search for: dxy

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 -0.341357727 -0.002645013 -0.338712715 40 R2 0.084694141 0.095440176 0.07907759...

Hello R Community, I'm continuing to work through logistic regression (thanks for all the help on score test) and have come up against a new opposition. I'm trying to compute Somers Dyx as some suggest this is the preferred method to Somers Dxy (Demaris, 1992). I have searchered the [R] archieves to no avail for a function or code to compute Dyx (not Dxy). The overview of Hmisc has mention of Dyx for the rcorr.cens function but this appears to be a misprint because the manual states the function finds Dxy. Peng and So (1998) state that...

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) M4.5<-matrix(rep(4.5,13689),nrow=117) REPLACE ALL VALUES >4.5 WITH 18.0 in Dxy for(i in 1:117) { for(j in 1:117) { Dxy[i,j]<-ife...

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 should have Dxy around 0. Am I doing something wrong or is there an...

Hi there, I'm new to R but I thought this is the most likely place I could get advice or hints w.r.t the following problem: I have a series of measurements xi with associated uncertainties dxi. I would like to construct the probability density histogram of this data where each density estimate has an associated error that is derived from the dxi. In other words, for large dxi the

Hi all, So I've started working on this and I ran into something that I didn't know, namely that for x a multi-dimensional (2+) array, head(x) and tail(x) ignore dimension completely, treat x as an atomic vector, and return an (unclassed) atomic vector: > x = array(100, c(4, 5, 5)) > dim(x) [1] 4 5 5 > head(x, 1) [1] 100 > class(head(x)) [1] "numeric" (For a

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 <- cph(S ~ age, x=TRUE, y=TRUE) > coef(f) age 0.0440095 > se...

...9;first') options(datadist = 'dd.bc') # a univariate Cox regression: cph.bc <- cph(formula = Surv(time1,status1)~bc1, data = frame.bc, x=TRUE, y=TRUE, surv=TRUE) anova(cph.bc) cph.bc summary(cph.bc) # the validate command for the Cox model: val.cph.bc <- validate(cph.bc, B=200, dxy=TRUE , pr=TRUE) ---------------------- Output from the validate command: training test Dxy -0.124360 -0.1423409 R2 1.000000 1.0000000 Slope 1.000000 0.7919584 D 0.016791 0.0147536 U -0.002395 0.0006448 Q 0.019186 0.0141088 training test Dxy -0...

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 0.0000 200.0000 39800.0000 19228.0000 0.0000 That S.D. confuses me!! It is obviously not the standard...

...he 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 <- d.time <= cens > d.time <- pmin(d.time, cens) > rcorrp.cens(x1, x2, Surv(d.time, death)) Dxy S.D. x1 more concordant x2 more concordant n missing uncensored Relevant Pairs -8.902077e-03 2.649712e-01 4.955490e-01 5.044510e-01 4.000000e+02 0.000000e+00 4.000000e+00 1.348000e+03 Unc...

...mfp package. The predictors in the model were selected by stepAIC(). library(Design); library(Hmisc); library(mfp); data(GBSG) fit <- cph( Surv( rfst, cens ) ~ htreat + tumsize + tumgrad + posnodal + prm, data=GBSG, x=T, y=T ) val <- validate.cph( fit, dxy=T, B=200 ) round(val, 3) index.orig training test optimism index.corrected n Dxy -0.377 -0.383 -0.370 -0.013 -0.364 200 R2 0.140 0.148 0.132 0.016 0.124 200 Slope 1.000 1.000 0.925 0.075 0.925 200 D...

...Import Binary Raw Data" module, I used: > rsaga.get.usage("io_grid", 4) SAGA CMD 2.0.3 library path: C:/Progra~1/saga_vc/modules library name: io_grid module name : Import Binary Raw Data Usage: 4 [-GRID <str>] [-FILE_DATA <str>] [-NX <num>] [-NY <num>] [-DXY <str>] [-XMIN <str>] [-YMIN <str>] [-UNIT <str>] [-ZFACTOR <str>] [-NODATA <str>] [-DATA_OFFSET <num>] [-LINE_OFFSET <num>] [-LINE_ENDSET <num>] [-DATA_TYPE <num>] [-BYTEORDER_BIG <num>] [-TOPDOWN <num>] -GRID:<str> Gr...

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) > cox1a<-cph(surv.obj~factor(ecog)+factor(grade)+factor(tumor)+factor(extra),x=T,y=T) > coef1a<-coef(cox1a)...

...date comorbidities. I wish to compare the discrimination of the two comorbidity measures, i.e. I have two non-nested Cox models. I get the following output with > rcorrp.cens(myscore.lp, charlson.lp, Surv(time, dead), method=1): x1 = My comorbidity score, x2 = Charlson [,1] Dxy "-0.0605" S.D. "0.00648" x1 more concordant "0.4697" x2 more concordant "0.5302" n "1.369e+04" missing "0" uncensored "9411" Relevant Pairs "1.587e+08&quot...

...comorbidities. I wish to compare the discrimination of the two comorbidity measures, i.e. I have two non-nested Cox models. I get the following output with > rcorrp.cens(myscore.lp, charlson.lp, Surv(time, dead), method=1): x1 = My comorbidity score, x2 = Charlson [,1] Dxy "-0.0605" S.D. "0.00648" x1 more concordant "0.4697" x2 more concordant "0.5302" n "1.369e+04" missing "0" uncensored "9411" Relevant Pairs "1.587e+08&quot...

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) * (-2 * x) * (2 * y) + exp(-2 * x * y) * 2 >...

Hello All, as I?m new to R and survival analysis, I?ve got a question about the Design::validate function: My Code: cox <- cph(Surv(t,status) ~ var1 + var2 + var3, data=data, x=TRUE, y=TRUE, surv=TRUE) cox.val <- validate(cox, B=10, dxy=TRUE, pr=TRUE); My output (cox.val): index.orig training test Dxy -0.3639222921368090891 -0.3591157308750822175 -0.3634294047761231106 R2 1.0000000000000000000 1.0000000000000000000 1.0000000000000000000 Slope 1.0000000000000000000 1.0000...

...roject,and try to internal validate the model through using the function ?validate( )?,and get the ROC through the function ?plot.roc( )?.The outcomes like this: At last I want to get the Logistic model ,and get the prediction accuracy .Now the ?Area under the curve?(0.6931) is not too bad,but the ?Dxy?(I think it as the prediction accuracy probability) is too low.And I don?t know which reason lead to the outcomes.Maybe I have a mistake understanding on the function ?lrm( )?,and apply it wrong. Could you please give me some idea on how to resulve this problem? Thanks in advance for your kind...

On 10/30/19 04:29, Martin Maechler wrote: >>>>>> Gabriel Becker >>>>>> on Tue, 29 Oct 2019 12:43:15 -0700 writes: > > > Hi all, > > So I've started working on this and I ran into something that I didn't > > know, namely that for x a multi-dimensional (2+) array, head(x) and tail(x) > > ignore dimension

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 p-values<0.01 I am wondering what the interpretation should be. I know my Nagelkerke R2 isn't very good but I compare my results with the exa...