similar to: an error in my using of nnet

Hi, there: I spent some time on this but I think I really cannot figure it out, maybe I missed something here: my data looks like this: > dim(trn3) [1] 7361 209 > dim(val3) [1] 7427 209 > mg.rf2<-randomForest(x=trn3[,1:208], y=trn3[,209], data=trn3, xtest=val3[, 1:208], ytest=val3[,209], importance=T) my test data has 7427 observations but after prediction, > dim(mg.rf2$votes)

Hello, I use nnet to do prediction for a continuous variable. after that, I calculate correlation coefficient between predicted value and real observation. I run my code(see following) several time, but I get different correlation coefficient each time. Anyone know why? In addition, How to calculate prediction accuracy for prediction of continuous variable? Aimin thanks, > m.nn.omega

Hi, there: I think i need to re-phrase my question since last time I did not get any reply but i think the question is not that hard, probably i did not make the question clear: I want to find cases like 35, 90, 330, 330, 335 from the rest which look like 3, 3, 3, 3.2, 3.3 4, 4.4, 4.5, 4.6, 4.7 .... basically there is one (or more) big 'gap' in the case i seek. thanks, weiwei --

Hi there, I need a network builder and it can change the node size and color; I am not sure if network package in R can do this or not. The other functions I wanted have been found in that package. BTW, if there is another package in R relating to this, please suggest too. Thanks, Weiwei -- Weiwei Shi, Ph.D Research Scientist "Did you always know?" "No, I did not. But I

Hi, I have a question on read.table. I have a dataset with 273,000 lines and 195 columns. I used the read.table to load the data into R: trn<-read.table('train1.dat', header=F, sep='|', na.strings='.') I found it takes forever. then I run 1/10 of the data (test) using read.table again. And this time it finished quickly. So, there might be something wrong in my data

Hi, I have a dataset and want to build a generalized linear model on it. Unfortunately, complete.cases(df) returns null, which means I have to find a way to "fill" those missings. One way is following my previous post to use median to replace(or use most freq. of level to replace for catergorical case), but I am wondering if there are other ways, when glm or something like it is

> From: Weiwei Shi > > it works. > thanks, > > but: (just curious) > why i tried previously and i got > > > is.vector(sample.size) > [1] TRUE Because a list is also a vector: > a <- c(list(1), list(2)) > a [[1]] [1] 1 [[2]] [1] 2 > is.vector(a) [1] TRUE > is.numeric(a) [1] FALSE Actually, the way I initialize a list of known length is by

Hi, I have a dataset which has around 138 variables and 30,000 cases. I am trying to calculate a mahalanobis distance matrix for them and my procedure is like this: Suppose my data is stored in mymatrix > S<-cov(mymatrix) # this is fine > D<-sapply(1:nrow(mymatrix), function(i) mahalanobis(mymatrix, mymatrix[i,], S)) Error in solve.default(cov, ...) : system is computationally

Hi, I have a question on write.table: I have a data.frame called t7 as below: > dim(t7) [1] 14015184 6 > t7[1:5,] uci uce par line graphical.forms stems 1 0 0 0 0 active activ 2 0 0 0 0 policy polici 3 0 0 0 0 wc PC 4 0 0 0 0 eff elf 5 0 0 0 0 icn ICC I want to write the

Dear listers: Here I have a question on clustering methods available in R. I am trying to down-sampling the majority class in a classification problem on an imbalanced dataset. Since I don't want to lose information in the original dataset, I don't want to use naive down-sampling: I think using clustering on the majority class' side to select "representative" samples might

Hi, forgive me if it is due to my "laziness" :) I am wondering if there are functionalities in R, which can do like "more" and "tab" in linux: more(one.data.frame) so I can browse through it. Sometimes I can use one.data.frame[1:100,], but still not as good as "more" in linux. tab: can I use tab to auto complete an defined object name in R so I don't

Hi, there: I am wondering if anyone here can provide an example using pca doing dimension reduction for a dataset. The dataset can be n*q (n>=q or n<=q). As to dimension reduction, are there other implementations for like ICA, Isomap, Locally Linear Embedding... Thanks, weiwei -- Weiwei Shi, Ph.D "Did you always know?" "No, I did not. But I believed..." ---Matrix III

Hi there, I have a matrix like this: > a4[1:20, 1:5] 194 211 294 314 315 GO:0000003 1 1 1 1 1 GO:0000072 0 0 0 0 0 GO:0000076 1 0 0 0 0 GO:0000082 1 3 1 1 1 GO:0000083 1 0 0 0 1 GO:0000086 0 1 0 1 1 GO:0000114 0 0 0 0 0 GO:0000115 0 0 0 0 0 GO:0000117 0 0 0 0 0 GO:0000160 0 0 1 0 0

Hi, there: I am wondering if there is a quick way to "reverse" a list like this: t0 <- list(a=1, b=1, c=2, d=1) reverst t0 to t1 > t1 $`1` [1] "a" "b" "d" $`2` [1] "c" thanks. -- Weiwei Shi, Ph.D Research Scientist GeneGO, Inc. "Did you always know?" "No, I did not. But I believed..." ---Matrix III

Hi, there: I am looking for a regression modeling (like regression trees) approach for a large-scale industry dataset. Any suggestion on a package from R or from other sources which has a decent accuracy and scalability? Any recommendation from experience is highly appreciated. Thanks, Weiwei -- Weiwei Shi, Ph.D "Did you always know?" "No, I did not. But I believed..."

Hi, I am wondering how to make a function Fun to make the following work: t0 <- (paste("hgu133a", "ENTREZID", sep="")) xx <- as.list(Fun(t0)) # make it work like xx<-as.list(hgu133aENTREZID) thanks, -- Weiwei Shi, Ph.D Research Scientist GeneGO, Inc. "Did you always know?" "No, I did not. But I believed..." ---Matrix III

Hi, I searched the archives and did not find a good solution to that. assume I have 10 sets and I want to have the common character elements of them. how could i do that? -- Weiwei Shi, Ph.D Research Scientist GeneGO, Inc. "Did you always know?" "No, I did not. But I believed..." ---Matrix III

Hi, I am using dlda algorithm from supclust package and I am wondering if the output can be a continuous probability instead of discrete class label (zero or one) since it puts some restriction on convariance matrix, compared with lda, while the latter can. thanks, -- Weiwei Shi, Ph.D Research Scientist GeneGO, Inc. "Did you always know?" "No, I did not. But I believed..."

hi, is there some good summary on clustering methods in R? It seems there are many packages involving it. And I have two questions on clustering here: 1. Is there a way of evaluate the effecitives (or seperation) of clustering (rather than by visualization)? 2. Is there a search method (like genetic search) which can help find the best subset of attributes which gives best seperation? Thanks,

Hi, I have one question on factor vector. I have 3 factor vectors: a<-factor(c("1", "2", "3")) b<-factor(c("a", "b", "c")) c<-factor(c("b", "a", "c")) what I want is like: c x 1 b 2 2 a 1 3 c 3 which means, I use b as keys and vector a as values and I find values for c. I used the following