search for: freeni

Hi Say I want to add manually an intercept in the function lm. Even if almost all results will be identical, few stats are different as DF counting will be different as intercept will not be included in "automatic" case, while it will be in "manual" case. See: ###usual lm on freeny fr<-lm(freeny.y~freeny.x) ###manual lm on freeny man<-cbind(1,freeny.x)

Full thread here: https://github.com/tidyverse/broom/issues/287 Reproducible example: is.object(freeny$y) # [1] TRUE attr(freeny$y, 'class') # [1] "ts" class(freeny$y) # [1] "ts" # ts attribute wiped by model.frame class(model.frame(y ~ ., data = freeny)$y) # [1] "numeric" attr(model.frame(y ~ ., data = freeny)$y, 'class') # NULL # but still:

Hi My goal is to do a (multiple) regression, just knowing that my Y variables will be the say k first variables of a matrix/data frame. I thought I should do it with eval(parse)) but encounter a strange problem. See: lm(y~.-y, data=freeny) #that's what I want to do in the one equation case #Problem is I don't know name of the variable... only that it is the first one... #so idea is to

Hello! I would love to be able to include an external variable to a lm call, I mean something: if(TRUE) a<-freeny.x[,4] else a<-NULL lm(freeny.y~freeny.x[,-4] +a) but it does not work with a<-NULL, whereas lm(freeny.y~freeny.x[,-4] +NULL) I don't understand why and did not find an answer in the manuals... do you see it? Any idea? Thanks!!

Dear all, I have discovered an object named 'freeny.x' which seems in-built. Can somebody tell me why it is there and what purpose it is serving? 'ls()' syntax does not seem to catch this object in the environment. Thanks,

This one showed up while looking at one of Ripley's other reports: > data(freeny) > model.frame(y~1,data=freeny,subset=1:10) y 1962.25 8.79236 1962.5 8.79137 1962.75 8.81486 1963 8.81301 1963.25 8.90751 1963.5 8.93673 1963.75 8.96161 1964 8.96044 1964.25 9.00868 1964.5 9.03049 > model.frame(y~1,data=freeny,subset=1:10)$y Warning: Replacement length not a

[Copied to R-devel for information] This applies to all versions of R I have: 0.62.2, 0.62.3, 0.63. Great care seems needed with glms with offsets, as many things seem wrong. Consider the following: > data(freeny) > freeny.glm <- glm(y ~ offset(lag.quarterly.revenue) + price.index + income.level + market.potential, data=freeny, subset=1:30) > predict(freeny.glm) Qtr1

Hi, A recent thread provided a (working) construct for lm: lm(as.matrix(freeny[ix]) ~., freeny[-ix]) Can someone explain what is meant by the formula in that expression, that is, what does "mymatrix~." do? I couldn't find any such example in the lm() or formula() help pages. thanks Carl

can you recommend a good manual for R that starts with a data set and gives demonstrations on what can be done using R? I downloadedR Langauage definition and An introduction to R but haven't found them overly useful. I'd really like to be able to follow some tutorials using a dataset or many datasets. The datasets I have available on R are Data sets in package 'datasets':

Hi, I’d like to fit one GLM model for each possible combination of inputs (i.e. exhaustive search). The package leaps can help me to generate all possible variable subsets, but I’ll appreciate your guidance as of how to generate one model for each of those possible subsets. I’m new in R! Thanks in advance for your help. library(leaps) subsets <- regsubsets( y ~ . , data=freeny, nbest=100,

Hi I have a few problems with tail/head when applied to multiple time series. I'm not sure as whether I did not understand the function or whether it correspond to an unexpected behavior. When head(a,n) is applied on data.frame or matrix, it returns a data-frame or matrix with first n obs of *each* variable. When applied to a mts object, it returns first n obs of *first* variable only,

Bug PR#9316 noted an inconsistency between the Cook's distance contours on plot.lm(x, which = 5) and the values given by cooks.distance(x) -- as shown in plot.lm(x, which = 4) -- for glms: http://bugs.r-project.org/cgi-bin/R/Analyses-fixed?id=9316;user=guest;selectid=9316 The suggested fix was to modify the contour levels by a dispersion factor, implemented as follows: dispersion <-

Full_Name: Luzi P. Schucan Version: R-base-0.90.1-2.i386.rpm OS: Linux Submission from: (NULL) (141.84.136.129) I just wanted to install the rpm package with rpm --v -i [file], and here is the log: (I DID run it as root!!) (the important thing is in the end: there must be a bug in the post install script. The problem is, that it does install all the files, but it doesn't correctly hang the

<<insert bug report here>> ------------------------------------------------------ Error: R : Copyright 2002, The R Development Core Team Version 1.5.1 (2002-06-17) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type `license()' or `licence()' for distribution details. R is a collaborative project with