similar to: aov or lme effect size calculation

Hi, All. I'd like to calculate effect sizes for aov or lme and seem to have a bit of problems. partial-eta squared would be my first choice, but I'm open to suggestions. Here is the aov version: > fit.aov <- (aov(correct ~ cond * palette + Error(subject), data=data)) > summary(fit.aov) Error: subject Df Sum Sq Mean Sq F value Pr(>F) Residuals 15 0.17326

Until a weeks ago I used stata for everything. Now I'm learning R and trying to move. But, in this stage I'm testing R trying to do the same things than I used to do in stata whit the same outputs. I have a problem with the logit, applying weights. in stata I have this output . svy: logit bach job2 mujer i.egp4 programa delay mdeo i.str evprivate (running logit on estimation sample)

Dear all, I am running a logistic regression and this is the output: glm(formula = educationUniv ~ brncntr, family = binomial) Deviance Residuals: Min 1Q Median 3Q Max # ???? ????? ?? ???????? -0.8825 -0.7684 -0.7684 1.5044 1.6516 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.06869 0.01155 -92.487 <2e-16 *** brncntrNo

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Dear All, I am in a trouble with reading data. It is in txt file looking like this. 0.00632 18.00 2.310 0 0.5380 6.5750 65.20 4.0900 1 296.0 15.30 396.90 4.98 24.00 0.02731 0.00 7.070 0 0.4690 6.4210 78.90 4.9671 2 242.0 17.80 396.90 9.14 21.60 0.02729 0.00 7.070 0 0.4690 7.1850 61.10 4.9671 2 242.0 17.80 392.83 4.03 34.70 0.03237 0.00

The message for aov1 was "Estimated effects <may> be unbalanced". The effects are not unbalanced. The design is 'orthogonal'. The problem is that there are not enough degrees of freedom to estimate all those error terms. If you change the model to: aov1 <- aov(RT~fact1*fact2*fact3+Error(sub/(fact1+fact2+fact3)),data=myData) or to aov2 <-

Hi, I downloaded both gee and geepack, and I am trying to understand the differences between the two libraries. I used the same data and estimated the same model, with a correlation structure autoregressive of order 1. Surprisingly for me, I found very different results. Coefficients are slightly different in value but sometimes opposite in sign. Moreover, the estimate of rho (correlation

In my undertstanding of the problem, the model lme1 <- lme(resp~fact1*fact2, random=~1|subj) should be ok, providing that variances are homogenous both between & within subjects. The function will sort out which factors & interactions are to be compared within subjects, & which between subjects. The problem with df's arises (for lme() in nlme, but not in lme4), when

Dear all R-users, I wanted to fit a Garch(1,1) model to a dataset by: >garch1 = garch(na.omit(dat)) But I got a warning message while executing, which is: >Warning message: >NaNs produced in: sqrt(pred$e) The garch parameters that I got are: > garch1 Call: garch(x = na.omit(dat)) Coefficient(s): a0 a1 b1 1.212e-04 1.001e+00 1.111e-14 Can any one

Dear ExpeRts, Suppose I have a typical psychological experiment that is a within-subjects design with multiple crossed variables and a continuous response variable. Subjects are considered a random effect. So I could model > aov1 <- aov(resp~fact1*fact2+Error(subj/(fact1*fact2)) However, this only holds for orthogonal designs with equal numbers of observation and no missing values.

Guys, I used Random Forest with a couple of data sets I had to predict for binary response. In all the cases, the AUC of the training set is coming to be 1. Is this always the case with random forests? Can someone please clarify this? I have given a simple example, first using logistic regression and then using random forests to explain the problem. AUC of the random forest is coming out to be

I have been trying to do a nls model and gives me the error of a nonnumeric argument table(file="c:/tt2.txt",header=T) > fit.model <- nls(TT~60*(1+alpha*(v/c)^beta),data=tt2, start=list(alpha=1, beta=3, v=1000)) Error in v/c : non-numeric argument to binary operator > is.numeric(tt2) [1] FALSE > is.character(tt2) [1] FALSE > as.numeric(tt2) Error: (list)