similar to: adjusted R squared

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:

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:

Dear list members, I am using a cross validation of a generalised linear model (glm). The cv.glm function (from boot package) returns an error as so-called ?delta? value. I would like to get to a (cross-validated) squared q, because I want to directly compare it to the squared correlation coefficient r. I tried to find an an equation for the raw and/or adjusted cross-validation estimate of

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

Hello Alexandra, R2 is only defined for regressions with intercept. See a decent econometrics textbook for its derivation. HTH, Bernhard -----Urspr??ngliche Nachricht----- Von: Alexandra R. M. de Almeida [mailto:alexandrarma at yahoo.com.br] Gesendet: Mittwoch, 11. Januar 2006 03:48 An: r-help at stat.math.ethz.ch Betreff: [R] Obtaining the adjusted r-square given the regression coefficients

I do not see an R-squared value after preforming the glm regression. Is there a separate command for this? Thanks -- View this message in context: http://r.789695.n4.nabble.com/Obtaining-R-squared-value-in-Logistic-Regression-tp4648954.html Sent from the R help mailing list archive at Nabble.com.

Hi there, I really tried hard to understand and find my own solution, but now I think I have to ask for your help. I already developed some script code for my problem but I doubt that it is correct. I have the following problem: Image you develop a logistic regression model with a binary outcome Y (0/1) with possible preditors (X1,X2,X3......). The development of the final model would be

Dear Colleagues, John Mandel ( Chain block designs with two-way elimination of heterogeneity. Biometrics 10, 251-272 ,1954). extended the class of chain block designs (Youden & Conner (1953) to elimination of both row and column (blocks) effects. These experimental designs can be useful in engineering and other fields. I am having difficulty obtaining his adjusted treatment means in his

The partial R-square (or coefficient of partial determination, or squared partial correlation coefficients) measures the marginal contribution of one explanatory variable when all others are already included in multiple linear regression model. The following link has very clear explanations on partial and semi-partial correlation: http://www.psy.jhu.edu/~ashelton/courses/stats315/week2.pdf In

Dear all, How do I make R show the R-squared (deviance explained by the model) in a logistic regression? Below is how I write my syntax. Basically I want to investigate density-dependence in parasitism of larvae. Note that in the end I perform a F-test because the dispersion factor (residual deviance / residual df) is significantly higher than 1. But how do I make R show the

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

Hi! I have some data that looks like this up down percentaje uew_21 20 14 58.82 uew_20_5 27 40 40.29 uew_20 8 13 38.09 uew_19_5 17 42 28.81 So I have 4 experimental conditions and I am counting number of animals in the up and down compartment and the calculating the percentage, I want to know which one of the conditions is different from each other. If the data wouldn't be percentage

Dear users, I want to compute r-squared values from a glm regression using a gamma distribution and an "identity" link-function, but find no such thing when using the summary() or names() function. My next guess was to calculate it by "hand", i.e. r2 = (sum((estimate - xbar)^2) /sum((x-xbar)^2)) but I am unsure if this is even allowed... Chris -- View this message in

Hi All, I am running a generalized linear model with gamma distribution in R (glm, family=gamma ) for my data (gene expression as response variable and few predictors). I want to calculate r-squared for this model. I have been reading online about it and found there are multiple formulas for calculating R2 (psuedo) for glm (in R) with gaussian (r2 from linear model), logistic regression

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???????????????? I know that if you make a linear regression, you enter the dataset and have in "summary" the adjusted r-square. But this is calculated using the coefficients that R obtained,and I want other coefficients

This is a linear regression of Y onto factors... If I take log of Y, and regress onto the factors, I got: Multiple R-squared: 0.4023, Adjusted R-squared: 0.2731 If I don't take log of Y, and directly regress Y onto the factors, I got: Multiple R-squared: 0.1807, Adjusted R-squared: -0.001112 Is this negative adjusted R^2 a problem? What observation can I make here and what might

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:

Dear, I want to compute coefficient of determination (R-squared) to complement AIC for model selection of multivariable GLM. However, I found this is not a built-in function in glm. neither is it available through reviewing the question in the R-help archive. Please kindly help and thanks a lot. Elaine [[alternative HTML version deleted]]

Hi I am trying to find the relationship between two variables. First I fitted a linear model between two variables and I found the following results: Residual standard error: 0.03253 on 2498 degrees of freedom Multiple R-squared: 0.5551, Adjusted R-squared: 0.5549 F-statistic: 3116 on 1 and 2498 DF, p-value: < 2.2e-16 Then I used the cor function to see the correlation between two variable

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