search for: adjusted

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

...mod's data adjustment tools. -2: I used Joshua Ulrich's DataCamp guidance ( https://campus.datacamp.com/courses/importing-and-managing-financial-data-in-r/importing-text-data-and-adjusting-for-corporate-actions?ex=10) (and quantmod documentation) to determine how Alpha Vantage's data is adjusted. Here are my findings: -It seems that Alpha Vantage's OHLC data are unadjusted, and the adjusted close column provided is adjusted for splits, and split-adjusted dividends. -If I use AV's adjusted close column to adjust my OHCL data, my data will be adjusted for splits, and split-adjusted...

...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 is no 10th observation! # Adjusted Real rate - P > Sweden.p.adjust <- c(4.70243, 1.3776655, 1.117755, 1.6695175, 1.59282, > 1.1017625, -0.04295, 2.2552875, 0.0552875) > > # Adjusted Inflation deviation > Sweden.infl.dev.adjust <- c(0.110382497, -0.261612509, 0.040847515, > -0.195062497, -0.234362485, -0.023...

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 p.adjust (pt(2.086, df=...

...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: | 2012-04-29 12:17:48 deliver(jrspieker at well-adjusted.de): Info: Loading modules from directory: /usr/lib/dovecot/modules/lda | 2012-04-29 12:17:48 deliver(jrspieker at well-adjusted.de): Info: Module loaded: /usr/lib/dovecot/modules/lda/lib10_quota_plugin.so | 2012-04-29 12:17:48 deliver(jrspieker at well-adjusted.de): Info: Module loaded: /usr/lib/d...

...th(newdata) + adjusts[7]* adjusts[2]*adjusts[5] ; cppf[4] <- coal[2]*(1-adjusts[4])*length(newdata) + adjusts[7]* adjusts[4]*adjusts[6] ; cppm[2] <- coal[2]*(1-adjusts[1])*length(newdata) ; cppm[4] <- coal[2]*(1-adjusts[3])*length(newdata) label(cppm) <- "cppm - > SW coal costs adjusted " label (cppf) <- "cppf -> WW coal costs adjusted " # Any help or suggests would be greatly appreciated.

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(), and which explains quite clearly the concept...

...<-NA > npadj<-1 > # assume you have five comparisons in this family > for(method in p.adjust.methods) { > padjs[npadj]<-p.adjust(pval,method=method,n=5) > npadj<-npadj+1 > } > plot(padjs,xaxt="n",main="P plot",xlab="Method",ylab="adjusted p values") > abline(h=0.05,col="lightgray") > library(plotrix) > staxlab(1,at=1:8,labels=p.adjust.methods) > > Jim > > > On Thu, Jul 13, 2017 at 12:53 AM, Kirsten Morehouse > <kmoreho1 at swarthmore.edu> wrote: > > Hi all, > > > > Th...

...t significant value it does not match up to the one computed by the method p.adjust: > setwd("C:/work/Methylation/IMA/GM/siteLists") > > hypTable <- read.delim("hypernormal vs others.txt") > pList <- hypTable$p > names(pList) <- hypTable$site > > adjusted <- p.adjust(pList, method="BH") > adjusted[1] cg27433479 0.05030589 > > pList[1]*nrow(hypTable)/1 cg27433479 0.09269194 I tried to recreate this is a small example of a vector of 5 p values but everything worked as expected there. I was wondering if there is some subtle...

...mary(km.glm) pval<-0.00845 padjs<-NA npadj<-1 # assume you have five comparisons in this family for(method in p.adjust.methods) { padjs[npadj]<-p.adjust(pval,method=method,n=5) npadj<-npadj+1 } plot(padjs,xaxt="n",main="P plot",xlab="Method",ylab="adjusted p values") abline(h=0.05,col="lightgray") library(plotrix) staxlab(1,at=1:8,labels=p.adjust.methods) Jim On Thu, Jul 13, 2017 at 12:53 AM, Kirsten Morehouse <kmoreho1 at swarthmore.edu> wrote: > Hi all, > > Thank you for taking the time to read my message. I'm t...

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 be the problem? Thanks!

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

...ns}" is desirable for all P-value adjustment methods. One argument for keeping ``n = #{all observations}'' at least for some correction methods is the following "continuity" one: If only a few ``irrelevant'' (let's say > 0.5) P-values are replaced by NA, the adjusted relevant small P-values shouldn't change much, ideally not at all. I'm really no scholar on this topic, but e.g. for "holm" I think I would want to keep ``full n'' because of the above continuity argument. BTW, for "fdr", I don't see a straightforward way to...

...moved the xaxs="s" arguement on line 80. The fdr function requires a list of p-values as input, a Q-value (*expected* false discovery rate control at level Q) and a required method of fdr controlling procedure. > As you can see after running the code, the p values are truly being > adjusted, but for what FDR? If I set my p value at 0.05, does that mean > my FDR is 5%? I have been told by someone that is the case but, > normally, when discussing FDR, q values are reported or just one p value > is reported--the threshold for a set FDR. The p.adjust documentation is > unclear...

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, whose errors one obtains by STDERR) I have tried help.search and RSiteSearch with several terms including "standard errors", &q...

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 can't be reliably compared with one another, then what is the reasoning behind adjusting R^2 for degrees of freedom? What are the good published authorities...

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 incidence ratio concept found in epidemiology. To answer your technical question: fit <- coxph(Surv(.........