similar to: confidence interval or error of x intercept of a linear regression

Dear List members, I would like to ask for advise on quantile regression in R. I am trying to perform an analysis of a relationship between species abundance and its habitat requirements - the habitat requirements are, however, codes - 0,1,2,3... where 0<1<2<3 and the scale is linear - so I would be happy to treat them as continuos The analysis of the data somehow does not work, I am

Hello, the more general thing I'd like to learn here is how to compute Function of Data on the basis of grouping determiend by n variables. In terms of the reason why I am interested in this, I need to compute the average of my data based on the value of the month and day across years. I have come up withy the code below which, as far as I can see, does what I need but getting either a more

Hi May you help me correct my loop function. I want optim to estimates al_j; au_j; sigma_j; b_j by looking at 0 to 20, 21 to 40, 41 to 60 data points. The final result should have 4 columns of each of the estimates AND 4 rows of each of 0 to 20, 21 to 40, 41 to 60. ###MY code is n=20 runs=4 out=matrix(0,nrow=runs) llik = function(x) { al_j=x[1]; au_j=x[2]; sigma_j=x[3]; b_j=x[4]

Hello, I have a data matrix consists of species as columns and sites as rows. N Chlamydiae Deferribacteres GN12 Hyd24-12 KSB1 PAUC34f SC4 SPAM Thermi WPS-2 ZB3 AD3 Elusimicrobia GAL15 ABY1_OD1 OP9 Fusobacteria HDBW-WB69 OP11 WS1 SR1 ZB2 AC1 OP3 OP8 NC10 NKB19 TM7 WS3 Gemmatimonadetes Lentisphaerae GN02 Armatimonadetes Tenericutes Spirochaetes Epsilonproteobacteria TM6 Acidobacteria Chlorobi

Dear R help, I have some readings in three dimensions (x, y, z) and an amplitude for each. I'd like to visualize the data using ggplot, using tile plots, as I have some additional point data I would like to eventually overlay on the tile plots. I would like to subset the data by sections, slices if you will, in the z dimension, and plot the data for that slice. I can do all of this, but am

#Here is a vector of IDs > cwaves [1] 86 90 185 196 197 209 210 215 216 217 218 #Here is a matrix. The rows and columns correspond to the IDs in cwaves, and the matrix is populated with a coefficient > mat 86 90 185 196 209 210 215 216 217 218 86 0 0 0 0 0 0.00000 0 0.000000 0.000000 0.000000 90 0 0 0 0 0 0.00000 0

Dear List, I need a barplot with vertical bars. Each bar should have a label. The problem is, that the labels are too long, so they overlap, or only every seccond label is displayed in the output. Here is a little syntax: dd <- c(100,110,90,105,95) barplot(dd,names.arg=c('Conduct Disorders','Attention Deficit', 'Eating Disorders',

Dear list colleagues, I'm trying to come up with a test question for undergraduates to illustrate comparison of means from a complex survey design. The data for the example looks roughly like this: mytest<-data.frame(harper=rnorm(500, mean=60, sd=1), party=sample(c("BQ", "NDP", "Conservative", "Liberal", "None", NA), size=500,

I am tangled with a syntax question. I want to calculate basic statistics for a large dataset provided in weights and values and I can't figure out an elegant way to expand the data. For example here are the counts: > counts n4 n3 n2 n1 p0 p1 p2 p3 p4 1 0 0 0 1 1 3 16 55 24 2 0 0 0 0 2 8 28 47 15 3 1 17 17 13 4 5 12 24 8 ... and the values: > values

That's consistent/not surprising if the problem lies in the numerical gradient calculation step ... On 2019-05-06 10:06 a.m., Ravi Varadhan wrote: > Optim's Nelder-Mead works correctly for this example. > > >> optim(par=10, fn=fn, method="Nelder-Mead") > x=10, ret=100.02 (memory) > x=11, ret=121 (calculate) > x=9, ret=81 (calculate) > x=8, ret=64

Is there a way to put a legend on an xyplot -- eg, something akin to the key function? Or, is there a way to get the R legend function to work on a lattice plot? To the people who have done the work on lattice and grid: Thanks for making lattice. I'm finding it very useful. Anne ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Anne E. York National Marine Mammal Laboratory Seattle WA 98115-0070 USA

I recently uploaded a developmental version of the Matrix package, Matrix_0.6-1.tar.gz, to CRAN where it is in the src/contrib/1.9.0/Other directory. It requires some of the packages that will appear in R-1.9.0. This version marks a major redesign of the Matrix package to use S4 classes and methods and to incorporate sparse matrix manipulations using routines from TAUCS

It seems that it's an old bug that was found in some other packages, but at that time not optim: https://bugs.r-project.org/bugzilla/show_bug.cgi?id=15958 and that Duncan Murdoch posted a patch already last Friday :) Thomas Am 06.05.2019 um 16:40 schrieb Ben Bolker: > That's consistent/not surprising if the problem lies in the numerical > gradient calculation step ... >

Hello there, I am trying to compute the 3 months return momentum with the timeSeries x.ts, which is just a subset of simple returns from a much bigger series, > class(x.ts) [1] "timeSeries" attr(,"package") [1] "timeSeries" > dim(x.ts) [1] 20 3 > x.ts[1:8,] GMT MS.US AAPL.US CA.FP 1996-01-31 0.15159065 -0.133391894

Hi dear all, In my laptop(ubuntu 11.10 64bit), I maintained a released R (2.14) and a developmental R, I can specify qplot(..., alpha = ) in R 2.14 , but when I try to use transparency in developmental R, I got a warning message and the plot is clearly not I want. minimal example: > qplot(data = mtcars, x = mpg, y = cyl, alpha = cyl) Warning message: In grid.Call.graphics(L_points, x$x, x$y,

Hi dear all, In my laptop(ubuntu 11.10 64bit), I maintained a released R (2.14) and a developmental R, I can specify qplot(..., alpha = ) in R 2.14 , but when I try to use transparency in developmental R, I got a warning message and the plot is clearly not I want. minimal example: > qplot(data = mtcars, x = mpg, y = cyl, alpha = cyl) Warning message: In grid.Call.graphics(L_points, x$x, x$y,

I built some reasonably successful tools in a graphical database for reconstructing the developmental turning points for feedback loop driven natural processes. I'm trying to move it to R and am having difficulty with the very basics, i.e. a) defining time series using time as a natural number rather than a place in a table. I was also hoping b) someone might have built some of the

Optim's Nelder-Mead works correctly for this example. > optim(par=10, fn=fn, method="Nelder-Mead") x=10, ret=100.02 (memory) x=11, ret=121 (calculate) x=9, ret=81 (calculate) x=8, ret=64 (calculate) x=6, ret=36 (calculate) x=4, ret=16 (calculate) x=0, ret=0 (calculate) x=-4, ret=16 (calculate) x=-4, ret=16 (memory) x=2, ret=4 (calculate) x=-2, ret=4 (calculate) x=1, ret=1

Dear Dr.Goslee and anyone may intrested in matrix manipulate, I am using your ecodist to do mantel and partial mantel test, I have locality data and shape variation data, and the two distance matrixs are given as belowings. When I run the analysis, it is always report that the matrix is not square, but I didn't know what's wrong with my data. Would you please help me on this. I am quite

I have a data set that I display using barplot. I don't know what you call it, but when I look at it, it looks like this: > lsu (0,0.1] (0.1,0.2] (0.2,0.3] (0.3,0.4] (0.4,0.5] (0.5,0.6] A 0.052631579 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 B 0.000000000 0.000000000 0.001007049 0.003021148 0.000000000 0.000000000 E 0.200000000 0.000000000