similar to: SEa nd CI

Dear all, I am trying to get an estimate of uncertainty surrounding a single predicted value from a beta regression model (this is similar to a logistic glm - in that it involves a link function and linear predictor - but it uses the beta distribution rather than discrete binomial). For example: library(betareg) data("GasolineYield")

Greetings, I'd be grateful if a good Samaritan helps me to approach this problem.... with my data, I've created the following model lm(formula = OUTCOME ~ VAR1 + VAR2) summary(model) Call: lm(formula = OUTCOME ~ VAR1 + VAR2) Residuals: Min 1Q Median 3Q Max -1.4341 -0.3621 0.1879 0.4994 0.7696 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.89020

Hello all, I have generated a principal components regression model using the pcr() function from the PLS package (R version 2.12.0). I am getting a "non-conformable arguments" error when I try to use the predict() function on new data, but only when I try to read in the new data from a separate file. More specifically, when my data looks like this #########training data

Full_Name: Yasuhiro Nakajima Version: 2.6.1 OS: WinXP SP2 Submission from: (NULL) (202.237.255.13) Dear all, I noticed the following behaviour of ci.plot in HH Package(ver.2.1-9): > library(HH) > data(women, package="datasets") > attach(women) > ft <- lm(height~weight) > windows() > ci.plot(ft,conf.level=0.95) > windows() > ci.plot(ft,conf.level=0.999) I

My data is called xc and has more than 15 variables. When I used summary(xc) it gave me the detail description of each variable. Summary(xc) Y1 x1 x2 x3 .. Min. :0.0000 Min. : 1.000 Min. : 1.000 Min. : 1.000 1st Qu. :0.0000 1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.: 2.000 Median :1.0000 Median : 1.000

Dear R users, Below I have written 4 functions CIT1, CIT2a and CIT2b and CIT3 which recode a variable CLD_ISCH into 3 new variables(T1 T2 T3), I wish to use T1, T2 and T3 based on the values of tf1 and tf2. (NOTE:- T2a is used to create T2 in a long winded manner due to my lack of programming experience) I then attach T1 T2 and T3 to a dataset KidneyT that contains other variables i wish to use

Dear all, How should I add several variables to a data frame without losing the attributes of the df? Consider the following: > require(Hmisc) > Xa <- iris > label(Xa, self=T) <- "Some df label" > str(Xa) 'data.frame': 150 obs. of 5 variables: $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9

Hello, I'm using pscl to run a hurdle model. Everything works great until I get to the point of making predictions. All of my "count" predictions are lower than my actual data, and lower than the "response" predictions, similar to the issue described here ( https://stat.ethz.ch/pipermail/r-help/2012-August/320426.html) and here (

Hi, I have run into another problem using offsets, this time with the predict function, where there seems to be a contradiction again between the behavior and the help page. On the man page for predict.lm, it says Offsets specified by offset in the fit by lm will not be included in predictions, whereas those specified by an offset term in the formula will be. While it indicates nothings about

As I understand it,  predict.lm(l ,newdata=nd ,interval="confidence") yields confidence bands for the predicted mean of new observations and lm.predict(l ,newdata=nd ,interval="prediction") yields confidence bands for new observations themselves, given an lm object l.   However with regard to {mgcv} although  predict.gam (g ,se.fit=TRUE ,interval= "prediction")

Greetings all, Ran into a strange problem with the krige function from geoR. The problem that I am having is that while the krige function seems to work well, the resulting predicted values are all NAs. Given the size of the datasets I am working with can't attach it, but I can provide snippets of the datasets. > casedata station year month day obs mpe bias type

Hello I carried out a logistic regression and found predicted values. Then I want to see both predictors (var1,var2..) and predicted values in same matrix. In other words, I need to know each combinations and predicted values. I used: cbind(var1,var2,var3,var4,predict(glm.obj,type="resp")) I got a somewhat strange result: var1 var2 var3 var4 var5 var6 predicted vals ------

I am trying to use the levelplot function from a command file. Here is the code: library(sp) library(gstat) library(lattice) gatherData <- read.table("~/gather.txt", header = TRUE) grd = makegrid(gatherData$x, gatherData$y, cell.size = 5) k <- krige(z~x+y, ~x+y, data = gatherData, newdata = grd, nmax = 5) levelplot(var1.pred~x+y, k, aspect = mapasp(k), main = "Predicted

Dear all, I'm using zeroinfl() from the pscl-package for zero inflated Poisson regression. I would like to calculate (aproximate) prediction intervals for the fitted values. The package itself does not provide them. Can this be calculated analyticaly? Or do I have to use bootstrap? What I tried until now is to use bootstrap to estimate these intervals. Any comments on the code are welcome.

Hi all, I try to make a split-plot with poisson errors using glmmPQL, but I have some doubts about the model simplification. Look my system: Block = 3 blocks Xvar1 = 2 levels Xvar2 = 13 levels Yvar = Count data Response I need know about the behaviour of Var1, Var2 and interaction Var1:Var2. Look the levels: > levels(Xvar1) [1] "A" "B" > levels(Xvar2) [1]

I'm pasting below a working R file featuring a function I'd like to polish up. I'm teaching regression this semester and every time I come to something that is very difficult to explain in class, I try to simplify it by writing an R function (eventually into my package "rockchalk"). Students have a difficult time with predict and newdata objects, so right now I'm

Hi, See below I reply your message for <https://stat.ethz.ch/pipermail/r-help/2008-April/160966.html>[R] predict.glm & newdata posted on Fri Apr 4 21:02:24 CEST 2008 You say it ##works fine but it does not: if you look at the length of yhat2, you will find 100 and not 200 as expected. In fact predict(reg1, data=x2) gives the same results as predict(reg1). So I am still looking for

hi all, I have a plot to which i would like to add text labels. And i cant find a way...here is the code : enaD2<-idw(D2~1, loca=dva, newdata=grd) pts = list("sp.points", dva, pch = 20, cex=1.5, col = "darkred spplot(enaD2, "var1.pred",sp.layout=pts, main = "globina 60 cm", sub="D2",

> On Dec 6, 2017, at 4:27 PM, Ashta <sewashm at gmail.com> wrote: > > Thank you Ista! Worked fine. Here's another (possibly more direct in its logic?): DM[ !ave(DM$x, DM$GR, FUN= function(x) {!length(unique(x))==1}), ] GR x y 5 B 25 321 6 B 25 512 7 B 25 123 8 B 25 451 -- David > On Wed, Dec 6, 2017 at 5:59 PM, Ista Zahn <istazahn at gmail.com> wrote:

Terry, I recently noticed the censor argument of survfit. For some analyses it greatly reduces the size of the resulting object, which is a nice feature. However, when combined with the id argument, only 1 prediction is made. Predictions can be made individually but I'd prefer to do them all at once if that change can be made. Chris ##################################### # CODE # create