similar to: Linear Regression

Hi, perhaps this question was answered previously however I could not find them. My problem is how how to extract a particular statistic from the result given by lm(). For e.g. ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69) group <- gl(2,10,20, labels=c("Ctl","Trt")) weight <- c(ctl, trt) >

Dear R-helpers, I was curious why the output of summary (and many other functions in R) does not add a separator between the name of a factor and the label of a level (e.g., in the example below, 'group Trt'). If the user had lower case labels (e.g., 'trt'), the output would be particularly hard to read. ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) trt

Hello everybody, i used xtable to get some latex output, which worked pretty well with my latex document. But somewhere i missed the part where they explain how to get these nice significance indicating *** into my latex table. it just stops right after Pr(> |t|) thanks in advance matthias

Hi! In R 3.0.0 from current SVN, ?summary.lm says: > Value [...] > df degrees of freedom, a 3-vector (p, n-p, p*), the last > being the number of non-aliased coefficients. ?summary.glm says: > df a 3-vector of the rank of the model and the number of residual > degrees of freedom, plus number of non-aliased coefficients. It seems to me that the description is reversed: p is

Hi, on a > summary(lm(y~x)) are the computed t-values for two.sided or one.sided. By looking on some tables they seem like they are for two.sided. Is it possible to have them for one.sided? If this makes sense... Thanks. [[alternative HTML version deleted]]

I would like to call both p-values and R-squared values from lm's in a function. I can get the p-values from coef(summary(name.lm))[r,c], however, I cannot figure out how to call the R-squared values without manually calling the summary and inserting them in the script - which negates the value of automating the process through a function. Thanks, Mike [[alternative HTML version deleted]]

Hi, I'm trying to get the p-value from the 'lm' regression function as a list object. For example, I can get r^2 from the following code by entering summary(fm)$r.squared. Is there a way to get the p-value? If not, is there a function where I can enter the f-value and degrees of freedom to get the p-value? Thanks. x <- c(1,2,3,4,5,6,7,8,9,10) y <- c(1,2,3,4,4,5,6,8,1,9) fm

Hi Everyone, I am trying to use lmFit function; however, i cannot find it function anywhere. I have been trying to find the function in Bioconductor and elsewhere. I re-install bioconductor source, update package and update R as well. no luck Is there a command in R where i can just type, and it will download it for me? -- View this message in context:

I'm new to R and some what new to the world of stats. I got frustrated with excel and found R. Enough of that already. I'm trying to test and correct for Heteroskedasticity I have data in a csv file that I load and store in a dataframe. > ds <- read.csv("book2.csv") > df <- data.frame(ds) I then preform a OLS regression: > lmfit <- lm(df$y~df$x) To

I try to send this message To Gordon Smyth at smyth at vehi,edu.au but it bounced back, so here it is to r-help I am trying to use limma, just downloaded it from CRAN. I use R 2.0.1 on Win XP see the following: > library(RODBC) > chan1 <- odbcConnectExcel("D:/Data/mgc/Chips/Chips4.xls") > dd <- sqlFetch(chan1,"Raw") # all data 12000 > # > nzw <-

Hi there, I am trying to use linear regression to solve the following equation - y <- c(0.2525, 0.3448, 0.2358, 0.3696, 0.2708, 0.1667, 0.2941, 0.2333, 0.1500, 0.3077, 0.3462, 0.1667, 0.2500, 0.3214, 0.1364) x2 <- c(0.368, 0.537, 0.379, 0.472, 0.401, 0.361, 0.644, 0.444, 0.440, 0.676, 0.679, 0.622, 0.450, 0.379, 0.620) x1 <- 1-x2 # equation lmFit <- lm(y ~ x1 + x2) lmFit Call:

My data: I have raw data points that form a logit style curve as if they were a time series. Which is to say they form 3 distinct lines with 3 distinct slopes in backwards z pattern. A certain class of my data looks essentially flat to the eye with marginal oscillation. What is important to me is the x value at which the state change is occurring, in other words, the break point Use of

Hi, I would like to generate a series in the following form (0.8^1, 0.8^2, ..., 0.8^600) Could anyone tell me how can I achieve that? I am really new to R. -- View this message in context: http://www.nabble.com/sequences-tf4019146.html#a11414836 Sent from the R help mailing list archive at Nabble.com.

Hallo, > fit12<-lmFit(qrg[,1:2]) > t12<-toptable(fit12,adjust="fdr",number=25,genelist=qrg$genes[,1]) > t12 ID logFC t P.Value adj.P.Val B 522 PLAU_OP -6.836144 -8.420414 5.589416e-05 0.01212520 2.054965 1555 CD44_WIZ -6.569622 -8.227938 6.510169e-05 0.01212520 1.944046 Can anyone tell me what the difference is between P.Value

Hi, I would like to plot a histogram with the following codes, and I would like to make the tick intervals smaller. I tried to add "lab=c(1,1,12)", but nothing changes. par(mfrow=c(1,1),font=1, cex=0.8) hist (data1, seq(-0.01,0.3,0.01),freq = FALSE, main="Figure1.",xlab="return",lab=c(1,1,12)) lines(density(data1), col="blue") Could anyone give me sone

Hi all,   I have loaded the LIMMA and Biobase package and tried these commands:   library(limma) library("Biobase") data <- read.table("c:/temp/data.txt",header=T,row.names=1) ExpressionData <- as.matrix(data[,c(2,3,4,6,7,8)]) eset <- new("ExpressionSet", exprs = ExpressionData) design <- cbind(WT=1,P=c(0,1,1,0,1,1),G=c(0,1,0,0,1,0)) fit <-

Hi, I have two data set of normalized Affymetrix CEL files, wild type vs Control type.(each set have further three replicates). > wild.fish AffyBatch object size of arrays=712x712 features (10 kb) cdf=Zebrafish (15617 affyids) number of samples=3 number of genes=15617 annotation=zebrafish notes= > Dicer.fish AffyBatch object size of arrays=712x712 features (10 kb) cdf=Zebrafish (15617

I know this has got to be simple, but I have a added an arrow to a graph with: arrows(5,8,8, predict(lmfit,data.frame(x=8)), length=0.1) but its in the wrong position, correcting it and running again adds an new arrow (which is what you would expect) so how do I a) edit the existing arrow, and b) delete it all together As so often seems to be the case, some of the simplist things seem also to

Hi All, I am trying to make a loop for a function and I am using the following codes. "p" and "var" are some matrix obtained before. I would like to apply the function "gpdlow" for i in 1:12 and get the "returnlow" for i in 1:12. But when I ask for "returnlow" there are warnings and it turns out some strange result. for (i in 1:12){ gpdlow

What is the way to add an arbitrary fit from a model to a lattice conditioning plot ? For example xyplot(v1 ~v2 | v3,data=mydata, panel=function(...){ panel.xyplot(...) panel.loess(...,col.line="red") } ) Will add a loess smoother. Instead, I want to put a fit from lm (but not a simple straight line) and the fit has to be done for each panel