similar to: nls and discontinuous functions

Good day, A function is used on a database with 50 rows and 200 collumns. every row is 1 seperate study that the function should run through. Desired outcome are [x,y] and a weight for that coordinate. I have problems with generating x. ## Generating random time or x coordinate (through random time of displacement - random recall) ## First generating random time within recall period

Hi all, I would like to fit a gamma pdf to my data using the method of RSS (Residual Sum-of-Squares). Here are the data: x <- c(86, 90, 94, 98, 102, 106, 110, 114, 118, 122, 126, 130, 134, 138, 142, 146, 150, 154, 158, 162, 166, 170, 174) y <- c(2, 5, 10, 17, 26, 60, 94, 128, 137, 128, 77, 68, 65, 60, 51, 26, 17, 9, 5, 2, 3, 7, 3) I have typed the following code, using nls method:

Greetings R users, maybe there is someone who can help me with this problem: I define a function "optim.fun" and want as output the sum of squared errors between predicted and measured values, as follows: optim.fun <- function (ST04, SM08b, ch2no, a, b, d, E) { predR <- (a*SM08b^I(2)+b*SM08b+d)*exp(E*((1/(283.15-227.13))-(1/(ST04+273.15-227.13)))) abserr

Hello, I try to plot the marginal effect by using package "effects" (example of the graph i want to get is in the attached picture). All variables are continuous. Here is regression function, results and error effect function gives: > mreg01 = lm(a90$enep1 ~ a90$enpres + a90$proximity1 + (a90$enpres * a90$proximity1), data=a90)> summary(mreg01) Call: lm(formula = a90$enep1 ~

Hi Everyone, I'm trying to write selfStart non-linear models for use with nls. In these models some combinations of parameter values are illegal; the function value is undefined. That's OK when calling the function directly [e.g. SSmodel(x, pars...)]; I return an appropriate non-value such as NA or Inf. However, when called from nls [e.g. nls(y~SSmodel(x, pars...), ...)] those

Hi, I have some data I want to fit with a non-linear function using nls, but it won't solve. > regresjon<-nls(lcfu~lN0+log10(1-(1-10^(k*t))^m), data=cfu_data, > start=(list(lN0 = 7.6, k = -0.08, m = 2))) Error in nls(lcfu ~ lN0 + log10(1 - (1 - 10^(k * t))^m), data = cfu_data, : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Tried to increase minFactor

Hi Everyone, I posted this a couple of weeks ago with no responses. My interface (via gmane) seemed a bit flakey at the time, so I'm venturing to repost with some additional information. I'm trying to write selfStart non-linear models for use with nls. In these models some combinations of parameter values are illegal; the function value is undefined. That's OK when calling the

Hi, I saved as a matrix a summary of a PCA analysis and I've used barplot to plot the PCA variances. I would like to print on the same graphic the values of my matrix m1 - in other words the summary of my PCA analysis. I can do it very painstaking with text for each row and make sure that everything aligns and so on but i wonder if there is a better method than that. My summary follows:

Dear all, I am trying to fit a generalized linear mixed model to deal with both temporal and spatial pseudorreplication. I have repeated seasonal measurements (3 seasons during 3 years and 2 season during the last year, factor named percod, with 11 levels) of a bird presence/absence (pres.f) on 14 artificial ponds (charca.f). The ponds are integrated in 4 greater spatial units (zepa). I have

Greetings R users, maybe there is someone who can help me with this problem: I'm trying to fit this discontinous model : GE<-data.frame( Ci<-c(81,87,91,111,159,173,295,453,629,984), A<-c(-0.9,1.2,3.5,8.3,13.1,14.4,22.9,27.3,29.6,32.6) ) rhs <- function(Ci, Vcmax, J, Rd) { ? ?R <-0.008314472 ? ?Tleaf <-25 ? ?Kc <-exp(38.05-79.43/(R*(Tleaf+273.15))) ? ?Ko

my programe is below: a=c(1,2,1,1,1); b=c(1,2,3,4,1); c=c(3,4,3,3,3); d=c(1,2,3,5,1); e=c(1,5,3,5,1) data.f=data.frame(a,b,c,d,e) origin.data<-data.f cor.matrix<-cor(origin.data) origin.cor<-cor.matrix m<-0 for(i in 1:(cor.matrix[1]-1)) { for(j in (i+1):(cor.matrix[2])) { if (cor.matrix[i,j]>=0.95) { data.f<-data.f[,-i]; i<-i+1 }

I am currently attempting to hack the recently featured profileModels package so that it can handle models generated by the lme4 (mixed models) package. I'm getting really confused by different behavior of summary() before and after loading the lme4 package, and inside and outside the profileMethod() function. The basic behavior is that with lme4 loaded, and "obj" a fitted object

I have the following data (see RawData using dput below) How do I get it in the following 3 column format (CO2 measurements are the elements of the original data frame). I'm sure the package reshape is where I should look, but I haven't figured out how. Thanks ahead of time Month Year CO2 J 1958 F 1958 M 1958 315.71 A 1958 317.45 M.1 1958 317.5 J.1 1958 J.2 1958 315.86 A.1 1958

Hi- I am using the following code: start=c(alpha=0.4,beta=0.4) warm.10<-nls(warming$umoles60~alpha*exp(beta*warming$T10cm),start = start,data=warming,na.action=na.omit) This code works for other columns in my dataset that are similar to $T10cm but the code does not work for this particular column (T10cm). I am assuming this is because warming$T10cm contains NAs. I have tried just

https://bugs.freedesktop.org/show_bug.cgi?id=80506 Priority: medium Bug ID: 80506 Assignee: nouveau at lists.freedesktop.org Summary: Video failed on resume from suspend - G94 / GeForce 9600 GT, 10de:0622 , kernel 3.16 rc2 QA Contact: xorg-team at lists.x.org Severity: major Classification: Unclassified

Dear List, i am trying to produce a 3d plot using wireframe using the code: wireframe(Residuals_FD ~ Elevation * Temperature, data = data2, scales = list(arrows = FALSE), drape = TRUE, colorkey = TRUE) As you can see when the code (using the data below) is run the plot area is set-up correctly but the actual surface is missing? Any help would be greatly appreciated. Chris #data Elevation

Dear all, I'm writing my manuscript to publish after analysis my final data with ANOVA, ANCOVA, MANCOVA. In a section of my result, I did correlation of my data (2 categirical factors with 2 levels: Quantity & Quality; 2 dependent var: Irid.area & Casa.PC1, and 1 co-var: SL). But as some traits (here Irid.area) are significantly influenced by the covariate (standard length, SL), I

https://bugs.freedesktop.org/show_bug.cgi?id=110660 Bug ID: 110660 Summary: GeForce GT 750M Mac Edition fullscreen issues Product: Mesa Version: unspecified Hardware: Other OS: All Status: NEW Severity: normal Priority: medium Component: Drivers/DRI/nouveau Assignee: nouveau

Hi, I have a question about estimating IRT models by using nlme, not just rasch model, but also other models. Behavior Research Methods <http://www.springerlink.com/content/1554-351x/> Volume 37, Number 2 <http://www.springerlink.com/content/1554-351x/37/2/>, 202-218, DOI: 10.3758/BF03192688 Using SAS PROC NLMIXED to fit item response theory models (2005). Ching-Fan