similar to: R code Error : Hybrid Censored Weibull Distribution

Define an array > v<-1:256 > dim(v)<-rep(4,4) Subarrays can be obtained as follows: > v[3,2,,2] [1] 71 87 103 119 > v[3,,,2] [,1] [,2] [,3] [,4] [1,] 67 83 99 115 [2,] 71 87 103 119 [3,] 75 91 107 123 [4,] 79 95 111 127 In the general case this procedure is very tedious. Given an array A, dim(A)=(dim_1,dim_2,...,dim_d) and two vectors

Hi all, I'm new on this list so I greet all. My question is: does exist in R a plot similar to candlestick plot but not based on xts (time series)? I have to plot a range of 4 value: for every item I have min value, max value and 2 intermediate values. I would like plot this like a candlestick, i.e. with a box between 2 intermediate values and 1 segment between box and min value and a segment

Dear R users, I have 2 SpatialPointsDataFrame's, pcs and East. The column str_1 in the first (pcs) is: > pcs[0:4,] coordinates cat str_1 int_1 int_2 dbl_1 dbl_2 1 (101000, 263000) 1 "SM06B" 101000 263000 4.978915 -4.293668 2 (101000, 265000) 2 "SM06C" 101000 265000 4.960478 -4.266742 3 (101000, 267000) 3 "SM06D" 101000 267000

Dear All, I am analysing some data for a colleague (not my data, gotta be published so I cannot divulge). My response variable is the number of matings observed per day for some fruitlies. My factors are: Day: the observations were taken on 9 days Regime: 3 selection regimes Line: 3 replicates per selection regime. I have 81 observations in total The lines are coded A to I, so I do not need

Hi r-users;   I have this values: eign_val <- c(137.810447,3.538721,2.995161,1.685670) alp    <- 1.6549 ;  lamda <- eign_val lamda_m <- min(lamda)   First I calculated manually: delta0 <- 1 delta1 <- alp*delta0*(4-lamda_m*(1/lamda[1]+1/lamda[2]+1/lamda[3]+1/lamda[4]))  delta1 delta2 <- (alp/2)*(delta1*(delta1/alp) + delta0*((1-lamda_m/lamda[1])^2+

Hello, In the following I tried 3 versions of an example in R help. Only the two first predict command work. After : library(splines) require(stats) 1) fm1 <- lm(weight ~ bs(height, df = 5), data = women) ht1 <- seq(57, 73, len = 200) ph1 <- predict(fm1, data.frame(height=ht1)) # OK plot(women, xlab = "Height (in)", ylab = "Weight (lb)") lines(ht1, ph1) 2)

Hi folks, I tried to retain the Weibull distribution using the `nls' function and proceeding along the lines of the example provided in the `SSweibull' help (at least I thought so): t <- (1:200)/100 v <- pweibull(t, shape=3, scale=1) df <- data.frame(Time=t, Value=v) Asym <- 1.0; Drop <- 1.0; lrc <- 0; pwr <- 1 df.estimate <- nls(Value ~ SSweibull(Time,

I am trying to it a particular nonlinear model common in Soil Science to moisture release data from soil. I have written the function as shown below according to the logist example in Ch8 of Pinheiro & Bates. I am getting the following error (R version 2.1.1) *Error in qr(attr(rhs, "gradient")) : NA/NaN/Inf in foreign function call (arg 1)* Below is the function and data. /#

Hello, I recently came across an OpenCL kernel in which an int vector type was subtracted from the INT_MIN constant, e.g. int2 v2 = INT_MIN - (int2)(0); INT_MIN was defined as #define INT_MIN (-2147483648) Clang in OpenCL modes (-x cl) produces the following error: vector_conversion.c:12:42: error: can't convert between vector values of different size ('long' and 'int2'

Hi all, I am a bit mystified by this error message that I get when I try to apply lmer() to a simple dataset with one between factor (age) and one within factor (item): "$ operator is invalid for atomic vectors" I'll just provide the code, because I don't see where the problem is: library(lme4) options(contrasts=c("contr.helmert","contr.poly")) data =

Dear all I am so glad the R can provide the efficient calculate about eigenvector and eigenvalue. However, i have some puzzle about the procedure of eigen. Fristly, what kind of procedue does the R utilize such that the eigen are obtained? For example, A=matrix(c(1,2,4,3),2,2) we can define the eigenvalue lamda, such as det | 1-lamda 4 | =0 | 2 3-lamda | then

Hello, I am working with the nls() function and inserting a formula into it that uses the pweibull function. However the pweibull function is annoyingly producing NaNs, which nls() refuses to handle. I have put a sample of the code below. Is there a way to prevent these NaNs from interfering, for example a method to catch them? I get the following error when I try to run the code: res.nls <-

Dear All, In Storey's estimator of the proportion of true nulls, the estimator depends on the tuning parameter lamda. Suppose now that an estimator of this proportion has been obtained by the qvalue package, what is the lamda that corresponds to the estimate? How to get this lamda? Thanks, -Chee [[alternative HTML version deleted]]

Hello R-world, I am facing a peculiar problem and hope someone out there can comment on it. In goodness-of-fit tests for evaluation of distributions, there are three well-known methods: 1. Chi-square 2. Anderson-Darling 3. Kolmogorov-Sminrov I am trying to use the second test. Many researchers have reported results using this test. I wrote programs in C and now in R to do this. I run into

Hello everyone, I have a quick question but I am stuck with it and I do not know how to solve it. Imagine I need the distribution function of a Weibull(1,1) at t=3, then I will write pweibull(3,1,1). I want to keep the shape and scale parameters in a list (or a vector or whatever). Then I have parameters<-list(shape=1,scale=1) but when I write pweibull(3,parameters) I get the following

Chris Lattner-2 wrote: > > > On Feb 11, 2009, at 4:07 AM, Alex wrote: > >> In my hardware there are two special registers cannot be copied but >> can only be assigned and referenced (read) in the other instruction. >> They are allocatable also. >> >> br i1 %if_cond, label %then, label %else >> then: >> %x1 = fptosi float %y1 to i32

Please allow me to add just a little more about this: nothing wrong with pweibull(), namely, the two cases I reported: pweibull(3:10, 2) and pweibull(3:10, 2.1), in rw1041 and earlier version. I wonder this might just due to the change from rw1041 to rw1050, however, I can't find anything relevant (seems to me) in the News or Readme. Thanks Sundar for the suggestion of using 1 -

Hello guys.I am working on implementing the PL2 weighting scheme of the DFR framework by Gianni Amati. It uses the Poisson approximation of the Binomial as the probabilistic model (P), the Laplace law of succession to calculate the after effect of sampling or the risk gain (L) and within document frequency normalization H2(2) (as proposed by Amati in his PHD thesis). The formula for w(t,d) in

I've now done two rebuilds of R-1.7.0 on NetBSD 1.6, one with the --without-zlib configure option, and one without. Both builds use the recently-installed gcc-3.2.3 compiler. As before, the one built normally gets a segment violation, whereas the one built with the --without-zlib option works. The odd thing is that neither uses shared libraries for zlib: % ldd /usr/local/lib/R/bin/R.bin

Please start a new thread for a new topic and use a meaningful subject for sake of the archives and everyone trying to follow. Using your Int1, correcting your Int2 and using your Y (which goes to Jul not Aug as per the comment): library(zoo) > as.ts(with(merge.zoo(Int1, Int2, Y, fill = 0), pmax(Int1, Int2))) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1992