similar to: Plotting Binned Data

Hi, this is probably quite simple but I can't seem to do it correctly. I have a data frame of counts of infections in different ages; something like: count=c(1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 4, 7, 8, 8, 9, 9, 10, 11, 15, 17, 17, 17, 17, 19, 19, 19, 19, 20, 20, 20, 21, 21, 21, 22, 22, 22, 22, 23, 23, 23, 23, 23, 24, 24, 25, 27, 31, 33 ) age=c(3, 8, 9, 7, 56, 58, 10, 13, 53, 55, 11,

Hi, I have a binned data that looks like this. (8.048,18.05] (-21.95,-11.95] (-31.95,-21.95] (18.05,28.05] (-41.95,-31.95] 81 76 18 18 12 (-132,-122] (-122,-112] (-112,-102] (-162,-152] (-102,-91.95] 6 6 6 5 5 (-91.95,-81.95] (-192,-182]

Dear all, I have the following object: > print(x) An object of class "matrix.csr" Slot "ra": [1] 0.992056718 1.000000000 1.000000000 1.000000000 1.000000000 1.000000000 [7] 1.000000000 1.000000000 1.000000000 1.000000000 1.000000000 1.000000000 [13] 1.000000000 1.000000000 1.000000000 1.000000000 1.000000000 1.000000000 [19] 1.000000000 1.000000000 1.000000000

Dear all, I have a simple plot using "gridBase" like this. The problem occurs whenever I execute this code there is always a blank page created before the actual plot. How can we disable that blank page? I am using: R version 2.7.2 (2008-08-25) and gridBase version: 0.4-3 __ BEGIN__ library(grid) library(gridBase) opar <- par(no.readonly=TRUE) par(opar) grid.newpage()

Add infrastructure to keep track of GPU virtual address (VA) mappings with a decicated VA space manager implementation. New UAPIs, motivated by Vulkan sparse memory bindings graphics drivers start implementing, allow userspace applications to request multiple and arbitrary GPU VA mappings of buffer objects. The DRM GPU VA manager is intended to serve the following purposes in this context. 1)

On 7/7/23 13:00, Boris Brezillon wrote: > On Fri, 30 Jun 2023 00:25:18 +0200 > Danilo Krummrich <dakr at redhat.com> wrote: > >> +/** >> + * drm_gpuva_for_each_va_range - iternator to walk over a range of &drm_gpuvas >> + * @va__: &drm_gpuva structure to assign to in each iteration step >> + * @mgr__: &drm_gpuva_manager to walk over >> + *

On 2/17/23 19:34, Liam R. Howlett wrote: > * Danilo Krummrich <dakr at redhat.com> [230217 08:44]: >> Split up the MA_STATE() macro such that components using the maple tree >> can easily inherit from struct ma_state and build custom tree walk >> macros to hide their internals from users. >> >> Example: >> >> struct sample_iter { >> struct

Is 'make check' supposed to work with llvm-gcc? I get the following: autogen -T /users/dag/projects/cascade/compiler/llvm-gcc/fixincludes/check.tpl /users/dag/projects/cascade/compiler/llvm-gcc/fixincludes/inclhack.def Your shell is '/bin/tcsh'. AutoGen will attempt to use 'sh' /bin/sh ./check.sh /users/dag/projects/cascade/compiler/llvm-gcc/fixincludes/tests/base

Add infrastructure to keep track of GPU virtual address (VA) mappings with a decicated VA space manager implementation. New UAPIs, motivated by Vulkan sparse memory bindings graphics drivers start implementing, allow userspace applications to request multiple and arbitrary GPU VA mappings of buffer objects. The DRM GPU VA manager is intended to serve the following purposes in this context. 1)

Add infrastructure to keep track of GPU virtual address (VA) mappings with a decicated VA space manager implementation. New UAPIs, motivated by Vulkan sparse memory bindings graphics drivers start implementing, allow userspace applications to request multiple and arbitrary GPU VA mappings of buffer objects. The DRM GPU VA manager is intended to serve the following purposes in this context. 1)

Dear all, I am trying to model number of samples from a given series. The series are modelled according Gamma function. In order to estimate the # samples, I use BIC/AIC with MLE (computed from dgamma function). Here is the code I have. __BEGIN__ mlogl <- function( x_func, theta_func, samp) { # computing log_likelihood return( - sum(dgamma(samp, shape = x_func, scale=theta_func, log

Okay, I've had some fun there. Let me tell I am no samba pro, so the patch below _should be tested and tested again_ in _your_ environment before doing anything close to putting your patched samba into production. It can possibly break everything. You have been warned. The mail is somehow longish, because sending a quick (and dirty) patch without any explanation seems wrong to me. And

Suppose I have a set of binned data, counts exceeding a series of arbitrary thresholds, a total N, a minimum and maximum, those sorts of things. Is there a "standard" method for estimating arbitrary quantiles from this? My initial thought is that the counts and min/max give me solutions at various points along the empirical cdf. As the data are roughly log-normal, I thought maybe I

Dear All, I am researching financial market microstructure and have approx 4 x 10^7 multivariate 2D data samples which I have counted into a 250 x 390 bin matrix (frequency counted 2D histogram) in order to more efficiently manage the volume of data. I now wish to construct a smooth kernel density estimate (Gaussian kernel function) using this binned data. Does anyone in the R community know

Hello I need to fit a distribution to a histogram data set. I have read Ricci's guide to distribution fitting, and am ready to begin experimenting with the techniques it mentions, but I am uncertain how to get my data in the format he uses. My problem is that my data is binned. So for example my data is in the following format #lb ub count 0 1 4 1 2 7 2 3 2

I have a table that looks like this: structure(list(speed = c(3,9,14,8,7,6), result = c(0.697, 0.011, 0.015, 0.012, 0.018, 0.019), house = c(1, 1, 1, 1, 1, 1), date = c(719, 1027, 1027, 1027, 1030, 1030), id = c("1000", "10000", "10001", "10002", "10003", "10004")), .Names = c("speed", "result",

How to use ggplot to do the binned quantile plots(one type of scatter plot)? Hi all, I have done scatter plot: plot(x, y). Now I wanted to do binned quantile plots... can ggplot2 help me? For example, we bin x data into 10 bins. For each bin, we draw the 10 deciles of the corresponding y data in that bin as points/dots. And then accross all bins, we would like to connect the corresponding

Hi, I'm a newbie in using R and I would like to have a few clues as to how I could compute and plot a distribution of daily rainfall intensity in different categories. I have daily values (mm/day) for several years and I need to show the frequency of 0-1, 1-2.5, 2.5-5, 5-10, 10-20, 20+ mm/day. Can this be done easily? Thanks, Etienne