similar to: error propagation - hope it is correct subject

Hi After melt you can change levels of your factor variable. Again with the toy example. > levels(temp$variable) [1] "y1" "y2" "y3" "y4" > levels(temp$variable) <- levels(temp$variable)[c(2,4,1,3)] > levels(temp$variable) [1] "y2" "y4" "y1" "y3" > And you will get graphs with this new levels ordering.

Hi! I'm learning mgcv, and reading Simon Wood's book on GAMs, as recommended to me earlier by some folks on this list. I've run into a question to which I can't find the answer in his book, so I'm hoping somebody here knows. My outcome variable is binary, so I'm doing a binomial fit with gam(). I have five independent variables, all continuous, all uniformly

Petr, there was a thinko in your response. tmp <- data.frame(m=factor(letters[1:4]), n=1:4) tmp tmp$m <- factor(tmp$m, levels=c("c","b","a","d")) ## right tmp[order(tmp$m),] tmp <- data.frame(m=factor(letters[1:4]), n=1:4) levels(tmp$m) <- c("c","b","a","d") ## wrong tmp[order(tmp$m),] changing levels

Dear all, Please suggest me how can I do it. I have a matrix which look like following: x1 x2 x3 t1 .01 0.3 0 t2 0 0.1 0.01 t3 0 .01 .01 t4 0 0 t5 5 0 0 t6 0 0 0 t7 0 0 0 t8 0 0 0 t9 0.6 0 0 t10 0 0 0.66 t11 0 0.6 0.11 t12 0 0.4 0 I want to sort decreasing order in each column based on rows. and then to display only those rows which has a value. The expected out put matrix will

Hi Petr and Richard; Thanks for your responses and supports. I just faced a different problem. I have the following R codes and work well. p <- ggplot(a, aes(x=Phenotypes, y=Metabolites, size=abs(Beta), colour=factor(sign(Beta)))) + theme(axis.text=element_text(size = 5)) p1<-p+geom_point() p2<-p1+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),

During some experimentation in preparing R lessons I encountered this behaviour which I can not explain fully mat <- matrix(1:16, 4,4) df1 <- data.frame(mat) > mean(df1) X1 X2 X3 X4 2.5 6.5 10.5 14.5 Expected, documented > median(df1) [1] 6.5 10.5 Rather weird, AFAIK there shall not be an issue with data frame at least I did not find any in help page. I tracked it

I assume that I have missed something fundamental and that it is there in front of me in "An Introduction to R", but I need someone to point me in the right direction. > x1 <- "1159 1129 1124 -5 -0.44 -1.52" > x2 <- c("1159","1129","1124","-5","-0.44","-1.52") > x3 <- unlist(strsplit(x1,"

Hi scale_colour_gradient(?red?, ?blue?) should do the trick. Actually I found it by Google ggplot colour http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/ http://www.sthda.com/english/wiki/ggplot2-colors-how-to-change-colors-automatically-and-manually#gradient-colors-for-scatter-plots question. So you could find it too and probably far more quickly then myself as I have also other duties. Cheers

Hi Maybe there are other ways but I would split data to several chunks e.g. in list and use for cycle to fill multipage pdf. With the toy data something like library(reshape2) library(ggplot2) temp <- melt(temp) temp.s<-split(temp, cut(1:nrow(temp), 2)) pdf("temp.pdf") for (i in 1: length(temp.s)) { p <- ggplot(temp.s[[i]], aes(x=par1, y=variable, size=abs(value),

Hi Petr; Thanks for your reply. It is much appreciated. A small example is given below for 4 independent and 4 dependent variables only. The values given are regression coefficients.I have looked ggplot documents before writing to you. Unfortunately, I could not figure out as my experience in ggplot is ignorable Regards. Greg y1 y2 y3 y4 x1 -0.19 0.40 -0.06 0.13 x2 0.45 -0.75 -8.67 -0.46 x3

Dear all, I want to compute the average of the three blocks for each x-variable which is equal slide in the code below. How can I do that ? block x1 x2 x3 x4 x5 1 23 22 23 24 23 1 21 25 26 21 39 1 23 24 22 23 23 2 20 21 23 24 28 2 32 23 34 24 26 2 19

Hi Petr; Thanks so much. Exactly this is what I need. I will play to change color and so on but this backbound is perfect to me. I do appreciate your help and support. Regards, Greg On Thu, Feb 8, 2018 at 1:29 PM, PIKAL Petr <petr.pikal at precheza.cz> wrote: > Hi > > I copied your values to R, here it is > > > > > dput(temp) > > > > temp <-

Hi Petr; Thanks so much. This is great! Although last Sunday, alternatively, I have solved the problem using the following statement at the very end of the program. * ggsave('circle.pdf', p4, height = 70, width = 8, device=pdf, limitsize = F, dpi=300).* This works very well too. Asa my categorical variables are in my Y axis, my R program reorders the names on Y-axis. However, I would

Given a large data.frame, a function trains a series of models by looping over two steps: 1. Create a model-specific subset of the complete training data 2. Train a model on the subset data The function returns a list of trained models which are later used for prediction on test data. Due to how models and closures work in R, each model contains a lot of data that is not necessary for

Question: I have matrix (n x3) that represents discrete data. Each row of matrix is 3-D point (x,y,z). I would like to get contour map (z value) at two dimension (x,y). How can I use related contour function to do this job? I am not sure if I clarify this question. For example, we can get point (x,y) at 2 dimension according to first two columns of matrix. Then I want to connect same value

Hi Peter; The R code you provided works very well. Once again thanks so much for this. The number of variables in my data set that should appear on the y-axis is 733 and they are not numerical (for example the name of one variable is *palmitoyl-arachidonoyl-glycerol (16:0/20:4) [1]**. So, the plot looks very messy in one page. How can I make the plot to print out on multiple pages? Regards,

Hi I copied your values to R, here it is > dput(temp) temp <- structure(list(par1 = structure(1:4, .Label = c("x1", "x2", "x3", "x4"), class = "factor"), y1 = c(-0.19, 0.45, -0.09, -0.16), y2 = c(0.4, -0.75, 0.14, -0.01), y3 = c(-0.06, -8.67, 1.42, 2.21), y4 = c(0.13, -0.46, 0.06, 0.06)), .Names = c("par1", "y1",

Dear R users, I try to compute this summation, http://www.nabble.com/file/p25054272/dd.jpg where f(y|x) = Negative Binomial(y, mu=exp(x' beta), size=1/alp) http://www.nabble.com/file/p25054272/aa.jpg http://www.nabble.com/file/p25054272/cc.jpg In fact, I tried to use "do.call" function to compute each u(y,x) before the summation, but I got an error, "Error in X[i, ]

Hi, I'd like to apply a 2^k factorial design with k=10 parameters. Obviously this results in a quite long term for the model equation due to the high number of combinations of parameters. How can I specify the equation for the linear model (lm) without writing all combinations explicitly down by hand? Does a R command exist for this problematic? Thanks for your help in advance, Sven

R-List, I'm trying to compute pairwise distances among pairs of observations, which each pair containing data from 2 groups. There are more than 100000 unique pairs. I have programmed a distance function that has three parameters, a vector of covariates from the ith observation in Group 1, a vector of covarites from the jth observation in Group 2, and a weighting matrix. I have used