.333 -1.852 -1.036 -0.41
3 -0.607 -2.364 -1.726 -1.064 -0.55
Mean -0.635 -2.344 -1.789 -1.073 -0.55
SD 0.029 0.018 0.063 0.042 0.14
CV -4.620 -0.774 -3.509 -3.889 -25.73
Test_2:5:1000 Unknown_3:1:16000 Unknown_3:2:8000 Unknown_3:3:4000
1 0.13 -2.5 -2.064 -1.284
2 0.33 -2.6 -1.924 -1.317
3 0.05 -2.6 -2.017 -1.313
Mean 0.17 -2.6 -2.002 -1.305
SD 0.14 0.1 0.071 0.018
CV 84.11 -4.0 -3.548 -1.387
Unknown_3:4:2000 Unknown_3:5:1000
1 -0.534 -0.044
2 -0.715 -0.144
3 -0.605 0.044
Mean -0.618 -0.048
SD 0.091 0.094
CV -14.749 -196.113
================================================================================
================================================================================
Dilution ration: 2
Factor: 20 100 45.8965738974058 20 100 56.7291261152691
Significance level alpha: 5 %
================================================================================
================================================================================
Regression, Restricted model (Common Slope), with adjusting for 'blocks'.:
--------------------------------------------------------------------------------
Call:
lm(formula = Response ~ -1 + factor(Sample) + Z, data = data)
Residuals:
Min 1Q Median 3Q Max
-0.200680 -0.061179 -0.005301 0.053056 0.190092
Coefficients:
Estimate Std. Error t value Pr(>|t|)
factor(Sample)Standard_1 -0.56790 0.02903 -19.559 < 2e-16 ***
factor(Sample)Test_2 0.13633 0.02903 4.695 2.97e-05 ***
factor(Sample)Unknown_3 -0.05528 0.02903 -1.904 0.0639 .
Z 0.90428 0.01325 68.266 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0871 on 41 degrees of freedom
Multiple R-squared: 0.9976, Adjusted R-squared: 0.9974
F-statistic: 4269 on 4 and 41 DF, p-value: < 2.2e-16
--------------------------------------------------------------------------------
Slope:
Z
0.904279
================================================================================
================================================================================
Regression, Unrestricted Model (Different slopes), with adjusting for 'blocks':
--------------------------------------------------------------------------------
Call:
lm(formula = Response ~ -1 + factor(Sample) + factor(Sample):Z,
data = data)
Residuals:
Min 1Q Median 3Q Max
-0.200281 -0.051667 -0.007188 0.053056 0.194530
Coefficients:
Estimate Std. Error t value Pr(>|t|)
factor(Sample)Standard_1 -0.59978 0.03892 -15.409 < 2e-16 ***
factor(Sample)Test_2 0.13553 0.03892 3.482 0.00124 **
factor(Sample)Unknown_3 -0.02261 0.03892 -0.581 0.56471
factor(Sample)Standard_1:Z 0.88128 0.02293 38.441 < 2e-16 ***
factor(Sample)Test_2:Z 0.90370 0.02293 39.419 < 2e-16 ***
factor(Sample)Unknown_3:Z 0.92785 0.02293 40.472 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.08704 on 39 degrees of freedom
Multiple R-squared: 0.9977, Adjusted R-squared: 0.9974
F-statistic: 2851 on 6 and 39 DF, p-value: < 2.2e-16
--------------------------------------------------------------------------------
Slopes:
factor(Sample)Standard_1:Z factor(Sample)Test_2:Z
0.8812844 0.9037023
factor(Sample)Unknown_3:Z
0.9278504
================================================================================
--------------------------------------------------------------------------------
Completly Randomized Design Analysis (CRD):
--------------------------------------------------------------------------------
Analysis of Variance Table
Response: Response
Df Sum Sq Mean Sq F value Pr(>F)
factor(Sample) 2 3.977 1.989 256.7030 <2e-16 ***
Dilution 1 35.359 35.359 4564.3757 <2e-16 ***
factor(Sample):Dilution 2 0.016 0.008 1.0091 0.3766
factor(Sample):factor(Dilution) 9 0.063 0.007 0.9042 0.5341
Residuals 30 0.232 0.008
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Df Sum Sq Mean Sq F value Pr(>F) DFresiduals
Test of Preparation: 2 3.977204 1.988602 256.703 1.348813e-19 30
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Df Sum Sq Mean Sq F value Pr(>F) F(critical)
Test of Regression: 1 35.35887 35.35887 4564.376 2.423905e-34 4.170877
This test passes if F(regression) > F(critical)
Test of Regression: Test passed!
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Df Sum Sq Mean Sq F value Pr(>F) F(critical)
Test of Linearity: 9 0.06304198 0.007004665 0.9042123 0.5340925 2.210697
This test passes if F(non-linearity) < F(critical)
Test of Linearity: Test passed!
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Df Sum Sq Mean Sq F value Pr(>F) F(critical)
Test of Parallelism: 2 0.01563435 0.007817173 1.009097 0.376588 3.31583
This test passes if F(non-parallelity) < F(critical)
Test of Parallelism: Test passed!
================================================================================
================================================================================
Sums of Squares, Slope, Variance, C, Potency and Confidence-intervals:
--------------------------------------------------------------------------------
SStot SSreg SSres SSsmp R Radj
39.64714805 35.35886612 0.23240111 3.97720449 0.99606920 0.99578097
DFres b s2 qt C
30.0000000000 0.9042790078 0.0077467037 2.0422724563 1.0009146249
V
2.8827180835
Width Log(Lower) Log(Potency) Log(Upper) CM
Test_2 0.076347525 0.70314448 0.77877971 0.85583953 0.77949201
Unknown_3 0.074616815 0.49278409 0.56688242 0.64201772 0.56740090
exp(Width) Lower Potency Upper
Test_2 - 20 21.586752 40.401898 43.576237 47.066985
Test_2 - 100 107.933761 202.009488 217.881187 235.334926
Test_2 - 45.8965738974058 49.537898 92.715434 100.000000 108.010668
Unknown_3 - 20 21.549424 32.737341 35.255258 38.006226
Unknown_3 - 100 107.747120 163.686706 176.276292 190.031131
Unknown_3 - 56.7291261152691 61.124000 92.858038 100.000000 107.803000
exp(CM)
Test_2 - 20 43.607287
Test_2 - 100 218.036437
Test_2 - 45.8965738974058 100.071254
Unknown_3 - 20 35.273542
Unknown_3 - 100 176.367712
Unknown_3 - 56.7291261152691 100.051862
--------------------------------------------------------------------------------
================================================================================
pla documentation built on May 2, 2019, 11:12 a.m.
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Description Usage Source Examples
Description
Example 07 - Five-dose parallel line assay; completely randomized; logarithmic transformation; explicit ratio notation, from CombiStats - EDQM, Council of Europe (http://combistats.edqm.eu).
Usage
1 | data("HepatitisBvaccine")
|
Source
EDQM Combistats: http://combistats.edqm.eu/images/stories/Examples/Hepatitis%20B.pdf
Examples
1 2 3 4 5 6 7 8 9 | Example <- "Example 7"
data(HepatitisBvaccine); Data <- HepatitisBvaccine
Data <- readAssayTable(paste(system.file(package = "pla"),
"vignettes/CombiStat/data/HepatitisBvaccine.txt",
sep = "/"), fun = log,
rows = "Dilutions & Samples", columns = "Replicates")
plaModel <- plaCRD(Data); plaModel
plots <- plot(plaModel)
|
Example output
Warning: Problem with label number 24 , S1/1000 : 1/2000 1/4000 1/8000
Warning: Problem with label number 27 , T1/1000 : 1/2000 1/4000 1/8000
Warning: Problem with label number 30 , U1/1000 : 1/2000 1/4000 1/8000
================================================================================
Project: CombiStats - EDQM, Council of Europe
Assay: Example 7: Hepatitis B vaccine
================================================================================
================================================================================
Data values:
--------------------------------------------------------------------------------
Standard_1:1:16000 Standard_1:2:8000 Standard_1:3:4000 Standard_1:4:2000
1 -3.147 -2.375 -1.839 -1.26
2 -3.101 -2.313 -1.871 -1.22
3 -2.976 -2.501 -1.796 -1.02
Mean -3.075 -2.396 -1.835 -1.17
SD 0.088 0.096 0.038 0.13
CV -2.874 -4.005 -2.052 -11.30
Standard_1:5:1000 Test_2:1:16000 Test_2:2:8000 Test_2:3:4000 Test_2:4:2000
1 -0.666 -2.333 -1.790 -1.118 -0.69
2 -0.633 -2.333 -1.852 -1.036 -0.41
3 -0.607 -2.364 -1.726 -1.064 -0.55
Mean -0.635 -2.344 -1.789 -1.073 -0.55
SD 0.029 0.018 0.063 0.042 0.14
CV -4.620 -0.774 -3.509 -3.889 -25.73
Test_2:5:1000 Unknown_3:1:16000 Unknown_3:2:8000 Unknown_3:3:4000
1 0.13 -2.5 -2.064 -1.284
2 0.33 -2.6 -1.924 -1.317
3 0.05 -2.6 -2.017 -1.313
Mean 0.17 -2.6 -2.002 -1.305
SD 0.14 0.1 0.071 0.018
CV 84.11 -4.0 -3.548 -1.387
Unknown_3:4:2000 Unknown_3:5:1000
1 -0.534 -0.044
2 -0.715 -0.144
3 -0.605 0.044
Mean -0.618 -0.048
SD 0.091 0.094
CV -14.749 -196.113
================================================================================
================================================================================
Dilution ration: 2
Factor: 20 100 45.8965738974058 20 100 56.7291261152691
Significance level alpha: 5 %
================================================================================
================================================================================
Regression, Restricted model (Common Slope), with adjusting for 'blocks'.:
--------------------------------------------------------------------------------
Call:
lm(formula = Response ~ -1 + factor(Sample) + Z, data = data)
Residuals:
Min 1Q Median 3Q Max
-0.200680 -0.061179 -0.005301 0.053056 0.190092
Coefficients:
Estimate Std. Error t value Pr(>|t|)
factor(Sample)Standard_1 -0.56790 0.02903 -19.559 < 2e-16 ***
factor(Sample)Test_2 0.13633 0.02903 4.695 2.97e-05 ***
factor(Sample)Unknown_3 -0.05528 0.02903 -1.904 0.0639 .
Z 0.90428 0.01325 68.266 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0871 on 41 degrees of freedom
Multiple R-squared: 0.9976, Adjusted R-squared: 0.9974
F-statistic: 4269 on 4 and 41 DF, p-value: < 2.2e-16
--------------------------------------------------------------------------------
Slope:
Z
0.904279
================================================================================
================================================================================
Regression, Unrestricted Model (Different slopes), with adjusting for 'blocks':
--------------------------------------------------------------------------------
Call:
lm(formula = Response ~ -1 + factor(Sample) + factor(Sample):Z,
data = data)
Residuals:
Min 1Q Median 3Q Max
-0.200281 -0.051667 -0.007188 0.053056 0.194530
Coefficients:
Estimate Std. Error t value Pr(>|t|)
factor(Sample)Standard_1 -0.59978 0.03892 -15.409 < 2e-16 ***
factor(Sample)Test_2 0.13553 0.03892 3.482 0.00124 **
factor(Sample)Unknown_3 -0.02261 0.03892 -0.581 0.56471
factor(Sample)Standard_1:Z 0.88128 0.02293 38.441 < 2e-16 ***
factor(Sample)Test_2:Z 0.90370 0.02293 39.419 < 2e-16 ***
factor(Sample)Unknown_3:Z 0.92785 0.02293 40.472 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.08704 on 39 degrees of freedom
Multiple R-squared: 0.9977, Adjusted R-squared: 0.9974
F-statistic: 2851 on 6 and 39 DF, p-value: < 2.2e-16
--------------------------------------------------------------------------------
Slopes:
factor(Sample)Standard_1:Z factor(Sample)Test_2:Z
0.8812844 0.9037023
factor(Sample)Unknown_3:Z
0.9278504
================================================================================
--------------------------------------------------------------------------------
Completly Randomized Design Analysis (CRD):
--------------------------------------------------------------------------------
Analysis of Variance Table
Response: Response
Df Sum Sq Mean Sq F value Pr(>F)
factor(Sample) 2 3.977 1.989 256.7030 <2e-16 ***
Dilution 1 35.359 35.359 4564.3757 <2e-16 ***
factor(Sample):Dilution 2 0.016 0.008 1.0091 0.3766
factor(Sample):factor(Dilution) 9 0.063 0.007 0.9042 0.5341
Residuals 30 0.232 0.008
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Df Sum Sq Mean Sq F value Pr(>F) DFresiduals
Test of Preparation: 2 3.977204 1.988602 256.703 1.348813e-19 30
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Df Sum Sq Mean Sq F value Pr(>F) F(critical)
Test of Regression: 1 35.35887 35.35887 4564.376 2.423905e-34 4.170877
This test passes if F(regression) > F(critical)
Test of Regression: Test passed!
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Df Sum Sq Mean Sq F value Pr(>F) F(critical)
Test of Linearity: 9 0.06304198 0.007004665 0.9042123 0.5340925 2.210697
This test passes if F(non-linearity) < F(critical)
Test of Linearity: Test passed!
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Df Sum Sq Mean Sq F value Pr(>F) F(critical)
Test of Parallelism: 2 0.01563435 0.007817173 1.009097 0.376588 3.31583
This test passes if F(non-parallelity) < F(critical)
Test of Parallelism: Test passed!
================================================================================
================================================================================
Sums of Squares, Slope, Variance, C, Potency and Confidence-intervals:
--------------------------------------------------------------------------------
SStot SSreg SSres SSsmp R Radj
39.64714805 35.35886612 0.23240111 3.97720449 0.99606920 0.99578097
DFres b s2 qt C
30.0000000000 0.9042790078 0.0077467037 2.0422724563 1.0009146249
V
2.8827180835
Width Log(Lower) Log(Potency) Log(Upper) CM
Test_2 0.076347525 0.70314448 0.77877971 0.85583953 0.77949201
Unknown_3 0.074616815 0.49278409 0.56688242 0.64201772 0.56740090
exp(Width) Lower Potency Upper
Test_2 - 20 21.586752 40.401898 43.576237 47.066985
Test_2 - 100 107.933761 202.009488 217.881187 235.334926
Test_2 - 45.8965738974058 49.537898 92.715434 100.000000 108.010668
Unknown_3 - 20 21.549424 32.737341 35.255258 38.006226
Unknown_3 - 100 107.747120 163.686706 176.276292 190.031131
Unknown_3 - 56.7291261152691 61.124000 92.858038 100.000000 107.803000
exp(CM)
Test_2 - 20 43.607287
Test_2 - 100 218.036437
Test_2 - 45.8965738974058 100.071254
Unknown_3 - 20 35.273542
Unknown_3 - 100 176.367712
Unknown_3 - 56.7291261152691 100.051862
--------------------------------------------------------------------------------
================================================================================
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