similar to: Ggplot2 equivalent of axis and problem with log scale

For a given linear regression, I wish to find the 2-tailed t-dist probability that Y-hat <= newly observed values. I generate prediction intervals in predict() for plotting, but when I calculate my t-dist probabilities, they don't agree. I have researched the issues with variance of individual predictions and been advised to use the variance formula below (in the code). I presume my

hello, all. while i have a mcmc running, i am looking at the frequestist method of my model. i have never done HLM so i am looking for ways to plot them that might yeild something useful like dr. fox's effects plot package. this is my model, where dem is democracy ranked continuous 1:10, trsut is a 3 level categorical variable, cpi is 1:10, etc... > hier.jags2.mod <- lmer(dem ~

Hi all. This is more of a stats question, I suppose. Let's say I have two separate simple regressions of weight on year from two different datasets. I want to combine the regressions so that I can come up with a single equation for the total weight regressed on year. In reality, there is missing data, so I can't just sum the data across datasets and come up with a regression on the

With this data x <- c(0,0,1,1,2,2) y <- c(5,6,4,3,2,6) lwr <- y-1 upr <- y+1 xlab <- c("Low","Low","Med","Med","High","High") mydata <- data.frame(x,xlab,y,lwr,upr) I would like to make a dot plot and use lwr and upr as error bars. Above 0=Low. I would like there to be some space between the 5 and the 6 corresponding

Dear R-developeRs, Attached follows a patch against svn 34959 that adds the printing of p-values to the TukeyHSD.aov function in stats package. I also updated the corresponding documentation file and added a 'see also' reference to the simint function of the multcomp package. As it was already brought up in a previous thread [1] in R-help, one can obtain the adjusted

I'm interested in restricting the pairwise comparisons of interaction effects in a multi-way factorial ANOVA, because I find comparisons of interactions between all different variables different to interpret. For example (supposing a p<0.10 cutoff just to be able to use this example): > summary(fm1 <- aov(breaks ~ wool*tension, data = warpbreaks)) Df Sum Sq Mean Sq F

In modelling functions some people like to use a weight of 0 to drop an observation instead of using a subset value of FALSE. E.g., weights=c(0,1,1,...) instead of subset=c(FALSE, TRUE, TRUE, ...) to drop the first observation. lm() and summary.lm() appear to treat these in the same way, decrementing the number of degrees of freedom for each dropped observation. However, predict.lm() does

Por favor ver comentario con las las letras mayúsculas data("CPS1985", package = "AER") cps <- CPS1985 cps [1:10,] dim( cps) names( cps) library("quantreg") #regresion usual cps_lm <- lm(log(wage) ~ experience + I(experience^2) + + education, data = cps) #regresion por quantiles, tau son los cuantiles

Dear all, I have a quite basic questions about anova analysis in R, sorry for this, but I have no clue how to explain this result. I have two datasets which are named: nmda123, nmda456. Each dataset has three samples which were measured three times. And I would like to compare means of them with Posthoc test using R, following please see the output: (CREB, mCREB and No virus are the name of

Hello all, I would like to relate this behaviour of: plot.table & plot.TukeyHSD. They don't work with some graphical parameters. Thanks for your attetion. Cleber > ### example: > plot( table(1:10), cex.axis=0.6) > plot( table(1:10), las=2) > > tukaov <- TukeyHSD( aov(breaks ~ wool + tension, data = warpbreaks) ) > > plot( tukaov, main='xxx' ) Error

Dear all,? I hope that you can help me on this. I have been struggling to figure this out but I haven't found any solution. I am running a generalised mixed effect model, gamm4, for an ecology project. Below is the code for the model: model<-gamm4(LIFE.OE_spring~s(Q95, by=super.end.group)+Year+Hms_Rsctned+Hms_Poaching+X.broadleaved_woodland? ? ? ? ? ? ?+X.urban.suburban+X.CapWks,

Hi R-users, I have a question relating to the HoltWinters() function. I am trying to forecast a series using the Holt Winters methodology but I am getting some unusual results. I had previously been using R for Windows version 2.7.2 and have just started using R 2.9.1. While using version 2.7.2 I was getting reasonable results however upon changing versions I found I started to see unusual

Hi Maria If you have problems just start with a small model with predictions and then plot with xyplot the same applies to xyplot Try library(gamm4) spring <- dget(file = "G:/1/example.txt") str(spring) 'data.frame': 11744 obs. of 11 variables: $ WATERBODY_ID : Factor w/ 1994 levels "GB102021072830",..: 1 1 2 2 2 3 3 3 4 4 ... $ SITE_ID

Hi, I'm trying to create a plot showing the density distribution of some shipping data. I like the look of violin plots, but my data is not continuous but rather binned and I want to make sure its binned nature (not smooth) is apparent in the final plot. So for example, I have the number of individuals per vessel, but rather than having the actual number of individuals I have data in the

Full_Name: Yasuhiro Nakajima Version: 2.6.1 OS: WinXP SP2 Submission from: (NULL) (202.237.255.13) Dear all, I noticed the following behaviour of ci.plot in HH Package(ver.2.1-9): > library(HH) > data(women, package="datasets") > attach(women) > ft <- lm(height~weight) > windows() > ci.plot(ft,conf.level=0.95) > windows() > ci.plot(ft,conf.level=0.999) I

Dear All, When I plot the values and linear regression line for one data set, it is fine. But for another one I see zigzags, when I plot the confidence interval >cd Depth CHAOsep12RNA 9,94 804 25,06 1476,833333 40,04 1540,561404 50,11 1575,166667 52,46 349,222222 54,92 1941,5 57,29 1053,507042 60,11 1535,1 70,04 2244,963303 79,97 1954,507042 100,31 2679,140625 >

Dear R gurus, I have the following model: appcov.aov <- aov(yield ~ prevyield + trt + block) where prevyield is a continuous numeric covariate and trt and block are factors (yes, I did factor()!) Now, when I do a TukeyHSD, my diff's are all screwed up! For instance: treatment mean for treatmen "E" is 277.25 and for treatment "O" is 279.5, so I figure the diff O-E

Hi, I am running a ANOVA on a factorial design, and using TukeyHSD for post hoc comparisons. I have 2 factors with three levels each: Factor B Factor A 1 2 3 1 2 3 When I look at the Tukey output (on the interaction of the factors) the comparisons come out numbered 1-36. e.g. $"A:B" diff lwr upr [1,] 49.1666667 -160.041022 258.3744 [2,]

Dear useRs, I'm working on multiple comparison design on two factor (2 3 levels) ANOVA. Each of the tests I have tried (Tukey, multcomp package) seem to do only with one factor at a time. fm1 <- aov(breaks ~ wool * tension, data = warpbreaks) tHSD <- TukeyHSD(fm1, "tension", ordered = FALSE) $tension diff lwr upr p adj M-L -10.000000 -19.35342

Hey, I have this TukeyHSD output from which I would like to extract only the P-values (p adj, last number). The problem is that the test output is a character list. How can I "break" this sentence to separate the Pv? Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = Fe1$Fe ~ Fe1$genotype) $`Fe1$genotype` diff lwr upr