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

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

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:

Dear R-Help, I am trying to determine the growth rate of bacteria under specific conditions using ipolygrowth function `ipg_multisample`. While this worked before, I got some data that give the error: ``` Error in if (tb.result$peak.growth.time == 0) { : missing value where TRUE/FALSE needed In addition: Warning message: In max(pgr[pgr > 0 & Re(x) >= 0 & Re(x) <= max]) :

?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