search for: plateau

...r it is constructed appropriately. Looking forward to your suggestions. (Additional info about my experimental design. Uppercase and lowercase words in the R object targets (see below in the posted program) have different meanings. The locusts on the plain [PLAIN] was treated [plain] in a simulated plateau environment while the locusts on the plateau [PLATEAU] was treated [plateau] in a simulated plain environment. They experienced different treatments. I think it is not a complete factorial design. Therefore I did not choose the design matrix for factorial designs. However, I do not know whether wha...

...y maternal trees, with eight groups of seedlings around each. Each tree has a TreeID, which I use as the random effect (blocking factor). There are two fixed effects: DISTANCE - distance to maternal tree; two levels 'CLOSE' or 'AWAY' (four groups of seedlings each per tree), and PLATEAU - whether the maternal tree grows on the 'UPPER' plateau (bad soil) or 'LOWER' plateau (good soil). In each group of seedlings, we randomly selected one seedling where we scored herbivory. Levels of herbivory for each of the seven herbivory categories was scored as proportion of...

Dear R colleagues: Am trying to fit a simple NL model to determine Economical Optimum Nitrogen Rates. The segmented (quadratic + plateau) model only works with some y's, in some cases I get a "singular gradient" error. I'll appreciate any ideas in how to solve the singular gradient error. Thanks, Jose # The following code works using yield2 in the nls model but not using yield. # Economical constants, nitrogen...

Dear All, I would like to make a quadratic with a plateau model in R. Is there a package in R doing this? The bentcableAR package seems won't work. The link below describes what I am looking for in R exactly: http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_nlin_sect033.htm -- Thanks so much! Orange help.ly20...

Dear All, I'm a beginner user in R and I would like to make a quadratic and plateau model in R. Can you help please with an example? Thanks so much -- Ahmed M. Attia Assistant Lecturer El-Khattara farm Station Agronomy Dept., Zgazig Univ., Egypt Visiting Scientist Haskell Agricultural laboratory Agronomy and Horticultural Dept., Univ. of Nebraska-Lincoln ahmedatia at zu.edu.e...

...my.func = function(x){ y=ifelse(x>-.5,0,ifelse(x< -.8,abs(x)/2,abs(x))) print(c(x,y)) #print what was tested and what the result is return(y) } curve(my.func,from=-1,1) #### When I attempt to find the maximum of this function, which should be -.8, I find that optimize gets stuck in the plateau area and doesn't bother testing the more interesting bits of the function: #### optimize(my.func,interval=c(-1,1),maximum=TRUE) #### I really don't understand why the search moves to the positive/ constant area of the function and neglects the more negative area of the function. On s...

Hello, I am working on extracting data from sound recording (fee bee song of the black-capped chickadee). I obtained a two column matrix with x=time(s) and y=frequency(khz). The part of the recording that is interesting me is when the frequency is stable. Does somebody ever used a fonction that extract only constant data among a huge list of data (sample= 180000)? Thanks Thibault Grava

Hello, I'm attempting to find multiple breakpoints in an association of my response variable (R.AUC) with two explanatory variables ('s.size' and 'bedekking'). The association between 's.size' and 'R.AUC' shows a plateau, but the value when this plateau is reached is differs for different values of 'bedekking'. Initially I thought these different values could be assessed by the 'segmented' function in the package of the same name, but this only seems to give one value of a breakpoint for each expla...

Hi there are some predefined self-start functions, like SSmicmen, SSbiexp, SSasymp, SSasympOff, SSasympOrig, SSgompertz, SSflp, SSlogis, SSweibull, Quadratic, Qubic, SSexp (nlrwr) Btw, do you know graphic examples for this functions? The SSexpDecay (exponential decay) for y = (y0 - plateau)*exp(-k*x) + plateau from http://stats.stackexchange.com/questions/17126/how-can-i-make-my-r-nls-model-more-stable works fine, too. But now I search a self-starter for y = a + b * c^x Do you know a package that implement such self-starter or is there a easy way to do it myself? thx Christof

...res R >=2.6.0). ~ The argument convert.path in read.image and write.image is no ~ longer needed and has been removed. - - make.image no longer has an argument gamma Adaptive smoothing: - - functions awsimage, awspimage: - - value of ladjust 1.0 -- 1.25, different lseq - - new arguments plateau=NULL, homogen=TRUE, earlystop=TRUE - - new varmodel "Quadratic" - - location kernel with choice "Plateau" - - statistical kernel changed to Plateau with spmin=0.25 - - FORTRAN subroutine awsimg has fewer parameters - - lseq extended (if it is to short) by last element rather tha...

...res R >=2.6.0). ~ The argument convert.path in read.image and write.image is no ~ longer needed and has been removed. - - make.image no longer has an argument gamma Adaptive smoothing: - - functions awsimage, awspimage: - - value of ladjust 1.0 -- 1.25, different lseq - - new arguments plateau=NULL, homogen=TRUE, earlystop=TRUE - - new varmodel "Quadratic" - - location kernel with choice "Plateau" - - statistical kernel changed to Plateau with spmin=0.25 - - FORTRAN subroutine awsimg has fewer parameters - - lseq extended (if it is to short) by last element rather tha...

...ine the initial intensity value before the bleaching. This pre-bleach value is also used for normalising the curve (pre-bleach is then set to 1). 2. Bleach this ROI (with high laser intensity). 3. Record/ measure the recovery of fluorescence over time in the ROI until it reaches a steady state (a plateau). . n. Fit the measured intensity for each time point and mesure the half time (the timepoint which the curve has reached half the plateau), and more... The recovery of fluorescence in the ROI is used as a measurement of protein diffusion in the time range of the experiment. A steep curve means th...

...ple: area %is NOT% large area %is% NOT large area %is% !large etc... 3) how brackets works (as in typical logical operation, are allowed - some expert system do not allow use brackets) Is proper following syntax? (slope %is% flat && elevation %is% is high) || area %is% large, form %is% plateau Where I can find more information about syntax rules used in that (fantastic!) package, except in "Generalised and Customisable Sets in R" where is nothing about rules. Maybe rules comes from other, more general rule syntax, if yes,I would be grateful for the source thanks and greeti...

...o the dev mailing list with your observations and findings. We've spent considerable time looking for memory leaks using Valgrind. While no tool is perfect, we believe that we've found all leaks related to source connections. All other memory activity is likely related to buffers and should plateau according to settings. We expect the next or latest the following release to be RC1. Source tar ball: http://downloads.xiph.org/releases/icecast/icecast-2.4-beta5.tar.gz Windows build: http://downloads.xiph.org/releases/icecast/icecast_win32_2.4_beta5.zip Also there are experimental packages fo...

...o the dev mailing list with your observations and findings. We've spent considerable time looking for memory leaks using Valgrind. While no tool is perfect, we believe that we've found all leaks related to source connections. All other memory activity is likely related to buffers and should plateau according to settings. We expect the next or latest the following release to be RC1. Source tar ball: http://downloads.xiph.org/releases/icecast/icecast-2.4-beta5.tar.gz Windows build: http://downloads.xiph.org/releases/icecast/icecast_win32_2.4_beta5.zip Also there are experimental packages fo...

...e the response variable has values between 0 and 1, I used a logistic GLM: > gmodel <- glm(R.AUC ~ bedekking + s.size, data=dat.al2, family = quasibinomial(link=logit)) R.AUC increases with increasing s.size and decreasing bedekking, looking at the graph shows that the association reaches a plateau at a s.size of 10 and a bedekking of 0.45. So these are the values I use in 'psi' argument in the 'segmented' function > psi.mod <- list(0.45, 10) > names(psi.mod) <- c("bedekking", "s.size") Then I attempt to run the 'segmented' function:...

...that is Kappa -> 0; the latter would even constitute the H0. If I log-transform Kappa to get in on the real line, will the chain then ever fulfill convergence criteria ? The values for logged Kappa should be on average I suppose less and less all the time. But suppose it finds an almost flat plateau. How do I then test against the H0 - by definition, I'll never get a Kappa = 0 exactly; so I can't compare against that. One idea I had: Define not only a parameter Kappa, but also one of an indicator function, which acts as switch between a uniform and a vonMises distribution. Call th...

When a whole-file checksum is performed, hashing was done in 64 byte blocks, causing overhead and limiting performance. Testing showed the performance improvement to go up quickly going from 64 to 512 bytes, with diminishing returns above, 4096 was where it seemed to plateau for me. Re-used CHUNK_SIZE (32 kB) as it already exists and should be fine to use here anyway. Noticed this because I'm playing with a parallel MD5 implementation, and it benchmarked about the same as xxhash on the CPUs I used for testing, which should not be the case. Discovered performance w...

...regression to model the trajectory of reproductive traits with age in a longitudinal data set using a mixed model framework. The aim is to find three slopes and two points- the slope from low performance in early age to a point of high performance in middle age, the slope (may be 0) of the plateau from the start of high performance to the end of high performance , and the slope of the decline from the end of high performance to the end of life. I've found the segmented package useful, but it cannot be implemented in a mixed model framework. I've also attempted piecewise regr...

...ade successive runs, adding one variable at a time from the most to the least important (not-too-correlated) variables. I then plot the error rate (err.rate) as a function of the number of variable used. As this number increase, the error first sharply decrease, but relatively soon it reaches a plateau . I assume that the point of inflexion can be use to derive the minimum number of variable to be used. Is that a sensible approach? Is there any other suggestion? A measure of the error on "err.rate" would also here really help. Is there any idea how to estimate this? From the variatio...