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.diffmich(x1, y1, x2, y2, permutations = 1000, a=3, b=0.5,
trace=FALSE, ...)fitmich(x, y, a=3, b=0.5)
## S3 method for class 'diffmich':
plot(x, y, which=3, two=2, ...)
x1
(for instance ETR measurements). Must have the same length as x1
.x2
(for instance ETR measurements). Must have the same length as x2
.lm
, which is used to calculate the regression lines)fitmich
is usually called only internally by diffmich
. Plotting object in the plot method.x1
(for instance ETR measurements). Must have the same length as x1
. Plotting object in the plotting method, optional when x
has appropriate structuretwo
) is automatically set to 1!which
is set to 3. Defaults to 2, which means that the display is divided in two halfs and the histogram-plots are plotted side by side. 3 causes histograms todiffmich
-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.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 ...
nls
, sample