similar to: add constraints to nls or use another function

Hi, I always have a question about how to do this best in R. I have a data frame and a set of criteria to filter points out. My procedure is to always locate indices of those points, check if index vector length is greater than 0 or not and then remove them. Meaning dftest <- data.frame(x=rnorm(100),y=rnorm(100)); qtile <- quantile(dftest$x,probs=c(0.05,0.95)); badIdx <- which((dftest$x

Hello, I am pretty new to R, I have always used SAS and SAS products. My target variable is binary ('Y' and 'N') and i have about 14 predictor variables. My goal is to compare different variable selection methods like Forward, Backward, All possible subsests. I am using misclassification rate to pick the winner method. This is what i have as of now, Reg <- glm (Graduation ~.,

Dear R-helpers, I am using confint() within a function, and I want to turn off the message it prints: x <- rnorm(100) y <- x^1.1+rnorm(100) nlsfit <- nls(y ~ g0*x^g1, start=list(g0=1,g1=1)) > confint(nlsfit) Waiting for profiling to be done... 2.5% 97.5% g0 0.4484198 1.143761 g1 1.0380479 2.370057 I cannot find any way to turn off 'Waiting for. .." I tried

AdvisoRs: Is the following a bug, feature, hinky error message, or dumb Bert? > mtest <- matrix(1:12,nr=4) > dftest <- data.frame(mtest) > ix <- cbind(1:2,2:3) > mtest[ix] <- NA > mtest [,1] [,2] [,3] [1,] 1 NA 9 [2,] 2 6 NA [3,] 3 7 11 [4,] 4 8 12 ## But ... > dftest[ix] <- NA Error in `[<-.data.frame`(`*tmp*`, ix, value

On Wed, Aug 1, 2012 at 3:08 PM, <fra.meucci@hotmail.it> wrote: > Dear Göran Broström, > I am trying to use AFTREG function for R to estimate a loglogistic > survival function, including time dependent covariates. > Actually, my Subset includes some partial events; the idea is to model > this kind of events using something similar to “weights” in the SURVREG > function.

Why fails nls with "singular gradient" here? I post a minimal example on the bottom and would be very happy if someone could help me. Kind regards, ########### # define some constants smallc <- 0.0001 t <- seq(0,1,0.001) t0 <- 0.5 tau1 <- 0.02 # generate yy(t) yy <- 1/2 * ( 1- tanh((t - t0)/smallc) * exp(-t / tau1) ) + rnorm(length(t))*0.01 # show the curve

I have data vectors x and y both with 179 observations. I'm trying to fit a nonlinear model with five parameters using nls. The formula is only defined within a range of x-values, it should be zero otherwise, thus my attempted use of ifelse: > df<-data.frame(x,y) >

I am trying to fit a nonlinear regression to infiltration data in order to determine saturated hydraulic conductivity and matric pressure. The original equation can be found in Bagarello et al. 2004 SSSAJ (green-ampt equation for falling head including gravity). I am also VERY new to R and to nonlinear regressions. I have searched the posts, but am still unable to determine why my data come up

write.table with large data frames takes quite a long time > system.time({ + write.table(df, '/tmp/dftest.txt', row.names=FALSE) + }, gcFirst=TRUE) user system elapsed 97.302 1.532 98.837 A reason is because dimnames is always called, causing 'anonymous' row names to be created as character vectors. Avoiding this in src/library/utils, along the lines of Index:

I am trying to fit a rank-frequency distribution with 3 unknowns (a, b and k) to a set of data. This is my data set: y <- c(37047647,27083970,23944887,22536157,20133224, 20088720,18774883,18415648,17103717,13580739,12350767, 8682289,7496355,7248810,7022120,6396495,6262477,6005496, 5065887,4594147,2853307,2745322,454572,448397,275136,268771) and this is the fit I'm trying to do: nlsfit

?summary.lm says, under the "Value" section, coefficients: a p x 4 matrix with columns for the estimated coefficient, its standard error, t-statistic and corresponding (two- sided) p-value. so try summary(fit.lm)$coefficients[,1:2] to get the coefficients and their SEs. For nls, it takes a bit more digging, as the documentation is a bit sparse. Something

Hello, I'm trying to reproduce some SAS result wit R (after I got suspicious with the result in R). I struggle with the contrasts in a linear model. I've got three factors > d$dose <- as.factor(d$dose) # 5 levels > d$time <- as.factor(d$time) # 2 levels > d$batch <- as.factor(d$batch) # 3 levels the data frame d contains 82 rows. There are 2 to 4 replicates of

I am trying to add a constant to the previous value of a variable based on certain conditions. Maybe there is a simple way to do this that I am missing completely. I have given an example below: df <- data.frame(x = c(1,2,3,4,5), y = c(10,20,30,NA,NA)) > df x y 1 1 10 2 2 20 3 3 30 4 4 NA 5 5 NA I want to add 2 to the previous value of y, if x exceeds 3 (also will have to handle NAs in

Hi there, Anyone has an idea how to put those two sets of code together so that I can get a 3-dimensional picture that includes points instead of 2 separate pictures which doesnt make that much sense at the end. #Let's say that these are the data we would like to plot: A<-c(62,84,53) B<-c(64,82,55) C<-c(56,74,41) D<-c(46,68,38) E<-c(71,98,72) data<-rbind(A,B,C,D,E)

I have an inquire about the RNG in R It is known that when we use the " rnorm " function , we pass the arguments : 1- number of variables to be generated 2- mean vector of the normal random errors. 3- standard deviation vector of the normal random errors. my question is the following Is the a way (a function) in R that we could specify the covariance matrix in step 3, instead of the

I am having problems with forward prediction using the output of the Basic Structural Model from StructTS. The following snippet illustrates the problem: t_end <- 139 nahead <- 20 data(AirPassengers) ap <- log10(AirPassengers)-2 fit <- StructTS(ts(ap[1:t_end], freq=12), type="BSM") p <- stats:::predict.StructTS(fit, n.ahead=nahead) plot(1:t_end, ap[1:t_end],

Perhaps I should have submitted this as a bug so that it would be officially tracked. It's not a big deal, but here it is again (I can't remember which version this patch is against, but I don't think legend() has changed since then ...) Basically, the problem is that if you want to have "opaque" points that overlay lines (rather than using type="b" and having

Hello I have the following problem : The dataframe TEST has multiple lines for a same person because : there are differents values of Nom or differents values of Prenom but the values of Matricule or Sexe or Date.de.naissance are the same. TEST <- structure(list(Matricule = c(66L, 67L, 67L, 68L, 89L, 90L, 90L, 91L, 108L, 108L, 108L), Nom = structure(c(1L, 2L, 2L, 4L, 8L, 5L, 6L, 9L, 3L, 3L,

In our smallest office, we have a Dell CentOS 7 system, a Windows system and an HP 8610 printer, all hard-wire Ethernet connected with a Linksys router. The router provides Internet connection. All of the network-connected systems get their IP address from the router at power up. Successful network connection of the printer at power up has recently started taking much longer than usual.? The

Hello, all. I have co-opted a number of functions that can be used to plot the hazard/survival functions and associated density distribution for a Gompertz mortality model, given known parameters. The Gompertz hazard model has been shown to fit relatively well to the human adult lifespan. For example, if I wanted to plot the hazard (i.e., mortality) functions: pop1 <- function (t) {