similar to: evaluation methods for logistic regression with proportion data

Sorry I have a very simple question: I used somers2 function from Design package: > z<- somers2(x,y, weights=w) results are: >z C Dxy n Missing 0.88 0.76 500 0.00 Now I want to call only the value of C to be used in further analyses, but I fail to do it. I have tried: > z$C NULL > z[,C] Error in z[,C]: incorrect number of dimensions and some other silly

Hi, I'm plotting a multiple histogram using the function multhist {package plotrix}, something like: library(plotrix) mh <- list(rnorm(200, mean=200, sd=50), rnorm(200, mean=250, sd=50)) multhist(mh) In this graph y-axis represents the frequency of observations.... but I would like it to be scaled into relative frequencies, does anybody know how to do this with multhist or similar

Dear all, I'm having a few problems trying to reshape a data frame. I tried with reshape{stats} and melt{reshape} but I was missing something. Any help is very welcome. Please find details below: ################################# # data in its original shape: indiv <- rep(c("A","B"),c(10,10)) level.1 <- rpois(20, lambda=3) covar.1 <- rlnorm(20, 3, 1) level.2

Dear all, I'm trying to estimate the parameters of a lognormal distribution fitted from some data. The tricky thing is that my data represent the time at which I recorded certain events. However, in many cases I don't really know when the event happened. I' only know the time at which I recorded it as already happened. Therefore I want to fit the lognormal from the cumulative

Dear useRs, In a data.frame (df) I have several columns (x1, x2, x3....xn) containing data as a continuous numerical response: df var x1 x2 x3 1 143 147 137 2 93 93 117 3 164 39 101 4 123 118 97 5 63 125 97 6 129 83 124 7 123 93 136 8 123 80 79 9 89 107 150 10 78 95 121 I want to

Dear useRs, I am fitting a mixed model using the function lmer from the package lme4, but I have some problems when I try to test the effect of my factors of interest. First let me explain the structure of the model: I'm measuring animal movements. Explicitly, I am interested in displacement (straight-line distance from an initial point). Displacements are measured longitudinally, with one

Dear mailing group, This is my first time here. Glad to have this resource! I am currently trying to load an Excel file into R (limma package loaded) using the source(*name of directory*) command, but it cannot open the file. I renamed the file as .R and .RData, to no avail. The Excel data contains one gene name per row and about 100 data points per gene (columns). I am only used to

Dear R Admins, I received an unsolicited e-mail from BlueInference as an R user. Does it mean that R that our e-mails (and names) is sharing it's user database with third parties without our consent? Or perhaps the BlueInference guys are using an e-mail address miner to get our contact details? [SNIP] Dear Gorden Jemwa, As a fellow R user, I am sure you agree with me that R is a

Hallo! Does anybody know how to calculate a sample size estimation for proportions with continuity correction? ? I only found EpiR which seems to calculate without continuity correction: library(epiR) epi.studysize(treat = .65, control = .50, n = NA, sigma = NA, power = 0.80, r = 1, conf.level = 0.95, sided.test = 2, method = "cohort") $n [1] 340 Thanks! Karl

Greetings all, I am using lmer (lme4 package) to analyze data where the response is a proportion (0 to 1). It appears to work, but I am wondering if the analysis is treating the response appropriately - i.e. can lmer do this? I have used both family=binomial and quasibinomial - is one more appropriate when the response is a proportion? The coefficient estimates are identical, but the standard

hi all, why we implement VM_metrics.VCPUs_utilisation as the proportion of all cpu time? Then XenAPI client (xm) can not get the accumulated cpu_time of each vcpu. I recommend we make VM_metrics.VCPUs_utilisation just the cpu_time which is maintained by hypervisor. Then XenAPI client (xm) not only can get the accumulated cpu_time of each vcpu, but also can calculate the proportion by getting

Hallo! I found a question exactly as mine, but I did not found an answer. Therefore?I post this again - hopefully there will be an answer! Thanks in advance! karl From: Berta <ibanez> Date: Tue, 27 Feb 2007 18:58:48 +0100 Hi R-users, I want to calculate the sample size needed to carry out a 2-sample proprotion test. I have the hypotesized treatment probability of success (0.80), the

Hello group, my question is deciding what test would be appropriate for following question. An experiment 'A' yielded 3200 observations of which 431 are significant. Similarly, using same method, another experiment 'B' on a different population yielded 2541 observations of which 260 are significant. There are 180 observations that are common between significant observations of A

Please forgive me, I feel exceptionally like a newbie. Although I've read screeds of documentation, I just can't see how this is done. I have a data frame that contains a number of pass/fails for certain variable sizes. From that, I would like to form another data frame that contains the proportions of pass/fails per variable. So, for example: df <- data.frame( Var=c(3,3,3,4,4),

Hi R users, I need some help in the followings: I'm doing factor analysis and I need to extract the loading values and the Proportion Var and Cumulative Var values one by one. Here is what I am doing: > fact <- factanal(na.omit(gnome_freq_r2),factors=5); > fact$loadings Loadings: Factor1 Factor2 Factor3 Factor4 Factor5 b1freqr2 0.246 0.486 0.145

Hello everyone, I am confused about how the dependent variable should be specified, e.g. say S and F denote series of successes and failures. Is it share<-S/(S+F) glm(share~x,family=quasibinomial) or glm(cbind(S,F)~x,family=quasibinomial) The two variants produce very different dispersion parameter and deviances. The book by Crawley, the only one R-book a have, says the second variant is

Using the survey package I find it is convenient and easy to get estimated proportions using svymean, and their corresponding estimated standard errors. But is there any elegant/simple way to calculate corresponding confidence intervals for those proportions? Of course +/- 1.96 s.e. is a reasonable approximation for a 95% CI, but (incorrectly) assumes symmetrical distribution for a proportion.

BODY { font-family:Arial, Helvetica, sans-serif;font-size:12px; }I am looking to draw what I hoped was a simple plot of proportion WITHIN a strata, save % males by site. I seem to be able to get proportion of males, by site, where the proportion is across the whole dataset, but not the proportion within each site. thanks in advance, Rob [[alternative HTML version deleted]]

Hi, All. I have scatterplot in 2-D I'd like to see which proportion of data goes into a range. How shall I figure it out? Perhaps some accumulative graph or something? Kind regards, Tammy _________________________________________________________________ Drag n’ drop—Get easy photo sharing with Windows Live™ Photos. http://www.microsoft.com/windows/windowslive/products/photos.aspx

Hi, ALL, Is there any function in R that does the exact test for the matched pair proportions (one sided), which I assume is binomial(b+c, .5). Thanks, Annie [[alternative HTML version deleted]]