similar to: linear model with similar response predictor

Hi, I'm using the R library(car) to draw confidence/prediction ellipses in a scatterplot. >From what i understood the ellipse() function return an ellipse based parameters: shape, center, radius . If i read dataEllipse() function i can see how these parameters are calculated for a confidence ellipse. ibrary(car) a=c(12,12,4,5,63,63,23) b=c(13,15,7,10,73,83,43) v <-

Hello all, I just noticed the following behavior of plot: x <- c(1,2,9) plot(x ~ x) # this is just like doing: plot(x) # when maybe we would like it to give this: plot(x ~ c(x)) # the same as: plot(x ~ I(x)) I was wondering if there is some reason for this behavior. Thanks, Tal ----------------Contact Details:------------------------------------------------------- Contact me:

Dear list, I have been using multivariate multiple regression (MMR) in the form lm(Y~X) where Y and X are matrices of response and predictor variables. I know that summary(mlm.object) would give the usual lm statistics for each response variable separately and that anova.mlm(mlm.object) will give the analysis of variance table of the mlm object. However, anova.mlm (also manova(mlm.object))

Hello, I have an experimental design where I would like to use separate ranking events to predict an independent ranking event. I have been using function clmm in the ordinal library but now realize that I am violating one of the assumptions. I have included a subset of the data. I am looking at how undergraduate students perceive the architecture and biological correctness of box and arrow

When I installed R 2.0.1 (replacing 1.9.0) for Windows, a code using "model.response" began acting up. Here are the first several lines of a code I had been tweaking for a spatial model (the code is mostly that of Roger Bivand--I am adapting it to a slightly different data structure and the problem I'm sure is with my changes, not his code). <command name> <- function

Hi folks, I use lm to run regression and I don't know how to predict dependent variable based on the model. I used predict.lm(model, newdata=80), but it gave me warnings. Also, how can I get the variance of dependent variable based on model. Thanks. [[alternative HTML version deleted]]

Dear all, I am quite new to R and I am having trouble writing the polynomial contrasts for an ordinal factor in MANOVA. # I have a model such as this fit<-manova(cbind(Y1,Y2,Y3)~Groups,data=Events) # where groups is an ordinal factor with 4 levels # how to set polynomial contrasts for the "Groups" factor ? Thank you very much in advance for any help! Best regards, Mauro -- Dr.

Colleagues, My interest is not in writing ad hoc functions (which I might use once to analyze my data), but rather what I will call a system function that might be part of a package. The lm function is a paradigm of what I call a system function. The lm function begins by processing the arguments passed to the function (represented in the function as parameters, see code below.) Much of this

I'm trying to write a formula method for canonical correlation analysis, that could be called similarly to lm() for a multivariate response: cancor(cbind(y1,y2,y3) ~ x1+x2+x3+x4, data=, ...) or perhaps more naturally, cancor(cbind(y1,y2,y3) ~ cbind(x1,x2,x3,x4), data=, ...) I've adapted the code from lm() to my case, but in this situation, it doesn't make sense to include an

Hi group, I have this type of data x(predictor), y(response), factor (grouping x into many groups, with 6-20 obs/group) I want to fit a linear regression with one common intercept. 'factor' should only modify the slopes, not the intercept. The intercept is expected to be >0. If I use y~ x + factor, I get a different intercept for each factor level, but one slope only if I use y~ x *

Interesting discussion. A few things occurred to me. Apologies to Iris Simmons: I mixed up his answer with Bert's question. Bert raises questions about promises, and I think they are related to John Sorkin's question. A big difference between R and most other languages is that function arguments are computed lazily. match.call & substitute tell us what expressions will be evaluated

I have a data with binary response variable, repcnd (pregnant or not) and one predictor continuous variable, svl (body size) as shown below. I did Hosmer-Lemeshow test as a goodness of fit (as suggested by a kind “R-helper” previously). To test whether the predictor (svl, or body size) has significant effect on predicting whether or not a female snake is pregnant, I used the differences between

Hi everyone, I am trying to construct a glm and am running into a couple of questions. The data set I am using consists of 6 categories for the response and 6 independent predictors representing nutrient concentrations at sample point locations. Ultimately I'd like to use the probabilities for each response category in a simulation model such that these probabilities are used to define a

I currently have a data frame whose rows correspond to each student and whose columns are different variables for the student, as shown below: Lastname Firstname CATALOG_NBR Email StudentID EMPLID Start 1 alastname afirstname 1213 *@uark.edu 10295236 # 12/2/2008 2 anotherlastname anotherfirstname 1213 **@uark.edu ## 10295236 9/3/2008 Xattempts Q1

Thanks Denis, I was looking for the option 'dns:x' in the wiki but I didn't find it. Now it works. I used    log level = 3 auth:3  dns:0 auth_audit:3 gives me unknown class message But where I can find a complete list of classes for log level? I'll also give a try on the last version of samba with json. Thanks again Giuseppe On 1/18/2018 4:52 PM, Denis Cardon wrote:

On 18/06/2014 04:17, Charles Lepple wrote: > On Jun 13, 2014, at 2:53 AM, Giuseppe Corbelli <giuseppe.corbelli at copanitalia.com> wrote: > >> As said in previous mail, I just finished a first working version of a driver for the UPS found on ASEM PB1300 device >> (http://www.asem.it/prodotti/industrial-automation/box-pcs/performance/pb1300/) >> Linux only, accessed

To whom it may concern, I happened to run the following R code just to check the layout of the output, but found that the code doesn't work the way I thought it should work. '' > lm(rnorm(100) ~ rnorm(100)) Call: lm(formula = rnorm(100) ~ rnorm(100)) Coefficients: (Intercept) -0.07966 Warning messages: 1: In model.matrix.default(mt, mf, contrasts) : the response appeared

There's an ancient (2003) document on the CRAN "developers' page" https://developer.r-project.org/model-fitting-functions.html that is sort of (but not exactly) what you're looking for ... On 2025-01-07 5:03 p.m., Sorkin, John wrote: > Colleagues, > > My interest is not in writing ad hoc functions (which I might use once to analyze my data), but rather what I

* * Hi , Can anyone help me please with this problem?* * *CASE-I* all_raw_data_NAomitted is my data frame.It has columns with names i1 ,i2, i3,i4…, till i15.It has 291 rows actually ,couldn’t show here. The data frame looks like this:-- i1 i2 i3 i4 i5 i6 i7 i8 i9 i10 i11 i12 i13 i14 i15 2 2 2 2 2 2 2 2 2 2 2 1 2 2 3 2 3 2 2 2 2 3 2 2 3 3

As said in previous mail, I just finished a first working version of a driver for the UPS found on ASEM PB1300 device (http://www.asem.it/prodotti/industrial-automation/box-pcs/performance/pb1300/) Linux only, accessed through i2c/SMBUS. If you can spare some time, please review. Expecially my autotools skills are ehm... a bit rusty. -- Giuseppe Corbelli WASP Software Engineer,