similar to: seq

Full_Name: Jeremiah Cohen Version: 2.9.0 OS: Windows XP Submission from: (NULL) (129.59.230.235) I believe there is a bug in the seq() function for certain values of the "from" argument. Here are examples: > seq(-.2, .1, .1) [1] -0.2 -0.1 0.0 0.1 > seq(-.3, .1, .1) [1] -3.000000e-01 -2.000000e-01 -1.000000e-01 5.551115e-17 1.000000e-01 > seq(-.4, .1, .1) [1] -0.4 -0.3

I came across this: shouldn't the last value be a more exact zero? It did not do that with 1 - sum( rep(0.1, 10) ) > 1 - cumsum( rep(0.1, 10) ) [1] 9.000000e-01 8.000000e-01 7.000000e-01 6.000000e-01 5.000000e-01 4.000000e-01 3.000000e-01 2.000000e-01 [9] 1.000000e-01 1.110223e-16 > version _ platform

Hello everyone, I am using the following program to get the p-value of some numbers (column 'LR' of the data.dat file). I want to write the 1st and 2nd column of the output file (data.out) as an integer while the program change them. Could anybody please tell me how I can write the code which writes the values of the first two columns as integer? For your convenience, I have attached the

Hi, I'm having trouble with the "which" or the "seq" function, I'm not sure. Here's an example : > lat=seq(1,2,by=0.1) > lat [1] 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 > which(lat==1) [1] 1 > which(lat==1.1) [1] 2 > which(lat==1.2) [1] 3 > which(lat==1.3) [1] 4 > which(lat==1.4) [1] 5 > which(lat==1.5) [1] 6 >

Dear all, After having set par(mfrow = c(2, 2)) and switching between plot() and xyplot() using R 2.0.0 Patched (2004-10-13) under Windows 2000, the lattice plot gets overplotted. I also tried this under 1.9.1 Patched (2004-09-22), since this is the only older version I've got installed, and it did the same thing apart from that the overplotting now starts in the opposite lower corner.

Hi, I've noted that not all `predict' methods are hidden in the namespace: > methods("predict") [1] predict.ar* predict.Arima* [3] predict.arima0* predict.glm [5] predict.HoltWinters* predict.lm [7] predict.loess* predict.mlm [9] predict.nls* predict.poly [11] predict.ppr* predict.prcomp* [13]

Dear all, I'm analyzing a binary outcome using glm() with a binomial distribution and a logit link, and have now reached the point where I'd like to do some model checking. Since my data are in binary form I'd like to collapse over the cross-classification of the factors before the model checking. Are there any nice and simple ways doing this? If so, how? If not, I'd be

>>>>> Henric Winell <nilsson.henric at gmail.com> >>>>> on Tue, 7 Feb 2017 13:37:42 +0100 writes: > Hi, On 2017-02-07 13:12, Benjamin Tyner wrote: >> Hello >> >> When loading a package, I'm wondering if it's frowned >> upon for the package to alter the state of the random >> number

I have the following output from pretty(): > pretty(c(-.1, 1)) # note 2nd element [1] -2.000000e-01 -2.775558e-17 2.000000e-01 4.000000e-01 6.000000e-01 [6] 8.000000e-01 1.000000e+00 > as.character(pretty(c(-.1, 1))) [1] "-0.2" "-2.77555756156289e-17" "0.2" [4] "0.4" "0.6"

Dear list, I've noticed that influence.measures and cooks.distance gives different results for non-gaussian GLMs. For example, using R-1.9.0 alpha (2003-03-17) under Windows: > ## Dobson (1990) Page 93: Randomized Controlled Trial : > counts <- c(18,17,15,20,10,20,25,13,12) > outcome <- gl(3,1,9) > treatment <- gl(3,3) > glm.D93 <- glm(counts ~ outcome +

Thank you for your help! I gave you an example, you could run it in R . Maybe you will understand my meaning clearly. x <- data.frame(main.name="AAA", x.name=rep(c("Apply","Watermelon","Lemon","Banana",

Hi everyone, I have a simple problem but don´t know how to solve it. I read in a table from a text file that looks like that: -3,932200E-01 -2,000000E-01 4,999995E-02 -3,932099E-01 -1,000000E-01 3,999996E-02 -3,932000E-01 0,000000E+00 3,999996E-02 -3,931899E-01 -1,000000E-01 4,499996E-02 -3,931800E-01 -1,000000E-01 4,499996E-02 -3,931699E-01

Dear all, I'm a fairly new R user having two questions regarding gam: 1. The prediction example on p. 38 in the mgcv manual. In order to get predictions based on the original data set, by leaving out the 'newdata' argument ("newd" in the example), I get an error message "Warning message: the condition has length > 1 and only the first element will be used in: if

The last line of the example in droplevels' manual page seems to be incorrect to me. I think it should read: "table(droplevels(aq$Month))". Amazingly (I don't understand) both variants seem to produce the same result (R 3.3.3): --- > aq <- transform(airquality, Month = factor(Month, labels = month.abb[5:9])) > aq <- subset(aq, Month != "Jul") >

Full_Name: Henric Nilsson Version: 2.3.0 Patched (2006-05-09 r38014) OS: Windows 2000 SP4 Submission from: (NULL) (83.253.9.137) When supplying an unavailable link to `quasibinomial', the error message looks strange. E.g. > quasibinomial("x") Error in quasibinomial("x") : 'x' link not available for quasibinomial family, available links are "logit",

Hi, I have a following vector: > smallch [1] 0.0652840 0.1181300 0.0319370 0.0155700 0.0464110 0.0107850 [7] 0.0158970 0.0375900 0.0603090 0.0310300 0.0105920 0.0540580 [13] -0.0177740 0.0039393 Pretty (R 1.5.1) has problems with zero: > pretty(smallch) [1] -2.000000e-02 -3.469447e-18 2.000000e-02 4.000000e-02 6.000000e-02 [6] 8.000000e-02 1.000000e-01 1.200000e-01

Hi, I have a following vector: > smallch [1] 0.0652840 0.1181300 0.0319370 0.0155700 0.0464110 0.0107850 [7] 0.0158970 0.0375900 0.0603090 0.0310300 0.0105920 0.0540580 [13] -0.0177740 0.0039393 Pretty (R 1.5.1) has problems with zero: > pretty(smallch) [1] -2.000000e-02 -3.469447e-18 2.000000e-02 4.000000e-02 6.000000e-02 [6] 8.000000e-02 1.000000e-01 1.200000e-01

Hello, I'm working on using the LLVM JIT for a little project of mine, amazing work first off! I have a question about optimization passes. I initially have this function I've created, in python it looks like this: OS_end = 50OS_start = 0OS_timestep = 1birth_rate = .3population = 30.0for time in range(OS_start, OS_end, OS_timestep): births = birth_rate * population deaths = 0.1

Dear all, I've now spent a couple of days trying to learn R and, in particular, the gam() function, and I now have a few questions and reflections regarding the latter. Maybe these things are implemented in some way that I'm not yet aware of or have perhaps been decided by the R community to not be what's wanted. Of course, my lack of complete theoretical understanding of what

I can't get confint.glm to work from within a function. Consider the following (using R 1.9.1, Windows 2000): # FIRST: SOMETHING THAT WORKS FROM A COMMAND PROMPT DF <- data.frame(y=.1, N=100) (fit <- glm(y~1, family=binomial, data=DF, weights=DF[,"N"])) Call: glm(formula = y ~ 1, family = binomial, data = DF, weights = DF[, "N"]) Coefficients: