cellsurvLQdiff: Comparison of two linear-quadratic cell survival curves
In CFAssay: Statistical analysis for the Colony Formation Assay
Description
Usage
Arguments
Details
Value
Author(s)
See Also
Examples
View source: R/cellsurvLQdiff.R
Description
The function does an ANOVA test for overall comparison of the parameters alpha and beta of two linear-quadratic cell survival curves. The parameters are fitted simultaneously to the data with this function, i.e. no other function is necessary to derive the fits.
Usage
1
cellsurvLQdiff(X, curvevar, method="ml", PEmethod="fit")
Arguments
X
A data frame which contains columns Exp, dose, ncells, ncolonies and a further column containing two different values (character strings), which identify the two curves. Moreover, if there is no 0-value in the dose-column, X has to contain a column pe for plating efficiencies.
curvevar
Character string, which has to be one of the column names of the data frame X, that contains the two different values (character strings that distinguishes between the two curves).
method
Determines the method used for the fit. "ml" is for maximum-likelihood, "ls" for least-squares. "franken" performs weigthed least-squares with weights as described in Franken et al. (2006).
PEmethod
Controls the value of the plating efficiencies, i.e. the colony counts for untreated cells. "fit" calculates fitted plating efficiencies as model parameters, "fix" uses fixed ones calculated from the observed zero dose data or from a column named pe in X.
Details
In the data frame X, Exp identifies the experimental replicates and may be numeric or non-numeric. method="ml" for maximum-likelihood uses R function glm with family "quasipoisson" and link function "log". method="ls" uses R function lm.
Value
The function returns an object of class cellsurvLQdiff containing three elements, fit1, fit2 and anv. fit1 and fit2 are objects of class glm when method="ml" or of class lm when method="ls". fit1 has parameters alpha and beta fitted in common for both cell survival curves. fit2 has parameters alpha and beta fitted differently for both curves. anv is of class anova and contains the F-test. Test results are printed, however, the full result inlcuding curve parameters is returned invisibly, i.e. the function has to be used with print or assigned to a variable, say for e.g. fitcomp as in the example below.
Author(s)
Herbert Braselmann
See Also
glm and family with references for generalized linear modelling. anova, cellsurvLQfit.
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
datatab<- read.table(system.file("doc", "expl1_cellsurvcurves.txt", package="CFAssay"), header=TRUE, sep="\t")
names(datatab) #contains a column "cline"
table(datatab$cline)
fitcomp<- cellsurvLQdiff(datatab, curvevar="cline") #using default options
print(fitcomp)
plot(cellsurvLQfit(subset(datatab, cline=="okf6TERT1")), col=1)
plot(cellsurvLQfit(subset(datatab, cline=="cal33")), col=2, add=TRUE)
legend(0, 0.02, c("okf6TERT1", "cal33"), text.col=1:2)
#using different options:
print(cellsurvLQdiff(datatab, curvevar="cline", method="ls"))
print(cellsurvLQdiff(datatab, curvevar="cline", PEmethod="fix"))
print(cellsurvLQdiff(datatab, curvevar="cline", method="ls", PEmethod="fix"))
print(cellsurvLQdiff(datatab, curvevar="cline", method="franken"))
Example output
[1] "cline" "Exp" "dose" "ncells" "ncolonies"
cal33 okf6TERT1
24 48
*** Overall comparison for two linear-quadratic cell survival curves ***
Compared curves: cal33 okf6TERT1
method: ml
PEmethod: fit
Test used: F-test
F Pr(>F)
values 7.3509 0.001463 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Use 'print' to see detailed results
Overall comparison test for coefficients alpha and beta of LQ-models
====================================================================
method = ml
PEmethod = fit
12 PEs fitted as intercepts. To look at, use simple R print function.
Null hypothesis (Model 1): one set of shape parameters alpha and beta for all data
----------------------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
alpha -0.39893900 0.047865098 -8.334653 1.687495e-11
beta -0.03434671 0.007828292 -4.387510 4.906364e-05
Goodness-of-fit values
Residual Deviance: 317.2604
Total sum of squared weighted residuals rsswTot: 319.7611
Residual Degrees of Freedom: 58
Dispersion parameter: 5.513123
Alternative hypothesis (Model 2): two sets of shape parameters alpha and beta
-----------------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
alpha:curvescal33 -0.26831918 0.062588637 -4.287027 7.206618e-05
alpha:curvesokf6TERT1 -0.51937898 0.059669207 -8.704305 5.439622e-12
beta:curvescal33 -0.04892355 0.010058934 -4.863691 9.739225e-06
beta:curvesokf6TERT1 -0.02102614 0.009918948 -2.119795 3.846873e-02
Goodness-of-fit values
Residual Deviance: 251.804
Total sum of squared weighted residuals rsswTot: 249.3271
Residual Degrees of Freedom: 56
Dispersion parameter: 4.452269
Analysis of Variance Table and F-test
Model 2 versus Model 1
Resid. Df Resid. Dev Df Deviance F Pr(>F)
1 58 317.26
2 56 251.80 2 65.456 7.3509 0.001463 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
method = ml
PEmethod = fit
dose dose2
-0.51937898 -0.02102614
Use 'print' to see detailed results
method = ml
PEmethod = fit
dose dose2
-0.26831918 -0.04892355
Use 'print' to see detailed results
*** Overall comparison for two linear-quadratic cell survival curves ***
Compared curves: cal33 okf6TERT1
method: ls
PEmethod: fit
Test used: F-test
F Pr(>F)
values 8.5624 0.0005695 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Use 'print' to see detailed results
Overall comparison test for coefficients alpha and beta of LQ-models
====================================================================
method = ls
PEmethod = fit
12 PEs fitted as intercepts. To look at, use simple R print function.
Null hypothesis (Model 1): one set of shape parameters alpha and beta for all data
----------------------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
alpha -0.44210271 0.058993395 -7.494105 4.330475e-10
beta -0.03097808 0.009374666 -3.304446 1.634899e-03
Goodness-of-fit values
Total sum of squared residuals rssTot: 5.138071
Residual Degrees of Freedom: 58
Multiple R-squared: 0.95907
Alternative hypothesis (Model 2): two sets of shape parameters alpha and beta
-----------------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
alpha:curvescal33 -0.25601286 0.09100092 -2.813300 6.751349e-03
alpha:curvesokf6TERT1 -0.53514763 0.06434737 -8.316543 2.336628e-11
beta:curvescal33 -0.04744575 0.01446100 -3.280946 1.783720e-03
beta:curvesokf6TERT1 -0.02274424 0.01022547 -2.224274 3.017867e-02
Goodness-of-fit values
Total sum of squared residuals rssTot: 3.934809
Residual Degrees of Freedom: 56
Multiple R-squared: 0.9686552
Analysis of Variance Table and F-test
Model 2 versus Model 1
Res.Df RSS Df Sum of Sq F Pr(>F)
1 58 5.1381
2 56 3.9348 2 1.2033 8.5624 0.0005695 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
*** Overall comparison for two linear-quadratic cell survival curves ***
Compared curves: cal33 okf6TERT1
method: ml
PEmethod: fix
Test used: F-test
F Pr(>F)
values 19.637 3.45e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Use 'print' to see detailed results
Overall comparison test for coefficients alpha and beta of LQ-models
====================================================================
method = ml
PEmethod = fix
Null hypothesis (Model 1): one set of shape parameters alpha and beta for all data
----------------------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
alpha -0.36939379 0.048055945 -7.686745 2.054860e-10
beta -0.03910804 0.009968124 -3.923310 2.339076e-04
Goodness-of-fit values
Residual Deviance: 897.6402
Total sum of squared weighted residuals rsswTot: 841.7324
Residual Degrees of Freedom: 58
Dispersion parameter: 14.51263
Alternative hypothesis (Model 2): two sets of shape parameters alpha and beta
-----------------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
alpha:curvescal33 -0.22802046 0.05241601 -4.350207 5.815804e-05
alpha:curvesokf6TERT1 -0.51079080 0.05609421 -9.105946 1.216308e-12
beta:curvescal33 -0.05423143 0.01101630 -4.922835 7.891563e-06
beta:curvesokf6TERT1 -0.02274661 0.01150507 -1.977095 5.296198e-02
Goodness-of-fit values
Residual Deviance: 538.1011
Total sum of squared weighted residuals rsswTot: 512.6458
Residual Degrees of Freedom: 56
Dispersion parameter: 9.154389
Analysis of Variance Table and F-test
Model 2 versus Model 1
Resid. Df Resid. Dev Df Deviance F Pr(>F)
1 58 897.64
2 56 538.10 2 359.54 19.637 3.45e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
*** Overall comparison for two linear-quadratic cell survival curves ***
Compared curves: cal33 okf6TERT1
method: ls
PEmethod: fix
Test used: F-test
F Pr(>F)
values 15.038 5.924e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Use 'print' to see detailed results
Overall comparison test for coefficients alpha and beta of LQ-models
====================================================================
method = ls
PEmethod = fix
Null hypothesis (Model 1): one set of shape parameters alpha and beta for all data
----------------------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
alpha -0.42775991 0.06443191 -6.638945 1.181206e-08
beta -0.03278904 0.01288638 -2.544472 1.362888e-02
Goodness-of-fit values
Total sum of squared residuals rssTot: 15.83755
Residual Degrees of Freedom: 58
Multiple R-squared: 0.984003
Alternative hypothesis (Model 2): two sets of shape parameters alpha and beta
-----------------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
alpha:curvescal33 -0.22201908 0.09160853 -2.423564 1.862519e-02
alpha:curvesokf6TERT1 -0.53063032 0.06477701 -8.191646 3.744222e-11
beta:curvescal33 -0.05173789 0.01832171 -2.823858 6.560102e-03
beta:curvesokf6TERT1 -0.02331461 0.01295540 -1.799605 7.731114e-02
Goodness-of-fit values
Total sum of squared residuals rssTot: 10.30378
Residual Degrees of Freedom: 56
Multiple R-squared: 0.9895589
Analysis of Variance Table and F-test
Model 2 versus Model 1
Res.Df RSS Df Sum of Sq F Pr(>F)
1 58 15.838
2 56 10.304 2 5.5338 15.038 5.924e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
*** Overall comparison for two linear-quadratic cell survival curves ***
Compared curves: cal33 okf6TERT1
method: franken
PEmethod: fix
Test used: F-test
F Pr(>F)
values 16.948 1.756e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Use 'print' to see detailed results
Overall comparison test for coefficients alpha and beta of LQ-models
====================================================================
method = franken
PEmethod = fix
Null hypothesis (Model 1): one set of shape parameters alpha and beta for all data
----------------------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
alpha -0.34268389 0.044434197 -7.712166 1.862428e-10
beta -0.03917355 0.009218242 -4.249568 7.867653e-05
Goodness-of-fit values
Total sum of squared weighted residuals rsswTot: 767.0095
Residual Degrees of Freedom: 58
Multiple R-squared: 0.990357
Alternative hypothesis (Model 2): two sets of shape parameters alpha and beta
-----------------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
alpha:curvescal33 -0.22859933 0.04836349 -4.726692 1.580129e-05
alpha:curvesokf6TERT1 -0.48879371 0.05293026 -9.234675 7.547585e-13
beta:curvescal33 -0.05214356 0.01011265 -5.156271 3.413201e-06
beta:curvesokf6TERT1 -0.02190780 0.01087920 -2.013733 4.885208e-02
Goodness-of-fit values
Total sum of squared weighted residuals rsswTot: 477.8013
Residual Degrees of Freedom: 56
Multiple R-squared: 0.9939996
Analysis of Variance Table and F-test
Model 2 versus Model 1
Res.Df RSS Df Sum of Sq F Pr(>F)
1 58 767.01
2 56 477.80 2 289.21 16.948 1.756e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/cellsurvLQdiff.R
Description
The function does an ANOVA test for overall comparison of the parameters alpha and beta of two linear-quadratic cell survival curves. The parameters are fitted simultaneously to the data with this function, i.e. no other function is necessary to derive the fits.
Usage
1 | cellsurvLQdiff(X, curvevar, method="ml", PEmethod="fit")
|
Arguments
X |
A data frame which contains columns |
curvevar |
Character string, which has to be one of the column names of the data frame |
method |
Determines the method used for the fit. |
PEmethod |
Controls the value of the plating efficiencies, i.e. the colony counts for untreated cells. |
Details
In the data frame X, Exp identifies the experimental replicates and may be numeric or non-numeric. method="ml" for maximum-likelihood uses R function glm with family "quasipoisson" and link function "log". method="ls" uses R function lm.
Value
The function returns an object of class cellsurvLQdiff containing three elements, fit1, fit2 and anv. fit1 and fit2 are objects of class glm when method="ml" or of class lm when method="ls". fit1 has parameters alpha and beta fitted in common for both cell survival curves. fit2 has parameters alpha and beta fitted differently for both curves. anv is of class anova and contains the F-test. Test results are printed, however, the full result inlcuding curve parameters is returned invisibly, i.e. the function has to be used with print or assigned to a variable, say for e.g. fitcomp as in the example below.
Author(s)
Herbert Braselmann
See Also
glm and family with references for generalized linear modelling. anova, cellsurvLQfit.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 | datatab<- read.table(system.file("doc", "expl1_cellsurvcurves.txt", package="CFAssay"), header=TRUE, sep="\t")
names(datatab) #contains a column "cline"
table(datatab$cline)
fitcomp<- cellsurvLQdiff(datatab, curvevar="cline") #using default options
print(fitcomp)
plot(cellsurvLQfit(subset(datatab, cline=="okf6TERT1")), col=1)
plot(cellsurvLQfit(subset(datatab, cline=="cal33")), col=2, add=TRUE)
legend(0, 0.02, c("okf6TERT1", "cal33"), text.col=1:2)
#using different options:
print(cellsurvLQdiff(datatab, curvevar="cline", method="ls"))
print(cellsurvLQdiff(datatab, curvevar="cline", PEmethod="fix"))
print(cellsurvLQdiff(datatab, curvevar="cline", method="ls", PEmethod="fix"))
print(cellsurvLQdiff(datatab, curvevar="cline", method="franken"))
|
Example output
[1] "cline" "Exp" "dose" "ncells" "ncolonies"
cal33 okf6TERT1
24 48
*** Overall comparison for two linear-quadratic cell survival curves ***
Compared curves: cal33 okf6TERT1
method: ml
PEmethod: fit
Test used: F-test
F Pr(>F)
values 7.3509 0.001463 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Use 'print' to see detailed results
Overall comparison test for coefficients alpha and beta of LQ-models
====================================================================
method = ml
PEmethod = fit
12 PEs fitted as intercepts. To look at, use simple R print function.
Null hypothesis (Model 1): one set of shape parameters alpha and beta for all data
----------------------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
alpha -0.39893900 0.047865098 -8.334653 1.687495e-11
beta -0.03434671 0.007828292 -4.387510 4.906364e-05
Goodness-of-fit values
Residual Deviance: 317.2604
Total sum of squared weighted residuals rsswTot: 319.7611
Residual Degrees of Freedom: 58
Dispersion parameter: 5.513123
Alternative hypothesis (Model 2): two sets of shape parameters alpha and beta
-----------------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
alpha:curvescal33 -0.26831918 0.062588637 -4.287027 7.206618e-05
alpha:curvesokf6TERT1 -0.51937898 0.059669207 -8.704305 5.439622e-12
beta:curvescal33 -0.04892355 0.010058934 -4.863691 9.739225e-06
beta:curvesokf6TERT1 -0.02102614 0.009918948 -2.119795 3.846873e-02
Goodness-of-fit values
Residual Deviance: 251.804
Total sum of squared weighted residuals rsswTot: 249.3271
Residual Degrees of Freedom: 56
Dispersion parameter: 4.452269
Analysis of Variance Table and F-test
Model 2 versus Model 1
Resid. Df Resid. Dev Df Deviance F Pr(>F)
1 58 317.26
2 56 251.80 2 65.456 7.3509 0.001463 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
method = ml
PEmethod = fit
dose dose2
-0.51937898 -0.02102614
Use 'print' to see detailed results
method = ml
PEmethod = fit
dose dose2
-0.26831918 -0.04892355
Use 'print' to see detailed results
*** Overall comparison for two linear-quadratic cell survival curves ***
Compared curves: cal33 okf6TERT1
method: ls
PEmethod: fit
Test used: F-test
F Pr(>F)
values 8.5624 0.0005695 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Use 'print' to see detailed results
Overall comparison test for coefficients alpha and beta of LQ-models
====================================================================
method = ls
PEmethod = fit
12 PEs fitted as intercepts. To look at, use simple R print function.
Null hypothesis (Model 1): one set of shape parameters alpha and beta for all data
----------------------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
alpha -0.44210271 0.058993395 -7.494105 4.330475e-10
beta -0.03097808 0.009374666 -3.304446 1.634899e-03
Goodness-of-fit values
Total sum of squared residuals rssTot: 5.138071
Residual Degrees of Freedom: 58
Multiple R-squared: 0.95907
Alternative hypothesis (Model 2): two sets of shape parameters alpha and beta
-----------------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
alpha:curvescal33 -0.25601286 0.09100092 -2.813300 6.751349e-03
alpha:curvesokf6TERT1 -0.53514763 0.06434737 -8.316543 2.336628e-11
beta:curvescal33 -0.04744575 0.01446100 -3.280946 1.783720e-03
beta:curvesokf6TERT1 -0.02274424 0.01022547 -2.224274 3.017867e-02
Goodness-of-fit values
Total sum of squared residuals rssTot: 3.934809
Residual Degrees of Freedom: 56
Multiple R-squared: 0.9686552
Analysis of Variance Table and F-test
Model 2 versus Model 1
Res.Df RSS Df Sum of Sq F Pr(>F)
1 58 5.1381
2 56 3.9348 2 1.2033 8.5624 0.0005695 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
*** Overall comparison for two linear-quadratic cell survival curves ***
Compared curves: cal33 okf6TERT1
method: ml
PEmethod: fix
Test used: F-test
F Pr(>F)
values 19.637 3.45e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Use 'print' to see detailed results
Overall comparison test for coefficients alpha and beta of LQ-models
====================================================================
method = ml
PEmethod = fix
Null hypothesis (Model 1): one set of shape parameters alpha and beta for all data
----------------------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
alpha -0.36939379 0.048055945 -7.686745 2.054860e-10
beta -0.03910804 0.009968124 -3.923310 2.339076e-04
Goodness-of-fit values
Residual Deviance: 897.6402
Total sum of squared weighted residuals rsswTot: 841.7324
Residual Degrees of Freedom: 58
Dispersion parameter: 14.51263
Alternative hypothesis (Model 2): two sets of shape parameters alpha and beta
-----------------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
alpha:curvescal33 -0.22802046 0.05241601 -4.350207 5.815804e-05
alpha:curvesokf6TERT1 -0.51079080 0.05609421 -9.105946 1.216308e-12
beta:curvescal33 -0.05423143 0.01101630 -4.922835 7.891563e-06
beta:curvesokf6TERT1 -0.02274661 0.01150507 -1.977095 5.296198e-02
Goodness-of-fit values
Residual Deviance: 538.1011
Total sum of squared weighted residuals rsswTot: 512.6458
Residual Degrees of Freedom: 56
Dispersion parameter: 9.154389
Analysis of Variance Table and F-test
Model 2 versus Model 1
Resid. Df Resid. Dev Df Deviance F Pr(>F)
1 58 897.64
2 56 538.10 2 359.54 19.637 3.45e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
*** Overall comparison for two linear-quadratic cell survival curves ***
Compared curves: cal33 okf6TERT1
method: ls
PEmethod: fix
Test used: F-test
F Pr(>F)
values 15.038 5.924e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Use 'print' to see detailed results
Overall comparison test for coefficients alpha and beta of LQ-models
====================================================================
method = ls
PEmethod = fix
Null hypothesis (Model 1): one set of shape parameters alpha and beta for all data
----------------------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
alpha -0.42775991 0.06443191 -6.638945 1.181206e-08
beta -0.03278904 0.01288638 -2.544472 1.362888e-02
Goodness-of-fit values
Total sum of squared residuals rssTot: 15.83755
Residual Degrees of Freedom: 58
Multiple R-squared: 0.984003
Alternative hypothesis (Model 2): two sets of shape parameters alpha and beta
-----------------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
alpha:curvescal33 -0.22201908 0.09160853 -2.423564 1.862519e-02
alpha:curvesokf6TERT1 -0.53063032 0.06477701 -8.191646 3.744222e-11
beta:curvescal33 -0.05173789 0.01832171 -2.823858 6.560102e-03
beta:curvesokf6TERT1 -0.02331461 0.01295540 -1.799605 7.731114e-02
Goodness-of-fit values
Total sum of squared residuals rssTot: 10.30378
Residual Degrees of Freedom: 56
Multiple R-squared: 0.9895589
Analysis of Variance Table and F-test
Model 2 versus Model 1
Res.Df RSS Df Sum of Sq F Pr(>F)
1 58 15.838
2 56 10.304 2 5.5338 15.038 5.924e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
*** Overall comparison for two linear-quadratic cell survival curves ***
Compared curves: cal33 okf6TERT1
method: franken
PEmethod: fix
Test used: F-test
F Pr(>F)
values 16.948 1.756e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Use 'print' to see detailed results
Overall comparison test for coefficients alpha and beta of LQ-models
====================================================================
method = franken
PEmethod = fix
Null hypothesis (Model 1): one set of shape parameters alpha and beta for all data
----------------------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
alpha -0.34268389 0.044434197 -7.712166 1.862428e-10
beta -0.03917355 0.009218242 -4.249568 7.867653e-05
Goodness-of-fit values
Total sum of squared weighted residuals rsswTot: 767.0095
Residual Degrees of Freedom: 58
Multiple R-squared: 0.990357
Alternative hypothesis (Model 2): two sets of shape parameters alpha and beta
-----------------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
alpha:curvescal33 -0.22859933 0.04836349 -4.726692 1.580129e-05
alpha:curvesokf6TERT1 -0.48879371 0.05293026 -9.234675 7.547585e-13
beta:curvescal33 -0.05214356 0.01011265 -5.156271 3.413201e-06
beta:curvesokf6TERT1 -0.02190780 0.01087920 -2.013733 4.885208e-02
Goodness-of-fit values
Total sum of squared weighted residuals rsswTot: 477.8013
Residual Degrees of Freedom: 56
Multiple R-squared: 0.9939996
Analysis of Variance Table and F-test
Model 2 versus Model 1
Res.Df RSS Df Sum of Sq F Pr(>F)
1 58 767.01
2 56 477.80 2 289.21 16.948 1.756e-06 ***
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Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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