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...ter [mailto:bgunter.4567 at gmail.com] Sent: 22 August 2017 20:38 To: Stephen O'hagan <SOhagan at manchester.ac.uk> Cc: r-help at r-project.org Subject: Re: [R] boot.stepAIC fails with computed formula OK, here's the problem. Continuing with your example: strt1 <- lm(y1 ~1, dat) strt2 <- lm(frm1,dat) > strt1 Call: lm(formula = y1 ~ 1, data = dat) Coefficients: (Intercept) 41.73 > strt2 Call: lm(formula = frm1, data = dat) Coefficients: (Intercept) 41.73 Note that the formula objects of the lm object are different: strt2 does not evaluate the formula...

OK, here's the problem. Continuing with your example: strt1 <- lm(y1 ~1, dat) strt2 <- lm(frm1,dat) > strt1 Call: lm(formula = y1 ~ 1, data = dat) Coefficients: (Intercept) 41.73 > strt2 Call: lm(formula = frm1, data = dat) Coefficients: (Intercept) 41.73 Note that the formula objects of the lm object are different: strt2 does not evaluate the formula...

...; Sent: 22 August 2017 20:38 > To: Stephen O'hagan <SOhagan at manchester.ac.uk> > Cc: r-help at r-project.org > Subject: Re: [R] boot.stepAIC fails with computed formula > > OK, here's the problem. Continuing with your example: > > strt1 <- lm(y1 ~1, dat) > strt2 <- lm(frm1,dat) > > >> strt1 > > Call: > lm(formula = y1 ~ 1, data = dat) > > Coefficients: > (Intercept) > 41.73 > >> strt2 > > Call: > lm(formula = frm1, data = dat) > > Coefficients: > (Intercept) > 41.73 > > &gt...

I'm trying to use boot.stepAIC for feature selection; I need to be able to specify the name of the dependent variable programmatically, but this appear to fail: In R-Studio with MS R Open 3.4: library(bootStepAIC) #Fake data n<-200 x1 <- runif(n, -3, 3) x2 <- runif(n, -3, 3) x3 <- runif(n, -3, 3) x4 <- runif(n, -3, 3) x5 <- runif(n, -3, 3) x6 <- runif(n, -3, 3) x7

...0, 16, 12, 8, 36, 32, 36, 20, 26, 34, 28)), row.names = c(NA, -15L), class = "data.frame") I did the following to try to fit a nonlinear regression model: #First, Procedure to Find Starting (initial) Values For Theta1, Theta2, and Theta3 mymod2 <- y ~ theta1 - theta2*exp(-theta3*x) strt2 <- c(theta1 = 1, theta2 = 2, theta3 = 3) trysol2<-nlxb(formula=mymod2, data=mod14data2_random, start=strt2, trace=TRUE) trysol2 trysol2$coefficients[[3]] #Fitting nonlinear Regression Model Using Starting Values From Previous Part nonlinearmod2 <- nls(mymod2, start = list(theta1 = trysol...

...), row.names = c(NA, -15L), class = "data.frame") > > I did the following to try to fit a nonlinear regression model: > > #First, Procedure to Find Starting (initial) Values For Theta1, Theta2, and > Theta3 > > mymod2 <- y ~ theta1 - theta2*exp(-theta3*x) > > strt2 <- c(theta1 = 1, theta2 = 2, theta3 = 3) > > trysol2<-nlxb(formula=mymod2, data=mod14data2_random, start=strt2, > trace=TRUE) > trysol2 > trysol2$coefficients[[3]] > > #Fitting nonlinear Regression Model Using Starting Values From Previous > Part > nonlinearmod2 &lt...

...a.frame") >> >> I did the following to try to fit a nonlinear regression model: >> >> #First, Procedure to Find Starting (initial) Values For Theta1, Theta2, >> and >> Theta3 >> >> mymod2 <- y ~ theta1 - theta2*exp(-theta3*x) >> >> strt2 <- c(theta1 = 1, theta2 = 2, theta3 = 3) >> >> trysol2<-nlxb(formula=mymod2, data=mod14data2_random, start=strt2, >> trace=TRUE) >> trysol2 >> trysol2$coefficients[[3]] >> >> #Fitting nonlinear Regression Model Using Starting Values From Previous >...

...I did the following to try to fit a nonlinear regression model: >>> >>> #First, Procedure to Find Starting (initial) Values For Theta1, Theta2, >>> and >>> Theta3 >>> >>> mymod2 <- y ~ theta1 - theta2*exp(-theta3*x) >>> >>> strt2 <- c(theta1 = 1, theta2 = 2, theta3 = 3) >>> >>> trysol2<-nlxb(formula=mymod2, data=mod14data2_random, start=strt2, >>> trace=TRUE) >>> trysol2 >>> trysol2$coefficients[[3]] >>> >>> #Fitting nonlinear Regression Model Using Starti...

...it a nonlinear regression model: >>>> >>>> #First, Procedure to Find Starting (initial) Values For Theta1, Theta2, >>>> and >>>> Theta3 >>>> >>>> mymod2 <- y ~ theta1 - theta2*exp(-theta3*x) >>>> >>>> strt2 <- c(theta1 = 1, theta2 = 2, theta3 = 3) >>>> >>>> trysol2<-nlxb(formula=mymod2, data=mod14data2_random, start=strt2, >>>> trace=TRUE) >>>> trysol2 >>>> trysol2$coefficients[[3]] >>>> >>>> #Fitting nonlinear R...

...>>>>> >>>>> #First, Procedure to Find Starting (initial) Values For Theta1, >>>>> Theta2, and >>>>> Theta3 >>>>> >>>>> mymod2 <- y ~ theta1 - theta2*exp(-theta3*x) >>>>> >>>>> strt2 <- c(theta1 = 1, theta2 = 2, theta3 = 3) >>>>> >>>>> trysol2<-nlxb(formula=mymod2, data=mod14data2_random, start=strt2, >>>>> trace=TRUE) >>>>> trysol2 >>>>> trysol2$coefficients[[3]] >>>>> >>>&...

SSweibull() :  problems with step factor and singular gradient Hello I am working with growth data of ~4000 tree seedlings and trying to fit non-linear Weibull growth curves through the data of each plant. Since they differ a lot in their shape, initial parameters cannot be set for all plants. That’s why I use the self-starting function SSweibull(). However, I often got two error messages: