search for: olkin

...st addresses like 199.129.206.255 199.129.207.255 So, I tried remote announce = 199.129.206.255 199.129.207.255 199.129.208.255 199.129.209.255 With this, network-neighborhood on the Windows 98 boxes show a terminal icon with "Olkin" on its right. This reflects Olkin which is my Linux box running Samba. However, clicking on the "olkin" icon produces \\Olkin is not accessible The computer or share could not be found. So, my Samba server appears to make itself known to the Windows 98 computers, but they can n...

I am a beginner when it comes to using R, though fortunately I already know something about statistics. I think factor analysis should be used sparingly, but I occasionally use it. It doesn't seem to me that factanal() provides Kaiser's Measure of Sampling Adequacy, which should be computed for factor problems based on a small number of subjects, though perhaps it is elsewhere. Does anyone

...2 7 5 5 6 16 14 11 11 10 4 10 10 10 10 15 1 1 1 8 4 16 16 17 16 23 23 3 4 3 3 3 3 17 4 4 6 9 8 17 8 18 18 8 8 5 9 7 7 7 9 19 6 7 8 3 8 4 4 7 20 8 11 9 3 2 2 2 3 ** *SPSS results for the above dataset:--------* Kaiser-Meyer-Olkin Measure of Sampling Adequacy.(KMO) 0.350911931549742 *Got a R-function:-----* ( http://www.opensubscriber.com/message/r-help@stat.math.ethz.ch/2776549.html) kmo.test <- function(df){ ### ## Calculate the Kaiser-Meyer-Olkin Measure of Sampling Adequacy. ## Input should be a data frame or matrix,...

Dear colleagues, I've been searching for information on the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy (MSA). This statistic is generated in SPSS and is often used to determine if a dataset is "appropriate" for factor analysis -- it's true utility seems quite low, but it seems to come up in stats classes a lot. It did in mine, and a glance thr...

In a classical meta analysis model y_i = X_i * beta_i + e_i, data {y_i} are assumed to be independent effect sizes. However, I'm encountering the following two scenarios: (1) Each source has multiple effect sizes, thus {y_i} are not fully independent with each other. (2) Each source has multiple effect sizes, each of the effect size from a source can be categorized as one of a factor levels

Well, it is no problem to use R or S-Plus for a principle component analysis (PCA). The function "princomp" works very well with my polychoric correlation matrix, that I use for this approach on ordinal variables. But I want to inspect the Kaiser-Meyer-Olkin-crieterion with the "measure of sampling adequancy (MSA)" and the anti-image-correlation matrix. I can''t find any possibility to compute this. Please give me a hint. Many Thanks! Tino [[alternate HTML version deleted]]

Hello, This is in response to a post from a couple of years back regarding Kaiser-Meyer-Olkin Measures of Sampling Adequacy. (http://tolstoy.newcastle.edu.au/R/help/05/12/17233.html) As it turns out, last year Trujillo-Ortiz et al. at the Universidad Autonoma de Baja California wrote and posted a script for MATLAB that does the job. You can see it (with a discussion of KMO statistics) at...

...e R to do it in a big layout for all the variables in the data frame. 3. Descriptive statistics of each variable. Jim Lemon's excellent dstats() function does this. Solved. 4. A large correlation matrix for the data frame. The built-in function cov() does this. Solved. 5. KMO (Kaiser-Meyer-Olkin Measure of Sampling Adequacy) and Bartlett test of sphericity on the data frame as a whole. I can't find ways to recreate these tests -- bartlett.test() doesn't produce the type of response that makes sense. 6. Anti-image matricies, including MSA (sampling adequacy) scores for each v...

I thought the point of adjusting the R^2 for degrees of freedom is to allow comparisons about goodness of fit between similar models with different numbers of data points. Someone has suggested to me off-list that this might not be the case. Is an ADJUSTED R^2 for a four-parameter, five-point model reliably comparable to the adjusted R^2 of a four-parameter, 100-point model? If such values

Hi All, I have conducted a meta analysis using the metabin function. I want to plot 5 subgroups on the same forest plot. I have managed to do this using the byvar argument but when i plot the forest plot in R graphics I am unable to view the very top and very bottom of the image. It is as though the plot is too long. Is there a way in which I can ask R to show the entire plot within the

Dear R-Community, I'm currently trying to find a way to conduct a meta-analysis in R. I would like to analyze data from mostly-cross-sectional survey-studies. The effect sizes would be correlations. The R packages "meta" and "rmeta" are, as far as I can see, set up for analysis with effect sizes for differences (i.e. comparison of the means/odds-ratios of experimental

...n how the pooled standard deviation is being calculated, but the help for escalc only says that the pooled standard deviation is "calculated inside the function", and I can''t find the exact formula for how this is being done. I''m trying to track down a copy of Hedges and Olkin (1985), but it would be great in the meantime somebody could enlighten me. Thanks, Stephen Ban *p. 226. Cooper, H., Hedges, L., and Valentine, J. The handbook of research synthesis and metaanalysis. Russell Sage Foundation, 2009. Stephen Ban, M.Sc. Ph.D. Candidate Australian Research...