similar to: calculate effect size from p-value?

Dear R-experts! In a meta-analysis (metafor) I would like to assess the effect of two categorical covariates (A & B) whereas they both have 4 levels. Is my understanding correct that this would require to dummy-code (0,1) each level of each covariate (A & B)? However I am interested in the main-effects and the interaction of these two covariates and the dummy-coding would only allow to

Dear R-experts! I am working on some meta-analysis using the metafor package. I would like to extract values of the confidence intervals of the effect sizes of the single studies from an rma object. Those values are printed out when plotting a forest plot using the forest function on the rma object, however I was not able to locate them. Many thanks for your help! Jokel [[alternative HTML

Dear R-experts! Running a meta-analysis (using the magnificent metafor-package), I use cohen's d as a main outcome measure in a random-effects model. For most of the samples cohen's d is derived form a comparison of two groups (A & B). However some studies report results from an ANOVA (one-factor with three levels: C,D,E) whereas two groups (C,D) correspond to one group in the other

Dear all, I calculate the test statistic for the KS test outside R, and wish to use R only to calculate the corresponding p-value. Is there a way for doing this? (as far as I see, ks.test() requires raw data as input). Alternatively, is there a way to provide the ks.test() the two CDFs (two samples test) rather than the (x, y) data vectors? Thanks in advance, Rani

Hello, I have I problem and I hope someone can help me. I have a data set with 66 variables and 30 values for each variable. I need to calculate the Pearson correlation and the p-values among variables. I have the ?large p, small n? problem so I used a shrinkage process to obtain the r values. My problem is that I can?t calculate the p-values because the degree of freedom of the t-student

Although I saw this issue being discussed many times before, I still did not find the answer to: why does R can not calculate p-values for data with ties (i.e. - sample with two or more values the same)? Can anyone elaborate some details about how does R calculate the p- values for the Kolmogorov Smirnov test statistics? I can understand the theoretical problem that continuous distributions do

Let's say you want to compare "one observation" with a sample, how would you use R to get a p-value for that single observation itself? To clarify what I'm asking: We know you use a one-sample t test to compare an actual sample to a hypothetical value, and a Wilcoxon test if it's not normally distributed, in R either "t.test( )" or "wilcox.test( )".

Dear R-experts! I am currently running a meta-analysis with the help of the great metafor package. However I have some difficulties setting up my raw data to enter it to the meta-analysis models. I have a group of subjects that have been measured in two continuous variables (A & B). I have the information about the mean of the two variables, the group size (n) and the standard deviations of

I am looking for an R package with which one can calculate an effect size for a set of contrasts given an omnibus chi-square test statistic (more than 1 degree of freedom). Is there such a package? Presumably, it would implement the procedure (or something like it) described by Roznow and Rosenthal 1996 Psychological Methods 1: 331-340. many thanks! Steven Orzack Fresh Pond Research Institute

Dear R-experts, The forest.rma() function from the metafor package creates nice forest plots for presenting the results of a meta-analysis. These plots can be annotated for e.g. giving names to the columns. E.g. as in the documentation of the package: data(dat.bcg) ### meta-analysis of the log relative risks using a random-effects model res <- rma(ai=tpos, bi=tneg, ci=cpos, di=cneg,

(A repost of this request with a bit more detail) Hi, All. I'd like to calculate effect sizes for aov or lme and seem to have a bit of a problem. partial-eta squared would be my first choice, but I'm open to suggestions. I have a completely within design with 2 conditions (condition and palette). Here is the aov version: > fit.aov <- (aov(correct ~ cond * palette +

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

Hi All, I would like to use meta.summaries from package rmeta to do a meta-analysis. I have available effect sizes as r's (which could be easily transformed to effect sizes in terms of d's). My problem is that I'm not sure what the se's of these r's should be ... The r-values are themselves computed from F-tests and t-tests for various studies. Are there R-functions that

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Dear R experts, I have conducted a power calculation in order to estimate the number of subjects needed to detect an effect size of d=0.28 (cohen's d) for a difference between two independent groups (alpha level should be 0.05 and the effect should be detected with 80% probability). The results from the code below indicates that I would need n=400 subjects (200 in each group). This is seems

Hello, I'm having trouble figuring out how to calculate a p-value for a 1-tailed test of beta_1 in a linear model fit using command lm. My model has only 1 continuous, predictor variable. I want to test the null hypothesis beta_1 is >= 0. I can calculate the p-value for a 2-tailed test using the code "2*pt(-abs(t-value), df=degrees.freedom)", where t-value and degrees.freedom

Dear R users, I have a linear model of the kind outcome ~ treatment + covariate where 'treatment' is a factor with three levels ("0", "1", and "2"), and the covariate is continuous. Treatments "1" and "2" both have regression coefficients significantly different from 0 when using treatment contrasts with treatment "0" as the

Here is my dataframe with 1000 rows: employee_id weigth p-value 100 150 101 200 102 300 103 180 ..... My question: how can I calculate the p-value in R for each employee? the distribution of the weigth will be established from the above 1000 samples. -- View this message in context:

Hi, I'm working with the ks.test() function and I have also implemented the test using Conover as the reference. My D value matches that produced by R. However to calculate the p value I am using the code described in Numerical Recipes in C++ (2nd Ed.) pg 631. The p value produced by the NRC code is generally larger than that produced by R by a factor of 10. Currently I am not in a position

Sir,   I am unable to get the source code for Durbin-Watson test, as I want to calculate the p-value for Durbin Watson statistic using interpolation method. I sent this mail to r-help, but it was rejected, please suggest me some way. I will be highly greatful to you. Thanks in advance Ramesh [[alternative HTML version deleted]]