Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/cellsurvLQdiff.R

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.

1 | ```
cellsurvLQdiff(X, curvevar, method="ml", PEmethod="fit")
``` |

`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. |

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`

.

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.

Herbert Braselmann

`glm`

and `family`

with references for generalized linear modelling. `anova`

, `cellsurvLQfit`

.

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"))
``` |

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