similar to: legend and values do not match in ggplot

Dear R-users, I have some human serum lipid concentrations (cholesterol, apoB ...), each lipid was measured (in the same person) for 6 times in different time points (start, 3 months, 6 months, 12 months, 18 months, 24 months). There were 2 groups of participants: one with a nutritional intervention and the other without it. I would like to know, whether lipid concentrations differ among different

Dear All, I hope this is not too off-topic. Essentially, I need to know if there is any R package which can help me with a deformulation project. Suppose e.g. that you know from a chemical analysis the fat, mineral, vitamin, energy [and so on] content of a certain food product. You also know the ingredients of this product (e.g. milk, lactose, vegetable oil) and you know the chemical

I'm using this code to plot a smoothed line. These two columns of data really represent 4 groups and I'd like to plot a separate line for each group but have them all in the same plot. The R-Docs for lowess do not seem to indicate some type of "GROUPS=var_name" option. What would be the syntax for this? plot(AWGT ~ lipid ) lines(lowess(lipid , AWGT, f=.8)) -- Dean

I use the step() function occasionally, and I think I understand its objective, proper use, and limitations. Now I see stepwise model selection being used in what seems to be an unusual way, and I wonder if it is right or wrong. May I describe? Genetic mapping tries to find where in an animal's genome are genetic elements that influence a particular physical trait. Say there are 100

Hi ALL, I have very simple question regarding pattern matching. Could anyone tell me how to I can use R to retrieve string pattern from text file. for example my file contain following information SpeciesCommon=(Human);SpeciesScientific=(Homo sapiens);ReactiveCentres=(N,C,C,C,+ H,O,C,C,C,C,O,H);BondInvolved=(C-H);EzCatDBID=(S00343);BondFormed=(O-H,O-H);Bond+

Hello, I am hoping someone can help me with the following multivariate issue: I have a model consisting of about 50 covariates. I would like to reduce this to about 5 covariate for the reduced model by combining cofactors that are strongly correlated. Is there a package or function that would help me with this in R? I appreciate any suggestions. Thanks, Ian

I have 8 cofactors possibly affecting one and only one variable. I make conditional histograms: <-pdf(file="tst3.pdf",paper="special",width=36,height=36) <-histogram(~Oversized|dat$c1*dat$c2*dat$c5*dat$c6*dat$c7*dat$c8*dat$c9*dat$c10,nint=21,layout=c(32,8),data=dat,type="count") <-dev.off() This works (compliments to R developers!) but it does generate a

I would like to add a footnote to this graph but do not see a "footnote" command in the package:lattice documentation. I would like to note the "span=.8" as the footnote. postscript(file= ?C:/Documents and Settings/dsonneborn/My Documents/Slovak/output/pcb_tables/smooth_PCB_lines_four.ps?, bg=?transparent?, onefile=FALSE, pointsize=20,paper=?letter?, horizontal=TRUE,

Hello Everyone, I have three variables: Waist circumference (WC), serum triglyceride (TG) level and gender. Waist circumference and serum triglyceride is numeric and gender (male and female) is categorical. From these three variables, I want to calculate the "Lipid Accumulation Product (LAP) Index". The equation to calculate LAP is different for male and females. I am giving both

Well, something like: LAP <- ifelse(gender =='male', (WC-65)*TG, (WC-58)*TG) The exact code depends on whether your variables are in a data frame or list or whatever, which you failed to specify. If so, ?with may be useful. Cheers, Bert On Fri, Nov 3, 2023 at 3:43?AM Md. Kamruzzaman <mkzaman.m at gmail.com> wrote: > Hello Everyone, > I have three variables: Waist

df$LAP <- with(df, ifelse(G=='male', (WC-65)*TG, (WC-58)*TG)) That will do both calculations and merge the two vectors appropriately. It will use extra memory, but it should be much faster than a 'for' loop. Regards, Jorgen Harmse. ------------------------------ Message: 8 Date: Fri, 3 Nov 2023 11:10:49 +1030 From: "Md. Kamruzzaman" <mkzaman.m at gmail.com>

I discovered recently that the phrase "Oja median" produces no hits in Jonathan Baron's very valuable R search engine. I found this surprising since I've long regarded this idea as one of the more interesting notions in the multivariate robustness literature. To begin to remedy this oversight I wrote a bivariate version and then decided that writing a general p-variate version

Just a minor point in the suggested solution: df$LAP <- with(df, ifelse(G=='male', (WC-65)*TG, (WC-58)*TG)) since WC and TG are not conditional, would this be a slight improvement? df$LAP <- with(df, TG*(WC - ifelse(G=='male', 65, 58))) -----Original Message----- From: R-help <r-help-bounces at r-project.org> On Behalf Of Jorgen Harmse via R-help Sent: Friday,

Dear help community, I'm a R-beginner and use it for my master thesis. I've got a mixed model and want to analyse it with lme. There are a lot Cofactors that coult be relevant. To extract the important ones I want to do the stepAIC, but always get an error warning. Structure of my data: data.frame': 72 obs. of 54 variables: $ Block : Factor w/ 3 levels

Yes, that will halve the number of multiplications. If you?re looking for such optimisations then you can also consider ifelse(G=='male', 65L, 58L). That will definitely use less time & memory if WC is integer, but the trade-offs are more complicated if WC is floating point. Regards, Jorgen Harmse. From: avi.e.gross at gmail.com <avi.e.gross at gmail.com> Date: Friday,

*H*i, I am trying to fit a GEE model with *geeglm* function. The model is the following: Modelo<-geeglm(sqrt ~Tra+ Mes, id=Lugar , data=datos, family=gaussian(identity), corstr="independence") *Tra( is a experimental treatment, 2 levels)*, *Mes* (is the month of take data, 4 levels) and *Lugar* (is the site of study, 3 levels) are categorical variables and *sqrt* (sqrt of Total

heatmap.2 defaults to dist for calculating the distance matrix and hclust for clustering. Does anyone now how I can set dist to use the euclidean method and hclust to use the centroid method? I provided a compilable sample code bellow. I tried: distfun = dist(method = "euclidean"), but that doesn't work. Any ideas? library("gplots") library("RColorBrewer") test

Hi All, (I have a degree in math, but I am too embarassed to ask my colleagues, so here goes:) I would like to get a vector normal (orthogonal) to a plane formed by two other vectors. In matlab I do this: v1 = [.4, .6, .8]; v2 = [.9, .7, .2]; nn = cross(v1,v2) (gives ~[-.48, .65, -.24] if I do R> cross(v1, v2), I get .94. Huh? Thanks for all your help, again. W

Hello, I have two tab-delimited files which I would like to combine. In the first one I have gene IDs (Unique) on column 1 and than various experimental results from microarray analysis (see attached files list1 ) the second arrays have the same genes IDs (more and in a different order, some are double) (see attached files list2 ) What I would like to do is to search in the second list for gene

Asterisk work does not pay all of my bills, so I have joined up with a company that has a very good payment plan. I have recently become a Mona Vie Independent Distributor. I am not going to go into a sales pitch. This is just an FYI to this opportunity. The company has grown into a Billion dollar company in just 2 years. This company's compensation plan is the best and quickest that I have