search for: pca1

...gest 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$center))) newdata <- newdata[, cn] Now Dana's example looks like: > predict(pca1, frz) Error in "[.data.frame"(newdata, , names(object$center)) : undefined columns selected > names(frz) <- c("x2", "x1") > predict(pca1, frz) Comp.1 Comp.2 1 -3.29329963 -1.24675774 2 0.15760569 0.09364550 3 1.90206906 0.06292855...

...s please advise how I can achieve this. I used locater() to obtain coordinates for below the Comp.1 axis. Using these coordinates the legend disappears. Below is the code for the PCA and legend. Thanks in advance for the help. Regards Tinus r.cols <- rainbow(length(unique(SEData$Seamount))) pca1 <- princomp(SEData3, scores=TRUE, cor=TRUE) biplot(pca1, var.axes= TRUE, xlabs=rep("",nrow(SEData3)),main="Seamounts PCA") rrr <- apply(pca1$scores[,1:2],2, range) par(usr=as.vector(rrr)) points(pca1$scores[,1:2], col=r.cols , pch=20) legend(-8, 2.95, sm, col = r.cols, tex...

...t;StationCode" "Abramisbrama" "Alburnoidesbipunctatus" "Alburnusalburnus" [5] "Ameiurusmelas" > Fish89PCA <-read.csv("Fish89PCA.csv") > names (Fish89PCA) [1] "StationCode" "PCA1" > # Check data (1:X rows, e.g. 1:3,=1.-3.row or else) > Fish89PCA [1:3,] StationCode PCA1 1 DE582 -1.5592527 2 DE38 0.3530658 3 FR02116570 2.8057854 > > names (Fish89PCA) [1] "StationCode" "PCA1" > Fish89TiTAN <- titan(...

Hi, I was trying as well as looking for an answer without success (a bit strange since it should be an easy problem) and therefore I will appreciate you help: My simple script is: # Loadings data of 5 columns and 100 rows of data data1<-read.csv("C:/?/MyPCA.csv") pairs(data1[,1:4]) pca1 <- princomp(data1[,1:4], score=TRUE, cor=TRUE) biplot(pca1) The biplot present the data points as numbers. How can I present the data point in color (depends on their group-column 5). I was thinking about doing it using ggplot2 but I can not succeed. Any idea how to do it? Thanks -- View t...

Is there an option to draw concentration ellipses in biplots ? It seems really nice to summarize large number of points of each group. Cheers../ Murli

...mpting? If so, can anyone tell me how to accomplish steps 1 and 2 above? Thanks, Dana Honeycutt P.S. Here's a script that demonstrates the problem: x1 <- rnorm(10) x2 <- rnorm(10) y <- rnorm(10) frx <- data.frame(x1,x2) frxy <- data.frame(x1,x2,y) lm1 <- lm(y~x1+x2,frxy) pca1 <- princomp(frx) rm(x1,x2,y,frx,frxy) z1 <- rnorm(10) z2 <- rnorm(10) frz <- data.frame(z1,z2) predict(lm1, frz) # gives error: Object "x1" not found predict(pca1, frz) # gives no error, indicating column names ignored z3 <- rnorm(10) fr3z <- data.frame(frz,z3) pred...

...ble 2 and variable 3. What I want is to see if the combination of var 2 and var 3, will give me a pattern that allows to distinguish populations. I found several packages like ade4, with pcaiv function and factoMineR. but there are not working. Using the ade4 package, when I try to build the pca: pca1 <- dudi.pca(D, scan = FALSE, nf = 2) Error in v * row.w : non-numeric argument to binary operator Does someone has suggestions? Thanks, Andy -- View this message in context: http://www.nabble.com/principal-component-analysis-for-class-variables-tp24792474p24792474.html Sent from the R help ma...

...) mod.vol.nlme<-nlme(fa20~(ah*habdiv+ads*ds+ads2*ds2+at*trout)+asymporig(da.p,th1,th2)+ asymporigb(vol,th1b,th2b), fixed=ah+ads+ads2+at+th1+th2+th1b+th2b~1, random=pdBlocked(list(th1~1,th2~1)), start=c(ah=.5524,ads=.8,ads2=-.1,at=-1,th1=2.542,th2=-7.117,th1b=2,th2b=-7), data=pca1.grouped,verbose=T) I am looking at potential multicollinearity among the fixed effects, in particular I am concerned about multicollinearity between da.p (drainage area) and vol (volume). How do I interpret the correlation reported in the summary command for th1 and th1b, which are the asymptotes...

Hi!! I'm having trouble selecting 10 out of 41 attributes of the KDD data set. In order to identify the components with the higher variance I'm using princomp. the result i get for summary(pca1) is: Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9 Comp.10 Standard deviation 9.882181e+05 3.303966e+04 7.083767e+02 3.282215e+02 9.839173e+01 4.642...

...del. asymporig<-function(x,th1,th2)th1*(1-exp(-exp(th2)*x)) mod1<-nlme(fa20~(ah*habdiv+ad*log(d)+ads*ds+ads2*ds2+at*trout)+asymporig(da.p,th1,th2), fixed=ah+ad+ads+ads2+at+th1+th2~1, random=th1+th2~1, start=c(ah=.9124,ad=.9252,ads=.5,ads2=-.1,at=-1,th1=2.842,th2=-6.917), data=pca1.grouped) However, the two random effects (th1 and th2) which describe the asymptotic relationship between richness (fa20) and area (da.p) are correlated: -0.904 with approximate 95% ci of -0.99 to -.32. I examined the anova of mod1 with both random effects and mod2 with just th1 and mod1 is prefer...

hello all R experts, how do I calculate the reliability between the two groups using the ICCs? I'll appreciate your reply, Thanks Sincerely, Supreet kaur, Biomedical research engineer, Nationwide Childrens Hospital, Columbus, OH (614)355-3509 [[alternative HTML version deleted]]

...(paquete "ade4") y "PCA" (paquete "FactoMineR"). Sucede que al comparar las coordenadas de cada individuo que obtiene cada función, las que corresponden al segundo componente principal tienen idéntica magnitud pero con signos contrarios: Función dudi.pca: Comando: PCA1 <- dudi.pca(df = DATOS[,(2:ncol(DATOS))], center = TRUE, scale = TRUE, scannf = FALSE, nf = 6) Individuo Comp1 Comp2 1 -14.18 -4.47 2 -14.63 -4.53 3 -14.77 -2.57 4 -14.12 -1.71 5 -16.32 4.22 6 -17.03 5.94 7 -16.90 3.68 8 -17.75 5.86 9 13...

...(paquete "ade4") y "PCA" (paquete "FactoMineR"). Sucede que al comparar las coordenadas de cada individuo que obtiene cada función, las que corresponden al segundo componente principal tienen idéntica magnitud pero con signos contrarios: Función dudi.pca: Comando: PCA1 <- dudi.pca(df = DATOS[,(2:ncol(DATOS))], center = TRUE, scale = TRUE, scannf = FALSE, nf = 6) Individuo Comp1 Comp2 1 -14.18 -4.47 2 -14.63 -4.53 3 -14.77 -2.57 4 -14.12 -1.71 5 -16.32 4.22 6 -17.03 5.94 7 -16.90 3.68 8 -17.75 5.86...

..." (paquete > "FactoMineR"). Sucede que al comparar las coordenadas de cada > individuo que obtiene cada función, las que corresponden al segundo > componente principal tienen idéntica magnitud pero con signos contrarios: > > Función dudi.pca: > > Comando: > PCA1 <- dudi.pca(df = DATOS[,(2:ncol(DATOS))], center = TRUE, scale = > TRUE, scannf = FALSE, nf = 6) > > Individuo Comp1 Comp2 > 1 -14.18 -4.47 > 2 -14.63 -4.53 > 3 -14.77 -2.57 > 4 -14.12 -1.71 > 5 -16.32 4.22 > 6 -17.03 5.94 &gt...

...las coordenadas de cada >>> individuo que obtiene cada función, las que corresponden al segundo >>> componente principal tienen idéntica magnitud pero con signos >>> contrarios: >>> >>> Función dudi.pca: >>> >>> Comando: >>> PCA1 <- dudi.pca(df = DATOS[,(2:ncol(DATOS))], center = TRUE, scale >>> = TRUE, scannf = FALSE, nf = 6) >>> >>> Individuo Comp1 Comp2 >>> 1 -14.18 -4.47 >>> 2 -14.63 -4.53 >>> 3 -14.77 -2.57 >>> 4 -14.12 -1....