similar to: basic question about t-test with adjusted p value

I have a simple barplot of 4 mean values, each mean value has an associated 95% confidence interval drawn on the plot as an error bar. I want to make a legend on the plot that uses the error bar symbol, and explains "95% C.I." How do I show the error bar symbol in the legend? I could not find any "pch" values that are appropriate Thanks [[alternative HTML version

I have a matrix with 12 rows (one for each month), 2 columns (baseflow, runoff). I would like to make a barplot similar to Excel’s “clustered column chart”. Here is my matrix ‘x’ 8.258754 13.300710 10.180953 10.760465 11.012184 13.954887 10.910870 13.839839 9.023519 11.511129 7.189241 12.519830 5.925576 17.101491 5.211613 13.585175

I am running R on Windows XP I first tried to install rJava and got this message utils:::menuInstallLocal() package 'rJava' successfully unpacked and MD5 sums checked updating HTML package descriptions then i tried this require(rJava) Loading required package: rJava Error in if (!nchar(javahome)) stop("JAVA_HOME is not set and could not be determined from the registry") :

About a week ago, XL Solutions Corporation posted to this list with a solicitation for course offerings in R. I looked at their site, and many courses do look interesting. Can anyone provide feedback on the quality of their courses? [[alternative HTML version deleted]]

I have a time series of about 1500 measurements. There are sporadic data gaps. I would like to plot the time series with type "line". I only want a line to connect the periods with data; I don't want a line to connect the points across a data gap. Is there a function or recommended method to deal with this? For example there are 2000 days days 1:1000 each have one

This seems like a simple thing, but I have been stuck for some time. My data has 2 columns. Column 1 is the value, and column 2 is the Site where data was collected. Column 2 contains 7 different Sites (i.e. groups). I am only interested in showing 3 groups on a single boxplot. I have tried various methods of subsetting the data, in order to only have the 3 groups in my subset. However

Dear List, I have been trying to use p.adjust() to do BH multiple test correction and have gotten some unexpected results. I thought that the equation for this was: pBH = p*n/i where p is the original p value, n is the number of tests and i is the rank of the p value. However when I try and recreate the corrected p from my most significant value it does not match up to the one computed by the

I am trying to plot 2 time series on the same graph. For example, X1 is the vector of dates and times, its class is POSIXt. Y1 is an environmental parameter, e.g. salinity. X2 is a second vector of dates and times, also of class POSIXt. X2 has a different length than X1, but they have the exact same range. Y2 is another environmental parameter, e.g. stream flow rate. My approach is

I have a time series with interval of 2.5 minutes, or 24 observations per hour. I am trying to find a 1 hr moving average, looking backward, so that moving average at n = mean(n-23 : n) The time series has about 1.5 million rows, with occasional gaps due to poor data quality. I only want to take a 1 hour moving average for those periods that are complete, i.e. have 24 observations in the

Hello, I am not sure about the p.adjust( , fdr). How do these adjusted p-values get? I have read papers of BH method. For independent case, we compare the ordered p-values with the alfa*i/m, where m is the number of tests. But I have checked that result based on the adjusted p-values is different with that by using the independent case method. Then how do the result of p.adjust( , fdr) come? And

The Partek package (www.partek.com) allows only two selections for Multiple Test Correction: Bonferroni and Dunn-Sidak. Can anyone suggest why Partek implemented Dunn-Sidak and not the other methods that R has? Is there any particular advantage to the Dunn-Sidak method? R knows about these methods (in R 2.1.1): > p.adjust.methods [1] "holm" "hochberg" "hommel"

I append below a suggested update for p.adjust(). 1. A new method "yh" for control of FDR is included which is valid for any dependency structure. Reference is Benjamini, Y., and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics 29, 1165-1188. 2. I've re-named the "fdr" method to "bh" but

L.S. In the current version of ?p.adjust.Rd, one needs to scroll down to the examples section to find confirmation of one's guess that "fdr" is an alias of "BH". Please find a patch in attachment which mentions this explicitly. Best, Tobias -------------- next part -------------- A non-text attachment was scrubbed... Name: p.adjust.Rd.patch Type: text/x-patch Size: 633

Hello, I am trying to use the p.adjust function for multiple testing. here is what i have 9997 201674_s_at 0.327547396 9998 221013_s_at 0.834211067 9999 221685_s_at 0.185099475 I import them from excel have have the gene symbol as well as the pvalue here is the issue > pa<-p.adjust(pt,method="BH") Error in p[nna] : object is not

Dear R-Help, I am using the function "topTable" from the LIMMA package. To estimate adjusted P-values there are several options (adjust="fdr" , adjust="BH") as shown below: topTable(fit, number = 10, adjust = "BH", fit$Name) I guess any of these options (fdr, BH, etc.) is using a default of FDR=0.05 which is quite conservative (i.e., very

Hello, I'm trying to do two things: -1. Ensure that I understand how quantmod adjust's OHLC data -2. Determine how I ought to adjust my data. My overarching-goal is to adjust my OHLC data appropriately to minimize the difference between my backtest returns, and the returns I would get if I was trading for real (which I'll be doing shortly). Background: -1. I'm using Alpha

Hi all, I am having a problem with the function "p.adjust" in stats. I have looked at the manuals and searched the R site, but didn't get anything that seems directly relevant. Can anybody throw any light on it or confirm my suspicion that this might be a bug? I am trying to use the p.adjust() function to do Benjamini/Hochberg FDR control on a vector of p-values that are the

You asked the same question on the Bioconductor mailing list back in August. At that time, you suggested yourself a solution for how the adjusted p-values should be interpreted. I answered your query and told you that your interpretation was correct. So I'm not sure what more can be said, except that you should read the article Wright (1992), which is cited in the help entry for p.adjust(),

hi list, i have to ask you again, having tried and searched for several days... i want to do a TukeyHSD after an Anova, and want to get the adjusted p-values after the Tukey Correction. i found the p.adjust function, but it can only correct for "holm", "hochberg", bonferroni", but not "Tukey". Is it not possbile to get adjusted p-values after

Hello, I am trying to calculate and plot mean and confidence intervall for a set of data. This is the code that I am currently using: means <- sapply(data, mean, na.rm=TRUE) n <- sapply(data,length) stdev <- sqrt(sapply(data, var, na.rm=TRUE)) ciw <- qt(0.98, n) * stdev / sqrt(n) par(mgp=c(2,0.6,0), las=2, fin=c(7,3), mai=c(1,0.5,0.2,0.2), cex=0.8) plotCI(x=means, uiw=ciw,