Description Usage Arguments Details Value Author(s) See Also
The function can be used to calculate the difference in the two parameters of the Michaelis-Menten Kinetik y=(a*x)/(b+x) between two datasets containg each two vectors. Through permutation it is possible to compute significance of the difference. fitmich
is used to calculate the Michaelis-Menten fit to the data. With the corresponding plot method a plot of the actual difference in the parameters against a histogram of the permuted values can easily be achieved.
1 2 3 4 5 6 7 |
x1 |
Vector containing an independent variable, for instance PAR measurements. |
y1 |
Vector containing a variable dependent on |
x2 |
Vector containing a second independent variable (for instance PAR measurements). |
y2 |
Vector containing a variable dependent on |
permutations |
Number of permutations. |
a |
start value for parameter a, defaults to 3, usually there is no change necessary, but if the function gets trapped in the first run, changing the parameters might solve the problem. |
b |
Start value for parameter b, defaults to 0.5. |
trace |
set to TRUE for displaying the progress of the calculation |
... |
Arguments to other functions (for instance to |
x |
Vector containing an independent variable, for instance PAR measurements. Function |
y |
Vector containing a variable dependent on |
which |
Which histogram should be plotted? 1 triggers the histogram for parameter a, 2 the one for parameter b. It defaults to 3: both histograms are plotted. If it is changed from default the next argument ( |
two |
Should the histograms be printed on a divided display? And how? Can only be set if |
As the function was initially built to easily calculate the difference of parameters of the Michaelis-Menten Kinetik for PAM measurements, the independent vectors are meant to contain PAR values whereas the dependent vectors should represent ETR values. But you can use it for anything else which can be fitted with Michaelis-Menten. The vectors belonging together are formed into a data.frame
. For each permutation run the rows are interchanged randomly between the two data.frame
s and the difference in the parameters is calculated and collected into a vector. The p-value is then computed as the ratio between the number of cases where the differences in Parameter exceed the difference in parameter of the inital configuration and the number of permutations.
As it uses a for
loop it takes a while to calculate. So get a coffee while it is running, or set trace
= TRUE to avoid boring moments ...
Returns a diffmich
-object with the function call, the difference in the two parameters and their significance. Furthermore the number of permutations. If you want to change the way fitmich
is computed you can change the starting values. Per default it is calculated with starting values a=3 and b=0.5. There's no change needed unless the function gets trapped.
Gerald Jurasinski
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