similar to: Angela's review

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

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

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

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

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

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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

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Hi, I' am interested in differences between sample's result when samples consist of full elements and consist of only distinct elements. When sample consist of full elements it take about 120 sec., but when consist of only distinct elements it take about 4.5 or 5 times more sec. I expected that opposite of this result, because unique(sample) has less elements than full sample. Code as

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?

(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@

Dear community, I uploaded an excel with read.xls. My xls file actually have a column which is an id, ("plot" is the id) : plot height area 34 7.6 5.4 85 3.2 4.1 89 5.4 8.4 121 6.7 6.2 ... 1325 2.1 1.5 However R uses another id, this way: r id plot height area 1 34 7.6 5.4 2 85 3.2 4.1 3 89 5.4 8.4 4 121

dear list! i am running an anlysis on proportion data using binomial (quasibinomial family) error structure. My data comprises of two continuous vars, body size and range size, as well as of feeding guild, nest placement, nest type and foragig strata as factors. I hope to model with these variables the preference of primary forests (#successes) by certain bird species. My code therefore looks

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Hi there, I am trying to interface c++ code in R and make a package. With R CMD SHLIB the dll was created, but when I try R CMD check, I am getting 'undefined reference to..' linkage error messages. The relevant c++ source from conf-infomap.cpp: #include "conf-infomap.h" #include "R.h" // R functions #include "Rinternals.h" #include "Rmath.h" //

Hi there, I'm sorry if I a send it for second time, I've just subscribed for the list. I am trying to interface c++ code in R and make a package. With R CMD SHLIB the dll was created, but when I try R CMD check, I am getting 'undefined reference to..' linkage error messages. The relevant c++ source from conf-infomap.cpp: #include "conf-infomap.h" #include

Hi all, I have run a (glm) analysis where the dependent 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

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

Hi there, I am trying to interface c++ code in R and make a package. With R CMD SHLIB the dll was created, but when I try R CMD check, I am getting 'undefined reference to..' linkage error messages. The relevant c++ source from conf-infomap.cpp: #include "conf-infomap.h" #include "R.h" // R functions #include "Rinternals.h" #include "Rmath.h" //

I just (finally!!!) got R version 1.8.1 to configure and build under Solaris 9 (after much travail; there were funnies in my environment variables that mucked things up, but that's another story). Anyhow, when I ran ``make check'' I got an error right toward the end. Looking in the directory ``tests'' I found that the error was associated with the file reg-tests-3.R, and the