search for: 0.313

Dear R users I have a big matrix like 6021 1188 790 290 1174 1015 1990 6613 6288 100714 6021 1 0.658 0.688 0.474 0.262 0.163 0.137 0.32 0.252 0.206 1188 0.658 1 0.917 0.245 0.331 0.122 0.148 0.194 0.168 0.171 790 0.688 0.917 1 0.243 0.31 0.122 0.15 0.19 0.171 0.174 290 0.474

Dear R-friends, For the following data: > xy x y i 1 731 0.313 2 2 739 0.340 2 3 790 0.373 2 4 855 0.451 2 5 980 0.608 2 6 575 0.156 1 7 608 0.207 1 8 630 0.249 1 9 670 0.332 1 10 838 0.377 1 11 964 0.466 1 > coplot(y ~ x|i, data=xy) coplot gives 3 panels, rather than 2, namely one for i=1 and one for i=2. Futhermore, when I extand data fram xy to have i=3 as follows:

Dear All, I'me having (much) trouble understanding why it happened and answering a referee's comment to part of a submitted manuscript. I've tried to google for help but... I'm really confident that although this is a R-Help list someone can help me! I used R to do an ANCOVA w/ RNA/DNA as the dep var, sl as the indep var and gut (a factor w/ levels: prey and empty) as the

Dear all; Thanks. Here are the dput results as Duncan suggested. Regards, Greg structure(list(Sub_Pathways = structure(c(3L, 3L, 3L, 3L, 3L), .Label = c("Acetylated_Peptides", "Advanced_Glycation_End-product", "Alanine_and_Aspartate", "Aminosugar", "Ascorbate_and_Aldarate", "Carnitine", "Ceramides", "Creatine",

Hi, I'm getting an error message using polr(): Error in optim(start, fmin, gmin, method = "BFGS", hessian = Hess, ...) : initial value in 'vmmin' is not finite The outcome variable is ordinal and factored, and the independant variable is continuous. I've checked the source code for both polr() and optim() and can't find any variable called

Hi, I'm trying to use sapply to compute the min of several variables, each of them stored in data.frames, grouped as a list: Is it normal that mean() and min() produce different objects dimensions? > str(dats) List of 5 $ log20:'data.frame': 83 obs. of 5 variables: ..$ DATE : int [1:83] 2001081500 2001081512 2001081600 2001081612 2001081700 2001081712

Until a weeks ago I used stata for everything. Now I'm learning R and trying to move. But, in this stage I'm testing R trying to do the same things than I used to do in stata whit the same outputs. I have a problem with the logit, applying weights. in stata I have this output . svy: logit bach job2 mujer i.egp4 programa delay mdeo i.str evprivate (running logit on estimation sample)

Some help with how to re-label the vertical axis in a histogram would be appreciated. qplot(off.sc,weight=rel.freq,binwidth=.29,main="test Figure"+ylab("New from inside"))+ylab("New from outside")+ xlab("off.sc\nAggregated frequency plots for 17 equal intervals.") The code

We are two french students and we have a problem concerning an exercize. We don't know how to resolve it. It would be fantastic if someone can help us. Thanks. Description: This study examined how the metabolic cost of locomotion varied with speed, stride frequency and body mass. Cost was determined by measuring oxygen consumption (?vo2?), analyzing the oxygen content in air inhaled and

Works fine for me. What do you object to in the following? Calling the above df "d", > dm <- as.matrix(d) > dm Sub_Pathways BMI_beta SAT_beta VAT_beta 1 "Alanine_and_Aspartate" " 0.23820" "-0.02409" " 0.94180" 2 "Alanine_and_Aspartate" "-0.31300" "-1.97510" "-2.22040" 3

Hi, I am using R to do a principal components analysis for a class which is generally using SPSS - so some of my question relates to SPSS output (and this might not be the right place). I have scoured the mailing list and the web but can't get a feel for this. It is annoying because they will be marking to the SPSS output. Basically I'm getting different values for the component

Hello, I would like to do a partial least square discriminant analysis (PLSDA) in R using the package "ropls" Which is in R available via the R command : source("https://bioconductor.org/biocLite.R") I try to do a PLSDA to illustrate the impact of two genders (AP,C) on 5 compounds measured in persons (samples) should be illustrated. When I try to do a PLSDA I get the warning

Dear All- I have a dataset that is comprised of the following: doy yr mon day hr hgt1 hgt2 hgt3 co21 co22 co23 sig1 sig2 sig3 dif flag 244.02083 2005 09 01 00 2.6 9.5 17.8 375.665 373.737 373.227 3.698 1.107 0.963 -0.509 PRE 244.0625 2005 09 01 01 2.6 9.5 17.8 393.66 384.773 379.466 15.336 11.033 5.76 -5.307 PRE 244.10417 2005 09 01 02 2.6 9.5 17.8 411.162 397.866 387.755 6.835 5.61 6.728

Dear R folk: Perhaps I'm just dense today, but I am having trouble reproducing the principal components plotted and summarized by clusplot. Here is a brief example using the pluton dataset. clusplot reports that the first two principal components explain 99.7% of the variability. But this is not what princomp is reporting. I would greatly appreciate any advice. With best regards, -- Tom

On Sun, Sep 23, 2018 at 12:26 PM, Wolfgang Rißler <wolle321 at freenet.de> wrote: > I try to send a friendly ping to my problem. > At the moment I'm running at 1600x1200 (monitor than has 1600x1200 > too), what looks better then 1920x1200 picture on that Monitor with > 960x1200. > I cant get 1920x1200 working, but I'm shure, that the monitor has this > native

Hi, I have a long list of lists from which I want to efficiently extract and rbind elements. So I'm using the approach below: f <- function(i){ out <- replicate(5, list(matrix(rnorm(80), nc=20))) names(out) <- letters[1:5] out } set.seed(1) lst <- lapply(1:1.5e6, f) (t0 <- system.time(tmp <- do.call(rbind, lapply(lst, '[[', 'b')))) Is there

R-help, I'm trying to test the significance of two regression lines , i.e. the significance of the slopes from two samples originated from the same population. Is it correct if I fit a liner model for each sample and then test the slope signicance with 'anova'. Something like this: lm1 <- lm(Y~ a1 + b1*X) # sample 1 lm2 <- lm(Y~ a2 + b2*X) # sample 2 anova(lm1, lm2)

Dear list, I am trying to replicate some Stata results but having a tough time doing it in R. The goal is to obtain a difference-in-difference estimate in a model with simple state fixed effects. The "state" variable is a factor, but some levels are missing. It appears that Stata automatically recognizes this and works around it. It also automatically pick "ME" as the base

Hi r-users,   I would like to extract the data that match.  Attached is my data: I'm interested in matchind the value in column 'intg' with value in column 'rand_no' > cbind(z=z,intg=dd,rand_no = rr)             z  intg rand_no    [1,]  0.00 0.000   0.001    [2,]  0.01 0.000   0.002    [3,]  0.02 0.000   0.002    [4,]  0.03 0.000   0.003    [5,]  0.04 0.000   0.003    [6,] 

A bit of a newbee to R and factor rotation I am trying to understand factor rotations and their implementation in R, particularly the GPArotation library. I have tried to reproduce some of the examples that I have found, e.g., I have taken the values from Jacksons example in "Oblimin Rotation", Encyclopedia of Biostatistics