search for: ycenter

...mary(fitNormalReg) > summary(fitZeroInt) > # There is a statistically significant difference # between the models - > the model with and intercetpt, # the "normal" regression is the better > fit. > anova(fit1,fit2) > > # Center y and x by subtracting their means. > yCentered<-y-mean(y) > xCentered<-x-mean(x) > # Regress the centered y values on the centered x values. This # will > give us a model with an intercept that is very, very # small. It would > be zero save for the precision limits # inherent in using a computer. > Plot the line. Notice t...

Hello, I wanted to create a histogram, but somehow I got stuck... The interval limits are: x = 1, 2, 3, 3.5, 4.5, 5, 5.5 The interval widths are therefore: 1, 1, 0.5, 1, 0.5, 0.5 Nothing I tried worked... Can anyone help me please? Thanks Tobias -- View this message in context: http://www.nabble.com/Histogram-tf4315900.html#a12288850 Sent from the R help mailing list archive at Nabble.com.

Hello, I plotted a nice tree with "ctree" . It shows 3 nodes with the prediction of my 2 groups. (see picture) Unfortunately I need a larger scale to read the exact prediction of my groups to get the specificity and sensitivity. I tried to change the scale with "axis" but it didn't work, my guess because it's not a normal graph with x and y axis. Has someone an idea

...[2,] -0.293414 -0.176453 -0.015322 -0.0111357 -0.072555 [3,] 0.179000 0.048471 -0.103974 0.3313531 -0.049797 [4,] -0.126606 -0.088371 0.214449 -0.2998246 0.063524 [5,] 0.133073 0.011817 -0.073828 -0.0278944 -0.081489 $xcenter [1] 1.9984e-16 2.2177e-15 -7.5495e-16 -2.6312e-15 1.5543e-16 $ycenter [1] -5.5511e-17 1.4683e-15 -3.1086e-16 -1.9984e-16 -3.5527e-16 So in this example, I took the second principal components each from a bunch of variables, stuck them together in matrices and then performed CCA on it. The results tell me that the correlation for two variables was quite high 0....

I try tu use mob() with my data.frame ('data.frame': 288 obs. of 81 variables; factors, numerics and ordered factors) My response is a binary variable and I should use for modelling a logistic regression (family=binomial). I read in the "MOB" Vignette that I could use a formula like this if I would like to have only partitioning variables apart from the response.

Dear all, I didn't find any graphical solution in the package "mva" to plot the canonical scores from a CCA (canonical correlation analysis). Does anybody knows how to plot or has anybody already programmed : - the map of the canonical scores, - the graph of the canonical weights, - the correlation circle i.e. the canonical loadings ? Thank you for help ...

R-1.7.0 built on NetBSD 1.6, but the validation test suite failed: Machinetype: Intel Pentium III (600 MHz); NetBSD 1.6 (GENERIC) Remote gcc version: gcc (GCC) 3.2.2 Remote g++ version: g++ (GCC) 3.2.2 Configure environment: CC=gcc CXX=g++ LDFLAGS=-Wl,-rpath,/usr/local/lib make[5]: Entering directory `/local/build/R-1.7.0/src/library' >>> Building/Updating