search for: 100,2,1

...ems to be attached to the wrong thread. Also, I extended my test a little and it seems to indicate the problem is with spm. I would appreciate any help. Thanks. ========================================================== library(plyr) library(SemiPar) data <- data.frame(id=c(rep("111",100),rep("222",200)), value=c(rnorm(100,2,1),rnorm(200,10,5)), lhs=c(rnorm(100,2,1),rnorm(200,10,5))) #this works d_ply(data, c("id"), function(x) { print(lm("lhs~value", data=x)) }) #this works data111 <- data[data$id=="111",] print(spm(data111$value ~...

Hi, I have created two separate overlapping density plots- see example code below. What I wish now to do is combine them into one figure where they sit side by side. Any help would be great! many thanks in advance, josh. ##################### thedataA <- data.frame(x1=rnorm(100,1,1),x2=rnorm(100,3,1)) #create data thedataA.m<-melt(thedataA) densityplot(~value, thedataA.m, groups=variable,auto.key=list(columns=2), panel = function(x, y, ...) { panel.densityplot(x, ...) panel.abline(v=0) } ) thedataB <- data.frame(x1=rnorm...

...tion that does the same thing, without having to specify that x2 should be held at its mean value? It works fine for this simple example, but might be cumbersome if the model was more complex (e.g., lots of x variables, and/or interactions). Many thanks, Mark #make some random data x1<-rnorm(100) x2<-rnorm(100,2,1) y<-0.75*x1+0.35*x2 #fit a model model1<-lm(y~x1+x2) #predict the effect of x1 on y, while controlling for x2 xv1<-seq(min(x1),max(x1),0.1) yhat_x1<-predict(model1,list(x1=xv1,x2=rep(mean(x2),length(xv1))),type="response") #plot the predicted values pl...

Full_Name: Richard Huggins Version: 1.7.1 OS: windows 2000 Submission from: (NULL) (131.172.4.44) > x<-rnorm(100,2,1) > mean(x) [1] 1.73299 > summary(fivenum(x)) Min. 1st Qu. Median Mean 3rd Qu. Max. -0.3655 1.1070 1.7430 1.7320 2.3840 3.7910 > summary(x) Min. 1st Qu. Median Mean 3rd Qu. Max. -0.3655 1.1070 1.7430 1.7330 2.3830 3.7910 > y<-log(abs(x)) > summ...

...s the density function but the actual code suggests that the hist.constructor function (which does not seem to be easily accessible). Any suggestion for computing the density values of foo$x in the following example will be welcome. require(lattice) set.seed(12345) foo1 <- data.frame(x=rnorm(100,0,0.1),grp=1,by=rep(1:2,each=50),by2=rep(1:2,times=50)) foo2 <- data.frame(x=rnorm(100,2,1),grp=2,by=rep(1:2,each=50),by2=rep(1:2,times=50)) foo <- rbind(foo1,foo2) xplot <- histogram(~x,data=foo, type='density') PS: the present question relates to a workaround for another probl...

Dear R'ers, I am fighting with a problem that is driving me crazy. I use "lm" in my user-defined function, but it seems to be looking for weights outside of my function's environment: ### Generating example data: x<-data.frame(y=rnorm(100,0,1),a=rnorm(100,1,1),b=rnorm(100,2,1)) myweights<-runif(100) data.for.regression<-x[1:3] ### Creating function "weighted.reg": weighted.reg=function(formula, MyData, filename,WeightsVector) { print(dim(MyData)) print(filename) print(length(WeightsVector)) regr.f<-lm(formula...

...tempts. Is this the way to go, or should I calculate the range outside the histogram call using some stat function (density?) on each group and use it as a ylim argument? Any advise would be greatly appreciated. Thank you Sebastien require(lattice) set.seed(12345) foo1 <- data.frame(x=rnorm(100,0,0.1),grp=1) foo2 <- data.frame(x=rnorm(100,2,1),grp=2) foo <- rbind(foo1,foo2) # # Some code to calculate ylim... # histogram(~x,data=foo,groups=grp, #prepanel=function(x,type,groups,...){???}, panel=function(x,type,groups,...){ panel.histogram(x,col='transparent',...)...

Please guide me through to resolve the error message that I get this is what i have done. >x1<- rnorm(100,2,1) >x1fitbeta<-fitdistr(x1,"beta") Error in fitdistr(x1, "beta") : 'start' must be a named list Yes, I do understand that sometime for the distribution to converge to the given set of data, it requires initial parameters of the distribution, to start off with. H...