similar to: how to be clever with princomp?

Hi R experts, I am interested on the effects of two dietry compunds on the growth of chicks. Rather than extracting linear growth functions for each chick and using these in an analysis I thought using ReML might provide a neater and better way of doing this. (I have read the pdf vignette("MlmSoftRev") and "Fitting linear mixed models in R" by Douglas Bates but I am not

Can you provide the summary(m2) results? > -----Original Message----- > From: Simon Pickett [mailto:S.Pickett at exeter.ac.uk] > Sent: Wednesday, August 16, 2006 7:14 AM > To: Doran, Harold > Cc: r-help at stat.math.ethz.ch > Subject: [SPAM] - RE: [R] REML with random slopes and random > intercepts giving strange results - Bayesian Filter detected spam > > Hi again,

Hi all, I have been bumbling around with r for years now and still havent come up with a solution for plotting reliable graphs of relationships from a linear regression. Here is an example illustrating my problem 1.I do a linear regression as follows summary(lm(n.day13~n.day1+ffemale.yell+fmale.yell+fmale.chroma,data=surv)) which gives some nice sig. results Coefficients:

Hi, I am an R beginner and having problems structuring my REML models. I have a model with y=weight x1=time x2=timesquared id=individual identity I need to structure the model such that in the random effects there is a constant intercept for all individuals but a separate individual slope for both x1 and x2 (a coefficient score for every individual).

Hi R experts, I have been using ReML as follows... model<-lmer(late.growth~mtf+year+treat+hatch.day+hatch.day:year+hatch.day:treat+ mtf:treat+ treat:year+ year:treat:mtf+(1|fybrood), data = A) then I wanted to plot the results of the three way interaction using lines() as follows... tmp<-as.vector(fixef(model)) graph1<-plot(mtf,fitted(f2), xlab=list("Brood Size"),

Hi, I would like to (if possible) set the default width and height for graphs at the start of each session and have each new graphic device overwrite the previous one. I only know how to do this using windows(width=,height=...) which opens up a new plotting device every time, so I end up with lots of graphs all over the place until I get the one I want! Thanks in advance, Simon Simon Pickett

Dear all, perhaps I am using princomp.formula and prcomp.formula in a way that is not documented to work, but then the documentation just says: formula: a formula with no response variable. Thus, to avoid a lot of typing, it would be nice if one could use '.' and '-' in the formula, e.g. > library(DAAG) > res <- prcomp(~ . - case - site - Pop - sex, possum)

Hi everyone, I have been using REML to derive intercepts and coeficients for each individual in a growth study. So the code is m2 <- lmer(change.wt ~ newwt+(newwt|id), data = grow) Calling coef(model.lmer) gives a matrix with this information which is what I want. However, as a test I looked at each individual on its own and used a simple linear regression to obtain the same information, then

Some webpage has described prcomp and princomp, but I am still not quite sure what the major difference between them is. Can they be used interchangeably? In help, it says 'princomp' only handles so-called R-mode PCA, that is feature extraction of variables. If a data matrix is supplied (possibly via a formula) it is required that there are at least as many units as

According to R help: princomp() uses eigenvalues of covariance data. prcomp() uses the SVD method. yet when I run the (eg., USArrests) data example and compare with my own "hand-written" versions of PCA I get what looks like the opposite. Example: comparing the variances I see: Using prcomp(USArrests) ------------------------------------- Standard deviations: [1] 83.732400 14.212402

Hi, I have been dabbling with str() to extract values from outputs such as lmer etc and have found it very helpful sometimes. but only seem to manage to extract the values when the output is one simple table, any more complicated and I'm stumped :-( take this example of the extracted coeficients from a lmer analysis... using str(coef(lmer(resp3~b$age+b$size+b$pcfat+(1|sex), data=b)))

In comparing the results of princomp and prcomp I find: 1. The reported standard deviations are similar but about 1% from each other, which seems well above round-off error. 2. princomp returns what I understand are variances and cumulative variances accounted for by each principal component which are all equal. "SS loadings" is always 1. 3. Same happens

Hi , To my knowledge, there're two functions that can do principal component analysis, princomp and prcomp. I don't really know the difference; the only thing I know is that when the sample size < number of variable, only prcomp will work. Could someone tell me the difference or where I can find easy-to-read reference? To access the first PC using princomp:

As of R 1.7.0, princomp no longer accept matrices with more coloumns than rows. I'm curious: Why was this decision made? I work a lot with data where more coloumns than rows is more of a rule than an exception (for instance spectroscopic data). To me, princomp have two advantages above prcomp: 1) It has a predict method, and 2) it has a biplot method. A biplot method shouldn't be too

When I look through archives at https://stat.ethz.ch/pipermail/r-help/2003-October/040525.html I see this: Liaw, Andy wrote: >In the `Detail' section of ?princomp: > >princomp only handles so-called Q-mode PCA, that is feature extraction of >variables. If a data matrix is supplied (possibly via a formula) it is >required that there are at least as many units as variables. For

This problem is not limited to R, but R is one of the packages in which it arises. princomp is a nice function which creates an object for which inspection methods have been written. Unfortunately, princomp does not admit cases in which the x matrix is wider than high (i. e. more variables than observations). Such cases are typical in spectroscopy and related disciplines. It would be nice if the

[Re-directing to R-devel, as I think this needs changes to the code.] Can I suggest a modification to stats:predict.princomp so that it will check for column (variable) names? In src/library/stats/R/princomp-add.R, insert the following after line 4: if (!is.null(cn <- names(object$center))) newdata <- newdata[, cn] Now Dana's example looks like: > predict(pca1, frz) Error in

Hi all: I am trying to do PCA on the following matrix. N1 N2 A1 A2 B1 B2 gene_a 90 110 190 210 290 310 gene_b 190 210 390 410 590 610 gene_c 90 110 110 90 120 80 gene_d 200 100 400 90 600 200 >dataf<-read.table("matrix") >

Hello fellow Rers, I have a no-doubt simple question which is turning into a headache so would be grateful for any help. I want to do a principal components analysis directly on a correlation matrix object rather than inputting the raw data (and specifying cor = TRUE or the like). The reason behind this is I need to use polychoric correlation coefficients calculated with John Fox's

Hello, I have a problem with the princomp method, it seems stupid but I don't know how to handle it. I have a dataset with some regular data and some outliers. I want to calculate a PCA on the regular data and get the scores for all data, including the outliers. Is this possible on R? Thank you for helping!!! -- View this message in context: