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# 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, anybody... could you help me to check the below coding for volcano. what is the mistake? what the plot could not display? # volcano_plot.r # # Author: Amsha Nahid, Jairus Bowne, Gerard Murray # Purpose: Produces a volcano plot # # Input: Data matrix as specified in Data-matrix-format.pdf # Output: Plots log2(fold change) vs log10(t-test P-value) # #

> # >> # 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 + >

Hello. My name is Akashah. i work at metabolic laboratory. From my study, i found that volcano plot can help a lot in my section.  i already studied about the volcano plot and get the coding to run in R software, unfortunately, there is may be something wrong with the coding. This is because  no graph appear, but no error (blue color text) was shown on the R console. Below is the coding for

Hi, what i need is as the example i attached in the box plot. As you can see, the boxplot have margins and it kind of follow the coding I've added, but, it does not happen for the heat map. Could you help me with this.. Thanks in advance. the following is the coding i've used: # Draw the heatmap # heatmap.r # # Purpose: Create a heatmap # # Input: Data matrix as

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

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,

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

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,

Well, ?factor does not say anything about this behaviour (assigning numeric code instead of level of factor). And actually if you do assignment for whole vector the result is different (vector in data frame is changed to factor). > temp2$fff[1]<-vec[1] > head(temp2,2) pokus minuty fff 1 T42 240 3 2 T42 300 <NA> > temp2$fff<-vec > head(temp2,2) pokus

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)))

Hi, I am using the lda function from the MASS library. I would to find the decision boundaries of each class and subsequently plot them. I wonder if anybody can offer any help on this topic? Below I applied the lda function on a small dataset of mine. Any help will be much appreciated. > library(MASS) >

I've writen this function (imagenrgb) to display a (m,n,3) array as a RGB image with ngris^3 colors and,optionally, stretching. If option ver=F, it does not display but saves a pseudocolor version of the image as a list (so that subsequent displays are faster). I'd appreciate feedback and improvements and hope that it's useful for others. Example of use: > dim(imatest) [1] 100

Hi, Thanks Sarah. Unfortunately I did not get a step further. My question, perhaps a bit clearer, is how to display the case control status (or any other arbitrary point label) after 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 <-

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 pdf() If I'm missing fonts, how

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

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

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

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

I use Widows, R version 2.4.1 I have 4 questions on lda (MASS) (code is pasted below): 1st. How can I obtain the statistics and p-value associated with discriminant analysis? Am I supposed to calculate that manually by squaring the svd value and looking the p value up in a table? I am writing the following code: training.mx<-read.table('C:\\Documents and Settings\\silvia\\My