similar to: plot Principal Components axes on original data

Hi there, I'm plotting some glass RI values just by plotting plot(x) then I put on my lowess smoother lines(lowess(x)) now I want to put on some 95% Confidence Interval bands of the lowess smoother, but don't know how?? Thanks -- Gareth Campbell PhD Candidate The University of Auckland P +649 815 3670 M +6421 256 3511 E gareth.campbell@esr.cri.nz gcam032@gmail.com [[alternative

Hi there, I know this is probably a really simple question, but without the correct keywords, or knowledge of the correct function it is hard to search for on the net or within R. How do I increment a dataframe (or similar) name within a loop and assign data? A simple example would be: for(i in 1:10){ test<-i } BUT I want it to be test[i] --- in other words I want my stored data to

Hey team, If I have a matrix: 1, 2, 3, 4, 4, 0, 1, 3, 0, 3 2 columns. I want to write an if command that looks at (in this case) row 3 and looks to see if either [3,1] or [3,2] has a zero in it. IF it does have a zero I want the zero to be placed in another matrix in the same position. I know how to do the latter part, I just can't get the if command to look at both cells and deal with

I have some data (REE plots - geochemistry) where I have values 1:14 for the x axis, but have no data for some x values. Here for example, let's say that I don't have data for x=2,5,8. So x<-1:14 y<-c(4, NA, 5, 9, NA, 3.4, 8, NA, 19, 22, 12, 14, 15.3, 15) if I plot the data plot(x,y) and then I want to join with lines lines(x,y) How do I get it so the points join across the

I have a vector: alleles.present<-c("D3", "D16", ... ) The alleles present changes given the case I'm dealing with - i.e. either all of the alleles I use for my calculations are present, or some of them. Depending on what alleles are present, I need to make matrices and do calculations on those alleles present and completely disregard any formula or other use of the

I've made an LDA model on some data from one source. I have some new data that I want to see if I can "place" to the sources in the LDA model. I used the predict function as follows: predict(wak.insitu.ld, wak.alr.alluvial) where wak.insitu.ld is an LDA model generated from some data and wak.alr.alluvial is new data of similar origin. When I look at the results, there is 86

Hi everyone, I have a PCA plot that I'm writing about in the text. There were so many symbols in different colours on it that I didn't include a legend in the plot as it would be useless. So what I was hoping to do was to talk about each set of replicates in the text and when I do that, use their coloured symbol in the text. So what I want to do is to get R to create some high quality

Hello, I have a time-series of standards measured for Refractive index. They are daily standards, however, I didn't run one everyday so some days have no data. I can plot the values, but the x-axis does not represent the correct time series (i.e. it's just an evenly spaced 1,2,3 type axis). I want to plot the points with some form of representitive date line on the x-axis. I don't

Hi, I'm doing anova on a matrix of multivariate data where I want to assess the effect of each column (element). My matrix is 86 rows x 31 columns. I've created a grouping factor of length 86 containing group assignments of 6 types. Then I run: x<- aov(matrix~grouping.factor) summary(aov.fit.raw, test="Wilks") This is working fine enough, but I'm getting different

When I run a summary(anova) I get output for all of the elements (columns) as these are multiple - single anova results. Can I store the F values? I can't find the attribute of the fitted model attributes(fit) that stores these F values, and for that matter, P values. Thanks -- Gareth Campbell PhD Candidate The University of Auckland P +649 815 3670 M +6421 256 3511 E

Hello all, I'm dealing with geochemical analyses of some rocks. If I use the full composition (31 elements or variables), I can get reasonable separation of my 6 sources. Then when I go onto do LDA with the 6 groups, I get excellent separation. I feel like I should be reducing the variables to thos that are providing the most discrimination between the groups as this is important

Hello, What I am trying to do is: Generate a density plot of a population of data. This data has a bimodal distribution so I've isolated a couple of possible sub-populations and I want to overlay these two density plots over the first to see whether they are contributing to the bimodal population. I can do this fine with plot(density(...)) and lines(density(...)) . But the resulting plots

Sorry, the code is incomplete. You get a better result this way... postscript('Circle.eps',paper='special',width=4,height=4) par(mar=c(0,0,0,0)) plot.new() points(0.5,0.5,pch=21,cex=50,bg='gray') dev.off() -----Mensagem original----- De: Rodrigo Aluizio [mailto:r.aluizio em gmail.com] Enviada em: ter?a-feira, 18 de novembro de 2008 19:28 Para: 'Gareth Campbell'

Hello, I have a matrix and I want to sample 20 rows that are the the percentiles of 0-100 in 0.05 increments. I have a vector of my sequence (0, 0.05, 0.10, 0.15,....1.0) and also a normalised vector of rownumbers. That is, there are 234 rows (for example) so I do perc<-c(1:234/234) which looks like a bunch of numbers from 0 - 1. In Excel (which I try not to use at every possible

Dear listserv, I am trying to perform a principal components analysis and create an output table of the eigenvalues for the dependent variables. What I want is to see which variables are driving each principal components axis, so I can make statements like, "PC1 mostly refers to seed size" or something like that. For instance, if I try the example from ?prcomp > prcomp(USArrests,

Hello, I have a matrix with 267 columns, all rows of which have at least one column missing (NA). All three methods i've tried (pcs, princomp, and prcomp) fail with either "Error in svd(zsmall) : infinite or missing values in 'x'" (latter two) or "Error in cov.wt(z) : 'x' must contain finite values only" The last one happens because of the check if

May be a simple question, but not understanding why in princomp I get different results for loadings and summary for my eigenvectors and eigenvalues. When I use pc.cr$loadings using the USArrests dataset the proportion of variance is equal for each of the components, but when summary(pc.cr) is used the proportion of variance is showing different proportions. My question is why do they differ? I

Dear R buddies, I’m trying to run Principal Component Analysis, package princomp: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/princomp.html. My question is: why do I get different results with pca = princomp (x, cor = TRUE) and pca = princomp (x, cor = FALSE) even when I standardize variables in my matrix? Best regards, Blaž Simčič [[alternative HTML version deleted]]

Hello R-ers, I'm trying to do a weighted principal components analysis. I couldn't find any such option with princomp or prcomp. Does anyone know of a package or way to do this? More specifically, the observations I'm working with are averages from populations of varying sizes. I thus need to weight the observations by sample size. Ideally I could apply these weights at the cell

Dear all, I am trying to find the PCs of a spatial data set (single variable). I want to calculate the PCs at each Lat-Lon location. The* 'princomp'* command gives the approximate standardized data (i.e* pca$scores*), stranded deviation ..etc. I tried* 'pca$loadings'*also, but it giving value 1 all time. Then I tried manually(without using* princomb*