Re: [R] significance test for difference of two correlations

Let r_1 be the correlation between the two variables for the first group with n_1 subjects and let r_2 be the correlation for the second group with n_2 subjects. Then a simple way to test H0: rho_1 = rho_2 is to convert r_1 and r_2 via Fisher's variance stabilizing transformation ( z = 1/2 *

Re: [R] Regarding Bivariate normal distribution.

No, x and y are not unique. In fact, there is an infinite number of x and y pairs that are roots to the equation P[Xx, Yy] = 0.05. -- Wolfgang Viechtbauer  Department of Methodology and Statistics  University of Maastricht, The Netherlands  http://www.wvbauer.com/ -Original

Re: [R] converting proc mixed to lme for a random effectsmeta-analysis

That was going to be my suggestion =) By the way, lme does not give you the right results because the residual variance is not constrained to 1 (and it is not possible to do so). Best, -- Wolfgang Viechtbauer  Department of Methodology and Statistics  University of Maastricht, The

[R] Boostrap p-value in regression [indirectly related to R]

Hello All, Despite my preference for reporting confidence intervals, I need to obtain a p-value for a hypothesis test in the context of regression using bootstrapping. I have read John Fox's chapter on bootstrapping regression models and have consulted Efron Tibshirani's An Introduction to the

Re: [R] coefficients regression

Try: regression - lm (biomass ~ poly (temperature, degree=2, raw=TRUE)) See the help page for poly what raw=TRUE does. Best, -- Wolfgang Viechtbauer  Department of Methodology and Statistics  University of Maastricht, The Netherlands  http://www.wvbauer.com/ Original Message From:

Re: [R] meta-regression, MiMa function, and R-squared

Dear All, I am actually in the process of turning the mima function (with additional functions for predict, resid, and so on) into a full package. Making the syntax of the function more like that for lm would indeed be useful. However, for that I would have to familiarize myself more with the

Re: [R] meta-regression, MiMa function, and R-squared

: Christian Gold [mailto:[EMAIL PROTECTED] Sent: Monday, March 12, 2007 13:35 To: Viechtbauer Wolfgang (STAT) Cc: r-help@stat.math.ethz.ch Subject: Re: meta-regression, MiMa function, and R-squared Dear Wolfgang Thanks for your prompt and clear response concerning the R^2. You write: Note

Re: [R] Mixed effects multinomial regression and meta-analysis

Here is my suggestion. Let P_i denote the true proportion in the ith study and p_i the corresponding observed proportion based on a sample of size n_i. Then we know that p_i is an unbiased estimate of P_i and if n_i is sufficiently large, we know that p_i is approximately normally distributed

[R] Distance between x-axis values and title

Dear All, I looked at help(par), but could not figure out which setting controls the distance between the x-axis values and the x-axis title. Any pointer would be appreciated! Thanks in advance, -- Wolfgang Viechtbauer  Department of Methodology and Statistics  University of Maastricht,

Re: [R] Distance between x-axis values and title

[mailto:[EMAIL PROTECTED] Sent: Monday, December 18, 2006 18:45 To: Viechtbauer Wolfgang (STAT); r-help@stat.math.ethz.ch Subject: Re: [R] Distance between x-axis values and title --- Viechtbauer Wolfgang (STAT) [EMAIL PROTECTED] wrote: Dear All, I looked at help(par), but could

Re: [R] Meta-regression with lmer() ? If so, how ?

I guess I'll chip in, since I wrote that function (which is going to be updated thoroughly in the near future -- I will probably expand it to an entire package). Have a look at MiMa at Wolfgang Viechtbauer's page. Is that what you are looking for? http://www.wvbauer.com/downloads.html

Re: [R] Testing the equality of correlations

It's more complicated than that, since Phi(X1,X2), Phi(X1,X3), and Phi(X1,X4) are dependent. Take a look at: Olkin, I., Finn, J. D. (1990). Testing correlated correlations. Psychological Bulletin, 108(2), 330-333. and Meng, X., Rosenthal, R., Rubin, D. B. (1992). Comparing correlated

Re: [R] Combination of Bias and MSE ?

The MSE of an estimator X for a parameter theta is defined as E(X - theta)^2, which is equal to Var[X] + (Bias[X])^2, so in that sense, the MSE is already taking the bias of X into account. Hope this helps, -- Wolfgang Viechtbauer  Department of Methodology and Statistics  University of

Re: [R] meta / lme

Hello Stephen, As far as I know, the meta package will not allow you to include moderator variables in the model. However, I have written a script for R/S-Plus that will allow you to fit such models (essentially, these are mixed-effects models with a random intercept). You can find the script

[R] Doubly Non-Central F-Distribution

Hello All, Has anyone written a function for the distribution function of a *doubly* non-central F-distribution? I looked through the archives, but didn't find anything. Thanks! Wolfgang __ R-help@stat.math.ethz.ch mailing list