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OpenCPU | Chart.js | Michaelis Menten

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R-squared [1] | R-squared [2] | RMSE

function(x,y){ nearest <- which.min(abs(y - 0.5*max(y))) #index of Vmax/2 nlmodel <- nls(y ~ A*x/(B + x), start = list(A = max(y), B = x[nearest])) fit_res<-summary(nlmodel)$coefficients # e.g. A is summary(nlmodel)$coefficients[1] #xtra values are for a smooth display (not needed when # points is large) S_xtra<-c(seq(0,min(x)*0.8,length.out=10),seq(min(x),max(x)*1.2,length.out = 39)) # create a dummy S (x) vector (49 values here) vfit_xtra<- fit_res[1]*S_xtra/(fit_res[2] + S_xtra) # calculate new v (y) values (according to those 49 new values) error_squared<-(predict(nlmodel)-y)^2 RMSE<-sqrt(mean(error_squared)) return(c(fit_res[1:4],RMSE,S_xtra,vfit_xtra)) }

#	Estimate	Error
Vmax
Km

RMSE:

Error log

Code (R)