similar to: Analyzing 2008 ANES using the weight variable

Hello, all. I'm new to R and just beginning to learn to write functions. I know I'm out of my depth posting here, and I'm sure my issue is mundane. But here goes. I'm analyzing the American National Election Study (nes), looking at mean values of a numeric dep_var (environ.therm) across values of a factor (partyid3). I use ddply from plyr and wtd.mean from Hmisc. The nes requires a

dear list, my question concerns the use of `eval' in defining the model formula for `nls' (version 2.6.2.). consider the following simple example, where the same model and data are used to perform unweighted and weighted fits. I intentionally used very uneven weights to guarantee large differences in the results #================================CUT=========================== ln

2 days ago I asked this on r-help, but no luck... since this is actually a programming question, I post it here again: my question concerns the use of `eval' in defining the model formula for `nls' when performing weighted fits. (I use version 2.6.2., but according to NEWS there were no changes to `nls' in 2.7.0, so the problem is still present). in this scenario their

Hi, If I used drop1 in a weighted lm fit, it seems to ignore the weights in the AIC calculation of the dropped terms, see the example below. Can this be right? Yan -------------------- library(car) > unweighted.model <- lm(trSex ~ (river+length +depth)^2- length:depth, dno2) > Anova(unweighted.model) Anova Table (Type II tests) Response: trSex Sum Sq Df F value

Hi, This is more of a general question than a pure R one, but I hope that is OK. I want to combine one-tailed independent p-values using the weighted version of fisher's inverse chi-square method. The unweighted version is pretty straightforward to implement. If x is a vector with p-values, then I guess that this will do for the unweighted version: statistic <- -2*sum(log(x)) comb.p <-

Greetings, I'm having trouble finding a simple way to calculate a weighted histogram where there may be zero raw counts in a given interval. Given equal-length vectors of data 'data' and weights 'w', and breaks (intervals) for the histogram, I calculate a weighted histogram as follows (see MASS's 'truehist' for an unweighted histogram): bin <- cut(data,

I'm trying to plot and compare weighted histograms and I can't seem find where to start. I have data similar to this: Miles2LAX RADAM2005Pct LAWA2005Pct 35.57 .00123 .00684 24.74 .00118 .00187 27.09 .00965 .00876 16.23 .00587 .00397 { ...

Dear all, I want to apply weights to my sample data set and I am struggling with the Efron Approximation with weights. I have got one sample data shown as below: customer week arrest fin age race weight 1 weight 2 weight 3 1 20 1 1 27 1 2 15 2 2 17 1 0 18 1 2 19 1 3 25 1 1 19 0 2 20 1 4 52 0 1 23 1 2 5 1 5 52 0 0 19 0 2 11 1 6 25 1 0 19 0 2 26 1 I applied four different weighted Efron

OK.. after school ended today, I poked around and found nothing so I started all over again. Followed Louis' instructions at https://github.com/thctlo/samba4/blob/master/full-howto-Ubuntu18.04-samba-AD_DC.txt all the way through but at the end, the resolver is not working - and kinit cannot find a KDC (I'm guessing because the resolver is not working!) This is the only server on the

In the HistData package, I have a data frame, PearsonLee, containing observations on heights of parent and child, in weighted form: library(HistData) > str(PearsonLee) 'data.frame': 746 obs. of 6 variables: $ child : num 59.5 59.5 59.5 60.5 60.5 61.5 61.5 61.5 61.5 61.5 ... $ parent : num 62.5 63.5 64.5 62.5 66.5 59.5 60.5 62.5 63.5 64.5 ... $ frequency: num 0.5 0.5

Package: logcheck-database Version: 1.2.40 Severity: wishlist Hi, Please duplicate the imap-login related lines and change them to filter out the equivalent messages emitted by pop3-login. regards Andrew -- System Information: Debian Release: testing/unstable APT prefers testing APT policy: (500, 'testing') Architecture: i386 (i686) Shell: /bin/sh linked to /bin/bash Kernel:

On 05/09/2020 07:46, Peter Pollock wrote: > I FINALLY DID IT!!!!! > > After following Louis van Belle's walk-through to create a new DC, and > having problems at the end, I realized there was nothing in the walk > through about modifying?/var/lib/samba/bind-dns/named.conf to let > Samba know the Bind version so I did that and Voila! > > We have name resolution, can

Hello all, I am using the function lm to do my weighted least square regression. model<-lm(Y~X1+X2, weight=w) What I am confused is the r.squared. It does not seem that the r.squared for the weighted case is an ordinary 1-RSS/TSS. What is that precisely? Is the r.squared measure comparable to that obtained by the ordinary least square? <I also notice that model$res is the unweighted

Hello R folks, Reproducible code below - I'm trying to do a weighted mean by a factor and can't figure it out. Thanks in advance for your assistance. Mike data<-data.frame(c(5,5,1,1,1), c(10,8,9,5,3), c("A","A","A","B","B"))

Dear R-experts: I am using survreg() to estimate the parameters of a Weibull density having right-censored observations. Some observations are weighted. To do that I regress the weighed observations against a column of ones. When I enter the data as 37 weighted observations, the parameter estimates are exactly the same as when I enter the data as the corresponding 70 unweighted observations.

I understand that the hat matrix is a function of the predictor variable alone. So, in the following example why do the values on the diagonal of the hat matrix change when I go from an unweighted fit to a weighted fit? Is the function hatvalues giving me something other than what I think it is? library(ISwR) data(thuesen) attach(thuesen) fit <- lm(short.velocity ~ blood.glucose)

Andrew, I very much appreciate your swift reply and your expertise. I readily admit I'm a little out of my depth here. I'm sitting here in California at almost midnight with just the weekend to get done all I need to do on the network and I fear this may be derailing my plans. This article on the Samba Wiki https://wiki.samba.org/index.php/Changing_the_IP_Address_of_a_Samba_AD_DC seems to

I would like to apply network measures (such as betweenness centrality, upper boundedness, etc.) to a weighted graph with non-integer weights, defined by a euclidean distance matrix. The package sna provides the measures that I want to use, but seems only to operate on binary graphs. I have read work by Mark Newman (http://aps.arxiv.org/abs/cond-mat/0407503/), who suggests that a weighted graph

I?m running some logistic regressions and I?ve been trying to include weights in the equation. However, when I run the model, I get this warning message: Here?s what it says: Warning message: In eval(expr, envir, enclos) : non-integer #successes in a binomial glm! I think it is because the weights are non-integer values. What is a good way to run logistic regressions in R when using

I sent this to r-devel the other day but didn't get any takers. This may not be a bug but rather an inconsistency. I'm not sure if this is intentional. summary.lm stores weighted residuals whereas I think most users will want print.summary.lm to summarize unweighted ones as if saying summary(resid(fit)). > set.seed(1) > dat <- data.frame(y = rnorm(15), x = rnorm(15), w = 1:15)