Displaying 8 results from an estimated 8 matches for "fitc".
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2007 Jul 26
2
logistic regression
...#39;t accept assignment of "fi1c==j" and it won't calculate the sum.
I am wondering whether someone might be able to offer me some assistance...my search of the archives was not fruitful.
Here is the code that I adapted from the lecture notes:
fit <- fitted(glm.lyme)
fitc <- cut(fit, br = c(0, quantile(fit, p = seq(.1, .9, .1)),1))
t<-table(fitc)
fitc <- cut(fit, br = c(0, quantile(fit, p = seq(.1, .9, .1)), 1), labels = F)
t<-table(fitc)
#Calculate observed and expected values in ea group
E <- matrix(0, nrow=10, ncol = 2)
O <- matrix(0,...
2009 Sep 07
1
Omnibus test for main effects in the face of an interaction containing the main effects.
...lieve I can get the omnibus test for the interaction by running the model:
fitb<-lme(Post~Time+factor(Group), random=~1|SS,data=blah$alldata)
followed by
anova(fita,fitb).
How do I get the omnibus test for the main effects i.e. for Time and factor(Group)? I could drop each from the model, i.e.
fitc<-lme(Post~ factor(Group)+factor(Group)*Time, random=~1|SS,data=blah$alldata)
fitd<-lme(Post~Time+ factor(Group)*Time, random=~1|SS,data=blah$alldata)
and then run
anova(fita,fitc)
anova(fita,fitd)
but I don't like this option as it will have in interactio...
2011 Oct 01
1
Problem with logarithmic nonlinear model using nls() from the `stats' package
Example:
> f <- function(x) { 1 + 2 * log(1 + 3 * x) + rnorm(1, sd = 0.5) }
> y <- f(x <- c(1 : 10)); y
[1] 4.503841 5.623073 6.336423 6.861151 7.276430 7.620131 7.913338 8.169004
[9] 8.395662 8.599227
> nls(x ~ a + b * log(1 + c * x), start = list(a = 1, b = 2, c = 3), trace = TRUE)
37.22954 : 1 2 3
Error in numericDeriv(form[[3L]], names(ind), env) :
Missing value or an
2003 Dec 16
0
Help w/ termplot & predict.coxph/ns
...lot(model.fit)
lines(newdata[,2], newpred)
Interestingly enough, predict(model.fit) does give back the correct
values for the actual data set used in the fitting:
max(predict(model.fit)-model.fit$linear.predictors)=0. Am I missing
something here?
3. Using the fitted coeficients:
# Coefficients
fitc<-coef(model.fit)
# Predictors
basis <- ns(x2, df = 3) ; # df= 3 were used to fit the model
newx2<- seq(0,30,length=60)
# new data in the coords of the basis and x1=1 for all obs
newdata2<-cbind(rep(1,60),predict(basis, newx2))
newpred<-newdata2%*%fitc
termplot(model.fit, ylim=c(0...
2009 Sep 08
3
Omnibus test for main effects in the face ofaninteraction containing the main effects.
...lieve I can get the omnibus test for the interaction by
running the model:
fitb<-lme(Post~Time+factor(Group), random=~1|SS,data=blah$alldata)
followed
by anova(fita,fitb).
How do I get the omnibus test for the main effects i.e. for Time and
factor(Group)? I could drop each from the model, i.e.
fitc<-lme(Post~ factor(Group)+factor(Group)*Time,
random=~1|SS,data=blah$alldata)
fitd<-lme(Post~Time+ factor(Group)*Time,
random=~1|SS,data=blah$alldata)
and then run
anova(fita,fitc)
anova(fita,fitd)
but I don't like this option as it will have in interaction...
2004 Dec 13
1
AIC, glm, lognormal distribution
I'm attempting to do model selection with AIC, using a glm and a lognormal
distribution, but:
fit1<-glm(BA~Year,data=pdat.sp1.65.04, family=gaussian(link="log"))
## gives the same result as either of the following:
fit1<-glm(BA~Year,data=pdat.sp1.65.04, family=gaussian)
fit1<-lm(BA~Year,data=pdat.sp1.65.04)
fit1
#Coefficients:
#(Intercept) Year2004
# -1.6341
2013 Mar 14
1
error: object of type 'closure' is not subsettable
...inge-hoch
#5: Ringe-niedrig
#6: Ringe-mittel
#7: Fitness-hoch
#8: Fitness-niedrig
# dye
#A: Hoechst42
#B: Hoechst80
#C: Vio
#D: Vibrant Orange gating 1
#E: Vibrant Orange gating 2
#F: NID APC
#G: NID PerCP
#H: DraQ
#I: Syber Green 1000
#J: Syber Green 2000
#K: Syber Green 5000
#L: Acridin Orange FITC
#M: Acridin Orange PerCP
#Standart Hoch & Hoechst42, Hoechst80, Vio, NID APC, NID PerCP, Syber Green
5000, Acridin Orange FITC, Acridin Orange PerCP
aa=mytable[mytable[,"con"]==1& mytable[,"dye"]=="A","differenz"]
ab=mytable[mytable[,"c...
2009 Mar 15
0
Axes crossing at origin
...tion.png",width=18,height=18,units="cm",res=600,pointsize=16)
newdata=data.frame(x=seq(0,0.6*max(fit$df$x),length=200))
newy=predict(fit,newdata)
plot.new()
plot.window(xlim=c(0,0.6*max(fit$df$x)),ylim=c(15.7,17.5))
lines(newdata$x, newy)
axis(1)
axis(2)
abline(v=0, h=0)
mtext("FITC-insulin [mol]",1,2.4)
mtext("log intensity [log(LAU)]",2,2.4)
myydata=c(16.35,16.5,16.65,16.95,17.1,17.25)
source("predict_amount.r")
myxdata=predict_amount(fit,myydata,uselog=TRUE)
for(i in 1:length(myydata)){
lines(c(myxdata[i],myxdata[i],max(newdata)),c(min(newy),myydat...