similar to: glm fit

Hi, I have fittet a gamma model, and is wondering if I can read the shape and the scale direct from the summary Estimate Std. Error t value Pr(>|t|) (Intercept) 1.612e+00 4.735e-02 34.052 <2e-16 *** myvalue 3.564e-02 2.787e-03 12.788 <2e-16 *** ... Is the shape = 1.1612e+00 and the scale = 3.564e-02 ?? is

Hi I am now doing a principal component analysis for a data set. This dataset inludes above 100 variables so the output matrix of the analysis is also big. Because the output matrix is so huge it is a problem to use the browser of R to look at the analysis. Therefor I want to export the solution of the principal component analysis to another tool which better fittet for viewing big

Dear all, Is there any method in R to find all possible nonnegative integer solutions to the linear equation with unit coefficients as follow: X1+X2+...+Xk=N Thank you, Amin Zollanvari

I have a table like the following. I want to fit Cm to Vm like this: Cm ~ Cl+Q1*b1*38.67*exp(-b1*(Vm-Vp1)*0.03867)/(1+exp(-b1*(Vm-Vp1)*0.03867))^2+Q2*b2*38.67*exp(-b2*(Vm-Vp2)*0.03867)/(1+exp(-b2*(Vm-Vp2)*0.03867))^2 I use nls, with start=list(Q1=2e-3, b1=1, Vp1=-25, Q2=3e-3, b2=1, Vp2=200). But I always get 'singlular gradient' error like this. But in SigmaPlot I can get the result. How

I am trying to reorder a factor data type so that when I plot stats associated with the factor, the ordering makes sense. For instance, if I have a factor entered as follows ... A = as.factor(c("1", "10", "3", "3", "10", "10")) levels(A) ... the ordering does not really make sense (assuming I want the factor ordered by integer

The warning gives some suggestions. E.g., replace funs(sum,prod) with list(sum=sum,prod=prod). % R CMD Rscript -e 'library(dplyr,warn.conflicts=FALSE); data.frame(X=1:3,Y=c(11,13,17)) %>% summarize_all(funs(sum,prod))' X_sum Y_sum X_prod Y_prod 1 6 41 6 2431 Warning message: `funs()` is deprecated as of dplyr 0.8.0. Please use a list of either functions or lambdas:

r-help Forum I'm using the dplyr:: summarize_all(funs(sum)) function and am receiving a warning message that the `funs()` is deprecated as of dplyr 0.8.0. Ok what should I be using to summarize all columns by sum? Jeff [[alternative HTML version deleted]]

Hi all, I try to test a linear slope using offset. I have: > m2 <- glm(Y~X*V) > summary(m2) Call: glm(formula = Y ~ X * V) Deviance Residuals: Min 1Q Median 3Q Max -2.01688 -0.56028 0.05224 0.53213 3.60216 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.3673 0.8476 1.613 0.119788 X

Hi, I want to test for a contrast from a regression where I am regressing the columns of a matrix. In short, the following. X <- matrix(rnorm(50),10,5) Y <- matrix(rnorm(50),10,5) lm(Y~X) Call: lm(formula = Y ~ X) Coefficients: [,1] [,2] [,3] [,4] [,5] (Intercept) 0.3350 -0.1989 -0.1932 0.7528 0.0727 X1 0.2007 -0.8505 0.0520

Hi, I would like to be able to access the calculated results from the nls package. Using the example in R, fm3DNase1 we can reurn certain parts of the calculations: > coef(fm3DNase1) Asym xmid scal 2.345179 1.483089 1.041454 > resid(fm3DNase1) [1] -0.0136806237 -0.0126806237 0.0089488569 0.0119488569 -0.0025803222 [6] 0.0064196778 0.0026723396 -0.0003276604

Hey, R-listers I am a new user of R and just found the package of PCURVE which can estimate principal curve for arbitrary dimensional data set. Now I have some 2-Dimensional data set X, which is stored as an Nx2 matrix in data.txt file and looks as following: -1.5551 2.4183 1.0051 1.0102 0.90644 0.82163 1.3248 1.7551 -1.3626 1.8568 -1.3554 1.8371 0.039396 0.0015521 -0.99622 0.99245 0.2314

Dear list, I'm trying to mimic the analysis of Wedderburn (1974) as cited by McCullagh and Nelder (1989) on p.328-332. This is the leaf-blotch on barley example, and the data is available in the `faraway' package. Wedderburn suggested using the variance function mu^2(1-mu)^2. This variance function isn't readily available in R's `quasi' family object, but it seems to me

I'm trying to extend some work in the car and heplots packages that requires getting a table of multivariate means for one (or later, more) terms in an mlm object. I can do this for concrete examples, using aggregate(), but can't figure out how to generalize it. I want to return a result that has the factor-level combinations as rownames, and the means as the body of the table

Dear R gurus I am analysing data from a study of behaviour and shade utilization of chimpanzees. I am using GLMs in R (version 2.13.0) to test whether shade/sun utilization is predicted by behaviour observed. I am thus interested in whether an interaction of behaviour (as a predictor) and presence in the sun/shade (also predictor) predicts the counts I have for the respective categories. I have

Hello R-help I don’t know how to interpret significance from the contr.poly() function . From the example below : how can I tell if data has a significant Linear/quadratic/cubic trend? > contr.poly(4, c(1,2,4,8))               .L         .Q          .C [1,] -0.51287764  0.5296271 -0.45436947 [2,] -0.32637668 -0.1059254  0.79514657 [3,]  0.04662524 -0.7679594 -0.39757328 [4,]  0.79262909 

Hallo! I have run a EEG experiment and got the following data group: 1 2 1 2 1 2 1 2 ... as factor, 2 levels between subjects fixed effect (patient vs control) subj: 1 2 ... 14 1 2 ... 14 as factor 7 patients 7 control random effect condition: 1 1 ... 2 2 ... 1 1 ... 2 2 as factor, 2 levels within subjects, ie every subject worked on every cond fixed effect roi: 1 ... 2 ... 3 ... 4 ... as factor,

Hi- I am having trouble with the syntax of looping through the rows and columns of a data frame. I have a table with 17 observations for 84 lines at n=5-10 per line. So the table is ~700x17. I want to pull out the median and stdev for each line and put it in a dataframe with rowname = linename. So I have tried the following.... #read in the table input.table <- read.table(file =

Hallo, I have this dataset with repeated measures. There are two within-subject factors, "formant" (2 levels: 1 and 2) and "f2 Ref" (25 levels: 670, 729, 788, 846, 905, 1080, 1100, 1120, 1140, 1170, 1480, 1470, 1450, 1440, 1430, 1890, 1840, 1790, 1740, 1690, 2290, 2210, 2120, 2040, 1950), and one between-subject factor, lang (2 levels:1 and 2). The response variable

Hi helpeRs, I've been working through some examples in Pinhiero & Bates( 2000) trying to understand how to translate to the new Lme4 syntax but without much luck. Below is what I think I should do, but either the answers don't come out the same or I get errors. In the Oxide problems I'm particularly interested in obtaining the levels coeficients but this options no longer seems

Are these 225 compile time regressions real? It sure looks bad! Ciao, Duncan. On 01/12/11 09:39, llvm-testresults at cs.uiuc.edu wrote: > > bwilson__llvm-gcc_PROD__i386 nightly tester results > > URL http://llvm.org/perf/db_default/simple/nts/380/ > Nickname bwilson__llvm-gcc_PROD__i386:4 > Name curlew.apple.com > > Run ID Order Start Time End Time > Current 380