similar to: Average R-squared of model1 to model n

Hi all, I have a data.frame with the following colnames pattern: x1 y11 x2 y21 y22 y23 x3 y31 y32 ... I.e. I have an x followed by a few y's. What I would like to do is turn this wide format into a tall format with two columns: "x", "y". The structure is that xi needs to be associated with yij (e.g. x1 should next to y11 and y12, x2 should be next to y21, y22, and

After searching in R- Introduction, FAQ, help... I don't understand this: I write a function in a file (.R): tt <- function(mc) { date() mc<-read.csv2("machines.txt",na.strings="") date() } I source it in R and I type tt(). The answer is > tt() [1] "Mon Aug 14 11:18:25 2000" > The instructions following the first "date()" are ignored. Why?

II am translating some gauss code into R, and gauss has a matrix product function called the horizontal direct product (*~), which is some sort of variant on the Kronecker product. For example if x is 2x2 and y is 2x2 the horizontal direct product, z, of x and y is defined (in the Gauss manual) as: row 1 = x11*y11 x11*y12 x12*y11 x12*y12 row 2 = x21*y21 x21*y22 x22*y21 x22*y22 Or in R

Dear R-lister I have a question about how to create multiple error bar plots. I found that I can use an "errbar" function from Hmisc package to create one error bar plot in which there are multiple data points (x, y) with the error bars. Thus, I know that I can get "one" error bar plot which consists of many data points. However, I did not find a way to put

Hi all. I have defined a plot thus: par(mar=c(5,5,4,5),las=1, xpd=NA) plot(Day, Ym1Imp, ylim=c(0,100), type="b", bty="l", main="Ym1 Expression", cex=1.3, xaxt="n", yaxt="n") #plot implant data axis(side=1, at=c(0,1,3,5,7,10,14,21), labels=c(0,1,3,5,7,10,14,21)) # label x axis mtext("Day", side =1, at=10, line=3, cex=1.2) # title x

Dear List, how can I obtain the value of r suqared for a non-linear model? For linear models it can be found in the summary() of the model but for non-linear models I just don't know. Please help! Anna

Hola grupo, estoy siguiendo una gu?a de la librer?a rLandsat que me la he descargado de: devtools::install_github("socialcopsdev/rLandsat") Y tras hacer los siguiente (obviamente tengo me he registrado previamente en la api correspondiente): product_id = c("LC08_L1TP_145049_20180301_20180308_01_T1", "LC08_L1TP_145049_20170330_20170414_01_T1",

Hola Ángel: Yo creo que tendrías que establecer el sistema de coordenadas de referencia de tu objeto raster antes de salvarlo como GTiff. Algo así: crs(r1) <-"+proj=utm +zone=14 +datum=WGS84" Saludos, Marcelino. El 20/02/2020 a las 1:42, Angel Cervantes escribió: > Hola a todos, quisiera pedirles su ayuda. Estoy tratando de crear un raster a partir de una tabla de datos

This code will read binary file and display it as a map. may problem is that this code is using a continuous colour scheme, even though I have discrete data (which is a classification scheme). How can I map numbers to colours with raster? Please require(raster) conne <- file("C:\\lai.bin", "rb") sd<- readBin(conne, integer(), size=1, n=360*720, signed=F)

This is one of those instances where a less superficial knowledge of R's technical details comes in really handy. What you need to do is convert the data frame to a single (numeric) vector for, e.g. a matrix() call. This can be easily done by noting that a data frame is also a list and using do.call(): ## imp is the data frame: do.call(c,imp) X11 X12 X13 X14 X15 X16 X17 X18 X19

Hi all!   My question is how to colour pixels by z value in persp plot in raster package. Here is an example:     x <- seq(-1.95, 1.95, length = 30) y <- seq(-1.95, 1.95, length = 35) z <- outer(x, y, function(a,b) a*b^2) r1 <- raster(nrows=35, ncols=30, xmn=0, xmx=30, ymn = 0, ymx = 35) r1[] <- c(z) persp(r1)   There already exist some function to produce persp plot for anothe

Hello everyone, I want to transform a data.frame into an array (lets call it mydata), where: mydata[[1]] is the first imputed dataset...and for each mydata[[d]], the first p columns are covariates X, and the last one is the outcome Y. Lets assume a simple data.frame: Imputed = data.frame( X1 = c(1,2,1,2,1,2,1,2, 1,2,1,2,1,2,1,2), X2 =

Hello everyone, Thank you for this. Nonetheless it is not exactly want i need. I need mydata[[1]] to provide the values for all 3 variables (Y, X1 and X2) of the first imputation only. As it stands it returns the whole database. Any ideas? Best, ioanna ________________________________ From: Bert Gunter <bgunter.4567 at gmail.com> Sent: 24 May 2018 16:04 To: Ioanna Ioannou Cc:

Dear experts, I have in all 14 matrices which stands for gene expression divergence and 14 matrices which stands for gene sequence divergence. I have tried joining them by using the concatanation function giving SequenceDivergence <- c(X1,X2,X3,X4,X5,X6,X7,X8,X9,X10,X11,X12,X13,X14) ExpressionDivergence <- c(Y1,Y2,Y3,Y4,Y5,Y6,Y7,Y8,Y9,Y10,Y11,Y12,Y13,Y14) where X1,X2..X14 are the

Dear experts, I have in all 14 matrices which stands for gene expression divergence and 14 matrices which stands for gene sequence divergence. I have tried joining them by using the concatanation function giving SequenceDivergence <- c(X1,X2,X3,X4,X5,X6,X7,X8,X9,X10,X11,X12,X13,X14) ExpressionDivergence <- c(Y1,Y2,Y3,Y4,Y5,Y6,Y7,Y8,Y9,Y10,Y11,Y12,Y13,Y14) where X1,X2..X14 are the

Dear all I am intrested in making iterative estimation (thro' loop statements) of, say, linear regression model. For this purpose, I have written the following programme and that I have made use of a sample data (viz., exp.txt): ? Programme: ? # Linear regression modelling with sample data (try5.txt) # Repeated estimation through loop statement

Hello- I understand that it's convention, when comparing two models using the anova function anova(model1, model2), to put the more "complicated" (for want of a better word) model as the second model. However, I'm using lme in the nlme package and I've found that the order of the models actually gives opposite results. I'm not sure if this is supposed to be the case

Hi All, I am making a serious effort to try to learn R, but one hurdle I am facing is that I need to "see" the data as I walk through the examples in the packages. For instance, many examples on the web start by a command like data("wines"). How can I actually view what the dataset looks like prior to transformations and analysis? I have tried to use edit() , print, and

°_§É§¿§¿ ----- Original Message ----- From: <r-help-request at stat.math.ethz.ch> To: <r-help at stat.math.ethz.ch> Sent: Sunday, December 08, 2002 7:00 PM Subject: R-help digest, Vol 1 #10 - 6 msgs > Send R-help mailing list submissions to > r-help at stat.math.ethz.ch > > To subscribe or unsubscribe via the World Wide Web, visit >

Hi all, I have happened to work on MS .NET for sometime now, and I found that this language offers RNG what is called as Donald E. Knuth's subtractive random number generator algorithm (found here: http://msdn.microsoft.com/en-us/library/system.random.aspx#Y12). ? Here I was wondering whether R also have same RNG in it's inventory, so looked at ?set.seed. There I found 2 related RNGs