similar to: Specifying ANCOVA models in R

Hi Group, Create some example data. set.seed(1) wide_data <- data.frame( id=c(1:10), predictor1 = sample(c("a","b"),10,replace=TRUE), predictor2 = sample(c("a","b"),10,replace=TRUE), predictor3 = sample(c("a","b"),10,replace=TRUE), measurement1=rnorm(10), measurement2=rnorm(10)) head(wide_data) id

R-help: I am trying to decide between using a multiple linear regression or a linear mixed effects model for my data: model1 <- lm (responsevariable ~ predictor1 + predictor2 + predictor3 + predictor4, data= data) model2 <- lme (responsevariable ~ predictor1 + predictor2 + predictor3 + predictor4, random = ~1 | site, data= data) anova (model1, model2) but I keep getting the

R experts - I'm using John Fox's sem package to analyze a simple path model (two correlated predictor variables directly influencing a single criterion variable): Predictor1 -> Criterion Predictor2 -> Criterion Predictor1 <-> Predictor2 I'm giving a presentation on this material next week, and I'd like to use component-residual plots (i.e., partial residual plots)

I am using the gbm package for generalized boosted regression models, and would like to be able to extract the coefficients produced for storage in a database. I am already using R to automatically generate formulas that I can export to a database and store. For example, I have been using Dr. Harrell's lrm package to perform logistic regression, e.g.: output <-

Hi, I use a mboost model to predict my dependent variable on new data. I get the following warning message: In bs(mf[[i]], knots = args$knots[[i]]$knots, degree = args$degree, : some 'x' values beyond boundary knots may cause ill-conditioned bases The new predicted values are partly negative although the variable in the training data ranges from 3 to 8 on a numeric scale. In order to

Hi, I would like to combine multiple xyplots into a single, multipanel display. Using R 3.0.1 in Ubuntu, I have used c() from latticeExtra to combine three plots, but the x-axis for two plots are on a log scale and the other is on a normal scale. I also have included equispace.log=FALSE to clean up the tick labels. However, when I try all of these, the x-axis scale of the first panel is used

I have built a Kalman Filter model for flu forecasting as shown below. Y - Target Variable X1 - Predictor1 X2 - Predictor2 While forecasting into the future, I will NOT have data for all three variables. So, I am predicting X1 and X2 using two Kalman filters. The code is below x1.model <- dlmModSeas(52) + dlmModPoly(1, dV=5, dW=10) x2.model <- dlmModSeas(52) + dlmModPoly(1, dV=10,

I was unsure of what suitable goodness-of-fit tests existed in R for logistic regression. After searching the R-help archive I found that using the Design models and resid, could be used to calculate this as follows: d <- datadist(mydataframe) options(datadist = 'd') fit <- lrm(response ~ predictor1 + predictor2..., data=mydataframe, x =T, y=T) resid(fit, 'gof'). I set up a

Hello, I came across a problem when building a randomForest model. Maybe someone can help me. I have a training- and a testdataset with a discrete response and ten predictors (numeric and factor variables). The two datasets are similar in terms of number of predictor, name of variables and datatype of variables (factor, numeric) except that only one predictor has got 20 levels in the training

Dear all, I want to manipulate a character string such as ex<-"cbind(data$response1,data$response2)" in R in two ways: 1) extracting the "response1" portion of ex 2) replacing "$" with "." I am wondering that is it possible efficiently doing these in R? Best Ozgur -- View this message in context:

Hi there, I'm having trouble working out how to change the colors of polygon shading lines. If I plot a polygon with polygon(poly,density=30,borders=gray(0.5)) I get gray borders but black shading lines; I have tried adding col=gray(0.5), bg=gray(0.5) and fg=gray(0.5), but no luck. How can I change the colors of the shading? The help file doesn't seem to detail this, so any

Hi there, I was wondering why x<-rnorm(n=5000,mean=1.5,sd=0.25) h<-hist(x) sum(h$density) [1] 10 I thought the integral of the histogram should be one? These seems strange to me, or am I being silly... Regards Luke Spadavecchia PS. I'm using R 2.5.1 on Mac OSX

Hello! I would like to draw a graph like the following: http://www.optics.rochester.edu/workgroups/cml/opt307/spr04/pavel/plot_small.jpg Aim is to plot confidence intervals of treatments for X(=response1) and Y(=response2) axis simultaneously to visualize aggreement of confidence interval for two responses. Can anyone please provide me some direction to start with? Thanks! -- Kim.

Hi there, I am trying to plot some output from a FORTRAN (ifort) program using R (2.5.1) under batch mode. In the FORTRAN code, I call R in batch mode to execute a script called fig1.R using something like PROGRAM test USE IFPORT IMPLICIT NONE DO !Some function which makes an output file called ~/fortran_output.txt CALL myfunc() !System call to R plotting routine CALL SYSTEM

Hi, I am trying to use CART to find an ideal cut-off value for a simple diagnostic test (ie when the test score is above x, diagnose the condition). When I put in the model fit=rpart(outcome ~ predictor1(TB144), method="class", data=data8) sometimes it gives me a tree with multiple nodes for the same predictor (see below for example of tree with 1 or multiple nodes). Is there a way

This is a reply to Luke Spadavecchia''s question about hist densities. If you want to construct a histogram where the bar heights are the percentage of observations in the intervals you can use this code: x<-rnorm(n=5000,mean=1.5,sd=0.25) h<-hist(x) h$density<-h$counts/sum(h$counts) plot(h,freq=F,ylab="Percentage") Sarah Hardy Sarah.hardy@maine.edu

Hello! Just would like to make sure I am not doing something wrong. I am running an OLS regression. I have several subgroups in the data set (locations) - and in each location I have weekly data for 2 years - on my DV and on all predictors. Looks like this: location week DV Predictor1 Predictor 2 location1 week1 xxx xxxxxxx xxxxxxxxx location1 week2 xxx xxxxxxx xxxxxxxxx . .

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Hi R-Users, I am working with sm.ancova (in the package sm) and I have two problems with the graph, which is automatically generated when sm.ancova() is run. 1-Besides of the fitted lines, the observed data appeared automatically in the graph. I prefer that only fitted lines appear. I check the sm.options, but I could not find the way that the observed data do not appear in the graph. 2-I

Hi list, does anybody know a easy to understand example in R which shows how to perfom an ANCOVA? I already tried to understand the example which you get when you type ?aov, but I did not really understand the output. Best, syrvn -- View this message in context: http://n4.nabble.com/R-example-for-ancova-tp949324p949324.html Sent from the R help mailing list archive at Nabble.com.