similar to: discriminant analysis with lda(MASS)

Hi, I have done different discriminant function analysis of multivariat data. With the CV=True option I was not able to perform the predict() call. What do I have to do? Or is there no possibility at all? You also need the predicted values to produce a plot of the analysis, as far as I know. Here my code: pcor.lda2<-lda(pcor~habarea+hcom+isol+flowcov+herbh+inclin+windprot+shrubcov+baregr,

Hi there, I am a master student in Denmark, and apply R to analyze the drug data. I use the function 'lda' to obtain a linear discriminant object, and then wish to use 'plot.lda' to do the plot on this object like below. drug.lda <- lda(Inhibition ~ NET_CHARGE + PKA_1 + MW + MLOGP, data = drug.class, method = "moment") plot.lda(drug.lda,

Dear R I'm trying to plot the lda means onto a 2 D plot of discriminant scores. Preferably I'd like these to be in a larger font compared to the discriminant scores. I tried skull.mean.pred <- predict(skulls.lda, as.data.frame(skulls.lda$means), dimen=2) from which I got skull.mean.pred $class [1] 1 2 3 4 5 Levels: 1 2 3 4 5 $posterior 1 2 3 4

Hi all. I would like to understand what are the units defined on the y-axis when you plot the one-dimensional predictions (histograms) from lda() (MASS) discriminant function objects? While the helpfile suggests that a histogram is returned by default, the presumably proportion-like values for each group seem to add up to more than 1, and I'm not sure how to interpret the code from ldahist(),

> # >> # Generate the figures (on screen) >> # >> # Image generation - function definition >> pic_onscr<-function(matrix, title, cex_val=1) > + {x11() + par(mgp=c(5,2,0), # axis margins + # (title, > labels, line) + mar=c(7,4,4,2), # plot margins (b,l,t,r) + > las=1) # horizontal labels + plot(matrix, # data to plot + >

Dear R users, I want to do discriminant analysis on my data. I have successfully followed the discriminant analysis in V & R on the iris data: > ir <- rbind (iris3[,,1],iris3[,,2],iris3[,,3]) > ir.species <- c(rep("s",50),rep("c",50),rep("v",50)) > a <- lda(log(ir),ir.species) > a$svd^2/sum(a$svd^2) [1] 0.996498601 0.003501399 > a.x <-

# lda.r > # > # Author: Amsha Nahid, Jairus Bowne, Gerard Murray > # Purpose: Perform Linear Discriminant Analysis (LDA) > # > # Input: Data matrix as specified in Data-matrix-format.pdf > # Output: LDA plot > # > # Notes: Missing values (if any) are replaced by the half of the lowest > # value in the entire data matrix. >

hello, i already include the error in blue color word. i hope it can help you to understand my question. if not burden you, please give me a guide how to correct the error or maybe you can correct the coding cause error. thank you. > # lda.r > # > # Author: Amsha Nahid, Jairus Bowne, Gerard Murray > # Purpose: Perform Linear Discriminant Analysis (LDA) > # > #

I am trying to get a random matrix based on an original matrix called disperser.mx, with dimensions 30x73 When I write the following code: >scramble = sample (disperser.mx) >newmat = matrix(scramble, nrow=30) I get the following warning message and a very weird matrix with 30 rows but only 3 columns shown below: Warning message: data length [73] is not a sub-multiple or multiple of the

Hi !! I am trying to analyze some of my data using linear discriminant analysis. I worked out the following example code in Venables and Ripley It does not seem to be happy with it. ============================ library(MASS) library(stats) data(iris3) ir<-rbind(iris3[,,1],iris3[,,2],iris3[,,3]) ir.species<-factor(c(rep("s",50),rep("c",50),rep("v",50)))

Hello R people I now know how to run my discriminant analysis with the lda function in MASS: lda.alain=lda(Groupes ~ Ht.D0 + Lc.Dc + Ram + IDF, gr, CV = FALSE) and it works fine. But I am missing a test and cannot find any help on how to get it, if it exist. The "S" equivalent: discrim(structure(.Data = Groupes ~ Ht.D0 + Lc.Dc + Ram + IDF, class = "formula"), data = gr,

Hi all, I obtained a strage result with LDA (MASS) function in R with NIR data. I tried both CV (leave one out cross validation) and splitting my data in odd (training) and even (prediction) sets. In all the cases the minimum error was near to 0. Due to the strange result, I tried with SPSS IBM software and it give me around 11% of minimum error with and without leave one out cross validation.

I work with Windows, R 2.4.1. I'm a beginner with R! After doing a Discriminant Function Analysis, I am trying to run manova to get a measure of significance of my lda results. I want to predict groups 1 through 4 using 78 variables (bad group/var ratio, I know, but I'm just exploring the possibilities right now). I've tried with a test matrix and I get my results fine, so I think

Hi all, I am trying to plot the results of a discriminant analysis done with lda(MASS) but my groups appear in two different plots (in the same graphics device) and I want to combine them in one plot. My code looks like: BirdTrain.lda <- lda(Bdisperser~., data=BirdTrain.mx) predict(BirdTrain.lda) plot(BirdTrain.lda) I have two types of Bdisperser, so I only get one linear discriminant

Dear all When plotting the results of lda and predict.lda, I expect the axis dimensions to remain essentially the same - predict.lda projects new observations onto linear discriminants. However, I am getting different ranges of values along the x-axis when producing plots following each: plot(lda) yields values in the range approximately -3 to +4, while plot(predict.lda) yields values for the

My objective is to look at differences in two species of fish from morphometric measurements. My morphometric measurements are head length, eye diameter, snout length, and measurements from tail to each fin. I want to use discrimanant function analyis to determine if there are differences between the two species. I am familiar with R but new to discrimannt function analysis. I want to learn

I work on Windows, R version 2.4.1. I'm very new with R! I am trying to build a classification tree using rpart but, although the matrix has 108 variables, the program builds a tree with only one split using one variable! I know it is probable that only one variable is informative, but I think it's unlikely. I was wondering if someone can help me identify if I'm doing something

I use Windows, R version 2.5.1 When I try to run stepclass (klaR) I get an error message/warning saying: 1: error(s) in modeling/prediction step in: cv.rate(vars = c(model, tryvar), data = data, grouping = grouping, ... Actually, I look 16 warnings of this type. Can anyone tell me what this means? Also, it returns only 2 out of the 79 variables as important, however these variables

This is a simple problem, but a mystery to me. I'm trying to grab $Family "Scelionidae" from one dataframe and put it into another dataframe occupied with NA in $Family. The result is a "158" ends up there instead of Scelionidae. Simply put fam$Family[1] <- least$Family[1] If I have made a mistake here, can somebody point it out. I've included the simple

Your data frame fam contains factors. Turn it into character strings using fam$Family = as.character(fam$Family) and try again. It may be helpful if you read up on R's factors, see ?factor. HTH, Peter On Tue, Nov 21, 2017 at 2:14 PM, Glen Forister <gforister at gmail.com> wrote: > This is a simple problem, but a mystery to me. > I'm trying to grab $Family