search for: yfit2

...;- rnorm(x, x, .8) w1 <- rep(1, 10) w2 <- w1; w2[7:10] <- 0.01 * w2[7:10] rr1 <- nls(y ~ a*x + b, data = list(x = x, y = y), start = list(a = 1, b = 0), weights = w1) rr2 <- nls(y ~ a*x + b, data = list(x = x, y = y), start = list(a = 1, b = 0), weights = w2) yfit1 <- fitted(rr1) yfit2 <- fitted(rr2) se.fit1 <- sqrt(apply(rr1$m$gradient(), 1, function(x) sum(vcov(rr1)*outer(x,x)))) luconf1 <- yfit1 + outer(se.fit1, qnorm(c(probex, 1 - probex))) se.fit2 <- sqrt(apply(rr2$m$gradient(), 1, function(x) sum(vcov(rr2)*outer(x,x)))) luconf2 <- yfit2 + outer(se.fit2, qnor...

...;- rnorm(x, x, .8) w1 <- rep(1, 10) w2 <- w1; w2[7:10] <- 0.01 * w2[7:10] rr1 <- nls(y ~ a*x + b, data = list(x = x, y = y), start = list(a = 1, b = 0), weights = w1) rr2 <- nls(y ~ a*x + b, data = list(x = x, y = y), start = list(a = 1, b = 0), weights = w2) yfit1 <- fitted(rr1) yfit2 <- fitted(rr2) se.fit1 <- sqrt(apply(rr1$m$gradient(), 1, function(x) sum(vcov(rr1)*outer(x,x)))) luconf1 <- yfit1 + outer(se.fit1, qnorm(c(probex, 1 - probex))) se.fit2 <- sqrt(apply(rr2$m$gradient(), 1, function(x) sum(vcov(rr2)*outer(x,x)))) luconf2 <- yfit2 + outer(se.fit2, qnor...

...rnorm(x, x, .8) w1 <- rep(1, 10) w2 <- w1; w2[7:10] <- 0.01 * w2[7:10] res1 <- nls(y ~ a*x + b, data = list(x = x, y = y), start = list(a = 1, b = 0), weights = w1) res2 <- nls(y ~ a*x + b, data = list(x = x, y = y), start = list(a = 1, b = 0), weights = w2) yfit1 <- fitted(res1) yfit2 <- fitted(res2) se.fit1 <- sqrt(apply(res1$m$gradient(), 1, function(x) sum(vcov(res1)*outer(x,x)))) luconf1 <- yfit1 + outer(se.fit1, qnorm(c(probex, 1 - probex))) se.fit2 <- sqrt(apply(res2$m$gradient(), 1, function(x) sum(vcov(res2)*outer(x,x)))) luconf2 <- yfit2 + outer(se.fit2,...

...uot;linear") #Computation of the weight vector w = t(svclass$coefs) %*% svclass$SV if (y[1] == -1) { w = -w } #Derivation of predicted class lavels #Using method in documentation yfit = (x %*% t(w) + svclass$rho) > 0 yfit = factor(2*yfit - 1) #Extracting them directly from the model yfit2 = svclass$fitted #Display where predictions differ from each other yfit != yfit2 Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm

Dear all, I found really difficult with the time series questions, please help me with this monthly airline series! I have run the following r code, and there is an error appeared at the end. The data files was enclosed in the email. I'm sorry the errors message appeared in chinese, but it says "plot.xy(xy.coords(x, y), type = type, ...) : errors in argument has more than 3

Dear useRs, I'm trying to simply fill in the area under a curve using RGL. Here' the set up: x <- c(0.75,75.75,150.75,225.75,300.75,375.75,450.75,525.75,600.75,675.75, 0.5,50.5,100.5,150.5,200.5,250.5,300.5,350.5,400.5,450.5, 0.25,25.25,50.25,75.25,100.25,125.25,150.25,175.25,200.25,225.25) y <- c(0.05,4.91,9.78,14.64,19.51,24.38,29.24,34.11,38.97,43.84,

...b<-.27 fn<-function(p) -sum( log(dweibull(rt,p[1],p[2])) ) cat("starting -log like=",fn(c(a,b)),"\n") out<-nlm(fn,p=c(a,b), hessian=TRUE) xfit<-seq(min(rt),max(rt),(max(rt)-min(rt))/100) yfit<-pweibull(xfit,out$estimate[1], out$estimate[2]) lines(xfit,yfit,lty=2) yfit2<-pweibull(xfit,a, b) lines(xfit,yfit2) list(out=out) } I got the starting values a=9, b=.27 from fitting the Weibull CDF by eye to a quantile plot of the data. The final values fitted by nlm() are a= 4.8299357, b= 0.2753897 I plotted both CDFs against the quantile plot of the data. I would hav...

Hello, I'm new to this and am trying to teach myself some R by plotting biological data. The growth curve in question is supposed to be fitted to the Verhulst equation, which may be transcribed as follows: f(x)=a/(1+((a-0.008)/0.008)*exp(-(b*x))) - for a known population density (0.008) at t(0). I am trying to rework the example from "An Introduction to R" (p. 72) for my case and