# Search results

1. ### Variable restriction in (exhaustive) model selection

Hi hlsmith, I agree with your comment. At this stage, I am simply doing an exploratory search, to see which of my variables could (in theory) be used in subsequent analyses / experiments. Statistical robustness with regards to significance and extrapolation are not yet my concerns, as this is...
2. ### Variable restriction in (exhaustive) model selection

Never mind, I think I solved it. glmulti() has a minsize and a maxsize parameter that (probably) do what I need.
3. ### Variable restriction in (exhaustive) model selection

Hi all, I have a high number of variables (around 80) with which to model an intermediate-size sample (around 50 points) using GLMs. I would like to do an exhaustive search for the "best" model, but using all of the variables in an exhaustive (or semi-exhaustive, like glmulti's genetic...
4. ### Sample size, effect size and power.

Hi ernie_aka, The larger the effect size, the easier it is to detect it. Therefore, it makes sense that you need a larger sample size to detect an effect size of 0.25, as compared to a fairly larger effect size of 0.5. As a rough example, imagine an experiment where you use 2 different...
5. ### Reporting Eta Squared in ANOVA

Greetings all, Let's suppose I have an ANOVA with three variables: Y ~ a + b + c. When I report the results, I would like to include the eta-squared for each of the three variables as a barplot. My question is the following: since the definition of eta-squared is "the proportion of the...
6. ### erase rows with zero values in R

I am not aware of a function that directly does that, but you could try the following. Let's suppose your data.frame is x: ind <- which(apply(x,1,is.element, el=0)) x <- x[-ind,] The object ind contains the rows that have at least one zero. Then, with a logical indexing you can omit these...
7. ### Reproducibility crisis

Hi All, I am looking for a good paper about the reproducibility crisis or any related issue (ideally a review), published in the last couple of years on a high impact factor journal. I am especially interested in the medical field, but a general review would be fine as well. Does anybody have a...
8. ### Paired t-test equivalent for non-normal, heteroscedastic data

Thank you everyone for the valuable input! I obviously need to read more about the matter, but you have given me clear directions. Maybe my original confusion comes from the fact that in R, which I use for my analyses, both the Wilcoxon Rank Sum and Signed Tests are run with the same function...
9. ### Paired t-test equivalent for non-normal, heteroscedastic data

Hello Karabiner! Thank you for your answer. I might say something stupid, but I thought the Wilcoxon signed rank test has similar assumptions to the Mann-Whitney U test... am I wrong? If so, the literature for the Mann-Whitney U test says that it is sensitive to departures for normality and...
10. ### Paired t-test equivalent for non-normal, heteroscedastic data

Thank you hlsmith! Unfortunately, I am not allowed to post the data here. However, I suspected that a permutation would be a solution - honestly, I tried to avoid it because I am not very knowledgeable on the matter. I will try to look into it. Thank you again for your help!
11. ### Paired t-test equivalent for non-normal, heteroscedastic data

Greetings good people, I work on a project where a number of subjects (N = 5) with a particular disease were measured in two specific regions of their bodies (say, region1 and region2) using a specific medical instrument. Based on the pathophysiology of the disease, region1 should give...