similar to: max possible rsquare

Hi, I know that Rsquare in glm or in non-linear models is "wrong", but some people like this. How I make to estimate the Rsquare from a model ajusted with glmmPQL or another GLMM? Thanks for all Ronaldo -- A simplicidade ?? o ??ltimo degrau da sabedoria. -- Victor Hugo -- |> // | \\ [***********************************] | ( ?? ?? ) [Ronaldo Reis J??nior

I think you've badly misinterpreted the purpose of the R listserv with this request: https://www.stat.math.ethz.ch/mailman/listinfo/r-help says "The `main' R mailing list, for announcements about the development of R and the availability of new code, questions and answers about problems and solutions using R, enhancements and patches to the source code and documentation of R,

Hi, I have something like this: > x <- 1:10 > y2 <- 30+5*x+rnorm(x,sd=3) > y <- c(y1,y2) > x <- c(x,x) > plot(x,y) > x <- 1:10 > y1 <- 1+5*x+rnorm(x,sd=2) > y2 <- 30+5*x+rnorm(x,sd=5) > y <- c(y1,y2) > x <- c(x,x) > f <- factor(rep(c("a","b"),c(10,10))) > m <- lm(y~x+f) > anova(m) Analysis of Variance

Dear R users, I am doing Cox regression using coxph(Survival) or cph(Design). I have time varying effects (diagnosed with schoenfeld residuals Chi2 test and graph) so I first want to split time into 2 separate intervals : t<6months and t>=6months, to estimate one hazard ratio (hr) for each interval. I am analysing Overall survival according to 3 prognostic factors (age,deep,ldh).

Is there a function/package that will allow you to test the hypothesis beta1 = x in a simple linear regression, where x is a constant? The AOV tests if beta1 in different from 0 but what if I want to know if beta1 one is different from 1? With best wishes and kind regards I am Sincerely, Corey A. Moffet Instructor Department of Range, Wildlife, and Fisheries Management Mail Stop 2125 Texas

Hello, my question is about the data handling. I have a data set that is lined as: 4 1 17 1 1 -5.1536 -0.1668 -2.3412 -0.5062 0.9621 0.3640 0.3678 -0.5081 -0.2227 0.8142 -0.0389 -0.0445 -0.0578 -0.1175 -0.1232 0.8673 -0.1033 -0.0796 -0.0341 -0.1716 -0.1801 -0.7014 0.6578 0.5611 4 1 17 2 1 -5.1536 -0.1668 -2.3412 -0.5062 0.9621 0.3640 0.3678 -0.5081 -0.2227 0.8142 -0.0389 -0.0445

I have the joint distribution of three discrete random variables z1, z2 and z3 which is captured by "z" and "prob" as described below. For example, the probability for z1=0.46667, z2=-1 and z3=-1 is 2.752e-13. Also, the probability adds up to 1. > head(z) z1 z2 z3 [1,] -0.46667 -1.0000 -1.0000 [2,] -0.33333 -0.9333 -0.9333 [3,] -0.20000 -0.8667 -0.8667

Hi, I've built a linear model using multiple linear regression which leads me a R-squared value of 73.58%. After that, I used this model to predicted some values based on the test data. Now I'm wondering how: 1. can I measure de R-squared value between the predicted(by the model) and real (observed) values.? 2. Measure the RMSE error . Example: suppose my data its below: REAL

Hello, In my lab we use a four parameter logistic fit model for our ELISA data (absorbance values). We are currently testing the use of different solvents and need to find a way to add a correlation value (such as an R squared or something similar) so we can test different solvents in making this standard curve. We currently use the drc package and this is our script for the 4 parameter:

Most likely, previous computations have ended up giving slightly different values of say 0.13333. A pragmatic way out is to round to, say, 5 digits before applying unique. In this particular case, it seems like all numbers are multiples of 1/30, so another idea could be to multiply by 30, round, and divide by 30. -pd > On 28 Jul 2017, at 17:17 , li li <hannah.hlx at gmail.com> wrote:

I calculate an anova test in the following way: expdata<-read.table("/home/dorien/UA/meta-music/optimuse/optimuse1-build-desktop/results/results_processedCP", header=TRUE)

Hi all, Can anyone explain why the following use of the subset() function produces a different outcome than the use of the "[" extractor? The subset() function as used in density(subset(mydf, ht >= 150.0 & wt <= 150.0, select = c(age))) appears to me from documentation to be equivalent to density(mydf[mydf$ht >= 150.0 & mydf$wt <= 150.0, "age"])

Hello, I'm doing some linear regression: >lm<-lm(osas~alp,data) >summary(lm) However, the Rsquared in the output of summary() is not the same as the "standard" Rsquared calculated by spreadsheets, and outlined in statistical guidebooks, being SSR/SSTO. The output says "multiple Rsquared", but it is no multiple regression... What's the difference? Thanks,

Hi all, I have this figure: http://docs.google.com/Doc?id=df5zfsj4_103rjt2v4d5 created with the following steps: > x [1] 90.4 57.8 77.0 103.7 55.4 217.5 68.1 85.3 152.0 113.0 97.1 89.9 [13] 68.1 83.7 77.4 34.5 104.9 170.3 88.6 88.1 108.8 77.4 85.6 82.7 [25] 81.3 108.0 49.5 71.0 85.7 99.3 203.5 275.9 51.1 84.8 16.5 72.6 [37] 160.5 158.3 136.7 140.0 98.4 116.1

When I do ARMA(2,2) using one lag of LCPIH data This is eview result > > *Dependent Variable: DLCPIH > **Method: Least Squares > **Date: 08/12/11 Time: 12:44 > **Sample (adjusted): 1970Q2 2010Q2 > **Included observations: 161 after adjustments > **Convergence achieved after 14 iterations > **MA Backcast: 1969Q4 1970Q1 > ** > **Variable Coefficient Std.

> Date: Thu, 29 Jun 2000 14:22:24 +0000 > From: Lilla Di Scala <lilla at dimat.unipv.it> > I have a problem regarding the anova() output. When I apply it to a > single regression model, I do not understand how the values > corresponding to the F statistics are obtained by the software. I > believe that they are computed using differences between residual sums > of

Hi, I just read the "Embedding R in Gnumeric" idea at http://www.r-project.org/SoC08/ideas.html. On my side, I intend to add as many statistics related plot types to the current gnumeric charting engine as possible. We already have boxplots and partial support for histograms. My immediate plans are to finish the histogram code and add probability plots

I am using R for a graduate class on linear models that I teach. I sent the class a sample transcript of explicit construction of the model matrix and use of the QR decomposition. One of the members of the class picked up on the fact that the model matrix was not what I advertised that it should be when a two-sided formula is used. A one-sided formula works properly. For example, >

Using caret on the Titanic data from Kaggle, I tried various models, including rfRules which produces a model, partly described as such: > caret.rfRules.cv$finalModel $model len freq err [1,] "2" "0.0368" "0" [2,] "2" "0.032" "0.05" [3,] "2"

I have a data set for different time intervals. The data has three comment lines before data for each time interval. For each time interval there are 500 data points. I want to change the dataset such that I have the following format: t1 t2 t3 ................ 0.00208 0.00417 0.00625 ................. a1 a2 a3 ...................