similar to: no doubt a dumb question, but..

Hi I have a problem accessing an array field in a Postgresql database. Here is the table definition. View "neil.flashing_codes" Column | Type | Modifiers -------------+-----------------------+----------- code | character varying(10) | description | text | folds | integer[] | View definition: SELECT

I am trying to run an ancova and am having trouble setting it up properly. I have nearly 10,000 measurements of fish length, girth and stage of sexual development. I am suspicious that the stage of development is affecting the length (as they get full of eggs they get more round and are more difficult to measure and measure shorter). My data looks somethign like this: Length girth stage 40 50

Dear all, The 'make check' step fails for the pacakge mva on IBM AIX. The tail of the Rout log file looks like: > for(factors in 2:4) print(update(Harman23.FA, factors = factors)) Call: factanal(factors = factors, covmat = Harman23.cor) Uniquenesses: height arm.span forearm lower.leg weight 0.170 0.107 0.166

I want to use the caret package and found out about the timingSamps obtion to obtain the time which is needed to predict results. But, as soon as I set a value for this option, the whole model generation fails. Check this example: ------------------------- library(caret) tc=trainControl(method='LGOCV', timingSamps=10) tcWithout=trainControl(method='LGOCV')

useRs, A no doubt simple question, but I am baffled. Indeed, I think I once knew the answer, but can't recover it. The default contrasts for aov (and lm, and...) are contr.treatment and contr.poly for unordered and ordered factors, respectively. But, how does one invoke the latter? That is, in a data.frame, how does one indicate that a factor is an *ordered* factor such that

Hy, I got the following problem when trying to build a rpart model and using everything but LOOCV. Originally, I wanted to used k-fold partitioning, but every partitioning except LOOCV throws the following warning: ---- Warning message: In nominalTrainWorkflow(dat = trainData, info = trainInfo, method = method, : There were missing values in resampled performance measures. ----- Below are some

Hi all, I'm an R newbie, I did this script to create a scatterplot using the "tree" matrix from "datasets" package: library('datasets') with(trees, { plot(Height, Volume, pch=3, xlab="Height", ylab="Volume") symbols(Height, Volume, circles=Girth/12, fg="grey", inches=FALSE, add=FALSE) } ) I'd like to use the column Named

Dear R helpers, I have a 3D scatter plot that I have generated from scatterplot3d (which looks great- thanks!) and I can see that the points in my graph fall in a plane. Following the example 5 from 3D scatter plot (below) I have fitted a regression plane. Now what I would like to do is a rotation so that my new co-ordinate system is about the fitted plane (by finding the normal to the plane

Hi John, Thank-you very much for replying, - in answer to your questions: *"As I understand what you wrote, all factors but linelabel are crossed, with linelabel nested within all the other factors?" That's exactly it, *" What is in the highest order cells? Single observations, or multiple scores?" I would say "single observations" - but i have to

I tried to run the following example from section 4.1.4 of the "Scatterplot3d - an R package for Visualizing Multivariate Data" vignette and got an error on the part that plots the regression plane: > library(scatterplot3d) > data(trees) > s3d <- scatterplot3d(trees, type = "h", color = "blue", + angle = 55, scale.y = 0.7, pch = 16, main = "Adding

The following dummy data frame has factor Q (with 2 levels) nesting factor P (with levels p1 and p2 nested under q1, and p3 and p4 nested under q2), but both crossing the random variate s, which has 8 levels. The dependent measure is dv. > # The data frame: > testnest dv s P Q 1 1 s1 p1 q1 2 2 s2 p1 q1 3 1 s3 p1 q1 4 2 s4 p1 q1 5 1 s5 p1 q1 6 3 s6 p1 q1 7 3 s7

Hi All. I'd like to programm a 4 dimensional chart in R. Acctually I wanted to solve that problem in Excel cause I had the data there. Here is a link of my actual problem description (there are some chart pictures as well).... http://www.mrexcel.com/board2/viewtopic.php?t=187336&highlight=stacked+column because I still couldn't solve that problem I came to R. The chart should be

Hi Everyone, I often use the 'safe prediction' feature available through glm(). Now, I'm at a situation where I must use biglm:::bigglm. ## begin example library(splines) library(biglm) ff <- log(Volume)~ns(log(Girth), df=5) fit.glm <- glm(ff, data=trees) fit.biglm <- bigglm(ff, data=trees) predict(fit.glm, newdata=data.frame(Girth=2:5)) ## -1.3161465 -0.2975659

Dear all, I would like to get glm() return its results (at least the deviance) for some given parameter values (ie without actually fitting the model). I tried to set `maxit = 0' but this does not work, eg: > glm(y ~ x, start = c(1, 1), maxit = 0) Error in glm.control(...) : maximum number of iterations must be > 0 Any idea? Thanks in advance. Emmanuel Paradis

R for OS X, version 2.0.1 Has the add parameter of plot commands been deprecated? It no longer occurs in the help listing for plot, and doesn't work, either. If it has been deprecated, what is the replacement? That is, how does one overlay an existing plot? -- John R. Vokey, PhD Professor B.E.R.G. - Behaviour and Evolution Research Group Micro-Cognition Laboratory Department of

guRus and useRs, As I instruct my students: "With R what is difficult in anything else, is easy, usually one line of code; but, what is easy in anything else, is *&^%$%#$... (translation: next to impossible) in R." I just ran into such a case that has, to put it mildly, driven me to use spreadsheets (if that is not a sign of complete desperation, I don't know what is).

I want to use the caret package to train linear models. I want to compare these models when using different degrees (aka degrees of interaction). This is possible for the 'earth' method (using the '.degree' parameter) but I found no possibility of customizing the degree for the 'lm' method. This might be due to the fact that the basic 'lm' function does not support

In a rather simple regression, I?d like to ask the question, for high trees, whether it makes a difference (for volume) whether a three is thick. If my interpretation is correct, for low trees, i.e. for which trees$isHigh == FALSE, the answer is yes. The problem is how to "merge" the standard errors. Code follows. data(trees) trees$isHigh <- trees$Height > 76 trees$isThick

The "earth" package is now available on CRAN. Earth builds models using Friedman's MARS. Earth's principal advantages over the existing function mda::mars are that it is much faster and provides plotting and printing methods. The general purpose model plotting function "plotmo" may also be useful to people who are not interested in earth itself. Example: > a

The "earth" package is now available on CRAN. Earth builds models using Friedman's MARS. Earth's principal advantages over the existing function mda::mars are that it is much faster and provides plotting and printing methods. The general purpose model plotting function "plotmo" may also be useful to people who are not interested in earth itself. Example: > a