similar to: Pearson curves and CPK

In running the following example of a loglinear model for the Titanic data, I was surprised to see NaN reported for the Pearson chisq > loglin(Titanic, margin=list(1:3, 4)) 2 iterations: deviation 2.273737e-13 $lrt [1] 671.9622 $pearson [1] NaN $df [1] 15 $margin $margin[[1]] [1] "Class" "Sex" "Age" $margin[[2]] [1] "Survived" Tracing it back,

Dear R users, pROC is a package to compare, visualize, and smooth receiver operating characteristic (ROC) curves. The package provides the following features: * Partial or full area under the curve (AUC) computation * Comparison of two ROC curves (curves and AUC) * Calculating and plotting confidence intervals * Smoothing of the ROC curve * Coordinates extraction ('coords' function).

Dear R users, pROC is a package to compare, visualize, and smooth receiver operating characteristic (ROC) curves. The package provides the following features: * Partial or full area under the curve (AUC) computation * Comparison of two ROC curves (curves and AUC) * Calculating and plotting confidence intervals * Smoothing of the ROC curve * Coordinates extraction ('coords' function).

Thank you for your reply. I am aware of the good reasons not to use the deviance estimate in binomial, Poisson, and gamma families. However, for the inverse Gaussian, the choice seems to me less clear cut. So I just wanted to compare two different options. I have used the dispersion parameter to compute the standardized deviance residuals: summary(model.gamma)$deviance.resid

Hi all, This is a very silly question or something escapes me: Let obj a simple gam poisson model. Let >obj<-gam(....,family=poisson) >obj1<-update(obj, family=quasi(link="log", var="mu")) >From summary.glm(obj1) the dispersion parameter is estimated 1.165; In fact it is: > (predict(obj1, se.fit=T)$se.fit[1:5]/predict(obj, se.fit=T)$se.fit[1:5])^2 4

R version 1.3.0 OS: SunOS 5.7, but I think the same problem occurs with Windows An incorrect formula seems to be used to calculate the pearson residuals for a generalized linear model with a binomial response. Here is a simple program which gives (a) the pearson residuals calculated directly, (b) the pearson residuals from glm, and (c) the deviance residuals from glm. The first and last

Can anyone show me how to add a log Pearson type III plot using the evdistq() command to an extreme value plot using the lmom package? Attached sample code below... Thanks in advance, Dave library(lmom) # annual maximum daily streamflows Mackenzie River mackenzieRiver = c(26600, 30300, 34000, 32000, 29200, 28300, 28600, 26400, 28300, 28800, 29000, 22100, 32900, 31800, 21600, 32100, 27000,

Dear R forum, I am bit confused and please guide me - (1) Is "Pearson Type III Distribution" as given in lmomco package same as Three Parameter Pearson 5 Distribution? If not, how do I estimate the parameters of Three Parameter Pearson 5 Distribution? (2) Is there any other R forum dealing with only Statistical queries? Kindly guide Regards Katherine [[alternative HTML version

I have used the correlation analysis (pearson) in the agricolae package to analyse my data and got unexpectedly low p-values (therefore making many more highly significant correlations in my data than I had expected). I am wondering if the p-values given should be subtracted from 1 to give the real p-value, because for each variable compared against itself has a p-value of 1 and I thought it

Full_Name: Carmen Fernandez Version: 1.3.1 OS: Submission from: (NULL) (138.251.202.115) I think there is a problem when computing Pearson residuals, in that they seem to be computed at the raw residuals divided by the square root of the corresponding diagonal element of the weight matrix W evaluated at the last step of the iterative model fitting procedure (IWLS), instead of dividing by the

Hello, I will be very obliged if someone can help me with this statistical R problem: I am trying to do a Pearson correlation on my datasets X, Y with randomization test. My X and Y datasets are pairs. 1. I want to randomize (rearrange) only my X dataset per row ,while keeping the my Y dataset as it is. 2. Then Calculate the correlation for this pair, and compare it to your true

Is there any reason that rstandard.glm doesn't have a "pearson" option? And if not, can it be added? Background: I'm currently teaching an undergrad/grad-service course from Agresti's "Introduction to Categorical Data Analysis (2nd edn)" and deviance residuals are not used in the text. For now I'll just provide the students with a simple function to use, but I

hello list, i use the code below [macro-chanspy] exten => s,1,Authenticate(${ARG1}) exten => s,n,ChanSpy(SIP/${EXTEN:3},dqs) exten => s,n,Hangup app-chanspy] exten => _0071XX,*1,*Macro(chanspy,1234) exten => _0072XX,*1,*Macro(chanspy,5678) exten => _0073XX,*1,*Macro(chanspy,8910) but when i do 007100 for exemple i spy another agnet 102 or 103 any help please thanks and

Dear Sir, I am referring to your package "FAdist". I wish to know how to estimate the parameters of the distribution - "Log-Pearson Type III Distribution"? Will it be possible for you to guide me or inform the package in R, I can use to estimate the parameters. Regards Katherine [[alternative HTML version deleted]]

Hi everyone, I've been trying to find a function that outputs the Pearson and/or Spearman correlation coefficients for several variables with the associated statistics in one single table/matrix. For what I've been able to understand the Stats package is only able to compute these coeficients/statistics only in defined pairs. This becomes time consuming when we want to determine these

Dear Sirs What is the best aproximation to the standardized normal distribution: necessidade = c("sem necessidade","com necessidade") tipo =c("CE-1", "CE-2", "CE-3") dados=c(20,34,44,69,9,3) Tabela =cbind(expand.grid(list(Necessidade=necessidade, Tipo=tipo)), count=dados) Tabela.array=tapply(Tabela$count, Tabela[,1:2], sum) ni =

Greetings, I have been using lm to perform weighted linear regressions and resid to extract the residuals. I happened upon some class notes on the internet that described how one can specify type="pearson" in resid to extract the weighted residuals. Where is this option documented? And if you know that, what is the best way to find such documentation if you don't know that the

Hi, Seeking suggestions to compute uncentered (pearson correlation) correlation efficiently. corr from stats library works on x and y columns. dist from amap library works on x and y rows. My data layout is slightly different such that row(i) of matrix x is compared to row(i) of matrix y. Thanks [[alternative HTML version deleted]]

How can I compute the pearson correlation p-values for all combinations of columns of 2 matrices ? > m <- matrix(rnorm(20), nrow=4, dimnames=list(LETTERS[1:4], letters[1:5])) > m1 <- matrix(rnorm(20), nrow=4, dimnames=list(LETTERS[1:4], letters[1:5])) > cor(m,m1) a b c d e a -0.67533294 -0.2516151 -0.3780815 0.55816011

Hello, I found an interesting program on Pierre Legendre's webpage: http://www.bio.umontreal.ca/casgrain/en/labo/corr_permute.html With this program one can compute a "Pearson correlation coefficient matrix with permutation test". This is exactly what I need as an R-package because so far I have only analyzed my data with the function cor(). However, I need additional