nls

问题 I have been through many questions on SO but I am not able to find a fix for my problem in related answers. I have a table like this stored as PAO1.data : Name P AO Prog1 0.654 59.702 Prog2 0.149 49.595 Prog3 0.505 50.538 Prog4 0.777 59.954 Prog5 0.237 49.611 Prog6 0.756 50.630 Prog7 0.560 118.014 Prog8 0.015 53.779 Prog9 0.789 68.096 Prog10 0.825 79.558 I tried to use nls to fit an exponential curve to the data. df = data.frame(PAO1.data) p = df$P ao = df$AO RMSE <- function(fit, act){ sqrt

问题 What static library should I use to link against to use the NormalizeString() function? In contrast with most functions documented on MSDN, the static library required to use the function is not declared. I tried using the name derived from the DLL: normaliz.lib and it successfully linked, but then I get a pop-up at runtime saying Normalization.dll could not be found on my computer and the process is shut down. 回答1: As pointer out by Hans Passant, the correct import library is normaliz.lib .

问题 I am trying to convert an "nlmrt object to an "nls" object using nls2 . However, I can only manage to do it if I write explicitly the names of the parameters in the call. Can't I define the parameter names programmatically? See the reproducible example: library(nlmrt) scale_vector <- function(vector, ranges_in, ranges_out){ t <- (vector - ranges_in[1, ])/(ranges_in[2, ]-ranges_in[1, ]) vector <- (1-t) * ranges_out[1, ] + t * ranges_out[2, ] } shobbs.res <- function(x) { # UNSCALED Hobbs weeds

问题 I am trying to fit non parametric functions to curve using nls. When I try to fit all the parameters nls was not able to solve the equations. So, I split the equations and applied nls on individual equations and later again as a final fit Here is the data Below is the code for what I did #Readin Data library(readr) library(nls2) Data <- read_csv("data.csv") t<- Data$`Elasped Time (min)` w <-Data$`S2 Weight` t2<- Data$`Elasped Time (min)` w2 <-Data$`S2 Weight` # Parametric functions to be

问题 I'd like to estimate a large number of models using R's nls() function on a user-defined function. Since many variables are fixed across my specifications, I'd like a way of pre-setting them in my function, but I don't properly understand how R looks for variables in functions contained in a formula. I've had a look at the section on metaprogramming in Hadley Wickham's advanced R book, but it hasn't enlightened me. Here is a simplified example of what I'm trying to acheive, using the mtcars

问题 As said in the help(predict.nls), when se.fit=TRUE,the standard errors of the predictions should be calculated. However, my codes in the following do not display that, but only the predictions. alloy <- data.frame(x=c(10,30,51,101,203,405,608,810,1013,2026,4052,6078, 8104,10130), y=c(0.3561333,0.3453,0.3355,0.327453,0.3065299,0.2839316, 0.2675214,0.2552821,0.2455726,0.2264957,0.2049573, 0.1886496,0.1755897,0.1651624)) model <- nls(y ~ a * x^(-b), data=alloy, start=list(a=.5, b=.1)) predict

问题 I'm trying to use nls to estimate the parameters of a non linear model. I first use nls2 to find good initial parameters with Random Search and I then use nls to improve the estimation with a Gauss-Newton approach. The problem is I always get an "singular gradient matrix at initial parameter estimates" error. I'm not sure I understand, because the input matrix doesn't seem to be a singular gradient matrix. Moreover even if the fits I'm looking for is not perfect for this data, nls should find

问题 I'm trying to fit a non-linear model to a series of measurements collected on several plots throughout the season. Below is a subsample from the larger dataset. data: dput(nee.example) structure(list(julian = c(159L, 159L, 159L, 159L, 159L, 159L, 159L, 159L, 159L, 159L, 159L, 159L, 159L, 159L, 169L, 169L, 169L, 169L, 169L, 169L, 169L, 169L, 169L, 169L, 169L, 169L, 169L, 169L, 169L), blk = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,

问题 I have data from the USGS Nation Water Data website. I am currently trying to plot and fit curves to the data to use in prediction for different measurements taken within the dataset (dissolved oxygen, pH, gage height and temp) all in relation to discharge rate. I used the "nls" command and I am using a book of equations to find which curve to use...for this example I have specifically used Schumacher’s equation (p.48 in the book). Find the link to data: curve book: http://www.for.gov.bc.ca

问题 Oracle 11.2.0.3.0, APEX 4.1.1.00.23. We need to display numbers in our application with the format FM999999999990.000 and dates with the English format DD-MON-YYYY . Even if the application language is going to change (french, spain, etc.), we always need this format for numbers (no space or comma for group separator, and a point for decimal separator, ie. -1254.010 ) and date (3 first letters from the English month name ie. 12-FEB-2012 ). Here are globalization attributes we are using (