Hello,Maybe a bit late but there is a contributed package [1] for quantitative PCR fitting non-linear models with the Levenberg-Marquardt algorithm.
estim and vector R below are your model and your fitted values vector. The RMSE of this fit is smaller than your model's.
Isn't this simpler? library(qpcR) df1 <- data.frame(Cycles = seq_along(high), high) fit <- pcrfit( data = df1, cyc = 1, fluo = 2 ) summary(fit) coef(estim) coef(fit) sqrt(sum(resid(estim)^2)) #[1] 1724.768 sqrt(sum(resid(fit)^2)) #[1] 1178.318 highpred <- predict(fit, newdata = df1) plot(1:45, high, type = "l", col = "red") points(1:45, R, col = "blue") points(1:45, highpred$Prediction, col = "cyan", pch = 3) [1] https://CRAN.R-project.org/package=qpcR Hope this helps, Rui Barradas Às 06:51 de 18/03/21, Luigi Marongiu escreveu:
It worked. I re-written the equation as: ``` rutledge_param <- function(p, x, y) ( (p$M / ( 1 + exp(-(x-p$m)/p$s)) ) + p$B ) - y ``` and used Desmos to estimate the slope, so: ``` estim <- nls.lm(par = list(m = halfCycle, s = 2.77, M = MaxFluo, B = high[1]), fn = rutledge_param, x = 1:45, y = high) summary(estim) R <- rutledge(list(half_fluorescence = 27.1102, slope = 2.7680, max_fluorescence = 11839.7745, back_fluorescence = -138.8615) , 1:45) points(1:45, R, type="l", col="red") ``` Thanks On Tue, Mar 16, 2021 at 8:29 AM Luigi Marongiu <marongiu.lu...@gmail.com> wrote:Just an update: I tried with desmos and the fitting looks good. Desmos calculated the parameters as: Fmax = 11839.8 Chalf = 27.1102 (with matches with my estimate of 27 cycles) k = 2.76798 Fb = -138.864 I forced R to accept the right parameters using a single named list and re-written the formula (it was a bit unclear in the paper): ``` rutledge <- function(p, x) { m = p$half_fluorescence s = p$slope M = p$max_fluorescence B = p$back_fluorescence y = (M / (1+exp( -((x-m)/s) )) ) + B return(y) } ``` but when I apply it I get a funny graph: ``` desmos <- rutledge(list(half_fluorescence = 27.1102, slope = 2.76798, max_fluorescence = 11839.8, back_fluorescence = -138.864) , high) ``` On Mon, Mar 15, 2021 at 7:39 AM Luigi Marongiu <marongiu.lu...@gmail.com> wrote:Hello, the negative data comes from the machine. Probably I should use raw data directly, although in the paper this requirement is not reported. The p$x was a typo. Now I corrected it and I got this error: ```rutledge_param <- function(p, x, y) ((p$M / (1 + exp(-1*(x-p$m)/p$s))) + p$B) - y estim <- nls.lm(par = list(m = halfFluo, s = slopes, M = MaxFluo, B = high[1]),+ fn = rutledge_param, x = 1:45, y = high) Error in dimnames(x) <- dn : length of 'dimnames' [2] not equal to array extent ``` Probably because 'slopes' is a vector instead of a scalar. Since the slope is changing, I don't think is right to use a scalar, but I tried and I got: ```estim <- nls.lm(par = list(m = halfFluo, s = 1, M = MaxFluo, B = high[1]),+ fn = rutledge_param, x = 1:45, y = high)estimNonlinear regression via the Levenberg-Marquardt algorithm parameter estimates: 6010.94, 1, 12021.88, 4700.49288888889 residual sum-of-squares: 1.14e+09 reason terminated: Relative error in the sum of squares is at most `ftol'. ``` The values reported are the same I used at the beginning apart from the last (the background parameter) which is 4700 instead of zero. If I plug it, I get an L shaped plot that is worse than that at the beginning: ``` after = init = rutledge(halfFluo, 1, MaxFluo, 4700.49288888889, high) points(1:45, after, type="l", col="blue") ``` What did I get wrong here? Thanks On Sun, Mar 14, 2021 at 8:05 PM Bill Dunlap <williamwdun...@gmail.com> wrote:rutledge_param <- function(p, x, y) ((p$M / (1 + exp(-1*(p$x-p$m)/p$s))) + p$B) - yDid you mean that p$x to be just x? As is, this returns numeric(0) for the p that nls.lm gives it because p$x is NULL and NULL-aNumber is numeric(). -Bill On Sun, Mar 14, 2021 at 9:46 AM Luigi Marongiu <marongiu.lu...@gmail.com> wrote:Hello, I would like to use the Rutledge equation (https://pubmed.ncbi.nlm.nih.gov/15601990/) to model PCR data. The equation is: Fc = Fmax / (1+exp(-(C-Chalf)/k)) + Fb I defined the equation and another that subtracts the values from the expectations. I used minpack.lm to get the parameters, but I got an error: ```library("minpack.lm") h <- c(120.64, 66.14, 34.87, 27.11, 8.87, -5.8, 4.52, -7.16, -17.39,+ -14.29, -20.26, -14.99, -21.05, -20.64, -8.03, -21.56, -1.28, 15.01, + 75.26, 191.76, 455.09, 985.96, 1825.59, 2908.08, 3993.18, 5059.94, + 6071.93, 6986.32, 7796.01, 8502.25, 9111.46, 9638.01, 10077.19, + 10452.02, 10751.81, 11017.49, 11240.37, 11427.47, 11570.07, 11684.96, + 11781.77, 11863.35, 11927.44, 11980.81, 12021.88, 12058.35, 12100.63, + 12133.57, 12148.89, 12137.09)high <- h[1:45] MaxFluo <- max(high) halfFluo <- MaxFluo/2 halfCycle = 27 find_slope <- function(X, Y) {+ Slope <- c(0) + for (i in 2:length(X)) { + delta_x <- X[i] - X[i-1] + delta_y <- Y[i] - Y[i-1] + Slope[i] <- delta_y/delta_x + } + return(Slope) + }slopes <- find_slope(1:45, high) rutledge <- function(m, s, M, B, x) {+ divisor = 1 + exp(-1* ((x-m)/s) ) + y = (M/divisor) + B + return(y) + }rutledge_param <- function(p, x, y) ((p$M / (1 + exp(-1*(p$x-p$m)/p$s))) + p$B) - y init = rutledge(halfFluo, slopes, MaxFluo, 0, high) points(1:45, init, type="l", col="red") estim <- nls.lm(par = list(m = halfFluo, s = slopes, M = MaxFluo, B = high[1]),+ fn = rutledge_param, x = 1:45, y = high) Error in nls.lm(par = list(m = halfFluo, s = slopes, M = MaxFluo, B = high[1]), : evaluation of fn function returns non-sensible value! ``` Where could the error be? -- Best regards, Luigi ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.-- Best regards, Luigi-- Best regards, Luigi
______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
