Hello,
We use drc to fit dose-response curves, recently we discovered that there are quite different standard error values returned for the same dataset depending on the drc-version / R-version that was used (not clear which factor is important) On R 2.9.0 using drc_1.6-3 we get an IC50 of 1.27447 and a standard error on the IC50 of 0.43540 Whereas on R 2.7.0 using drc_1.4-2 the IC50 is 1.2039e+00 and the standard error is 3.7752e-03 Normally I would use the most recent version (both R and drc library) but it seems to me that a standard error of 0.4 on a mean of 1.2 is too big, so I trust the values we get with the older versions more Has anyone suggestions on - how to solve these discrepancies, if possible - how to calculate which one of the 2 solutions is the correct one? Thanks a lot, Hans Vermeiren Demo (on a windows machine, while the issue was actually discovered on our ubuntu linux server): 1) sessionInfo() R version 2.7.0 (2008-04-22) i386-pc-mingw32 locale: LC_COLLATE=Dutch_Belgium.1252;LC_CTYPE=Dutch_Belgium.1252;LC_MONETARY=Du tch_Belgium.1252;LC_NUMERIC=C;LC_TIME=Dutch_Belgium.1252 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] drc_1.4-2 plotrix_2.4-2 nlme_3.1-89 MASS_7.2-41 lattice_0.17-6 [6] alr3_1.1.7 loaded via a namespace (and not attached): [1] grid_2.7.0 d<-data.frame(dose=c(2.00e-05, 4.00e-06, 8.00e-07, 1.60e-07, 3.20e-08, 6.40e-09, 1.28e-09, 2.56e-10, 5.10e-11, 1.00e-11, 2.00e-05, 4.00e-06, 8.00e-07, 1.60e-07, 3.20e-08, 6.40e-09, 1.28e-09, 2.56e-10, 5.10e-11, 1.00e-11), response=c(97.202,81.670,47.292,16.924, 16.832, 6.832, 11.118, 1.319, 5.495, -3.352, 102.464, 83.114, 50.631, 22.792, 18.348, 19.066, 27.794, 14.682, 11.992, 12.868)) m<- drm(response ~ (log10(dose*1e6)), data = d, fct = l4(fixed = c(NA,NA,NA,NA), names = c("hs", "bottom", "top", "ec50")), logDose = 10, control = drmc(useD = T)) summary(m) results in: Model fitted: Log-logistic (ED50 as parameter) (4 parms) Parameter estimates: Estimate Std. Error t-value p-value hs:(Intercept) -9.8065e-01 2.5821e-03 -3.7979e+02 2.248e-33 bottom:(Intercept) 1.0955e+01 2.2546e-02 4.8591e+02 4.364e-35 top:(Intercept) 1.0502e+02 9.0935e-02 1.1549e+03 4.210e-41 ec50:(Intercept) 1.2039e+00 3.7752e-03 3.1890e+02 3.681e-32 Residual standard error: 7.026655 (16 degrees of freedom) ======================================================================== =========================================== 2) sessionInfo() R version 2.9.0 (2009-04-17) i386-pc-mingw32 locale: LC_COLLATE=Dutch_Belgium.1252;LC_CTYPE=Dutch_Belgium.1252;LC_MONETARY=Du tch_Belgium.1252;LC_NUMERIC=C;LC_TIME=Dutch_Belgium.1252 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] drc_1.6-3 plotrix_2.5-5 nlme_3.1-90 MASS_7.2-46 magic_1.4-4 abind_1.1-0 lattice_0.17-22 alr3_1.1.7 loaded via a namespace (and not attached): [1] grid_2.9.0 tools_2.9.0 d<-data.frame(dose=c(2.00e-05, 4.00e-06, 8.00e-07, 1.60e-07, 3.20e-08, 6.40e-09, 1.28e-09, 2.56e-10, 5.10e-11, 1.00e-11, 2.00e-05, 4.00e-06, 8.00e-07, 1.60e-07, 3.20e-08, 6.40e-09, 1.28e-09, 2.56e-10, 5.10e-11, 1.00e-11), response=c(97.202,81.670,47.292,16.924, 16.832, 6.832, 11.118, 1.319, 5.495, -3.352, 102.464, 83.114, 50.631, 22.792, 18.348, 19.066, 27.794, 14.682, 11.992, 12.868)) m<- drm(response ~ (log10(dose*1e6)), data = d, fct = l4(fixed = c(NA,NA,NA,NA), names = c("hs", "bottom", "top", "ec50")), logDose = 10, control = drmc(useD = T)) summary(m) gives: Model fitted: Log-logistic (ED50 as parameter) (4 parms) Parameter estimates: Estimate Std. Error t-value p-value hs:(Intercept) -0.95266 0.25778 -3.69564 0.0020 bottom:(Intercept) 10.97437 2.24421 4.89009 0.0002 top:(Intercept) 106.38373 9.98378 10.65565 1.127e-08 ec50:(Intercept) 1.27447 0.43540 2.92712 0.0099 Residual standard error: 7.020175 (16 degrees of freedom) -- This e-mail and its attachment(s) (if any) may contain confidential and/or proprietary information and is intended for its addressee(s) only. Any unauthorized use of the information contained herein (including, but not limited to, alteration, reproduction, communication, distribution or any other form of dissemination) is strictly prohibited. If you are not the intended addressee, please notify the orginator promptly and delete this e-mail and its attachment(s) (if any) subsequently. Galapagos nor any of its affiliates shall be liable for direct, special, indirect or consequential damages arising from alteration of the contents of this message (by a third party) or as a result of a virus being passed on. ______________________________________________ [hidden email] mailing list 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. |
On May 20, 2009, at 11:20 AM, Hans Vermeiren wrote:
> Hello, > > We use drc to fit dose-response curves, recently we discovered that > there are quite different standard error values returned for the same > dataset depending on the drc-version / R-version that was used (not > clear which factor is important) > On R 2.9.0 using drc_1.6-3 we get an IC50 of 1.27447 and a standard > error on the IC50 of 0.43540 > Whereas on R 2.7.0 using drc_1.4-2 the IC50 is 1.2039e+00 and the > standard error is 3.7752e-03 > Normally I would use the most recent version (both R and drc library) > but it seems to me that a standard error of 0.4 on a mean of 1.2 is > too > big, so I trust the values we get with the older versions more > Has anyone suggestions on > - how to solve these discrepancies, if possible > - how to calculate which one of the 2 solutions is the correct one? > > Thanks a lot, > Hans Vermeiren > > Demo (on a windows machine, while the issue was actually discovered on > our ubuntu linux server): > 1) > sessionInfo() > R version 2.7.0 (2008-04-22) > i386-pc-mingw32 > > locale: > LC_COLLATE=Dutch_Belgium.1252;LC_CTYPE=Dutch_Belgium. > 1252;LC_MONETARY=Du > tch_Belgium.1252;LC_NUMERIC=C;LC_TIME=Dutch_Belgium.1252 > > attached base packages: > [1] stats graphics grDevices utils datasets methods base > > > other attached packages: > [1] drc_1.4-2 plotrix_2.4-2 nlme_3.1-89 MASS_7.2-41 > lattice_0.17-6 > [6] alr3_1.1.7 > > loaded via a namespace (and not attached): > [1] grid_2.7.0 > > d<-data.frame(dose=c(2.00e-05, 4.00e-06, 8.00e-07, 1.60e-07, 3.20e-08, > 6.40e-09, 1.28e-09, 2.56e-10, 5.10e-11, 1.00e-11, 2.00e-05, 4.00e-06, > 8.00e-07, 1.60e-07, 3.20e-08, 6.40e-09, 1.28e-09, 2.56e-10, 5.10e-11, > 1.00e-11), > response=c(97.202,81.670,47.292,16.924, 16.832, 6.832, 11.118, > 1.319, 5.495, -3.352, 102.464, 83.114, 50.631, 22.792, 18.348, > 19.066, 27.794, 14.682, 11.992, 12.868)) > > m<- drm(response ~ (log10(dose*1e6)), data = d, fct = l4(fixed = > c(NA,NA,NA,NA), names = c("hs", "bottom", "top", "ec50")), logDose = > 10, > control = drmc(useD = T)) > > summary(m) > results in: > Model fitted: Log-logistic (ED50 as parameter) (4 parms) > > Parameter estimates: > > Estimate Std. Error t-value p-value > hs:(Intercept) -9.8065e-01 2.5821e-03 -3.7979e+02 2.248e-33 > bottom:(Intercept) 1.0955e+01 2.2546e-02 4.8591e+02 4.364e-35 > top:(Intercept) 1.0502e+02 9.0935e-02 1.1549e+03 4.210e-41 > ec50:(Intercept) 1.2039e+00 3.7752e-03 3.1890e+02 3.681e-32 > > Residual standard error: 7.026655 (16 degrees of freedom) > = > = > ====================================================================== > =========================================== > 2) > sessionInfo() > R version 2.9.0 (2009-04-17) > i386-pc-mingw32 > > locale: > LC_COLLATE=Dutch_Belgium.1252;LC_CTYPE=Dutch_Belgium. > 1252;LC_MONETARY=Du > tch_Belgium.1252;LC_NUMERIC=C;LC_TIME=Dutch_Belgium.1252 > > attached base packages: > [1] stats graphics grDevices utils datasets methods base > > > other attached packages: > [1] drc_1.6-3 plotrix_2.5-5 nlme_3.1-90 MASS_7.2-46 > magic_1.4-4 abind_1.1-0 lattice_0.17-22 alr3_1.1.7 > > loaded via a namespace (and not attached): > [1] grid_2.9.0 tools_2.9.0 > > d<-data.frame(dose=c(2.00e-05, 4.00e-06, 8.00e-07, 1.60e-07, 3.20e-08, > 6.40e-09, 1.28e-09, 2.56e-10, 5.10e-11, 1.00e-11, 2.00e-05, 4.00e-06, > 8.00e-07, 1.60e-07, 3.20e-08, 6.40e-09, 1.28e-09, 2.56e-10, 5.10e-11, > 1.00e-11), > response=c(97.202,81.670,47.292,16.924, 16.832, 6.832, 11.118, > 1.319, 5.495, -3.352, 102.464, 83.114, 50.631, 22.792, 18.348, > 19.066, 27.794, 14.682, 11.992, 12.868)) > > m<- drm(response ~ (log10(dose*1e6)), data = d, fct = l4(fixed = > c(NA,NA,NA,NA), names = c("hs", "bottom", "top", "ec50")), logDose = > 10, > control = drmc(useD = T)) > > summary(m) > > gives: > Model fitted: Log-logistic (ED50 as parameter) (4 parms) > > Parameter estimates: > > Estimate Std. Error t-value p-value > hs:(Intercept) -0.95266 0.25778 -3.69564 0.0020 > bottom:(Intercept) 10.97437 2.24421 4.89009 0.0002 > top:(Intercept) 106.38373 9.98378 10.65565 1.127e-08 > ec50:(Intercept) 1.27447 0.43540 2.92712 0.0099 > > Residual standard error: > > 7.020175 (16 degrees of freedom) Hans, You have three important factors changing here. The version of R, the version of drc and the versions of any relevant drc dependencies (alr3, lattice, magic, MASS, nlme, plotrix). I would first try to install the newer version of drc on the older R system (all else staying the same) and see what you get. Don't run update.packages() here, lest you change other things. Just install the newer version of drc. If you get the same results as the older version, then it might lead you to something in R or one of the package dependencies changing. If you get a different result, then it would lead to something in drc changing. You can also install the old version of drc on your more recent R system to see what you get, which might help to confirm behavior. The old source version of drc would be available from: http://cran.r-project.org/src/contrib/Archive/drc/ I also found a Windows binary of the old package here: http://cran.r-project.org/bin/windows/contrib/2.6/drc_1.4-2.zip I have also copied Christian Ritz, the drc package author/maintainer, so that he may be able to assist you further with the problem. HTH, Marc Schwartz ______________________________________________ [hidden email] mailing list 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. |
Hello
Thanks a lot Marc, for the suggestion to explore the issue a bit more systematically So I did and the conclusion is that with the old drc 1.4-2, I get a SE=0.003, with the new drc 1.5-2, I get a SE=0.4 irrespective of the R version or the version of the packages drc depends on I hope somebody can help me further, I still have the feeling that a SE of 0.003 on an IC50 of 1.2, for a reasonable good fit is more realistic than a SE of 0.4 on an IC50 of 1.2 Regards, Hans Here are the results of the following test script: d<-data.frame(dose=c(2.00e-05,4.00e-06,8.00e-07,1.60e-07,3.20e-08,6.40e- 09,1.28e-09,2.56e-10,5.10e-11,1.00e-11,2.00e-05,4.00e-06,8.00e-07,1.60e- 07,3.20e-08,6.40e-09,1.28e-09,2.56e-10,5.10e-11,1.00e-11),response=c(97. 202,81.670,47.292,16.924, 16.832, 6.832, 11.118, 1.319, 5.495, -3.352, 102.464, 83.114, 50.631, 22.792, 18.348, 19.066, 27.794, 14.682, 11.992, 12.868)) m<- drm(response ~ (log10(dose*1e6)), data = d, fct = l4(fixed = c(NA,NA,NA,NA), names = c("hs", "bottom", "top", "ec50")), logDose = 10, control = drmc(useD = T)) summary(m) RESULTS: sessionInfo() R version 2.7.0 (2008-04-22) i386-pc-mingw32 locale: LC_COLLATE=Dutch_Belgium.1252;LC_CTYPE=Dutch_Belgium.1252;LC_MONETARY=Du tch_Belgium.1252;LC_NUMERIC=C;LC_TIME=Dutch_Belgium.1252 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] drc_1.5-2 plotrix_2.4-2 nlme_3.1-89 MASS_7.2-41 lattice_0.17-6 [6] alr3_1.1.7 loaded via a namespace (and not attached): [1] grid_2.7.0 tools_2.7.0 RESULT: ec50:(Intercept)=1.27447 SE=0.43541 CONCLUSION: R 2.7.0 with recent drc 1.5-2 (older dependencies) gives SE=0.43541 ======================================================================== ======= R version 2.9.0 (2009-04-17) i386-pc-mingw32 locale: LC_COLLATE=Dutch_Belgium.1252;LC_CTYPE=Dutch_Belgium.1252;LC_MONETARY=Du tch_Belgium.1252;LC_NUMERIC=C;LC_TIME=Dutch_Belgium.1252 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] drc_1.4-2 plotrix_2.5-5 nlme_3.1-90 MASS_7.2-46 [5] lattice_0.17-22 alr3_1.1.7 loaded via a namespace (and not attached): [1] grid_2.9.0 tools_2.9.0 RESULT: ec50:(Intercept) SE=1.2039e+00 CONCLUSION: R 2.9.0 with old drc 1.4-2 (newer dependencies) gives SE=0.003 Hans, You have three important factors changing here. The version of R, the version of drc and the versions of any relevant drc dependencies (alr3, lattice, magic, MASS, nlme, plotrix). I would first try to install the newer version of drc on the older R system (all else staying the same) and see what you get. Don't run update.packages() here, lest you change other things. Just install the newer version of drc. If you get the same results as the older version, then it might lead you to something in R or one of the package dependencies changing. If you get a different result, then it would lead to something in drc changing. You can also install the old version of drc on your more recent R system to see what you get, which might help to confirm behavior. The old source version of drc would be available from: http://cran.r-project.org/src/contrib/Archive/drc/ I also found a Windows binary of the old package here: http://cran.r-project.org/bin/windows/contrib/2.6/drc_1.4-2.zip I have also copied Christian Ritz, the drc package author/maintainer, so that he may be able to assist you further with the problem. HTH, Marc Schwartz -- This e-mail and its attachment(s) (if any) may contain confidential and/or proprietary information and is intended for its addressee(s) only. Any unauthorized use of the information contained herein (including, but not limited to, alteration, reproduction, communication, distribution or any other form of dissemination) is strictly prohibited. If you are not the intended addressee, please notify the orginator promptly and delete this e-mail and its attachment(s) (if any) subsequently. Galapagos nor any of its affiliates shall be liable for direct, special, indirect or consequential damages arising from alteration of the contents of this message (by a third party) or as a result of a virus being passed on. ______________________________________________ [hidden email] mailing list 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. |
In reply to this post by Hans Vermeiren
Hi Hans,
I hope I can resolve your problems below (Marc, thank you very much for cc'ing me on your initial response!). Have a look at the following R lines: ## Fitting the model using drm() (from the latest version) m1<- drm(response ~ dose, data = d, fct = LL.4()) summary(m1) plot(m1) ## Checking the fit by using nls() ## (we have very good guesses for the parameter estimates) m2 <- nls(response ~ c + (d - c)/(1 + (dose/e)^b), data=d, start=list(b=-0.95, c=10, d=106, e=1.2745e-06)) summary(m2) The standard errors agree quite well. The minor discrepancies between to two fits are attributable to different numerical approximations of the variance-covariance matrix being used in drm() and nls(). So I would use the latest version of 'drc', especially for datasets with really small doses. One recent change to drm() was to incorporate several layers of scaling prior to estimation (as well as subsequent back scaling after estimation): 1) scaling of parameters with the same scale as the x axis 2) scaling of parameters with the same scale as the y axis 3) scaling of parameters in optim() The effect of scaling is to temporarily "convert" the dataset (and the model) to scales that are more convenient for the estimation procedure. Any feedback on this would be much appreciated. Therefore it should also not be necessary to manually do any scaling prior to using drm() (like what you did). Compare, for instance, your specification of drm() to mine above. Is this explanation useful?! Christian ______________________________________________ [hidden email] mailing list 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. |
Yes, thanks that's very useful
Apart from checking the fit with nls() as you suggested, I've also used Prism, which gave the following results Equation 1 Best-fit values BOTTOM 10.96 TOP 106.4 LOGEC50 -5.897 HILLSLOPE 0.9501 EC50 1.2670e-006 Std. Error BOTTOM 2.196 TOP 9.337 LOGEC50 0.1439 HILLSLOPE 0.2270 95% Confidence Intervals BOTTOM 6.301 to 15.61 TOP 86.62 to 126.2 LOGEC50 -6.202 to -5.592 HILLSLOPE 0.4689 to 1.431 EC50 6.2750e-007 to 2.5560e-006 Goodness of Fit Degrees of Freedom 16 R² 0.9622 Absolute Sum of Squares 787.5 Sy.x 7.015 Data Number of X values 20 Number of Y replicates 1 Total number of values 20 Number of missing values 0 In other words: also in line with the drc 1.6-3 and nls() results As for the scaling: yes this is useful because I can't predict whether concentrations are in molar, micromolar,..., right now I indeed scaled dose-values "manually", it's better/ more robust when the drm-function takes care of that I suppose this also means I don't have to do the log transformation anymore? Thanks (both of you) for your swift feedback Hans -----Original Message----- From: Christian Ritz [mailto:[hidden email]] Sent: vrijdag 22 mei 2009 11:30 To: Hans Vermeiren Cc: [hidden email]; [hidden email] Subject: Re: [R] drc results differ for different versions Hi Hans, I hope I can resolve your problems below (Marc, thank you very much for cc'ing me on your initial response!). Have a look at the following R lines: ## Fitting the model using drm() (from the latest version) m1<- drm(response ~ dose, data = d, fct = LL.4()) summary(m1) plot(m1) ## Checking the fit by using nls() ## (we have very good guesses for the parameter estimates) m2 <- nls(response ~ c + (d - c)/(1 + (dose/e)^b), data=d, start=list(b=-0.95, c=10, d=106, e=1.2745e-06)) summary(m2) The standard errors agree quite well. The minor discrepancies between to two fits are attributable to different numerical approximations of the variance-covariance matrix being used in drm() and nls(). So I would use the latest version of 'drc', especially for datasets with really small doses. One recent change to drm() was to incorporate several layers of scaling prior to estimation (as well as subsequent back scaling after estimation): 1) scaling of parameters with the same scale as the x axis 2) scaling of parameters with the same scale as the y axis 3) scaling of parameters in optim() The effect of scaling is to temporarily "convert" the dataset (and the model) to scales that are more convenient for the estimation procedure. Any feedback on this would be much appreciated. Therefore it should also not be necessary to manually do any scaling prior to using drm() (like what you did). Compare, for instance, your specification of drm() to mine above. Is this explanation useful?! Christian -- This e-mail and its attachment(s) (if any) may contain confidential and/or proprietary information and is intended for its addressee(s) only. Any unauthorized use of the information contained herein (including, but not limited to, alteration, reproduction, communication, distribution or any other form of dissemination) is strictly prohibited. If you are not the intended addressee, please notify the orginator promptly and delete this e-mail and its attachment(s) (if any) subsequently. Galapagos nor any of its affiliates shall be liable for direct, special, indirect or consequential damages arising from alteration of the contents of this message (by a third party) or as a result of a virus being passed on. ______________________________________________ [hidden email] mailing list 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. |
Yes, you're right: taking logarithms is no longer needed!
Christian ______________________________________________ [hidden email] mailing list 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. |
I've been having trouble making sense of the drc results for my binomial response toxicity data. Firstly, the standard errors are far too large for how well the data fit the log-logistic model, particularly compared to other methods of LC50 estimation (e.g. probit and trimmed spearman-karber). Secondly, the LC50 estimates produced do not even fit with the DRC curve (see example below). I've tried all sort of log-logistic functions with varying fixed parameters (i.e. upper and lower limits for mortality data) and still fail to produce reliable estimates. Furthermore, my data response curves exhibit asymmetry so I feel the need to use the 5 parm function. There seems to be some inconsistency here which is causing me a serious headache.
#---------------data 1------------------# n<-c(20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20) x<-c(256,256,256,847,847,847,2208,2208,2208,3565,3565,3565,5756,5756,5756,7313,7313,7313,15000,15000,15000) r<-c(0,0,0,0,2,0,1,3,4,7,6,9,17,13,18,18,17,20,20,20,20) p<-r/n d<-data.frame(x=x,n=n,r=r,p=p) fct <- LL2.5(fixed = c(NA,0,1,NA,NA),names = c("Hillslope", "Lower Limit", "Upper Limit", "LC50", "Asymmetry")) drc <- drm(p~log(x), weight=n, data=d, fct=fct, type="binomial", logDose=exp(1)) summary(drc) plot(drc) est1<-8.67 se1<-0.14 points(est1,0.5) arrows(est1,0.5,est1-se1,angle=90,code=2,length=0.025) arrows(est1,0.5,est1+se1,angle=90,code=2,length=0.025) #---------------data 2------------------# n<-c(20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20) x<-c(798,798,798,6770,6770,6770,12130,12130,12130,21736,21736,21736,38948,38948,38948,69791,69791,69791) r<-c(0,0,0,4,3,3,5,8,7,12,12,7,17,15,17,20,20,19) p<-r/n d2<-data.frame(x=x,n=n,r=r) d2 fct <- LL2.5(fixed = c(NA,0,1,NA,NA),names = c("Hillslope", "Lower Limit", "Upper Limit", "LC50", "Asymmetry")) drc2 <- drm(p~log(x), weight=n, data=d2, fct=fct, type="binomial", logDose=exp(1)) summary(drc2) plot(drc2) est2<-10.56 se2<-0.271 points(est2,0.5) arrows(est2,0.5,est2-se2,angle=90,code=2,length=0.025) arrows(est2,0.5,est2+se2,angle=90,code=2,length=0.025) The idea is to use the drc package for these types of data to produce isoboles. Has anyone else encountered this issue before and can help me? OR I noticed Dr. Ritz has responded to this thread, do you have an suggestions? As far as I know, I have the latest version of every package for R. Sincerely, Patrick |
P.S.
Here are the results I get for; #---------------data 1------------------# Model fitted: Generalised log-logistic (log(ED50) as parameter) (3 parms) Parameter estimates: Estimate Std. Error t-value p-value Hillslope:(Intercept) -6.05453 2.18455 -2.77152 0.0056 LC50:(Intercept) 8.67707 0.14016 61.90663 0.0000 Asymmetry:(Intercept) 0.32382 0.16608 1.94978 0.0512 #---------------data 2------------------# Model fitted: Generalised log-logistic (log(ED50) as parameter) (3 parms) Parameter estimates: Estimate Std. Error t-value p-value Hillslope:(Intercept) -3.98218 1.91035 -2.08453 0.0371 LC50:(Intercept) 10.55736 0.27120 38.92901 0.0000 Asymmetry:(Intercept) 0.26840 0.18244 1.47120 0.1412 Pat |
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