search for: mydata1

I can run one-sample t-test on an array, for example a matrix myData1, with the following apply(myData1, 2, t.test) Is there a similar fashion using apply() or something else to run 2-sample t-test with datasets from two groups, myData1 and myData2, without looping? TIA, Gang

R helpers I am getting to grips with R but came across a small problem today that I could not fix by myself. I have 3 text files, each with a single column of data. I read them in using: myData1<-scan("C:/Program Files/R/myData1.txt") myData2<-scan("C:/Program Files/R/myData2.txt") myData3<-scan("C:/Program Files/R/myData3.txt") I wanted to produce a chart with 3 boxplots of the data and used: boxplot(myData1, myData2, myData3) This worked fine so...

...n't matter here mypos <- c("topleft", "topright","bottomleft") #run the function for columns 3&4 as y (seq.yvar) with column 2 as x (ind.xvar) for all 3 datasets (mydata_list) par(mfrow=c(2,1)) for (i in seq_along(seq.yvar)){ k <- seq.yvar[i] plot(mydata1[[k]]~mydata1[[ind.xvar]], type="p", xlab=names(mydata1)[ind.xvar], ylab=names(mydata1)[k]) for (j in seq_along(mydata_list)){ foo_reg(dat=mydata_list[[j]], xvar=ind.xvar, yvar=k, mycol=j, pos=mypos[j], name.dat=names(mydata_list)[j]) } } I tried with lapply() or mapply() but...

Hi I am really new using R, so this is really a beginner stuff! I created a very small data set on excel and then converted it to .csv file. I am able to open the data on R using the command "read.table ("mydata1.csv", sep=",", header=T)" and it just works fine. But when I want to work on the data (e.g. calculate the mean of variable "X") R says "object not found". I tried the "attach" command or "mean ("mydata1.csv"&X)" but still...

Dear Jim, Here is again exactly what I did and with the output of Rprof (with this reduced dataset and with a simpler function, it is here much faster than in real life). Thanks you again for your help! ## CODE ## mydata1<- structure(list(species = structure(1:8, .Label = c("alsen","gogor", "loalb", "mafas", "pacyn", "patro", "poabe", "thgel"), class = "factor"), fruit = c(0.52, 0.45, 0.43, 0.82, 0.35, 0.9, 0.68, 0), Asfc...

Hello I have dataframes. mydata1 <-data.frame(value=c(15,20,25,30,45,50),dates=c("2005-05-25 07:00:00 ","2005-05-25 19:00:00","2005-06-25 07:00:00","2005-06-25 19:00:00 ","2005-07-25 07:00:00","2005-8-25 19:00:00")) or mydata2 <-data.frame(value=c(15,20,25,30,45,50)...

...lumns with lots of NA's and I used knn imputation to fill in other gaps. When I load in my dataset, R uses no more than 100 megs of RAM. I'm running a 64-bit R with ~4 gigs of RAM available. When I execute the randomForest() function, however I get memory complaints. Example: > summary(mydata1.clean[,3:10]) cmp_fname_c1 cmp_lname_c1 cmp_sex cmp_bd cmp_bm cmp_by cmp_plz is_match Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.00000 FALSE:572820 1st Qu.:0.2857 1st Qu.:0.10...

Hi all, I am trying to understand Writing R Extension... Section 1.1.5, data: I include two datasets in a package, one using 'save', the other using 'write.table': --- 8< ---- myData1 <- data.frame(x=1:10) write.table(myData1,file="myData1.txt") myData2 <- data.frame(x=2:10) save(myData2,file="myData2.Rdata") --- 8< ---- Then R CMD check aks me to document myData1, but does not ask me to document myData2. In the R session, after 'library(myPack)...

...f1<-formula(MaxNASC40_50~hu3+flcmax+TidalFlag) vf1Exp<-varExp(form=~hu3) B1D<-gls(f1,correlation=corGaus(form=Lat~Lon, nugget=TRUE),weights=vf1Exp , data=ocean) ochu3<-seq(from=2.9,to=4,length=120) ocflc<-seq(from=0,to=0.8,length=120) tidal1<-rep(c("1"),length=120) mydata1<-data.frame(TidalFlag=factor(tidal1),hu3=ochu3,flcmax=ocflc) lineNASC1<-predict(B1D,newdata=mydata1,type="response") lineNASC1<-as.data.frame(lineNASC1) plot(ocean$MaxNASC40_50[ocean$TidalFlag==1]~ocean$flcmax[ocean$TidalFlag==1) lines(lineNASC1$lineNASC1~mydata1$flcmax) Tidal...

...;s correct too, but stll too complex, so my question is: Can I do the same thing just use another similar intersect function (without for/repeat/while). What's this simple function's name ? 2.how to do a relative computation after split (notice: not befor split)? create data: myData1 <- data.frame(product = c(1,2,3,1,2,3), year=c(2009,2009,2009,2010,2010,2010),value=c(1104,608,606,1504,508,1312),relative=0) mySplit1<- split(myData1,myData1$year) mySplit1 $`2009` product year value relative 1 1 2009 1104 0 2 2 2009 608 0 3 3 200...

...est.sav". *-----------------------------------------------------------------------------------------. 2) Import the Spss Data in R. Via Hmisc or foreign - both work fine. #import Spssdata in R spssfile <- "C:/tmp/test.sav" #via Hmisc library(Hmisc) Signs <- c("_") mydata1 <- spss.get(spssfile,lowernames=TRUE, allow=Signs) #via foreign library(foreign) mydata2 <- read.spss(spssfile,use.value.labels=TRUE, max.value.labels=Inf, to.data.frame=TRUE) #freq in r describe(mydata1) describe(mydata2) *-----------------------------------------------------------------...

Dear useRs, How could I obtain the confidence intervals for the means of my treatments, when my data was fitted to a GLM? I need the CI's for the Poisson and Negative Binomial distributions. Here's what I have: mydata1 <- data.frame('treatments'=gl(4,20), 'value'=rpois(80, 1)) model1 <- glm(value ~ treatments, data=mydata1, family=poisson) means1 <- with(mydata1, tapply(value, treatments, mean)) now I need the confidence intervals for the means And then again for a negative binomial GLM...

...R code####### fit1s <-glm(mydata$vzv~mydata$age.c+mydata$age2+mydata$sex1, family=Gamma(link=log)) x.betahat1<-fit1s$fitted.values res1<-fit1s$residuals b<-1000 for (i in 1:b){ b.i <- sample(index, size=n, replace=T) res.star1=res1[b.i] bst1=x.betahat1+res.star1 mydata1 <-data.frame(age,age2,sex,bst1) ########Modeling ################ fit11 <-glm(bst1~age+age2+sex, family=Gamma(link=log),data=mydata1) } Can someone help me correct this code? Kindly advice on Binomial data as well Happy New year2013! -- _______________________________ Paul...

...col(Mydata))] locus # Locus1 Locus2 Locus3 # Samp1 GG <NA> GG # Samp2 AG CA GA # Samp3 AG CA GG # converts geno matrix to genind object (adegenet) Mydata1 <- df2genind(locus, ploidy = 2, ind.names = ind[1:SAMPS], pop = population[1:SAMPS], sep="") Mydata1$tab # get stats on newly created genind object # Locus1.G Locus1.A Locus2.C Locus2.A Locus3.G Locus3.A # Samp1 2 0...

Hello, I have some data file, say, mydata 1,2,3,4,5,6,7 3,3,4,4,w,w,1 w,3,6,5,7,8,9 4,4,w,5,3,3,0 i want to replace some percentages of "mydata" file in to NAs for those values that are NOT w's. I know how to apply the percentage thing here but don't know how to select those values that are not "w"s. So far, i was able to do it but the result replaces the w's

...atically if specified condition met. This can solve my problem that I need to set the NA values manually and reset j in for-loop every time error returned. Doing this manually would be very tedious. m1<-matrix(ncol=3,nrow=0) for (j in 1:1000){ fit<-lme(Expression~1,random=~1|MonkeyID, data=mydata1,subset=Probes==as.character(Probes_Blood_sub[j,"probe"])) ci<-intervals(fit)$reStruct$MonkeyID^2 m1<-rbind(m1,ci)} Thanks,Sityee _________________________________________________________________ The i’m Talkathon starts 6/24/08.  For now, give amongst yourselves. [[alternative...

...an array with dim (22,161,56), which is populated by either a number (NTOTAL), or an NA. I then converted the NAs to zeroes; mydata[is.na(mydata)] <- 0 To get the total number of records per sitesurvey i used; array.dat1<-array(0,dim=c(22,1,56)) for (i in 1:56){ array.dat1[,,i]<-rowSums(mydata1[,,i],1) } array.dat1[which(array.dat1==0)] = NA SO when array.dat1=0, that represents that there was no survey. To combine this information with the original matrix i used new.mydata<-array(0,dim=c(22,162,56)) new.mydata[1:22,1:161,1:56]<-mydata new.mydata[1:22,162,1:56]<-array.dat1 wh...

I'm using ks.test (mydata, dnorm) on my data. I know some of my different variable samples (mydata1, mydata2, etc) must be normally distributed but the p value is always < 2.0^-16 (the 2.0 can change but not the exponent). I want to test mydata against a normal distribution. What could I be doing wrong? I tried instead using rnorm to create a normal distribution: y = rnorm (68,mean=mydata, s...

Hi, I am trying to discretize a numeric attribute of a data.frame using chiMerge mydata2<-chiMerge(mydata1, c(7), alpha = 0.05) but this command never returns, and I have to forcefully STOP the operation. Is this a bug or am I missing somthing? Can anybody help me. please? Thanks in advance.

...a new net for the old ponts plus the next 1000 points (81000 datapoints total) what would again cost much calculation time. So I want to pre-init the new net with the former wonnen net to reduce the necessary iteration numbers. Is thee a possibility to do that and how? i.e.: x=ts(scan("C:/mydata1.csv")) mod.nnet<-nnetTs(x,m=2,size=8) x=ts(scan("C:/mydata2.csv")) mod.nnetnew<-nnetTs(x,m=2,size=8,control=list(init=mod.nnet)) ??? Thanks for any help !! -- View this message in context: http://r.789695.n4.nabble.com/Init-nnetTs-or-nnet-with-a-former-Neural-Net-tp3514825p3...