similar to: Logistic regression goodness of fit tests

Hello, I am attempting to train a random forest model using the randomForest package on 500,000 rows and 8 columns (7 predictors, 1 response). The data set is the first block of data from the UCI Machine Learning Repo dataset "Record Linkage Comparison Patterns" with the slight modification that I dropped two columns with lots of NA's and I used knn imputation to fill in other gaps.

Dear All: I run a logistic regression (using lrm in the Design package), and after that, I use the command "summary" to get the marginal effects of each variable. But one strange thing happens on my binary dependent variable: The marginal effect of it jumping from 0 to 1 is 1.77. I believe the marginal effect of binary variable x1 has interpretation should be P(Y=1|x1=1,

Dear Sir or Madam? I am a doctor of urology,and I am engaged in developing a nomogram of bladder cancer. May I ask for your help on below issue? I set up a dataset which include 317 cases. I got the Binary Logistic Regression model by SPSS.And then I try to reconstruct the model ?lrm(RECU~Complication+T.Num+T.Grade+Year+TS)? by R-Project,and try to internal validate the model through

Hi, I am trying to construct a logistic regression model from my data (104 patients and 25 events). I build a full model consisting of five predictors with the use of penalization by rms package (lrm, pentrace etc) because of events per variable issue. Then, I tried to approximate the full model by step-down technique predicting L from all of the componet variables using ordinary least squares

[this message needed manual improvement by the mailing list administrator since it was `HTMLified' .. ``please do not''] Apologies for bothering you about a fairly trivial matter. I have been getting some inconsistencies with the display digits in R V1.5. I have been using the hypergeometric distribution function, and have found that when printing out the results from this

Hi, My data.frame "A" has FID like this FID a a b b b c c d d d d Now my second data.frame "B" has age value for a, b, c, d like FID Age a      5 b      7 c      9 d      3 How can search for the Age column in "B" and replace the values in "A" so that my new "A" looks like this FID Age a      5 a      5 b      7 b      7 b      7

Dear Forum, I have a question about interaction estimate in the Cox model: why the hazard ratio of the interaction is not produced in the summary of the model? (Instead, the estimate of the coefficient is given in the print of the model.) # Example: modINT <-cph( Surv(T_BASE, T_FIN,STATUS)~ NYHA + ASINI + RFP + FE_REC + XX_PR*XX_DISF) print(modINT) coef se(coef) z

I did have a verbose description of why but rather then make everyone's eyes bleed with useless details I ask the following :) To make a long story short: How can I make newmcReg[[i]]["PreIO308"] go away in the following list... er vector... no wait array.... dataframe.... awww crap... summary(newmcReg[[i]]) UNITBUILD UNITDB ITBUILD ITDB Mode :logical

My data is called xc and has more than 15 variables. When I used summary(xc) it gave me the detail description of each variable. Summary(xc) Y1 x1 x2 x3 .. Min. :0.0000 Min. : 1.000 Min. : 1.000 Min. : 1.000 1st Qu. :0.0000 1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.: 2.000 Median :1.0000 Median : 1.000

OK: > formatC(as.double(c(1,0,NA))) [1] "1" "0" "NA" NOT OK: > formatC(as.integer(c(1,0,NA))) [1] "0" "1072693248" "NA" > formatC(as.integer(c(0,1,NA))) [1] "0" "0" "NA" BUG TRACED TO R-code of formatC() where x[!Ok] <- 0 unintendedly changes the storage.mode of x to

Hello everybody! I'm trying to use nut-2.4.1 with brand new UPS Powercom Imperial IMD-825AP USB. I've faced a problem that the driver powercom despite specifying type=IMP automatically re-detects the UPS as "KIN" and then interprets raw data incorrectly. The same problem was reported by other Powercom users on the official support forum ( http://forum.pcm.ru ). I suppose the

The coxph function in R is not working for me when I use a continuous predictor in the model. Specifically, it fails to converge, even when bumping up the number of max iterations or setting reasonable initial values. The estimated Hazard ratio from the model is incorrect (verified by an AFT model). I've isolated it to the "x1" variable in the example below, which is log-normally

Hi I am working with this data: my data summary is: > summary(spi) open high low close volume Min. :4315 Min. :4365 Min. :4301 Min. :4352 Min. : 0 1st Qu.:4480 1st Qu.:4497 1st Qu.:4458 1st Qu.:4475 1st Qu.:11135 Median :4609 Median :4631 Median :4594 Median :4614 Median :14439 Mean :4620

Hi all! I've got this error while running example(Glm) library("rms") > example(Glm) Glm> ## Dobson (1990) Page 93: Randomized Controlled Trial : Glm> counts <- c(18,17,15,20,10,20,25,13,12) Glm> outcome <- gl(3,1,9) Glm> treatment <- gl(3,3) Glm> f <- glm(counts ~ outcome + treatment, family=poisson()) Glm> f Call: glm(formula = counts ~

Hey guys, I'd really appreciate any help. I have a multivariate analysis done, the output of which is: > GraphData <-read.table("eigen.coa") > GraphData V1 V2 V3 V4 1 1 0.371970 0.8552 0.8552 2 2 0.061785 0.1420 0.9972 3 3 0.001211 0.0028 1.0000 4 4 0.000000 0.0000 1.0000 > summary(GraphData) V1 V2 V3

I have been attempting to use cph models to get better calibration of my models for which I had originally used logistic regression. I tried running with 40 repetitions and got an error. I then tried 500 repetitions (thinking that the NaNs in the output below might be caused by that choice) and then let my computer crunch for several hours and got only the same error message and

I am exploring neural networks (adding non-linearities) to see if I can get more predictive power than a linear regression model I built. I am using the function nnet and following the example of Venables and Ripley, in Modern Applied Statistics with S, on pages 246 to 249. I have standardized variables (z-scores) such as assets, age and tenure. I have other variables that are binary (0 or 1). In

Hi, I would like to combine multiple xyplots into a single, multipanel display. Using R 3.0.1 in Ubuntu, I have used c() from latticeExtra to combine three plots, but the x-axis for two plots are on a log scale and the other is on a normal scale. I also have included equispace.log=FALSE to clean up the tick labels. However, when I try all of these, the x-axis scale of the first panel is used

Dear R list, members of my course have encountered the following error message: > slm <- lm(price ~ engsize, autoframe) Error in get(x, envir, mode, inherits) : variable "FUN" was not found [more context is given in the fuller listing below]. Once the error is encountered it seems to persist; for example early in one session: > summary(blin.fit) Call: lm(formula = Response

I think you are not using the best function for what your intentions are. Try: > by(data=mtcars, INDICES=list(as.factor(mtcars$am)), FUN=colMeans) : 0 mpg cyl disp hp drat wt qsec vs 17.1473684 6.9473684 290.3789474 160.2631579 3.2863158 3.7688947 18.1831579 0.3684211 am gear carb 0.0000000