search for: fullfit

....81, 17.91, 17.85, 17.70, 17.67, 17.45, 17.58, 16.99, 17.10) t=c(7, 37, 58, 79, 96, 110, 114, 127, 146, 156, 161, 169, 176, 182, 190, 197, 209, 218, 232, 240) I use the method to fit a curve, but it is different from the real curve, which can be seen in the figure. linFit <- lm(y ~ cos(t)) fullFit <- nls(y ~ A*cos(omega*t+C) + B, start=list(A=coef(linFit)[1],B=coef(linFit)[2],C=0,omega=.4)) #omega cannot be set to 1, don't know why. co <- coef(fullFit) fit <- function(x, a, b, c, d) {a*cos(b*x+c)+d} plot(x=t, y=y) curve(fit(x, a=co['A'], b=co['omega'], c=co['...

..., 17.10) t=c(7, 37, 58, 79, 96, 110, 114, 127, 146, 156, 161, 169, 176, 182, 190, 197, 209, 218, 232, 240) lidata <- data.frame(y=y, t=t) #I use the method to fit a curve, but it is different from the real curve, #which can be seen in the figure. linFit <- lm(y ~ cos(t)) library(nlsr) #fullFit <- nls(y ~ A*cos(omega*t+C) + B, #start=list(A=coef(linFit)[1],B=coef(linFit)[2],C=0,omega=.4)) #omega cannot be set to 1, don't know why. fullFit <- nlxb(y ~ A*cos(omega*t+C) + B, data=lidata, start=list(A=coef(linFit)[1],B=coef(linFit)[2],C=0,omega=.04), trace=TRUE) co <- coef(full...

...7.45, 17.58, 16.99, 17.10) > t=c(7, 37, 58, 79, 96, 110, 114, 127, 146, 156, 161, 169, 176, 182, > 190, 197, 209, 218, 232, 240) > > I use the method to fit a curve, but it is different from the real curve, > which can be seen in the figure. > linFit <- lm(y ~ cos(t)) > fullFit <- nls(y ~ A*cos(omega*t+C) + B, > start=list(A=coef(linFit)[1],B=coef(linFit)[2],C=0,omega=.4)) #omega cannot > be set to 1, don't know why. > co <- coef(fullFit) > fit <- function(x, a, b, c, d) {a*cos(b*x+c)+d} > plot(x=t, y=y) > curve(fit(x, a=co['A'], b=c...

...t different results of coef. Can someone kindly explain. # lasso using penalized library(penalized) pena.fit2<-penalized(HRLNM,penalized=~CN+NoSus,lambda1=1,model="logistic",standardize=TRUE) pena.fit2 coef(pena.fit2) opt<-optL1(HRLNM,penalized=~CN+NoSus,fold=5) opt$lambda coef(opt$fullfit) prof<-profL1(HRLNM,penalized=~CN+NoSus,fold=opt$fold,steps=20) plot(prof$lambda, prof$cvl, type="l") plotpath(prof$fullfit) pena.fit2<-penalized(HRLNM,penalized=~CN+NoSus,lambda1=opt$lambda,model="logistic",standardize=TRUE,steps=20) plotpath(pena.fit2) pena.fit2<-pena...

If you know the period and want to fit phase and amplitude, this is equivalent to fitting a * sin + b * cos > >>> > I don't know how to set the approximate starting values. I'm not sure what you meant by that, but I suspect it's related to phase and amplitude. > >>> > Besides, does the method work for sine curve as well? sin is the same as cos with

I'm trying the different parameters, but don't know what the error is: Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial parameter estimates Thanks for any suggestions. On Tue, Jun 20, 2017 at 7:37 PM, Don Cohen <don-r-help at isis.cs3-inc.com> wrote: > > If you know the period and want to fit phase and amplitude, this is > equivalent to

...the cpquery in debug mode for such a case (n=10^5, method="lw") creates the following output: generated a grand total of 1e+05 samples. > event has a probability mass of 14982.37 out of NaN (p = NaN). [1] NaN The cpquery command takes the following structure: cpquery(fullFitted,event=(C1_class=="Med"), evidence=list(GK_class = "ModHi", GTh_class = "Lo", GU_class = "Lo", El_class = "Hi", E50_class = "Med",...

...paste("mylme<-lme(fixed= y ~ ns(x, df=", splineDF, ") , random= ~ 1 | ID , correlation = corAR1() , data=longdat)") thecommandstring eval(parse(text=thecommandstring)) } else { stop(paste("WhichApproach=", WhichApproach, " not valid.")) } mylme longdat$fullfit<-predict(mylme) library(ggplot2) print( ggplot( longdat, aes(x,y)) + geom_point(shape=1) + facet_wrap( ~ ID ) + geom_line( aes(x, fullfit), color="red") ) invisible(mylme) } Jacob A. Wegelin Assistant Professor Department of Biostatistics Virginia Commonwealth University 830...

...aste("mylme<-lme(fixed= y ~ ns(x, df=", splineDF, ") , random= ~ 1 | ID , correlation = corAR1() , data=longdat)") thecommandstring eval(parse(text=thecommandstring)) } else { stop(paste("WhichApproach=", WhichApproach, " not valid.")) } mylme longdat$fullfit<-predict(mylme) library(ggplot2) print( ggplot( longdat, aes(x,y)) + geom_point(shape=1) + facet_wrap( ~ ID ) + geom_line( aes(x, fullfit), color="red") ) invisible(mylme) } Jacob A. Wegelin Assistant Professor Department of Biostatistics Virginia Commonwealth University...

...creates the following output: > > > > generated a grand total of 1e+05 samples. > > > event has a probability mass of 14982.37 out of NaN (p = NaN). > > [1] NaN > > > > > > The cpquery command takes the following structure: > > > > cpquery(fullFitted,event=(C1_class=="Med"), > > evidence=list(GK_class = "ModHi", > > GTh_class = "Lo", > > GU_class = "Lo", > > El_class = "Hi", > >...

...6, 182, >>> > 190, 197, 209, 218, 232, 240) >>> > >>> > I use the method to fit a curve, but it is different from the real >>> > curve, >>> > which can be seen in the figure. >>> > linFit <- lm(y ~ cos(t)) >>> > fullFit <- nls(y ~ A*cos(omega*t+C) + B, >>> > start=list(A=coef(linFit)[1],B=coef(linFit)[2],C=0,omega=.4)) #omega >>> > cannot >>> > be set to 1, don't know why. >>> > co <- coef(fullFit) >>> > fit <- function(x, a, b, c, d) {a*cos(...

...)/.5)), y<-18/(1+exp((2-time)/.5)) fixed effect for param a = 10,10, 20,20, random effects= +2, -2, +2, -2 fixed effect for param b=2, fixed effect for c = .5 normal noise with sd=1 was added to all y values. I do: df<-read.table(file="papers/alex/junk.dat",header=TRUE) attach(df) fullfit<-nlsList(y~SSlogis(Time,Asym,xmid,scal)|Subject,data=df) and get the separate fits of the logistic to each subject's data. 4 curves and 4 sets of param values (12 params total for the model). How do I get the fit of the model which is the true one as stated above: Asym for group A (fixed ef...