search for: lmfit

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...

...trated 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 test for Heteroskedasticity, I run the BPtest: > bptest(lmfit) studentized Breusch-Pagan test data: lmfit BP = 11.6768, df = 1, p-value = 0.0006329 From the above, if I'm interpreting this correctly, there is Heteroskedasticity present. To correct fo...

...t;- cbind(dd$NZW1C,dd$NZW2C,dd$NZW3C,dd$NZW1T,dd$NZW2T,dd$NZW3T) > akr <- cbind(dd$AKR1C,dd$AKR2C,dd$AKR3C,dd$AKR1T,dd$AKR2T,dd$AKR3T) > bas <- cbind(dd$NZW1C,dd$NZW2C,dd$NZW3C,dd$AKR1C,dd$AKR2C,dd$AKR3C) > # > design<-matrix(c(1,1,1,1,1,1,0,0,0,1,1,1),ncol=2) > fit1 <- lmFit(nzw,design) > fit1 <- eBayes(fit1) > topTable(fit1,adjust="fdr",number=5) M t P.Value B 12222 3679.480 121.24612 7.828493e-06 -4.508864 1903 3012.405 118.32859 7.828493e-06 -4.508866 9068 1850.232 92.70893 1.178902e-05 -4.508889 10635 2...

...reakpoints, how is this test able to assess the proper breakpiont? It appears to only give 1 best breakpoint, which is not consistent with the breakpoints found by segmented(). Also, is K the number of breakpoints or the number of iterations that it evaluates the breakpoint? Thanks in advance. lmfit<-glm(TotRad_KW~HRRPUA_kWm2,data=d1) davies.test(lmfit,seg.Z=~HRRPUA_kWm2,k=1000,alternative="less", beta0=0,dispersion=NULL) Davies' test for a change in the slope data: Model = gaussian , link = identity formula = TotRad_KW ~ HRRPUA_kWm2 segmented variable = HRRPUA_kWm2 `Best&...

...e 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: lm(formula = y ~ x1 + x2) Coefficients: (Intercept) x1 x2 0.30521 -0.09726 NA I would like to *constraint the coefficients of x1 and x2 to be between 0,1*. Is there a way of adding constraints to lm? I looked through the ol...

...y(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 <- lmFit(eset,design)   But I keep getting an eroor message after "lmFit" that says: Error in possibleExtends(cl, class2, class1Def, class2Def) : trying to get slot "subclasses" from an object (class "classRepresentation") that is not an S4 object   I am using R-2.9.2. Whats...

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...

...ds) 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 affyids) number of samples=3 number of genes=15617 annotation=zebrafish notes= Now, I have to combine these two S4 objects and use lmFit function of Limma package.I am able to combine the two S4 objects using merge function. > merge.fish <-merge(wild.fish,Dicer.fish) > merge.fish AffyBatch object size of arrays=712x712 features (17833 kb) cdf=Zebrafish (15617 affyids) number of samples=6 number of genes=15617 annotation=z...

Hello, I would like to fit a linear regression and when I use summary(), I got the following result: Call: lm(formula = weight ~ group - 1) Residuals: Min 1Q Median 3Q Max -1.0710 -0.4938 0.0685 0.2462 1.3690 Coefficients: Estimate Std. Error t value Pr(>|t|) groupCtl 5.0320 0.2202 22.85 9.55e-15 *** groupTrt 4.6610 0.2202 21.16 3.62e-14

...d$NZW2C,dd$NZW3C,dd$NZW1T,dd$NZW2T,dd$NZW3T) >> akr <- cbind(dd$AKR1C,dd$AKR2C,dd$AKR3C,dd$AKR1T,dd$AKR2T,dd$AKR3T) >> bas <- cbind(dd$NZW1C,dd$NZW2C,dd$NZW3C,dd$AKR1C,dd$AKR2C,dd$AKR3C) >> # >> design<-matrix(c(1,1,1,1,1,1,0,0,0,1,1,1),ncol=2) >> fit1 <- lmFit(nzw,design) >> fit1 <- eBayes(fit1) >> topTable(fit1,adjust="fdr",number=5) > M t P.Value B > 12222 3679.480 121.24612 7.828493e-06 -4.508864 > 1903 3012.405 118.32859 7.828493e-06 -4.508866 > 9068 1850.232 92.70893 1.1...

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 be the...

...Yes, lm.fit() is already faster, and .lm.fit() {added to base R by me, when a similar question was asked years ago ...} is even an order of magnitude faster in some cases. See ?lm.fit and notably example(lm.fit) which uses pkg microbenchmark for timing and after which png("lmfit-ex.png") boxplot(mb, notch=TRUE) dev.off() produces the attached nice image. > Also try the high-performance computing task view on CRAN > Cheers, > Andrew > -- > Andrew Robinson Chief Executive Officer, CEBRA and > Professor of Biosecurity,...

...l.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 separately, not one fit for the full data set, so sth like an lm equivalent of panel.locfit (there is no panel.lmfit) Thank you. Stephen B [[alternative HTML version deleted]]

...in packages doc. best regards, Claire Pujoll Paris, France library(marray) library(limma) ....... Rawdata <- read.GenePix(targets=Macro_Mono_targets,skip=107) design<- cbind(individual=c(0,0,1,1,2,2,3,3,4,4,5,5), dim1=c(1,0,1,0,1,0,1,0,1,0,1,0),dim2=c(0,1,0,1,0,1,0,1,0,1,0,1)) fit <- lmFit(normdata@maM, design) contrast.matrix<-makeContrasts(dim1vsdim2=dim2-dim1, levels=design) fit2 <- contrasts.fit(fit,contrast.matrix) fiteb <- eBayes(fit2) Toptable <- topTable(fiteb,number = 10600,genelist=maGeneTable(normdata), sort.by="P", resort.by= "M", adjust=&q...

Hello, I am trying to run a code (see reference section) and when I run the line: fit<-lmFit(xen1dataeset,design), I get the error message: Error in lm.fit(design, t(M)) : incompatible dimensions I will really appreciate if someone can help me understand this error message and possibly help me debug the problem. Thanks manisha Reference section xen1data<-ReadAffy()...

...] is a column of the fold change values and data[12] contains the P values and I am getting a following error: Error: geom_point requires the following missing aesthetics: x, y What can be done for the same ? And if not what other package may I use for the same in which I don't have to use a lmfit model? Thanks -- View this message in context: http://r.789695.n4.nabble.com/Volcano-Plot-tp4428622p4428622.html Sent from the R help mailing list archive at Nabble.com.

...wer and upper model, Caret still seems to default to "backward". Any thoughts on how I can make this work? Here is what I tried: itemonly <- susbstitute(~i1+i2+i3+i4+i5+i6+i7+i8+i9+i10) #this is my full model #I want my "lower" model to consist of the intercept only stepLmFit.i <- train(xtraindata.i, ytraindata,"lmStepAIC", scope=list(upper=itemonly,lower=~1),direction="forward") Any guidance on how I can make this work would be greatly appreciated. Dan [[alternative HTML version deleted]]

...(X) ym <- mean(y) X <- X - rep(Xm, rep(n, p)) y <- y - ym #scale X, as in MASS::lm.ridge Xscale <- drop(rep(1/n, n) %*% X^2)^0.5 X <- as.matrix(X/rep(Xscale, rep(n, p))) XPX <- crossprod(X) XPy <- crossprod(X,y) I <- diag(p) lmfit <- lm.fit(X, y) MSE <- sum(lmfit$residuals^2) / (n-p) # prepare output coef <- matrix(0, length(lambda), p) cov <- as.list(rep(0, length(lambda))) mse <- rep(0, length(lambda)) # loop over lambdas for(i in seq(length(lambda))) {...

...gdat$x + longdat$x^3 + (longdat$x-1)^4 / 5 + 1/(abs(longdat$x/50) + 0.02) + longdat$IDeffect + rnorm(1:nrow(longdat)) * 2 ) longdat<-longdat[order(longdat$x),] library(splines) # Calling ns within lm works fine: mylm<- lm( y ~ ns(x,df=splineDF), data=longdat) longdat$lmfit<-predict(mylm) library(ggplot2) print( ggplot(longdat, aes(x, y)) + geom_point(shape=1) + geom_line(aes(x=x, y=lmfit), color="red") ) cat("Enter to attempt lme.") readline() library(nlme) if(WhichApproach==1) { # returns: "Error in eval(expr, envir, enclos) : ob...

...file. Can anyone tell me how I would begin to approach this? I have looked into using the genefilter() function but can't figure out if it can take the right parameters (i.e. specific probe set id's). Thanks in advance, -M This is the code I used to generate my topTable > fit <- lmFit(eset, design) > cont.matrix <- makeContrasts(NormalvsTumor=Tumor-Normal, levels=design) > fit2 <- contrasts.fit(fit, cont.matrix) > fit2 <- eBayes(fit2) > topTable(fit2, number=100, adjust="BH") -- View this message in context: http://r.789695.n4.nabble.com/filterin...