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...le, labels, line) + mar=c(7,4,4,2), # plot margins (b,l,t,r) + las=1) # horizontal labels + plot(matrix, # data to plot + cex=cex_val, # font size + dimen=2 # dimensions to plot + ) + title(main=title) # title of plot + } > # Plot LDA scores with sample names > pic_onscr(lda_result,"Linear Discriminant Analysis") Error in plot(matrix, cex = cex_val, dimen = 2) :...

...le, labels, line) + mar=c(7,4,4,2), # plot margins (b,l,t,r) + las=1) # horizontal labels + plot(matrix, # data to plot + cex=cex_val, # font size + dimen=2 # dimensions to plot + ) + title(main=title) # title of plot + } > # Plot LDA scores with sample names > pic_onscr(lda_result,"Linear Discriminant Analysis") Error in plot(matrix, cex = cex_val, dimen = 2) :...

...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, panel = panel.lda, cex = 0.7, dimen = 1, abbrev = FALSE, type ="density") but I got an error message: Error: couldn't find function "plot.lda" and then I tried plot(drug.lda, panel = panel.lda, cex = 0.7, dimen = 1, abbrev = FALSE, type ="density") still an error message...

...ed the simple steps I got there showing the structure and heads of the objects. ===== add a col of NA = Family > least$Family <- NA; str(least) 'data.frame': 243 obs. of 6 variables: $ sp : int 1 3 5 6 8 11 13 15 18 19 ... $ Fallon: int 14 11 109 6 1 44 70 23 4 100 ... $ Dimen : int 10 13 52 2 1 19 18 0 2 116 ... $ Farm : int 6 2 3 0 0 2 0 1 2 1 ... $ Sums : int 30 26 164 8 2 65 88 24 8 217 ... $ Family: logi NA NA NA NA NA NA ... >head(least,2) sp Fallon Dimen Farm Sums Family 1 1 14 10 6 30 NA 3 3 11 13 2 26 NA > >...

...>> 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 + > cex=cex_val, # font size + dimen=2 # dimensions to plot + > ) + title(main=title) # title of plot + } >> # Plot LDA scores with sample names >> pic_onscr(lda_result,"Linear Discriminant Analysis") > Error in plot(matrix, cex = cex_val, dimen = 2) : error in > evaluating the argume...

...re showing the structure and heads of the objects. > ===== add a col of NA = Family >> least$Family <- NA; str(least) > 'data.frame': 243 obs. of 6 variables: > $ sp : int 1 3 5 6 8 11 13 15 18 19 ... > $ Fallon: int 14 11 109 6 1 44 70 23 4 100 ... > $ Dimen : int 10 13 52 2 1 19 18 0 2 116 ... > $ Farm : int 6 2 3 0 0 2 0 1 2 1 ... > $ Sums : int 30 26 164 8 2 65 88 24 8 217 ... > $ Family: logi NA NA NA NA NA NA ... >>head(least,2) > sp Fallon Dimen Farm Sums Family > 1 1 14 10 6 30 NA > 3 3 11...

...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, data=pcor.df, CV=T) table2<-table(pcor.df$pcor, pcor.lda2$class) table2 #doesn't work, becoause CV=True? pcor.ld2<-predict(pcor.lda2, dimen=1)$x plot(pcor.ld2) plot(pcor.lda2, type="density", dimen=1) #kernel density estimates I am happy if I get an answer from somebody! Stefanie von Felten Institut für Umweltwissenschaften Universität Zürich Winterthurerstrasse 190 CH-8057 Zürich e-mail: sfelten@uwinst.unizh.ch telefon:...

hi, When I try to use the function coordProj {mclust} " coordProj(diabetes[,-1],dimens=c(2,3),what="uncertainy",uncertainty=diabetesModel$uncertainty,parameters=diabetesModel$parameters) " to identify uncertainty, it errors and send this warning message: " Warning message: In coordProj(diabetes[, -1], dimens = c(2, 3), what = "uncertainy", : what...

...of the > objects. > > ===== add a col of NA = Family > >> least$Family <- NA; str(least) > > 'data.frame': 243 obs. of 6 variables: > > $ sp : int 1 3 5 6 8 11 13 15 18 19 ... > > $ Fallon: int 14 11 109 6 1 44 70 23 4 100 ... > > $ Dimen : int 10 13 52 2 1 19 18 0 2 116 ... > > $ Farm : int 6 2 3 0 0 2 0 1 2 1 ... > > $ Sums : int 30 26 164 8 2 65 88 24 8 217 ... > > $ Family: logi NA NA NA NA NA NA ... > >>head(least,2) > > sp Fallon Dimen Farm Sums Family > > 1 1 14 10...

...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))) ir.lda<-lda(log(ir),ir.species) ir.ld<-predict(ir.lda,dimen=2)$x eqscplot(ir.ld, type="n", xlab = "First linear discriminant", ylab = "second linear discriminant") text(ir.ld, labels= as.character(ir.species[-143]), col =3 +codes(ir.species),cex =0.8) ====================================== eqscplot does not plot anything a...

...topic? Below I applied the lda function on a small dataset of mine. Any help will be much appreciated. > library(MASS) > AA<-read.table("http://www.natursyn.dk/online/fingerprinting.txt",header=T) > aa.lda<-lda(as.matrix[3:9],AA$group) > aa.ld<-predict(aa.lda,dimen=2)$x > eqscplot(aa.ld,type="n",xlab="LD1", ylab="LD2",las=1) > text(aa.ld,c(rep('f',13),rep('b',10),rep('p',10))) > aa.mean<-lda(aa.ld,AA$group)$means > points(aa.mean,pch=3) Best, Thomas Larsen Leibniz-Laboratory for Stabl...

...(134.155.91.46) Merging of two data frames by variables with different names (using by.x and by.y) is no longer possible (it was possible in version 1.4.1). Call: mvoteint.classd.matadd <- merge( unique(mvoteint.classd), unique(subset(dimens.mpp, select=c(nation,year,mecode, econlrdiff, eeconlrdiff, welfdiff, labmiddiff,...

...p=F) dim(cols) <- c(m,n) #4.Display or save if(ver) imagen(cols,col=cols.unicos) else list(ima=cols,cols=cols.unicos) } Functions called: > rescale function(vector, oldmin = min(vector), oldmax = max(vector), newmin = 0, newmax = 255) { rango <- oldmax - oldmin dimen <- dim(vector) vector <- (vector - oldmin)/rango vector <- newmin + (newmax - newmin) * vector vector[vector<newmin]<- 0 vector[vector>newmax]<- newmax dim(vector) <- dimen vector } > reclas function(matriz, origen,...

Hello, I am using the parameterized EM- Algorithm in clustering context. I use generated datasets, containing 5000 points in 20 dimensions with 5 clusters. The following poses a problem for me: emEst<-me(modelName="EEV",data=data[,1:numDimensions],z=unmap(x)) emEst<-me(modelName="VEV",data=data[,1:numDimensions],z=unmap(x)) coordProj(main="Model EEV",data,dimens=c(1,2),type=&q...

...clustering in a plot: With a bit of pseudo code, where dataset is a data.frame, parameters are those column names where we find numerical values (no NAs) and nclasses is the desired number of classes. fit <- kmeans(dataset[, parameters], nclasses) plot(dataset[, parameters], col = fit$cluster, dimen = nclasses) This gives us a nicely coloured plot where all points are circled. Yet, my desire is to see the case / control-Status encoded in a column "Status" with single characters, in place of those circled points. Any hints? I did not get pch() nor text() to work. Perhaps the solutio...

...quality=100, # image quality (percent) # bg="white", # background colour # res=300, # image resolution (dpi) # units="in", width=8.3, height=5.8) # image dimensions (inches) # par(mgp=c(5,2,0), # axis margins # # (title, labels, line) # mar=c(6,6,4,2), # plot margins (b,l,t,r) # las=1 # horizo...

Hi, I'm using plot.lda (R1.4.0 on linux) and, if dimen > 2, I get the error: Error in text.default(x, y, txt, cex = cex, font = font) : X11 font at size 16 could not be loaded The traceback indicates that the problem comes from pairs(). I've tried several cex and font values, always with the same error. The same command works fine with...

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 v...

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 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 5 1 0.30359530 0.2980478 0.1985440 0.1194298 0.08038303 2 0.28170499 0.2869483 0.1998210 0.1344000 0.09712566 3 0.17098847 0.1820718 0.2614599 0.2195304 0.1659...