similar to: R-squared with and without constant

Hello, I'm trying to create a vector of r^2 values for using a function which I will run in a "for" loop. Example: per<-rnorm(100,.5,.2)^2 x<-rnorm(100,10,5) y<-rnorm(100,20,5) fr<-data.frame(x,y,per) test<-rep(0,9) plotter<-function(i){ temp.i<-fr[fr$per <=(i*.10),] with(temp.i, plot(x, y, main=(i*.10),)) mod<-lm(y~x-1,data=temp.i)

Dear R People: I have the following data: > ail.df[,1] [1] 47677 47602 47678 47905 47909 47906 47605 47673 47607 > cut(ail.df[,1],breaks=3) [1] (4.76e+04,4.77e+04] (4.76e+04,4.77e+04] (4.76e+04,4.77e+04] [4] (4.78e+04,4.79e+04] (4.78e+04,4.79e+04] (4.78e+04,4.79e+04] [7] (4.76e+04,4.77e+04] (4.76e+04,4.77e+04] (4.76e+04,4.77e+04] Levels: (4.76e+04,4.77e+04] (4.77e+04,4.78e+04]

Dear all - I do fret this to be a revealing beginner question - fortunately, this mailing list have been good to me in the paste .-) I am looking for a good R editor/environment in ubuntu. To that end, I have decided to dive into gvim as the modality offered here seems to make sense for editing. I want to use Johannes Ranke's vim r-plugin, but I can't make it work. I have the plugin

I've found a problem when using categorical variables in lmp() from package lmPerm According to help(lmp): "This function will behave identically to lm() if the following parameters are set: perm="", seq=TRUE, center=FALSE.") But not in the case of including categorical variables: require(lmPerm) set.seed(42) testx1 <- rnorm(100,10,5) testx2 <-

Dear R help, I have some readings in three dimensions (x, y, z) and an amplitude for each. I'd like to visualize the data using ggplot, using tile plots, as I have some additional point data I would like to eventually overlay on the tile plots. I would like to subset the data by sections, slices if you will, in the z dimension, and plot the data for that slice. I can do all of this, but am

Hi, I have some doubt about how qualitative factors are coded in R. For instance, I consider a response y, a quantitative factor x and a qualitative factor m at 3 levels, generated as follow : y_c(6,4,2.3,5,3.5,4,1.,8.5,4.3,5.6,2.3,4.1,2.5,8.4,7.4) x_c(3,1,3,1,2,1,4,5,1,3,4,2,5,4,3) m_gl(3,5) lm(y~x+m) Coefficients: (Intercept) x m2 m3 3.96364 0.09818

Hello All, i've a set of observations that is in the form : a, b, c, d, e, f 67.12, 4.28, 1.7825, 30, 3, 16001 67.12, 4.28, 1.7825, 30, 3, 16001 66.57, 4.28, 1.355, 30, 3, 16001 66.2, 4.28, 1.3459, 13, 3, 16001 66.2, 4.28, 1.3459, 13, 3, 16001 66.2, 4.28, 1.3459, 13, 3, 16001 66.2,

Hi everyones, my function like; e <- rnorm(n=50, mean=0, sd=sqrt(0.5625)) x0 <- c(rep(1,50)) x1 <- rnorm(n=50,mean=2,sd=1) x2 <- rnorm(n=50,mean=2,sd=1) x3 <- rnorm(n=50,mean=2,sd=1) x4 <- rnorm(n=50,mean=2,sd=1) y <- 1+ 2*x1+4*x2+3*x3+2*x4+e x2[1] = 10 #influential observarion y[1] = 10 #influential observarion data.x <- matrix(c(x0,x1,x2,x3,x4),ncol=5) data.y

Hi, When I use Anova(car) to produce type III SS, 'Sum Sq' is reported in integers: > Anova(bot.lm3, type ="III") Anova Table (Type III tests) Response: bottemp Sum Sq Df F value Pr(>F) (Intercept) 45295 1 29436.4440 < 2e-16 fungroup 3 2 0.8259 0.44006 numsp.fun 11 2

I have fitted a number of models with receipt of social assictance (toim1) during a year (values 0 or 1) with a number of covariates. The data include sampling weights which I use in the models. Using the exact same data, glm() under 1.0.1 and 1.1.0 give different results in many (but not all) of the models. I have re-installed 1.0.1 to check this and I found now mention in the NEWS file that

THANKS so very much for your help (previous and future!). I have a two follow-up questions. 1) You say that dispersion = 1 by definition ....dispersion changes from 1 to 13.5 when I go from binomial to quasibinomial....does this suggest that I should use the binomial? i.e., is the dispersion factor more important that the 2) Is there a cutoff for too much overdispersion - mine seems to be

----------------------------------------------------------------------------------------------------- Howdy, Gurus I am appying R package for regression analysis as followings. A dependent variable is jhnet that means ratio of dividing internal trip with all trips in a traffic zone. There are many indepentent variables including factor or dummy varibles such as parkfee, ohouse, Devt2, corridor1.

Hi, I'm trying to do a filled.contour plot where some points are labelled as NA. How do I could plot this kind of graphics, so NA points are coloured black, keeping the levels of remaining points. NA's values represent land points (meaningless), and what I want to plot is the levels of a variable over the sea. x<-seq(length=21, from=-10, by=.25) y<-seq(length=8, from=36,

Hi, I' am interested in differences between sample's result when samples consist of full elements and consist of only distinct elements. When sample consist of full elements it take about 120 sec., but when consist of only distinct elements it take about 4.5 or 5 times more sec. I expected that opposite of this result, because unique(sample) has less elements than full sample. Code as

Hi all, Please excuse the long post, but I wanted to make sure you have all the information.... I have a NewsModel that looks like this: class News < ActiveRecord::Base belongs_to :user validates_associated :user validates_length_of :title, :description, :minimum => 5, :allow_nil => false acts_as_commentable acts_as_taggable def self.find_recent News.find(:all,

Hi, There is a question that I am confused. I have a set of data like this: hsa-miR-205--GATA3 0.797882767 1.08E-13 hsa-miR-205--ITGB4 0.750217593 1.85E-11 hsa-miR-187--PGF 0.797604155 3.24E-11 hsa-miR-205--SERPINB5 0.744124886 3.28E-11 hsa-miR-205--PBX1 0.734487224 7.89E-11 hsa-miR-205--MCC 0.72499934 1.80E-10 hsa-miR-205--WNT5B 0.717705259 3.33E-10 hsa-miR-200c--PKN2 0.721746815

Hi all, I have been trying to reproduce an analysis from Douglas Montgomery?s book on design and analysis of experiments. Table 6.10 of example 6.2 on page 246, gives a table as follows: > NPK <- expand.grid(A=mp,B=mp,C=mp,D=mp) > Rate <- c(45,71,48,65,68,60,80,65,43,100,45,104,75,86,70,96) > filtration <- cbind(NPK,Rate) > filtration A B C D Rate 1 - - - - 45 2

Dear users, Is anyone out there on a Saturday to answer this easy question? I have the "yo" object data in a "list" format: > str(yo) num [1:259, 1:173] 16.3 NA NA NA NA ... - attr(*, "dimnames")=List of 2 ..$ x: chr [1:259] "367319" "367329" "367309" "367339" ... ..$ y: chr [1:173] "780175"