search for: adjustings

I'm able to recieve fax with pure SpanDSP 0.0.2 + Asterisk successfully but I have problems with some fax machine so I wanted to try using HylaFax to recieve Fax instead of SpanDSP hoping that it'll solve my problem. I'm trying to connect Asterisk with SpanDSP using iaxmodem. My system looks like this: ISDN <---> Asterisk <---> IAXModem <---> Hylafax Asterisk and

I'm able to recieve fax with pure SpanDSP 0.0.2 + Asterisk successfully but I have problems with some fax machine so I wanted to try using HylaFax to recieve Fax instead of SpanDSP hoping that it'll solve my problem. I'm trying to connect Asterisk with SpanDSP using iaxmodem. My system looks like this: ISDN <---> Asterisk <---> IAXModem <---> Hylafax Asterisk

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! I'm trying to do a lm() test on three objects. My problem is that R protests and says that the variable lengths differ for one of the objects (Sweden.GDP.gap). But I have double checked that the number of observations are the same. All three objects should contain 9 observations but R only accepts 9 observations in two of the objects. The third must have 10! Very confusing because there

I have read the R manual and help archives, sorry but I'm still stuck. How would I do a t-test with an adjusted p-value? Suppose that I use t.test ( ) , with the function argument alternative = "two.sided", and data such that degrees of freedom = 20. The function calculates a t-statistic of 2.086, and p-value =0.05 How do I then adjust the p-value? My thought is to do

Hi, I want to use Sieve filtering with my Dovecot 1.2 installation on Debian squeeze. I have a virtual domain setup using Portgresql. ManageSieve works fine so far, I can edit and activate/deactive scripts (using Thunderbird + Plugin) and they show up in the filesystem where I expect them to be, see below. The problem is that LDA doesn't find the script. From /var/log/dovecot-deliver.log:

# I have a simple function problem. I thought that I could write a function to modify a couple of vectors but I am doing something wrong #I have a standard cost vector called "fuel" and some adjustments to the #costs called "adjusts". The changes are completely dependend on the length #of the dataframe newdata I then need to take the modifed vectors and use # them later. I

Hi all, Thank you for taking the time to read my message. I'm trying to make a figure that plots p-values by a range of different adjustment values. (Using the **logit** function in package **car**) My Statistical analyses were conducted on probability estimates ranging from 0% to 100%. As it's not ideal to run linear models on percentages that are bounded between 0 and 1, these

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 Jim, Thanks for your help, I really appreciate it. Perhaps I'm misunderstanding, but does this formula run different ajustment values for this function? logit(p = doc$value, adjust = 0.025) I'm looking to plot the p-values of different adjustment values. Thanks so much, Kirsten On Wed, Jul 12, 2017 at 8:49 PM, Jim Lemon <drjimlemon at gmail.com> wrote: > Hi Kirsten,

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

Hi Kirsten, Perhaps this will help: set.seed(3) kmdf<-data.frame(group=rep(1:4,each=20), prop=c(runif(20,0.25,1),runif(20,0.2,0.92), runif(20,0.15,0.84),runif(20,0.1,0.77))) km.glm<-glm(prop~group,kmdf,family=quasibinomial(link="logit")) summary(km.glm) pval<-0.00845 padjs<-NA npadj<-1 # assume you have five comparisons in this family for(method in p.adjust.methods) {

This is a linear regression of Y onto factors... If I take log of Y, and regress onto the factors, I got: Multiple R-squared: 0.4023, Adjusted R-squared: 0.2731 If I don't take log of Y, and directly regress Y onto the factors, I got: Multiple R-squared: 0.1807, Adjusted R-squared: -0.001112 Is this negative adjusted R^2 a problem? What observation can I make here and what might

I have a, no doubt, simple question. I wish to write a function such that a <- 9 b <- 10 changer _ function(x,y) { if (y>x){ x <<- Y+1}} Of course there are easier ways to accomplish the task above, but I am more interested in how to have the "x <<- Y+1" part of the function to change x in place for purposes of a much larger function. I have been wrestling with

>>>>> "GS" == Gordon K Smyth <smyth@wehi.edu.au> >>>>> on Sat, 8 Jan 2005 01:11:30 +1100 (EST) writes: <.............> GS> p.adjust() unfortunately gives incorrect results when GS> 'p' includes NAs. The results from topTable are GS> correct. topTable() takes care to remove NAs before GS> passing

Mark, there is a fdr website link via Yoav Benjamini's homepage which is: http://www.math.tau.ac.il/%7Eroee/index.htm On it you can download an S-Plus function (under the downloads link) which calculates the false discovery rate threshold alpha level using stepup, stepdown, dependence methods etc. Some changes are required to the plotting code when porting it to R. I removed the

Hello, I have the following model: lm.7 <- lm(Y ~ F + C1 + C2 , data = EM4) F is a 4-level factor, the rest are covariates centered at their mean (Y is a two-column matrix). I have tried to find functions to give the model-adjusted means (adjusted at the covariates'means) and their standard deviations for each. (That is, what I believe is called in SAS "least square or LS-means,

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

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

First a statistical issue: The survfit routine will produce predicted survival curves for any requested combination of the covariates in the original model. This is not the same thing as an "adjusted" survival curve. Confusion on this is prevalent, however. True adjustment requires a population average over the confounding factors and is closely related to the standardized