similar to: patterns of missing data: determining monotonicity

Dear, I would like to post the following question to the r-help on Nabble (thanks in advance for the attention, Gustavo Vieira): Hi there. I have a data set on hands with 5,220 cases and I'd like to automate some procedures (but I have almost no programming knowledge). The data has some continuous variables that are grouped by 2 others: the name of species and the locality where they were

I am scraping data from a web page using XML (excellent package BTW - that's scraping data the easy way!). So far, I've got the code: tables <- readHTMLTable(theurl) rhf <- tables$tabResHistFull div1 <- rhf[which(rhf$V1=="Div ps"),] div1 which is giving me the result:        V1 V2    V3    V4    V5    V6    V7          V8    V9   V10   V11   V12   V13   V14  V15 15

This seems to be a recurrent topic, but I don't remember hearing a definitive answer. I also apologies for cross-posting. Say I have a numerical response variable and a bunch of multi-level factors I want to use for modeling. I don't expect factor interaction to be important so there will be no interactions in the model. All this would be a perfect job for ANOVA except for one additional

Hi all, I'm looking for smooth monotone estimation packages, preferably using splines. I downloaded the 'cobs' package and intend to use it, but since it offers only quadratic splines based on L1 minimization, I'd like to compare its performance to that of a more 'mainstream' cubic-spline, L2-norm minimizing spline. Preferably a smoothing spline. Does anyone know of such

Here is a programming question. The code I am using is quite slow and I was wondering if there is a way to get rid of the for loop. I am dealing with "interaction" in 2x2 table, and am using Edwards's G_I (Likelihood, p. 194). I label the cells in the table as follows stim response "y" "n" total -------------------------------- y hit miss nsignal

I have a data frame that looks like this: > print(df) V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 1 FN 8.637 28.890 31.430 31.052 29.878 33.215 32.728 32.187 29.305 31.462 2 FP 19.936 30.284 33.001 35.100 30.238 34.452 35.849 34.185 31.242 35.635 3 TN 0.000 17.190 16.460 21.100 17.960 15.120 17.200 17.190 15.270 15.310 4 TP 22.831 31.246 33.600 35.439 32.073

I am having a problem with creating a vector from a rows or columns, I searched around and found as.vector(x), but it does not seem to do what it says it does I have included an example below, of doing what would seem to be the method required to create a vector, but instead it creates a one row data frame. What is required to actually create a vector. Many thanks Philip > data

I'm using stepAIC to fit a model. Then I'm trying to use that model to predict future happenings. My first few variables are labeled as their column. (Is this a problem?) The dataframe that I use to build the model is the same as the data I'm using to predict with. Here is a portion of what is happening.. This is the value it is predicting = > [1] 9.482975 Summary of the

Hola!! tengo un data.frame donde cada fila corresponde a un año y cada columna a un mes (De enero a diciembre) > head(valT) V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 1941 18.0 16.3 15.2 10.1 8.1 8.3 8.8 9.2 7.9 12.2 11.9 14.6 1942 17.2 15.9 13.6 11.6 8.7 6.2 6.4 7.2 9.7 12.0 14.1 16.7 1943 17.6 17.3 13.5 12.5 10.5 7.0 8.2 7.9 -999.9 -999.9

hi i have a dataframe with the name "obsdata" V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 1 1001 3 24 12 24.7 44.4 70.1 49.3 33.7 3.0 6.8 2.7 NA 2 1001 3 25 0 70.1 49.3 33.7 138.2 152.5 NA 4.2 6.9 17.5 3 1001 3 25 12 33.7 187.7 286.5 386.7 NA 16.2 46.0 48.8 43.1 4 1001 3 26 0 88.6 129.4 NA NA

A mi también me funciona para los dos casos: > dat <- read.csv("d11-16.csv", header=FALSE, sep=",", dec=".", skip=11, nrows=1) > dat V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 1 masa total en µg 30.04633 ug PEAKS MUY PEQUENOS NA NA NA NA NA NA NA > dat18 <- read.csv("d11-18.csv", header=FALSE,

Hi all, I want to make all possible combination from dataset below: V1 <- c(0,1,2) V2 <- c(0,1) V3 <- c(0,1) V4 <- c(0,1) V5 <- c(0,1) V6 <- c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20) V7 <- c(0,1,2,3,4,5,6) V8 <- c(0,1) V9 <- c(0,1) V10 <- c(0,1) V11 <- c(0,1) V12 <- c(0,1) V13 <- c(0,1) V14

Hi, I am trying to use R to mimic what I did in SAS. proc transreg data=x ; model identity(GSI)=monotone(group1); output out=d2 pprefix=M; run; Accroding to SAS documentation, the MONOTONE transfomation algorithm comes from (Kruskal 1964, secondary approach to ties). I have tried ace. it does provide some kind of monotone transformation, but it is not what I expected. Here is how

I am working with some survival data with missing values. I am using the mice package to do multiple imputation. I have found code in this thread which handles pooling of the MI results: https://stat.ethz.ch/pipermail/r-help/2007-May/132180.html Now I would like to plot a survival curve using the pooled results. Here is a reproducible example: require(survival) require(mice) set.seed(2) dt

Dear R- User,   I have a dataset that looks like the following:   jh<-data.frame(  'id'=seq(1,10,1),   'time0'=c(8,5,8,8,9,NA,NA,2,4,5),   'time4'=c(NA,NA,9,8,NA,2,3,2,4,5),  'time8'=c(NA,2,8,NA,5,NA,2,3,NA,4),  'time12'=c(NA,2,NA,NA,NA,3,3,2,3,NA),  

Dear all, I have been attempting to use multiple imputation (MI) to handle missing data in my study. I use the mice package in R for this. The deeper I get into this process, the more I realize I first need to understand some basic concepts which I hope you can help me with. For example, let us consider two arbitrary variables in my study that have the following missingness pattern: Variable 1

I'm trying to apply a function defined in the VW R docs, that attemps to convert a data.table object to Vowpal Wabbit format. In the process i'm getting the error in printf mentioned in the subject. The original function is here: https://github.com/JohnLangford/vowpal_wabbit/blob/master/R/dt2vw.R Below there is a small example that reproduces the error. The function works great with

R-helpers: I have a dataset that has 168 subjects and 12 variables. Some of the variables have missing data and I want to use the multiple imputation capabilities of the "mice" package to address the missing data. Given that mice only supports linear models and generalized linear models (via the lm.mids and glm.mids functions) and that I need to fit Cox models, I followed the previous

Hi, I have written the following function to check whether a vector has elements satisfying monotonicity. is.monotone <- function(vec, increase=T){ # check for monotonicity in time-stamp data for cortisol collection ans <- TRUE vec.nomis <- vec[!is.na(vec)] if (increase & any(diff(vec.nomis,1) < 0, na.rm=T)) ans <- FALSE if (!increase & any(diff(vec.nomis,1) > 0,

Hi, I tried to load the package "kernlab" under R-v11 and R-v10, however it gave error message: Error in library.dynam(lib, package, package.lib) : shared library 'kernlab' not found In addition: Warning message: package 'kernlab' was built under R version 2.12.0 Error: package/namespace load failed for 'kernlab' Has anybody loaded this successfully before?