similar to: to get R-squared value

Dear all, I know there are 'coef', 'predict', 'fit' to get the corresponding outputs. Is that possible to get the value of R-squared from a 'lm' output? Many thanks.

What is the exact formula used in R lm() for the Adjusted R-squared? How can I interpret it? There seem to exist several formula's to calculate Adjusted R-squared. Wherry’s formula [1-(1-R2)·(n-1)/(n-v)] McNemar’s formula [1-(1-R2)·(n-1)/(n-v-1)] Lord’s formula [1-(1-R2)(n+v-1)/(n-v-1)] Stein 1-(n-1/n-k-1)(n-2)/n-k-2) (n+1/n) Theil's formula (found here:

A few days ago, I had asked this question. Consider this situation: > x1 <- runif(100); x2 <- runif(100); y <- 2 + 3*x1 - 4*x2 + rnorm(100) > m1 <- summary(lm(y ~ x1)) > m2 <- summary(lm(y ~ x2)) > m3 <- summary(lm(y ~ x1 + x2)) You have estimated 3 different "competing" models, and suppose you want to present the set of models in one table. xtable(m1) is

Dear R users I have run an regression and want to extract the p value of the F statistics, but I can find a way to do that. x<-summary(lm(log(RV2)~log(IV.m),data=b)) Call: lm(formula = log(RV2) ~ log(IV.m), data = b[[11]]) Residuals: Min 1Q Median 3Q Max -0.26511 -0.09718 -0.01326 0.11095 0.29777 Coefficients: Estimate Std. Error t value Pr(>|t|)

Hi, Sorry for the naive question, but what exactly does the 'Adjusted R-squared' coefficient in the summary of linear model adjust for? Sample code: > x <- rnorm(15) > y <- rnorm(15) > lmr <- lm(y~x) > summary(lmr) Call: lm(formula = y ~ x) Residuals: Min 1Q Median 3Q Max -1.7828 -0.7379 -0.4485 0.7563 2.1570 Coefficients:

Hi, I'm analyzing a large number of simulations using lm(), a sample of the resulting data is pasted below. In some simulations, the response variable doesn't vary, ie: > tmp[[2]]$richness [1] 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 When I analyze this using R version 2.8.0 (2008-10-20) on a linux cluster, I get an appropriate result: ## begin R

Hi R community I have a question. I'll explain my situation. I have to build a climate model to obtain monthly and annual temperature from 2004 to 2008 from a specif area in Almeria (Spain). To build this climate model, I will use Multiple regression. My dependant variable will be monthly and annual temperature and independant variables will be Latitute, Longitude and Altitude and I will work

Greetings R Users, I have a system of equations for which I would like to output all the R-squares. Assume there are four equations in my system, the only way I found to output all the R-squares is by calling them out one by one as this: summary(fitSUR$eq[[1]])$r.squared summary(fitSUR$eq[[2]])$r.squared summary(fitSUR$eq[[3]])$r.squared summary(fitSUR$eq[[4]])$r.squared But isn't there a

Dear all, I cannot find to explicitly get the R-squared or adjusted R-squared from summary(lm()) Thanks a lot! [[alternative HTML version deleted]]

Hi people, I want to obtain the adjusted r-square given a set of coefficients (without the intercept), and I don't know if there is a function that does it. Exist???????????????? Dear Alexandra, Without knowing what routine you were using that returned an R-Square value too you, it is a little difficult to tell. I am not able to comment on your routine, but I'll try to give a

Dear all, I produced a part of code which does what needed but by estimations I would need to wait for that for 300 hours!!! Maybe someone can give a me a glance advice or something. Thanks a lot! I need to define maximum adjusted R-squares from regression model: returns as dependent variable( here simulated to be normal) and 3 factors out of say 14 independent (simulated here). I need to run lm

The R code I just mailed out had a small error in it. This one works. Now what one needs is a way to get decimal alignment in LaTeX tabular objects. x1 <- runif(100); x2 <- runif(100); y <- 2 + 3*x1 - 4*x2 + rnorm(100) m1 <- summary(lm(y ~ x1)) m2 <- summary(lm(y ~ x2)) m3 <- summary(lm(y ~ x1 + x2)) # What I want is this table: # #

Hello. Consider the response-variable of data.frame df is constant, so analytically perfect fit of a linear model is expected. Fitting a regression line using lm result in residuals, slope and std.errors not exactly zero, which is acceptable in some way, but errorneous. But if you use summary.lm it shows inacceptable error propagation in the calculation of the t value and the corresponding

Hi, R Users I find a problem in extracting the R-squared and P-value from the lm results described below (in Italic), *Residual standard error: 2.25 on 17 degrees of freedom* *Multiple R-squared: 0.001069, Adjusted R-squared: -0.05769 * *F-statistic: 0.01819 on 1 and 17 DF, p-value: 0.8943 * * * Any suggestions will be appreciated. Thanks. Wenjun [[alternative HTML version deleted]]

I would like to call both p-values and R-squared values from lm's in a function. I can get the p-values from coef(summary(name.lm))[r,c], however, I cannot figure out how to call the R-squared values without manually calling the summary and inserting them in the script - which negates the value of automating the process through a function. Thanks, Mike [[alternative HTML version deleted]]

Full_Name: Adriano Azevedo Filho Version: 1.9.1 OS: Windows, Linux Submission from: (NULL) (200.171.246.212) R-squared and Adjusted R-squared appear to be wrong when the formula in lm() is specified without intercept. Problem present in both Windows and Linux 1.9.1 version. Also in the 1.8.1 version for Windows (other versions not checked). Possible example which reproduces the problem:

Greetings Listers! the R-squared value reported by summary of lm is calculated as 1 - RSS/RSS_m where RSS_m is the residual sum of squares of a minimal model. In most cases, the minimal model is simply y = mean(y), but when a constant is left out of the model, the minimal model is y = 0. However, if you manually add a constant, R still considers y = 0 the minimal model. This also causes

Hi, I have 12 independent variables and one dependent variable. Now I want to select the best adj. R squared model by using the regsubsets command, so I code: > plot(regsubsets(Gesamt ~ CommunistSocialist + CountrySize + GNI + Lifeexp + Schoolyears + ExpMilitary + Mortality + + PopPoverty + PopTotal + ExpEdu + ExpHealth, data=olympiadaten, nbest=1, nvmax=12), scale='adjr2') Then I

Dear Sir or Madam, I am a student at MSc Probability and Finance at Paris 6 University/ Ecole Polytechnique. I am using R and I can't find an answer to the following question. I will be very thankful if you can answer it. I have two vectors rendements_CAC40 and rendements_AlcatelLucent. I use the lm function as follows, and then the sumarry function: regression=lm(rendements_CAC40 ~

Hi there, I tried it many times but didn't get it worked. I just want to export the summary of a OLS regression (lm() function) into a csv-file including the "call"-formula", "coefficients", "r-squared", " adjusted r-squared" and "f statistic". I know I can export: write.csv2(Regression_60d_ann$coefficients,