search for: cm2

...that evaluates the NA in the demo dataframe. As an example, I want to assign rows to classes based on values in demo$Area. Some of the values in demo$Area are "NA" for (i in 1:nrow(demo)) { if (demo$Area[i] > 0 && demo$Area[i] < 10) {Class[i]<-"S01"} ## 1-10 cm2 if (demo$Area[i] >= 10 && demo$Area[i] < 25) {Class[i] <- "S02"} ## 10-25cm2 if (demo$Area[i] >= 25 && demo$Area[i] < 50) {Class[i] <-"S03"} ## 25-50 cm2 if (demo$Area[i] >= 50 && demo$Area[i] < 100) {Class[i] <-"S04&...

...ows only, nothing happens. install worked, the icon's on my desktop, it has been made executable, but after a bit of harddrive action nothing happens. terminal says: Code: mzwo at mzwo-laptop:~$ env WINEPREFIX="/home/mzwo/.wine" wine "C:\Program Files\Becker\Content Manager 2\cm2.exe" fixme:actctx:parse_depend_manifests Could not find dependent assembly L"Microsoft.VC80.MFCLOC" (8.0.50608.0) err:module:attach_process_dlls "odbc32.dll" failed to initialize, aborting err:module:LdrInitializeThunk Main exe initialization for L"C:\\Program Files\\...

...tion the two average main effects. Can some one point me to some literature where I can learn these things? Here the example, 2x2 factorial with interaction, estimation of average main effects: cov2 <- matrix( c(1,-1,-1, 1, -1, 2, 1,-2, -1, 1, 2,-2, 1,-2,-2, 4) , nrow=4, ncol=4, byrow=TRUE) cm2 <- matrix(c(0, 1, 0, 1/2, 0, 0, 1, 1/2), nrow = 2, ncol=4, byrow=TRUE, dimnames = list(c("A", "B"), c("C.1", "C.2", "C.3", "C.4"))) v2 <- csimint(estpar=c(100, 6, 5, 2), df=4*n-4, covm=cov2*sigma^2/n, cmatrix=cm2, conf.level=0.95)...

...ted(svm.model) > cm <- table(predict, data.df$Response) > cm #Model2: With 10-fold cross-validation: > svm.model2 <- svm(Response ~ ., data=data.df, type="C-classification", > kernel="linear", cost=1, cross=10) > predict2 <- fitted(svm.model2) > cm2 <- table(predict2, data.df$Response) > cm2 However, when I compare cm and cm2, I notice that the confusion matrices are identical although the accuracy of each model is diffent. What am I doing wrong? Thanks for you help, ----- TO GET MORE DETAILS CLICK HERE -- View this message...

Well, one way to do it is xyplot(y~time|id, data=dat,type='l', panel=function(x,y,subscripts,...){ panel.xyplot(x,y,subscripts,...) panel.abline(v=dat[subscripts,"cm1"]) panel.abline(v=dat[subscripts,"cm2"]) } ) Since I don't know what the dataframe 'mergeData' is I have used 'dat'. Best regards Frede Aakmann T??gersen Scientist Danish Institute of Agricultural Sciences Research Centre Foulum Dept. of Genetics and Biotechnology Blichers All?? 20, P.O. BOX 50 DK-8830 T...

Consider this data frame: dat<-data.frame(id=gl(3,5),time=rep(1:5,3),cm1=rep(c(2,3,4),each=5),cm2=rep(c(2.5,3.5,4.5),each=5),y=rnorm(15)) > dat id time cm1 cm2 y 1 1 1 2 2.5 -1.0824549 2 1 2 2 2.5 -0.7784834 3 1 3 2 2.5 -1.7783560 4 1 4 2 2.5 0.5056637 5 1 5 2 2.5 -0.1967505 6 2 1 3 3.5 1.1643136 7 2 2 3 3.5 -1.4323765 8 2...

...D() #library(KernSmooth) #dest0 <- bkde2D(lbt[,wh], bandwidth=.5, range.x=list(c(-16, 8), c(-5,5))) #dest1 <- bkde2D(lbt[,1+wh], bandwidth=.5, range.x=list(c(-16, 8), c(-5,5))) #dest2 <- bkde2D(lbt[,2+wh], bandwidth=.5, range.x=list(c(-16, 8), c(-5,5))) #save(dest0, dest1, dest2, cm0, cm1, cm2, file="bkde2D-boot.RData") con <- url("http://euclid.psych.yorku.ca/SCS/Private/Test/bkde2D-boot.RData") ## print the value to see what objects were saved. print(load(con)) close(con) col <- c("black", rainbow(5, start=.6, end=.1)) clr <- col[c(1,3,6)] con...

...fitted(svm.model) > cm <- table(predict, data.df$Response) > cm #Model2: With 10-fold cross-validation: > svm.model2 <- svm(Response ~ ., data=data.df, type="C-classification", > kernel="linear", cost=1, cross=10) > predict2 <- fitted(svm.model2) > cm2 <- table(predict2, data.df$Response) > cm2 However, when I compare cm and cm2, I notice that the confusion matrices are identical although the accuracy of each model is diffent. What am I doing wrong? Thanks for you help, Chris -- View this message in context: http://r.789695.n4.nabble.c...

...Ltd - Software Systems Design 2, The Business Courtyard, Marl Pits Lane, Trudoxhill, Frome, Somerset, BA11 5DL, UK +44 (0)1373 837900 fax: +44 (0)8700 518155 Registered in England: 2756481 VAT Reg: UK 601 1137 11 Registered Office: NSO Associates LLP, 75 Springfield Road, Chelmsford, Essex CM2 6JB All e-mail is subject to contract and is not intended to create a legally binding agreement. Hawkgrove Ltd will only be bound by an agreement in writing signed by an authorized signatory. All outgoing email is scanned by Kerio, using ClamAV 0.95.1/10094/Mon Nov 30 11:45:20 2009 Known viruses:...

Is that possible to create superscript text on the graph legend, for example to put "cm2" (centimeter square) on the legend. Please show me how to do it. Thanks. -- Agus Susanto [[alternative HTML version deleted]]

...nfiltration in cm per min df <- data.frame(time, cumul) df$cumul.m <- df$cumul/100/60 #convert to meters per second df$time.s <- df$time*60 #convert to seconds b2 <- 1-(0.196/(0.06/0.01131)) #relationship between soil moisture and the size of the ring infiltrometer (6 cm radius by 113.1 cm2 cross sectional area) theta <- 0.196 #difference in residual soil water and field capacity Here is the formula: #Where a = K_fs and b=psi_f nlsfit <- nls(time.s~(theta/a*b2)*((cumul.m/theta)-(((0.16-b)/b2)*log(1+((cumul.m*b2)/(theta*(0.16-b)))))), data = df, start=list (a=1, b=0.5), trace=TR...

...Ltd - Software Systems Design 2, The Business Courtyard, Marl Pits Lane, Trudoxhill, Frome, Somerset, BA11 5DL, UK +44 (0)1373 837900 fax: +44 (0)8700 518155 Registered in England: 2756481 VAT Reg: UK 601 1137 11 Registered Office: NSO Associates LLP, 75 Springfield Road, Chelmsford, Essex CM2 6JB All e-mail is subject to contract and is not intended to create a legally binding agreement. Hawkgrove Ltd will only be bound by an agreement in writing signed by an authorized signatory. All outgoing email is scanned by Kerio, using ClamAV 0.94.2/9865/Sat Oct 3 03:45:19 2009 Known viruses: 6...

...Ltd - Software Systems Design 2, The Business Courtyard, Marl Pits Lane, Trudoxhill, Frome, Somerset, BA11 5DL, UK +44 (0)1373 837900 fax: +44 (0)8700 518155 Registered in England: 2756481 VAT Reg: UK 601 1137 11 Registered Office: NSO Associates LLP, 75 Springfield Road, Chelmsford, Essex CM2 6JB All e-mail is subject to contract and is not intended to create a legally binding agreement. Hawkgrove Ltd will only be bound by an agreement in writing signed by an authorized signatory. All outgoing email is scanned by Kerio, using ClamAV Antivirus. -------------- next part -------------- An...

...Ltd - Software Systems Design 2, The Business Courtyard, Marl Pits Lane, Trudoxhill, Frome, Somerset, BA11 5DL, UK +44 (0)1373 837900 fax: +44 (0)8700 518155 Registered in England: 2756481 VAT Reg: UK 601 1137 11 Registered Office: NSO Associates LLP, 75 Springfield Road, Chelmsford, Essex CM2 6JB All e-mail is subject to contract and is not intended to create a legally binding agreement. Hawkgrove Ltd will only be bound by an agreement in writing signed by an authorized signatory. All outgoing email is scanned by Kerio, using ClamAV Antivirus. -------------- next part -------------- An...

I'm working with large data frames and running out of memory. I hope some of you may be able to suggest a more efficient approach. I have grid/lattice data representing a time series of 1 m2 quadrats in a grassland: Each 1 cm2 cell or pixel contains one ecological state (ie grass or bare ground). The goal is to calculate, for each cell, the transition probabilities to all available states (given that a cell is occupied by grass, what are the probabilities it will change to grass or bare ground in the next time step?...

Dear all, I'm facing a problem in estimation of glm model with weibull distribution. I run this : eqn0<-formula(fdh~cup1+cup2+cup3+cup4+fin1+vd1+cm2+cm4+milieu+cpro1+cpro2+cpro3a+cpro3b+schef+log(y)) regWeib0<-vglm(eqn0,family=weibull,subset(br, fdh<1)) I have en estimation but there is a message saying that regularity conditions are violated : Message d'avis : In eval(expr, envir, enclos) : ? MLE regularity conditions are violated(...

Hi, I'm trying to figure out how anova works in R by translating the examples in Sokal And Rohlf's (1995 3rd edition) Biometry. I've hit a snag with planned comparisons, their box 9.4 and section 9.6. It's a basic anova design: treatment <- factor(rep(c("control", "glucose", "fructose", "gluc+fruct",

...c/hosts # Do not remove the following line, or various programs # that require network functionality will fail. 127.0.0.1 localhost.localdomain localhost ::1 localhost6.localdomain6 localhost6 172.17.2.1 gateway gateway.inexs.com 172.17.2.50 cm cm.inexs.com #172.17.2.52 cm2 cm2.inexs.com 172.17.2.51 cs1 cs1.inexs.com #172.17.2.53 cs2 cs2.inexs.com 172.17.2.137 ingrid ingrid.inexs.com 172.17.2.161 vail vail.inexs.com 172.17.2.162 beaver beaver.inexs.com 172.17.2.175 plotting plotting.inexs.com 172.17.2.242 watchdog watc...

Hello, R-i-zens. I'm working on an data set with a factorial ANOVA that has a significant interaction. I'm interested in seeing whether the simple effects are different from 0, and I'm pondering how to do this. So, I have my.anova<-lm(response ~ trtA*trtB) The output for which gives me a number of coefficients and whether they are different from 0. However, I want the

Some of you may remember back in May the thread on using Asterisk as a voicemail server for a Cisco Callmanager system. My own Callmanager system is integrated into an Asterisk server for voicemail (and other things). Back in May I was using H323 for integration, but since I've upgraded to CCM 4.1 I have switched over to SIP. The integration with H323 required using Call forwarding to send