search for: 3.00000

Dear All, In R 3.6.3 (and earlier), method dispatch used to work for methods stored in local environments that are attached to the search path. For example: myfun <- function(y) { out <- list(y=y) class(out) <- "myclass" return(out) } print.myclass <- function(x, ...) print(formatC(x$y, format="f", digits=5)) myfun(1:4) # prints: [1]

Dear Sir, I get an error message when I use polr() in MASS package. My data is "ord.dat". I made "y" a factor. y y1 y2 x lx 1 0 0 0 3.2e-02 -1.49485 2 0 0 0 3.2e-02 -1.49485 3 0 0 0 1.0e-01 -1.00000 4 0 0 0 1.0e-01 -1.00000 5 0 0 0 3.2e-01 -0.49485 6 0 0 0 3.2e-01 -0.49485 7 1 1 0 1.0e+00 0.00000 8 0 0 0 1.0e+00 0.00000 9 1 1 0

Hi! I am studying differences between R and S-PLUS smooth() functions. I know from the help that they worked differently, so I ask: - exist a package that permit to have the same results? - alternatively, someone know how can I obtain the same results in R, using a self made script? I know that S-PLUS use the 4(3RSR)2H running median smoothing and I try to implement it with the code below. I

Dear list, I'm currently trying to use the rms package to get predicted ordinal responses from a conditional ratio model. As you will see below, my model seems to fit well to the data, however, I'm having trouble getting predicted mean (or fitted) ordinal response values using the predict function. I have a feeling I'm missing something simple, however I haven't been able to

Hi, is there a way I can calculate a summary statistics for a columns matrix let say we have this matrix x <- matrix(sample(1:8000),nrow=100) colnames(x)<- paste("Col",1:ncol(x),sep="") if I used summary summary(x) i get the output for each column but I need the output to be in matrix with rownames and all the columns beside it this how I want it

Dear Wolfgang, I think this new behaviour is related to the following R 4.0.0 NEWS item: > S3 method lookup now by default skips the elements of the search path between the global and base environments. Your environment "myenv" is attached at position 2 of the search() path and thus now skipped in S3 method lookup. I have just noticed that

hey what should I do to replace in a data frame NA?s with zeroes? Thanks in advance Juan Pablo

Hi, I have a question about the computation of confidence intervals in the zyp package, in particular using the functions zyp.sen and confint.zyp, or zyp.yuepilon. (1) I'm a bit confused about the confidence intervals given by zyp.sen and confint.zyp. When I request a certain confidence interval in the function, the R output seems to deliver another confidence interval, e.g. when I set

Dear R users, I am using 'R' version 2.2.1 and 'S-PLUS' version 6.0; and I loaded the MASS library in 'S-PLUS'. I am running a logistic regression using glm: --------------------------------------------------------------------------- > mydata.glm<-glm(COMU~MeanPycUpT+MeanPycUpS, family=binomial, data=mydata)

Hi, I'm trying to use adonis on a subset of data from a dataframe. The actual data is in columns 5:118, and the first four columns are various factors. There are 3 levels of the factor Habitat, and I want to examine differences among only two of them. So I started with: > CoastNear = subset(gel_data, Habitat != "I") The resulting data.frame has three levels for Habitat, but

--l4Siqd0eqV Content-Type: text/plain; charset=us-ascii Content-Transfer-Encoding: 7bit Hello, I run the following job. Please, compare the results of summary and table concerning berufl. From similar SPSS/PSPP runs, the result of table is correct. Did I misunderstand anything or is there a bug? What does the difference come from? What does '(other)' mean? What about the strange

Hi, I am performing a two-way ANOVA (2 factors with 4 and 5 levels, respectively). If I'm interpreting the output of summary correctly, then the interaction between both factors is significant: ,---- | ## Two-way ANOVA with possible interaction: | > model1 <- aov(log(y) ~ xForce*xVel, data=mydataset) | | > summary(model1) | Df Sum Sq Mean Sq F value Pr(>F) |

Hi, I have a sparse matrix.I want to fill values into the entries whose value is 0.The new generated values should come from the interpolation of the values have existed.Does R provide such interpolation functions which operate on Matrix, for example ,such a matrix below 0 0 0 0 2.3 0 0 0 0 0 0 3.1 0 0 0 0 1.4 0 0 0 0 0 0 0 0 0 0 1.1 0 0 0 0 0 0 0 0 0 0 0 4

Dear R users I'm new in R management and maybe It's a silly question. I'm working with GLM to obtain predictive models. I have some problesm with the prediction instruction: > DatosTotal <- read.csv("Var_perdizcsv.csv", sep =";") > edvariable <- edit(DatosTotal) > pre <- predict(rlfinal, DatosTotal, type = 'probs') Erro en

hi, i'm trying to figure out if there's any possibility to write a whole vector into a matrix or data.frame or something like that. i don't mean transormation. Here an example: [,1] [,2] [1,] "a" "d" [2,] "b" "e" [3,] "c" "f" where e.g. a is a<-c(0,1) vector of length 2, b a vector of length 4,... (i know that

I wrote a function which does "moving window" regressions. E.g. if there are 100 observations and the window width is 50, then I first run the regression for observations 1..50, then for 2..51, and so on. I am extremely pleased with R in my experience with writing this, since I was able to pass the model as an argument into the function :-) Forgive me if I sound naive, but that's

I am relatively new to R so I am not confident enough in what I am doing to be certain this is a bug. I am running R 2.1.1 on a Windows XP machine and the lme4 package version 0.98-1. The following code fits the model I want using the nlme package version 3.1-60. mltloc$loc <- factor(mltloc$loc) mltloc$block <- factor(mltloc$block) mltloc$trt <- factor(mltloc$trt) Mltloc <-

To calculate Percentile for a set of observations Excel has percentile() function. R function quantile() does the same thing. Is there any significant difference btw percentile and quantile? Regrads, -- View this message in context: http://www.nabble.com/Diff-btw-percentile-and-quantile-tp22328375p22328375.html Sent from the R help mailing list archive at Nabble.com.

Hi, I've got the following data: x<-c(1,3,5,7) y<-c(37.98,11.68,3.65,3.93) penetrationks28<-dataframe(x=x,y=y) now I need to fit a non linear function so I did: fit <- nls(y ~ I(a+b*exp(1)^(-c * x)), data = penetrationks28, start = list(a=0,b = 1,c=1), trace = T) The error message I get is: Error in nls(y ~ I(a + b * exp(1)^(-c * x)), data = penetrationks28, start = list(a =

Dear R Users, I am looking for an alternative to AIC or BIC to choose model parameters. This is somewhat of a general statistics question, but I ask it in this forum as I am looking for a R solution. Suppose I have one dependent variable, y, and two independent variables, x1 an x2. I can perform three regressions: reg1: y~x1 reg2: y~x2 reg3: y~x1+x2 The AIC of reg1 is 2000, reg2 is