search for: casewise

...g correlation between two variables of unequal length. when i use cor(x,y,na.rm=T,use="complete") where x has observations from 1928 to 2006 & y has observations from 1950 to 2006. I used na.rm=T to use the "complete observations". So missing values should be handled by casewise deletion. But it gives me error Error in cov(close, close1, na.rm = T, use = "complete") : unused argument(s) (na.rm ...) Please help me with this as I am new to R. Thanks, Sonal

...sign ensured, that each household in the population has the same probability of being sampled. However the data were gathered from only one adult individual in each household, who was randomly choosen by an interviewer (via "Kish grid"). To equalize the probabilities for each INDIVIDUAL a casewise weighting factor is introduced. It is proportional to the reciprocal of the number of adults in the household and rescaled so it's sum equals the sample size. This weighting factor is neccessery to perform inferences for population of individuals. I had no problems with estimating models which...

Full_Name: Marek Ancukiewicz Version: 1.8.1 OS: Linux Submission from: (NULL) (132.183.12.87) Function cor() incorrectly handles missing observation with method="spearman": > x <- c(1,2,3,NA,5,6) > y <- c(4,NA,2,5,1,3) > cor(x,y,use="complete.obs",method="s") [1] -0.1428571 >

Arrrrr, I think I've found a bug in the behavior of the stats::cor function when NAs are present, but in case I'm missing something, could you look over this example and let me know what you think: > a = c(1,3,NA,1,2) > b = c(1,2,1,1,4) > cor(a,b,method="spearman", use="complete.obs") [1] 0.8164966 > cor(a,b,method="spearman",

Dear r-helpers, I am developing a smoothing function that performs a casewise deletion of all the non-informative observations in a bivariate dataset following the Poisson - D'Avril algorythm. But I am now facing two problems: 1)Being based on loops, the function takes a lot of time before having done its work and even if I tried I was not able to vectorize it. 2)...

A question for R (and perhaps S and SPlus) historians. Does anyone know the reason for the inconsistency in the way that the action that should be taken when data are missing is specified? There are several variants, na.action, na.omit, "T", TRUE, etc. I know that a foolish consistency is the hobgoblin of a small mind, but consistency can make things easier. My question is not meant

...and Cutler's latest Fortran version. There is also plan to add some functions so that the package will work with Adele Cutler's Java visualization program (RAFT). Best, Andy ==================================================== Changes in 4.3-0: * Thanks to Adele Cutler, there's now casewise variable importance measures in classification. Similar feature is also added for regression. Use the new localImp option in randomForest(). * The `importance' component of randomForest object has been changed: The permutation-based measures are not divided by their `standard er...

...and Cutler's latest Fortran version. There is also plan to add some functions so that the package will work with Adele Cutler's Java visualization program (RAFT). Best, Andy ==================================================== Changes in 4.3-0: * Thanks to Adele Cutler, there's now casewise variable importance measures in classification. Similar feature is also added for regression. Use the new localImp option in randomForest(). * The `importance' component of randomForest object has been changed: The permutation-based measures are not divided by their `standard er...

Barrett & Ling, JASA, 1992, v.87(417), pp184-191 define general classes of influence measures for multivariate regression models, including analogs of Cook's D, Andrews & Pregibon COVRATIO, etc. As in univariate response models, these are based on leverage and residuals based on omitting one (or more) observations at a time and refitting, although, in the univariate case, the

Hi there I have been using R to perform kmeans on a dataset. The data is fed in using read.table and then a matrix (x) is created i.e: [ mat <- matrix(0, nlevels(DF$V1), nlevels(DF$V2), dimnames = list(levels(DF$V1), levels(DF$V2))) mat[cbind(DF$V1, DF$V2)] <- DF$V3 This matrix is then taken and a distance matrix (y) created using dist() before performing the kmeans clustering. My query

Dear list, I just tried to use the function cluster.stat in the package fpc. I just have a couple of questions about the syntax: cluster.stats(d,clustering,alt.clustering=NULL, silhouette=TRUE,G2=FALSE,G3=FALSE) 1) the distance object (d) is an object obtained by the function dist() on my own original matrix? 2) clustering is the clusters vector as result of one of the many clustering methods?

...o library() was changed so that when the methods package is attached it no longer complains about formal generic functions not specific to the library. o list.files()/dir() have a new argument `recursive'. o lm.influence() has a new `do.coef' argument allowing *not* to compute casewise changed coefficients. This makes plot.lm() much quicker for large data sets. o load() now returns invisibly a character vector of the names of the objects which were restored. o New convenience function loadURL() to allow loading data files from URLs (requested by Frank Harrell)....

...o library() was changed so that when the methods package is attached it no longer complains about formal generic functions not specific to the library. o list.files()/dir() have a new argument `recursive'. o lm.influence() has a new `do.coef' argument allowing *not* to compute casewise changed coefficients. This makes plot.lm() much quicker for large data sets. o load() now returns invisibly a character vector of the names of the objects which were restored. o New convenience function loadURL() to allow loading data files from URLs (requested by Frank Harrell)....