search for: loadingplot

Hi, I am a beginner with R. I would like to get a loading plot of PC 3 vs PC 1. For PC 1 vs PC 2 I use library(pls) loadingplot(pca.result, comps = 1:2, scatter = TRUE, labels="names") if I try loadingplot(pca.result, comps = 1:3, scatter = TRUE, labels="names") I get the loading plots of PC 1 vs PC 2, PC 1 vs PC 3 and PC 2 vs PC 3. What do I have to do to get just a single loading plot of PC 3 vs PC...

Dear colleagues, I a m a beginner with R and I would like to add labels (i.e. the variable names) on a pca loading plot to determine the most relevant variables. Could you please tell me the way to do this kind of stuff. The command I use to draw the pca loading plot is the following : Plot(molprop.pc$loading[,1] ~ molprop.pc$loading[,2]) Thanks for your help Fred Ooms

....$ : chr "X" "X.1" "X.12" "X.13" ... .. ..$ : NULL Because of this I am not able to do a loadings plot >plot(BHPLS1, "loadings", comps = 1:2, legendpos = "topleft", labels = "numbers", >xlab = "nm") Error in loadingplot.default(x, ...) : Could not convert variable names to numbers. What am I doing wrong Thanks in advance [[alternative HTML version deleted]]

...do this using the gasoline data that comes with the pls package. However when I conduct this on my dataset i get the following error message. >plot(BHPLS1, "loadings", comps = 1:2, legendpos = "topleft", labels = "numbers", >xlab = "nm") Error in loadingplot.default(x, ...) : Could not convert variable names to numbers. str(BHPLS1_Loadings) loadings [1:8892, 1:60] -0.00717 0.00414 0.02611 0.00468 -0.00676 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:8892] "PCIList1" "PCIList2" "PCIList3" "PCILi...

...in changes since 1.0-3 are - mvr, mvrCv and predict.mvr now has builtin support for scaling of X. - A new function stdize for explicit centering and/or scaling. - Correlation loadings plot (corrplot). - New argument `varnames' in coefplot, to label the x tick marks with the variable names. - loadingplot, coefplot and plot.mvrVal can now display legends, with the argument 'legendpos'. See CHANGES in the sources for all changes. -- Bj??rn-Helge Mevik and Ron Wehrens _______________________________________________ R-packages mailing list R-packages at stat.math.ethz.ch https://stat.eth...

...issing values, if na.action is na.exclude. - `ncomp' is now reduced when it is too large for the requested cross-validation. - Line plot parameter arguments have been added to predplotXy(), so one can control the properties of the target line in predplot(). - MSEP(), RMSEP(), loadings(), loadingplot() and scoreplot() are now generic. See the file CHANGES in the sources for all changes. -- Ron Wehrens and Bj??rn-Helge Mevik _______________________________________________ R-packages mailing list R-packages at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-packages

...gs, and calculation of (R)MSEP and R2 - Functions for plotting predictions, validation statistics, coefficients, scores, loadings, biplots and correlation loadings. The main changes since 1.2-0 are - There is now an options mechanism for selecting default fit algorithms. See ?pls.options. - loadingplot() and coefplot() now try to be more intelligent when plotting x axis labels. - The handling of factors in X has been improved, by changing the way the intercept is removed from the model matrix. - All PLSR and PCR algorithms, as well as mvrCv(), have been optimised. Depending on the algorithm...

...issing values, if na.action is na.exclude. - `ncomp' is now reduced when it is too large for the requested cross-validation. - Line plot parameter arguments have been added to predplotXy(), so one can control the properties of the target line in predplot(). - MSEP(), RMSEP(), loadings(), loadingplot() and scoreplot() are now generic. See the file CHANGES in the sources for all changes. -- Ron Wehrens and Bj??rn-Helge Mevik _______________________________________________ R-packages mailing list R-packages at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-packages

...gs, and calculation of (R)MSEP and R2 - Functions for plotting predictions, validation statistics, coefficients, scores, loadings, biplots and correlation loadings. The main changes since 1.2-0 are - There is now an options mechanism for selecting default fit algorithms. See ?pls.options. - loadingplot() and coefplot() now try to be more intelligent when plotting x axis labels. - The handling of factors in X has been improved, by changing the way the intercept is removed from the model matrix. - All PLSR and PCR algorithms, as well as mvrCv(), have been optimised. Depending on the algorithm...

...in changes since 1.0-3 are - mvr, mvrCv and predict.mvr now has builtin support for scaling of X. - A new function stdize for explicit centering and/or scaling. - Correlation loadings plot (corrplot). - New argument `varnames' in coefplot, to label the x tick marks with the variable names. - loadingplot, coefplot and plot.mvrVal can now display legends, with the argument 'legendpos'. See CHANGES in the sources for all changes. -- Bj??rn-Helge Mevik and Ron Wehrens _______________________________________________ R-packages mailing list R-packages at stat.math.ethz.ch https://stat.eth...