search for: a2b3

...ld be monotonic increasing but the required conditions are not satisfied # Check values of alpha and beta for this curve m <- FailMonSpline(x, deriv = 1) nx <- length(x) n1 <- nx - 1L dy <- y[-1] - y[-nx] dx <- x[-1] - x[-nx] Sx <- dy/dx alpha <- m[-nx]/Sx beta <- m[-1]/Sx a2b3 <- 2 * alpha + beta - 3 ab23 <- alpha + 2 * beta - 3 ok <- (a2b3 > 0 & ab23 > 0) ok <- ok & (alpha * (a2b3 + ab23) < a2b3^2) # If the curve is monotonic then all ok should be FALSE however this is not the case ok # Alternatively can easily seen to be non-monotonic by...

...ld be monotonic increasing but the required conditions are not satisfied # Check values of alpha and beta for this curve m <- FailMonSpline(x, deriv = 1) nx <- length(x) n1 <- nx - 1L dy <- y[-1] - y[-nx] dx <- x[-1] - x[-nx] Sx <- dy/dx alpha <- m[-nx]/Sx beta <- m[-1]/Sx a2b3 <- 2 * alpha + beta - 3 ab23 <- alpha + 2 * beta - 3 ok <- (a2b3 > 0 & ab23 > 0) ok <- ok & (alpha * (a2b3 + ab23) < a2b3^2) # If the curve is monotonic then all ok should be FALSE however this is not the case ok # Alternatively can easily seen to be non-monotonic by...

...vert my own similar course to R for my students for next fall. I have been successful at analyzing a segment of the data as a 2-way repeated measures design. Here is my code: > your.data=read.table(pipe("pbpaste"),header=T) > your.data partic A1B1 A1B2 A1B3 A1B4 A2B1 A2B2 A2B3 A2B4 A3B1 A3B2 A3B3 A3B4 1 p1 1 1 2 3 1 2 4 7 1 3 7 10 2 p2 2 2 3 3 2 2 5 6 2 4 6 9 3 p3 1 2 2 3 2 3 2 6 1 4 7 9 4 p4 1 1 2 2 1 2 3 6 2 3 8 1...

...els of the within factor. Below I report the list of commands used in R ("fattA" is the beteween factor and "fB" is the within factor): > a1b1<-c(10,9,8,7) > a1b2<-c(7,6,4,5) > a1b3<-c(3,2,3,4) > a2b1<-c(9,9,8,7) > a2b2<-c(8,7,9,7) > a2b3<-c(7,8,8,6) > > M3<-matrix(0,8,4) > M3[,1]<-cbind(a1b1,a2b1) > M3[,2]<-cbind(a1b2,a2b2) > M3[,3]<-cbind(a1b3,a2b3) > M3[,4]<-rep(c(1,2),each=4) > > colnames(M3)<-c("b1","b2","b3","fattA") > > M3<...

Hi all, In preparation for teaching a class next week, I've been reviewing R's standard modelling algebra. I've used it for a long time and have a pretty good intuitive feel for how it works, but would like to understand more of the technical details. The best (online) reference I've found so far is the section in "An Introduction to R"

...* factor expected.....................................^ +a A2A7 BB 0000 mov bx, # ***** factor expected.....................................^ +a A2AD BB 0000 mov bx, # ***** factor expected.....................................^ +a A2B3 BB 0000 mov bx, # ***** factor expected.....................................^ +a A2B9 BB 0000 mov bx, # ***** factor expected.....................................^ +a A2BF BB 0000 mov bx, # ***** factor e...