search for: myfit

...values of parameters a and b of > function myfun which give the best fit for the data set, mydata. > > mydata=data.frame(x=c(0,5,9,13,17,20),y = c(0,11,20,29,38,45)) > > myfun=function(a,b,r,t){ > prd=a*b*(1-exp(-b*r*t)) > return(prd)} > > and using nlsLM > > myfit=nlsLM(y~myfun(a,b,r=2,t=x),data=mydata,start=list(a=2000,b=0.05), > lower = c(1000,0), upper = c(3000,1)) > > It works. But now I would like to introduce a constraint which is a*b<1000. At the moment your coefficients do satisfy that constraint so that dataset is not...

I am using nlsLM {minpack.lm} to find the values of parameters a and b of function myfun which give the best fit for the data set, mydata. mydata=data.frame(x=c(0,5,9,13,17,20),y = c(0,11,20,29,38,45)) myfun=function(a,b,r,t){ prd=a*b*(1-exp(-b*r*t)) return(prd)} and using nlsLM myfit=nlsLM(y~myfun(a,b,r=2,t=x),data=mydata,start=list(a=2000,b=0.05), lower = c(1000,0), upper = c(3000,1)) It works. But now I would like to introduce a constraint which is a*b<1000. I had a look at the option available in nlsLM to set constraint via nls.lm.control. But it's...

...; function myfun which give the best fit for the data set, mydata. >> >> mydata=data.frame(x=c(0,5,9,13,17,20),y = c(0,11,20,29,38,45)) >> >> myfun=function(a,b,r,t){ >> prd=a*b*(1-exp(-b*r*t)) >> return(prd)} >> >> and using nlsLM >> >> myfit=nlsLM(y~myfun(a,b,r=2,t=x),data=mydata,start=list(a=2000,b=0.05), >> lower = c(1000,0), upper = c(3000,1)) >> >> It works. But now I would like to introduce a constraint which is a*b<1000. > > At the moment your coefficients do satisfy that constraint so...

...ivative if it can, else squawks. JN # Here's the script # # # Manoranjan Muthusamy <ranjanmano167 at gmail.com> # library(minpack.lm) mydata=data.frame(x=c(0,5,9,13,17,20),y = c(0,11,20,29,38,45)) myfun=function(a,b,r,t){ prd=a*b*(1-exp(-b*r*t)) return(prd)} # and using nlsLM myfit=nlsLM(y~myfun(a,b,r=2,t=x),data=mydata,start=list(a=2000,b=0.05), lower = c(1000,0), upper = c(3000,1)) summary(myfit) library(nlsr) r <- 2 myfitj=nlxb(y~a*b*(1-exp(-b*r*x)),data=mydata,start=list(a=2000,b=0.05), trace=TRUE) summary(myfitj) print(myfitj) myfitj2<-nlxb(y~ab...

...x <- c(rnorm(5000, 3, 5), rnorm(2000, -5, 7)) # Two normal distributions ... # Histogram h <- hist(x, freq = FALSE, breaks = seq(min(x)-2, max(x)+2, by = 1), col = "#cfd7fa", main = "", ylab = "density", xlab = "x") # Fit myFit <- fitNorm(h$mids, h$density) # Now: what I can do is, patch together the model function ... mF <- function(x, a = coef(myFit)["a"], mu = coef(myFit)["mu"], sig = coef(myFit)["sig"]){ a / (sig*sqrt(2*pi)) * ex...

...jdtass <- aggregate( ( objdta$myfun - objdta$y )^2 , objdta[ , c( "a", "b" ) ] , FUN = function( x ) sum( x, na.rm=TRUE ) ) objdtassmin <- objdtass[ which.min( objdtass$x ), ] myfit <- nlsLM( y ~ myfun( a, b, r=2, t=x ) , data = mydata , start = list( a = 2000 , b = 0.05 ) , lower = c( 1000, 0 ) , upper = c( 3000, 1 ) ) a <- as.vector( coef...

Hi, I have problem getting the standard deviation from the manova output. I have used the manova function: myfit <- manova(cbind(y1, y2) ~ x1 + x2 + x3, data=mydata) . I tried to get the predicted values and their standard deviation by using: predict(myfit, type="response", se.fit=TRUE) But the problem is that I don't get the standard deviation values, I only get the predicted value...

...fun - objdta$y )^2 > , objdta[ , c( "a", "b" ) ] > , FUN = function( x ) > sum( x, na.rm=TRUE ) > ) > objdtassmin <- objdtass[ which.min( objdtass$x ), ] > > myfit <- nlsLM( y ~ myfun( a, b, r=2, t=x ) > , data = mydata > , start = list( a = 2000 > , b = 0.05 > ) > , lower = c( 1000, 0 ) > , upper = c( 3000, 1 ) >...

...5, 7)) # Two normal distributions ... > > # Histogram > h <- hist(x, freq = FALSE, > breaks = seq(min(x)-2, max(x)+2, by = 1), > col = "#cfd7fa", > main = "", ylab = "density", xlab = "x") > # Fit > myFit <- fitNorm(h$mids, h$density) > > # Now: what I can do is, patch together the model function ... > mF <- function(x, > a = coef(myFit)["a"], > mu = coef(myFit)["mu"], > sig = coef(myFit)["sig&quo...

Hi all, Can anyone who is familar with CART tell me what I missed in my tree code? library (MASS) myfit <- tree (y ~ x1 + x2 + x3 + x4 ) # tree.screens () # useless plot(myfit); text (myfit, all= TRUE, cex=0.5, pretty=0) # tile.tree (myfit, fgl$type) # useless # close.screen (all= TRUE) # useless My current tree plot resulted from above code shows as: 1. overlapped #s cau...

Dear List, I think I'm going crazy here...can anyone explain why do I get the same predictions in train and test data sets below when the second has a missing input? y <- rnorm(1000) x1 <- rnorm(1000) x2 <- rnorm(1000) train <- data.frame(y,x1,x2) test <- data.frame(x1) myfit <- glm(y ~ x1 + x2, data=train) summary(myfit) all(predict(myfit, test) == predict(myfit, train)) [1] TRUE Thanks, Axel. [[alternative HTML version deleted]]

...on this e-mail list: for (y in df[, 3:5]) { mod = lm(y ~ Trt*Dose, data = x, contrasts = list(Trt = contr.sum, Dose = contr.sum)) Anova(mod, type = "III") } ## by John Fox or for (myname in names(myframe)){ mycall <- substitute(lm(myvar~etc.etc.....),list(myvar=as.name(myname))) myfit <- eval(mycall) print(summary(myfit)) } ## by Peter Dalgaard But instead of printing summary or Anova results, I need to generate a list containing all the lm() objects. Is that possible? How? Thanks, Gabriela.

...A beginners question which I could not find the answer to in earler posts. My thought process: Here "z" is a 119 x 15 data matrix Step 1: start at column one, bind every column with column 1 Step2: use the new matrix, "test", in the fitCopula package Step3: store each result in myfit, bind each result to "answer" Step4: return "answer" copula_est <- function(z) { for(i in 1:length(z[1,])) { my.cop <- normalCopula(param = 0.5, dim = 2) test <- cbind(z[,1],z[,i]) myfit[i] <- fitCopula(test,my.cop, start=0.3) } answer <- cbind(myfit[i...

Hi, I am trying to estimate the effects of covariates on the hazard function, rather than on the survival. I know this is actually the same thing. For example, using the survival package, and doing: > myfit <- survreg( Surv(time, event) ~ mymodel ) all I have to do to get the quantities of my interest is > -myfit$coefficients/myfit$scale The standard erros are easily worked out, as the absolute z-statistics are the same. Ok, so I can get easily what I want from survreg(). But I'd like to...

...y ~ 1 + x1 + x2") cat("in fit",NROW(train),"\n") model <- do.call(trial at fit$func,trial at fit$args) print(model$call) select <- do.call(trial at step$func,trial at step$args) } mydf <- data.frame(y <- runif(100),x1 <- runif(100),x2 <- runif(100)) myfit <- list(func="wrap.lm",args=alist(formula=null,data=train)) mystep <- list(func="wrap.step",args=alist(object=model,scope=full,trace=0)) setClass("trial",representation(n="numeric",fit="list",step="list")) trial1 <- new("tri...

Hi there, I am trying to export a regression output to Latex. I am using the xtable function in the xtable library. Doing myfit <- lm(myformula, mydata) print.xtable(xtable(myfit), file="myfile") only returns the estimated coefficients and the correspondent standard erros, t-statiscs and p-values. But I wish to get a bit more, say, the number of observations used in the regresion, the R^2 and F statistics. A...

...l isn't clear to me how to do this in the context of lattice. Guidance would be greatly appreciated. Thanks, Mike Babyak Duke University Medical Center My code using R 2.10.1/windows XP myx<-c(1,2,3,4) myy<-c(1,2,3,5) library(rms) d<-datadist(myx) options(datadist="d") myfit<-ols(myy~myx,x=T,y=T) p1<-Predict(myfit,myx =.) library(lattice) #change line to black line <- trellis.par.get("plot.line") line$col <- 1 trellis.par.set("plot.line", line) plot(p1, data=llist(myx),col.fill="lightgray", lwd=1.5) [[alternative HTML ver...

...ough a set of data. I have created a dataframe (df) of Stimulus Intensities (xstim) and Normalized Responses (yresp), and I've used nls() to calculate a nonlinear regression, like so: -------------------- > f <- yresp ~ xstim^n / (xstim^n + B^n) > starts <- list(n=.6, B=11) > myfit <- nls(formula=f, data=df, start = starts) >myfit Nonlinear regression model model: yresp ~ xstim^n/(xstim^n + B^n) data: df n B 0.8476233 5.7943791 residual sum-of-squares: 0.03913122 > ----------- Which seems great, but I'd like to be able to plot this curv...

Hi all. If I run the simple regression when x is a categorical variable ( x <- factor(x) ): > MyFit <-coxph( Surv(start, stop, event) ~ x ) How can I get the overall p-value on x other than for each dummy variable? > anova(MyFit) does NOT provide that information as previously suggested on the list. All the best, Kare [[alternative HTML version deleted]]

A question on lme() : details: nlme() in R 2.1.0 beta or 2.0.1 The data,y, consisted of 82 data value in 5 groups of sizes 3 9 8 28 34 . I fit a simple one level random effects model by: myfit <- lme( y~1, rand = ~1|Group) The REML estimates of between and within Group effects are .0032 and .53, respectively; the between group component is essentially zero as is clearly evident from a plot of the data. So, thus far, no problem. However, the confidence interval for the between Groups...