similar to: Somers Dyx

Displaying 20 results from an estimated 800 matches similar to: "Somers Dyx"

2002 Jun 20
1
Bug in printing symbolic derivative (PR#1697)
A colleague pointed out the following error in D(): f <- expression(exp(-2*x*y)) Dx <- D(f, 'x') Dy <- D(f,'y') Dxy <- D(Dx, 'y') Dyx <- D(Dy, 'x') Then > Dx # this is right -exp(-2 * x * y) * (2 * y) > Dy # this is right exp(-2 * x * y) * (-2 * x) > Dxy # this is wrong in the sign of the second term!!! -exp(-2 * x * y) *
2009 Jul 15
1
negative Somers D from Design package
Dear R help My problem is very similar to the analysis detailed here. If we use the mayo dataset provided with the survivalROC package the estimate for Somer's Dxy is very negative -0.56. The Nagelkerke R2 is positive though 0.32. I know there is a difference between explained variation and predictive ability but I am surprised there is usch a difference given that even a non predictive model
2011 May 05
1
Confidence interval for difference in Harrell's c statistics (or equivalently Somers' D statistics)
Dear All, I am trying to calculate a 95% confidence interval for the difference in two c statistics (or equivalently D statistics). In Stata I gather that this can be done using the lincom command. Is there anything similar in R? As you can see below I have two datasets (that are actually two independent subsets of the same data) and the respective c statistics for the variables in both cases.
2007 Mar 23
1
lmer estimated scale
I have data consisting of binary responses from a large number of subjects on seven similar items. I have been using lmer with (crossed) random effects for subject and item. These effects are almost always (in the case of subject, always) significant additions to the model, testing this with anova. Including them also increases the Somers' Dxy value substantially. Even without those
2006 Apr 18
6
lambda, uncertainty coefficient (& Somers D)
Dear colleagues in R, Has anybody implemented the 1) (Goodman & Kruskal) lambda or the 2) (Thiel's) uncertainty coefficient statistics (in the asymmetric and symmetric forms), or is anyone aware that they might reside in some package? A search in the R archives does indicate that they are (somehow) part of the CoCo package, but I would rather not start learning how to transform my
2009 Jul 15
0
Nagelkerkes R2N
I am interested Andrea is whether you ever established why your R2 was 1. I have had a similar situation previously. My main issue though, which I'd be v grateful for advice on, is why I am obtaining such negative values -0.3 for Somers Dxy using validate.cph from the Design package given my value of Nagelkerke R2 is not so low 13.2%. I have this output when fitting 6 variables all with
2011 Mar 01
1
which does the "S.D." returned by {Hmisc} rcorr.cens measure?
Dear R-help, This is an example in the {Hmisc} manual under rcorr.cens function: > set.seed(1) > x <- round(rnorm(200)) > y <- rnorm(200) > round(rcorr.cens(x, y, outx=F),4) C Index Dxy S.D. n missing uncensored Relevant Pairs Concordant Uncertain 0.4831 -0.0338 0.0462 200.0000
2011 Feb 19
0
contrasting Somer's D from Design package
Dear R help, I am having a problem with the Design package and my problem is detailed here. I fit a cox model to my data and validate the Somer's Dxy using the Design package. (Because of computation time problem, i only try 10 bootstrap samples for the time being) This is the model without stratification: > library(Design) >
2007 Feb 19
1
random effect nested within fixed effects (binomial lmer)
I have a large dataset where each Subject answered seven similar Items, which are binary yes/no questions. So I've always used Subject and Item random effects in my models, fit with lmer(), e.g.: model<-lmer(Response~Race+Gender+...+(1|Subject_ID)+(1| Item_ID),data,binomial) But I recently realized something. Most of the variables that I've tested as fixed effects are properties
2011 Feb 21
2
Interpreting the example given by Prof Frank Harrell in {Design} validate.cph
Dear R-help, I am having a problem with the interpretation of result from validate.cph in the Design package. My purpose is to fit a cox model and validate the Somer's Dxy. I used the hypothetical data given in the help manual with modification to the cox model fit. My research problem is very similar to this example. This is the model without stratification: > library(Design) > f1
2011 Sep 14
0
Confidence interval or p-value for difference in two c-statistics
Dear All, Apologies if you have a seen a question like this from me before. I am hoping that if I re-word my question more carefully someone may be able to offer more help than the last time I asked something similar. I am using R 2.9.2 and Windows XP. I am trying to determine if there is a statistically significant difference between two c-statistics (or equivalently D statistics). In Stata
2008 Mar 25
3
derivatives in R
Hi, I posted this message earlier in "Rmetrics" and I don't know whether I posted in the wrong place, so I'm posting it again in Rhelp. I have a function in x and y and let's call it f(x,y). I need to get the Hessian matrix. i.e I need (d^2f/dx^2), (d^2f/dxdy), (d^2f/dydx), (d^2f/dy^2).I can get these using the D function. now I need to evaluste the hessian matrix for
2003 Apr 25
1
validate function in Design library does not work with small samples
Hi, I am using the validate function in the design library to get corrected Somer's Dxy for cox ph models. When my sample size is reduced from 300 to 150, the function complains (length of dimnames[1] not equal to array) and does not produce any results. There are no missing values in the data. Any suggestions for a work-around? Thank you in Advance. >
2004 Jun 04
1
use of "rcorr.cens" with binary response?
Dear R-helpers, I recently switched from SAS to R, in order to model the occurrence of rare events through logistic regression. Is there a package available in R to calculate the Goodman-Kruskal Gamma? After searching a bit I found a function "rcorr.cens" which should do the job, but it is not clear to me how to define the input vectors? Is "x" a vector with the fitted
2008 Sep 08
2
ROC curve from logistic regression
I know how to compute the ROC curve and the empirical AUC from the logistic regression after fitting the model. But here is my question, how can I compute the standard error for the AUC estimator resulting form logistic regression? The variance should be more complicated than AUC based on known test results. Does anybody know a reference on this problem? [[alternative HTML version deleted]]
2006 Jul 27
1
replace values in a distance matrix
Hi to everybody! I´m just a beginner in R, and I´m trying to replace values in a distance matrix with a concret condition: replace all values (elements) lower than 4.5 with value=18. I´ve tried this, but it doesn´t work... Dxy would be my 117 x 117 euclidean distance matrix M18 and M4.5 would be 117 x 117 matrices: M18<-matrix(rep(18,13689),nrow=117)
2005 Jul 19
1
ROC curve with survival data
Hi everyone, I am doing 5 years mortality predictive index score with survival analysis using a Cox proportional hazard model where I have a continous predictive variable and a right censored response which is the mortality, and the individuals were followed a maximum of 7 years. I'd like to asses the discrimination ability of survival analysis Cox model by computing a ROC curve and area
2004 Mar 11
5
Receiver Operator Characteristic curve
Dear R-helpers: I want to calculate area under a Receiver Operator Characteristic curve. Where can I find related functions? Thank you in advance Xiao
2011 Aug 19
1
help Dxy and C-index calculation
Dear professor, I am currently using Design package and the cph formula for assessing multivariable analysis. I am tryng to get the C-index for my survival model based on Dxy coefficient. I am confused since there is a negative value. Do I need to used the absolute Dxy ? index.orig training test optimism index.corrected n Dxy -0.341357727 -0.344002740
2009 Sep 08
1
rcorrp.cens and U statistics
I have two alternative Cox models with C-statistics 0.72 and 0.78. My question is if 0.78 is significantly greater than 0.72. I'm using rcorrp.cens. I cannot find the U statistics in the output of the function. This is the output of the help example: > x1 <- rnorm(400) > x2 <- x1 + rnorm(400) > d.time <- rexp(400) + (x1 - min(x1)) > cens <- runif(400,.5,2) > death