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...ry(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 the # slope of the centered is t...

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

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.

Dear r-help list, I'm trying to use multiple imputation for my MSc thesis. Having good exemples using the Hmisc package, I tried the aregImpute function. But with my own dataset, I have the following error : Erreur dans matxv(X, xcof) : columns in a (51) must be <= length of b (50) De plus : Warning message: In f$xcoef[, 1] * f$xcenter :   la taille d'un objet plus long n'est pas

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.

Hi, from what I understand about the canonical correlation function 'cancor', it looks for correlations in two sets of variables, each represented in matrix form. Right? Sounds exactly like what I need. I have tried the following but I am not sure how to interpret the results. AudioPCs <- c(ArTHarF0PCA$x[,2], ArTHarF1PCA$x[,2], ArTHarF2PCA$x[,2], ArTHarF3PCA$x[,2],

Dear colleagues, following John Fox' advice in this article (http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-cox-regression.pdf), I'm trying to create a new data frame to examine the differential survival curves from a combination of covariates. These are derived from a Cox Proportional Hazards model I fit to data about the diffusion of a particular policy across American

How do I calculate the confidence interval for the value x given by the intersection of two quadratics (i.e. parabolas)? I fit two quadratics of the form: y = C1 + B1*x + A1*x^2 y = C2 + B2*x + A2*x^2 to two sets of points N1 and N2. I test for whether they intersect, if they do then I calculate the roots of: 0 = (C1 - C2) + (B1 - B2)*x + (A1 - A2)*x^2 to determine where they intersect

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 ...

Dear list, I'd like to use multiple imputations to try and save a somewhat badly mangled dataset (lousy data collection, worse than lousy monitoring, you know that drill... especially when I am consulted for the first time about one year *after* data collection). My dataset has 231 observations of 53 variables, of which only a very few has no missing data. Most variables have 5-10% of

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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