search for: influenti

If I have a column with 2 levels, but one level has no remaining observations. Can I remove the level? Had intended to do it as listed below, but soon realized that even though there are no observations, the level is still there. For instance summary(dbs3.train.sans.influential.obs$HAC) yields 0 ,1 4685,0 nlevels(dbs3.train.sans.influential.obs$HAC) yields [1] 2 drop.list <- NULL for (i in 1:ncol(dbs3.train.sans.influential.obs)) { if (nlevels(dbs3.train.sans.influential.obs[,i]) < 2) {drop.list <- cbind(drop.list,i)}} yields nothing becau...

I was wondering if someone might be able to tell me what formula R's influence.measures function uses for determining whether the hat value it computes is influential (i.e., the true/false value in the "hat" column of the returned is.inf data frame). The reason I'm asking is that its results disagree with what I've just learned in my statistics class, namely that a point should be considered influential if h_ii > 2(k+1)/n, where k+1 is th...

influence.measures gives several measures of influence for each observation (Cook's Distance, etc) and actually flags observations that it determines are influential by any of the measures. Looks good! But how does it discriminate between the influential and non- influential observations by each of the measures? Like does it do a Bonferroni-corrected t on the residuals identified by the influence measures or some other test? Cheers, Frank Tamborello,...

On 7/27/2016 12:17 PM, Chris Bieneman wrote: > > This is a really bad argument for large influential changes like this. Quite the contrary---anybody can participate and anybody can express their concerns, explain their goals, their workflow, etc. For a large influential changes like this, "zoning out" is a poor choice of action. > I suspect this is why the idea of having a surv...

Hi everyones, my function like; e <- rnorm(n=50, mean=0, sd=sqrt(0.5625)) x0 <- c(rep(1,50)) x1 <- rnorm(n=50,mean=2,sd=1) x2 <- rnorm(n=50,mean=2,sd=1) x3 <- rnorm(n=50,mean=2,sd=1) x4 <- rnorm(n=50,mean=2,sd=1) y <- 1+ 2*x1+4*x2+3*x3+2*x4+e x2[1] = 10 #influential observarion y[1] = 10 #influential observarion data.x <- matrix(c(x0,x1,x2,x3,x4),ncol=5) data.y <- matrix(y,ncol=1) data.k <- cbind(data.x,data.y) dataX <- data.k[,1:5] dataY <- data.k[,6] theta <- function(data) { B.cap <- solve(crossprod(dataX)) %*% crossprod(dataX...

...s less elements than full sample. Code as follows; e <- rnorm(n=50, mean=0, sd=sqrt(0.5625)) x0 <- c(rep(1,50)) x1 <- rnorm(n=50,mean=2,sd=1) x2 <- rnorm(n=50,mean=2,sd=1) x3 <- rnorm(n=50,mean=2,sd=1) x4 <- rnorm(n=50,mean=2,sd=1) y <- 1+ 2*x1+4*x2+3*x3+2*x4+e x2[1] = 10 #influential observarion y[1] = 10 #influential observarion X <- matrix(c(x0,x1,x2,x3,x4),ncol=5) Y <- matrix(y,ncol=1) Design.data <- cbind(X, Y) for (j in 1:nrow(X)) { result <- vector("list", ) for( i in 1: 3100) { data <- Design.data[sample(50,50,replace=TRUE),] ##### a...

Hi! I'm using a large panel data, and now I have faced some difficulties with my analysis. The predictors are not normally distributed and there are quite many outliers (some of them are influential though). I have tried to change the logarythm, but i'm not sure, how to do that. I want also draw a plot picture in which logarythms of predictors x and y are changed. How could I do that? Thanx before-hand! Liz [[alternative HTML version deleted]]

(I'm not subscribed; Mail-Followup-To set.) Contrary to the claim in the output of ./configure --help, $CPPFLAGS is in fact not influential. --- rsync-2.5.5/Makefile.in~ 2002-03-24 23:36:34.000000000 -0500 +++ rsync-2.5.5/Makefile.in 2002-11-12 17:52:04.000000000 -0500 @@ -9,6 +9,7 @@ LIBS=@LIBS@ CC=@CC@ CFLAGS=@CFLAGS@ +CPPFLAGS=@CPPFLAGS@ LDFLAGS=@LDFLAGS@ INSTALLCMD=@INSTALL@ @@ -45,7 +46,7 @@ # note that the -I. i...

Dear R-users, dear Ron I use pls from the pls.pcr package for classification. Since I need to know which variables are most influential onto the classification performance, what criteria shall I look at: a) B, the array of regression coefficients for a certain model (means a certain number of latent variables) (and: squared or absolute values?) OR b) the weight matrix RR (or R in the De Jong publication; in Ding & Gentl...

A lot of us wouldn't be here without him. DEC made good, really reliable hardware. mark <http://www.networkworld.com/news/2011/020711-kenneth-olsen-dec-obit.html>

...Biomedical o Environmental o Manufacturing o Adserving * Typical Post-Processing Steps * Techniques: o Generalized Path Seeker (GPS): modern high-speed LASSO-style regularized regression. o Importance Sampled Learning Ensembles (ISLE): identify and reweight the most influential trees. o Rulefit: ISLE on "steroids." Identify the most influential nodes and rules. * Case Study Example: o Output/Results without Post-Processing o Output/Results with Post-Processing o Demo [[alternative HTML version deleted]]

...s. o Financial Services o Biomedical o Environmental o Manufacturing o Adserving III. Typical Post-Processing Steps IV. Techniques: o Generalized Path Seeker (GPS): modern high-speed LASSO-style regularized regression. o Importance Sampled Learning Ensembles (ISLE): identify and reweight the most influential trees. o Rulefit: ISLE on "steroids." Identify the most influential nodes and rules. V. Case Study Example: o Output/Results without Post-Processing o Output/Results with Post-Processing o Demo [[alternative HTML version deleted]]

Hi, I am a new user of R. This is a conceptual doubt regarding screeing out outliers from the dataset in regression. I read up that Cook's distance can be used and if we want to remove influential observations, we can use the metric (>4/n) (n=no of observations) to remove any outliers. I also came across Grubb's test to identify outliers in univariate distns. (assumed normal) but i was not able to find contexts in Regression where Grubb's test is used (may be I didn't searc...

...7 6.2 ... 314 1325 2.1 1.5 I'd like that R used "plot" id because I delete some rows while studying regression, and R seems to be using the first id 1,2,3,4,...,314. Sometimes it's a mess to understand what R means in the plots when, for instance, states that data 200 is influential Thanks in advance, user at host.com -- View this message in context: http://r.789695.n4.nabble.com/delete-excel-id-automatically-generated-tp3492147p3492147.html Sent from the R help mailing list archive at Nabble.com.

...e-Location", "Cook's distance", "Residuals vs Leverage", "Cook's distance vs Leverage"), ......... ) {..............} So we now have 6 possible plots, where 1,2,3 and 5 are the defaults (and 1,2,3,4 where the old defaults). For the influential points and combination of 'influential' and 'outlier' there have been quite a few more proposals in the past. R <= 2.1.x has been plotting the Cook's distances vs. observation number, whereas quite a few people in the past have noted that all influence measures being more...

...gression (Crawley "The R Book"). I subsequently try to analyse each feeding guild seperately, but to no avail.overdispersion remains. Given the number of categorical variables in my study, is there a convenient way to handle the overdispersion? I was trying tree models to see the most influential variables but again, to no avail. BTW: It may well be that the data is just bad... thanks a lot!

...ndent variable is the gender (family=binomial) and the predictors are percentages. I get a warning saying "fitted probabilities numerically 0 or 1 occurred" that is indicating that quasi-separation or separation is occurring. This makes sense given that one of these predictors have a very influential effect that is depending on a specific threshold separating these effects, in other words in my analysis one of these variables predicts males about the 80% of times when its values are less or equal to zero and females about the 80% when its values are greater than zero. I have been looking at o...

...--------------- INSERTED LINE # fit <- lm(formula = 1000/MPG.city ~ Weight + Cylinders + Type + EngineSize + DriveTrain, data = Cars93) print(lm.influence(fit)) ## row 57 should have hat = 1 and resid=0. summary(influence.measures(fit)) } ## only last two cols in row 57 should be influential ===+===+===+===+===+===+===+===+===+===+===+===+===+===+===+===+===+===+=== Then I re-ran make check, and everything seemed to go OK. I had a brief scan of the r-help mailing list archives just now and could find no allusions to this problem. Has anyone else encountered the problem? ***Should...

...going to focus on self-descriptive names rather than literary or fantasy references… Advanced Compiler Kit, affectionately known as “ACK!”? It has an ill- deserved nod to NeXT, even. (Completely the wrong language, after all.) Core Compiler? Heh, I don't think that'd get past certain influential marketing departments. Nor is this a C API, but that didn't stop Core Data or Core Image, so y'never know. Veloce, schnell, rapido, rápide. Various translations of fast. Schnell is fun, but to an English speaker it has an imperative sense to it that makes it useless as an adject...

...o compile a check-list for diagnostic measures for OLS regression. My question: Can you offer more (or newer) tests/measures for the validity of a linear model then what is given here: http://www.statmethods.net/stats/rdiagnostics.html This resource gives a list of measures to test for: OUTLIERS, INFLUENTIAL OBSERVATIONS, NON-NORMALITY, NON-CONSTANT ERROR VARIANCE, MULTI-COLLINEARITY, NONLINEARITY, NON-INDEPENDENCE OF ERRORS and some global validation. I came across it after searching online for ways to validate a regression model. Although this is a great list, I am wondering if there is any newer...