similar to: Options for viewing / using results from lm

Hello, Below are the first two rows from my dataset and the header. This dataset has 5749 rows and I want to select only certain rows to be used based on existing grouping values. I am trying to group the data based on the values under 'ex_bin'. (e.g a group for 250, 251, 252, 500, 501, 502) I would then like to perform a lm for each grouping. My data: > all[1:2,] year extent scape

Hello, I am trying to export the results from symnum() while maintain their readability. I tried using sink to text file and also copying and pasting but the results end up looking like this: > symnum(c5.s) bC bED bEN bLP bLS bPA bPD bPR p bbContag 1 bbED + 1 bbENN_MN + B 1 bbLPI , , , 1 bbLSI + B B , 1 bbPAFRAC , * * , * 1 bbPD , B B

Dear R Team, I am a new R user and I am currently trying to subset my data under a special condition. I have went through several pages of the subsetting section here on the forum, but I was not able to find an answer. My data is as follows: ID NAME MS Pol. Party 1 John x F 2 Mary s S

Hi, I use lm or loess to make smoothing. After smoothing I need "Residual Standard Error" in my script. Could you please tell me how can I get this information? Thanks,

I have been using the "map" function in R and I was wondering if there is a way to map multi-state regions just showing the outline of the region without interior state borders. For instance, I am calculating aggregated statistics using 10 multi-state regions. I would like to color-code individual states based on my dataset but then to create black outlines around each of these 10

Hello, I am trying to produce some graphics to visualize my data. I think I want histograms. I have a grouping variable that has 48 different groups. I would like to produce a graphic that contains three of of these groups in the same device (they are time steps). I would like create graphics for about 6 different variables (6 variables X 16 groupings of there = 96 graphics). I am unsure of how

I don´t found any patent around this but i ned help with the patents. I can´t found all patents for interlazed video and I only read the patents around MPEG , the optimization for field based video can be aplied without problem, this is compatible whih the progresive encoding and in a few scenarios they can be positive. <p>process: <p>common: -2 bits to indicate video type 0x

Dear list members, I would like to know the difference in outputs and calculation processes between residuals.glm(object, type="response") and residuals.lm(object). For above-ground biomass estimation of trees, I estimated parameters of an allometric equation (ln y = b0 + b1*ln x) using glm as follows: fm <- glm(Ws~log(Wb), family=quasi(link="log",

Hi, I was playing around with something else and I noticed this matrix code for residuals in a linear model doesn't say what lm() says. Please tell me if I am completely misguided here. data(mtcars) Y <- as.matrix(mtcars[,1]) X <- as.matrix(mtcars[,c(2:11)]) # shouldnt this: H <- X %*% solve(t(X) %*% X) %*% t(X) (diag(dim(H)[1]) - H) %*% Y # be equal to this: residuals(lm(Y~X))

Dear list, I would like to know how the function residuals.lm calculates the partial residuals from an lm object with more than one predictor variable. In other words what is residuals.lm(...,type="partial") doing behind the scenes? According to the help file for residuals.lm (?residuals.lm), "The partial residuals are a matrix with each column formed by omitting a term from

I'm very new to R so maybe i'm doing something wrong so please let me know it that is that case. Here is an example where the summary() and residuals() applied to lm object produce different results (I think the residuals() results is correct since SAS produces those numbers - the second residual corresponding to observation with weight 4 is wrong in summary()). x <- c( 10, 20, 30, 40

Dear R community, Can I please get some advice on the following: I wish to obtain a list of residuals, padded by NAs for NAs in my source data. I tried several options of "na.action", but did not succeed... Example: I would like to get "0,0,0,0,NA,NA": > a [1] 1 2 3 4 NA 9 > b [1] 3 4 5 6 7 NA > lm(a~b)$residuals 1 2 3 4 0 0 0 0 >

I am using the lm function in R to fit several linear models to a fair-sized dataset (~160 collections of ~1000 data points each). My data have intrinsic, systematic uncertainty much greater than the measurement errors on any individual point. My thought is to use the residuals of my linear fits to quantify this intrinsic uncertainty, but I am puzzled over the correct interpretation of R's

Hello, I have been using: model <- lm(y~x+I(x^2)) I am namely interested in the values of the residuals. If I use the 'names' command I get the following: names(model) [1] "coefficients" "residuals" "effects" "rank" [5] "fitted.values" "assign" "qr" "df.residual" [9]

Dear list members, I want to save residuals above or less than a certain value to an R object. I have performed a multiple linear regression, and now I want to find out which cases have a residual of above + 2.5 and ? 2.5. Below I provide the R commands I have used. Reg<-lm(a~b+c+d+e+f) # perform multiple regression with a as the dependent variable. Residuals<-residuals(reg) # store

Dear friends, In s-plus, lm() generates the the studentized residuals automatically for us, and In R, it seems don't have the results: After i fitted lm(), i use attibutes() to see the objects and didn't find studentized residuals . How to get the the studentized residuals in lm(),have i missed something? thanks very much! -- Kind Regards, Zhi Jie,Zhang ,PHD Department of Epidemiology

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)

Residuals are stored as a numeric vector. The R software comes with a document "Introduction to R" that discusses basic math functions and logical operators that can create logical vectors: abs( stdresiduals ) > 2.5 It also discusses indexing using logical vectors: stdresiduals[ abs( stdresiduals ) > 2.5 ] Note that in most cases it is worth going the extra step of making your

Here's a bug one of my students noticed. When you call plot() on a glm object, plot.lm gets called. The second plot it shows is supposed to give a normal QQ plot of the standard deviance residuals, but it doesn't. The glm object created by glm.fit returns something (the IRLS weights?) in fit$weights which plot.lm takes as observation weights, so you get strange residuals in the QQ

Hi, I have always been wondering why S-Plus/R can not fit a linear model to an arbitrary large data set given that, I thought, it should be pretty straightforward. Sometime ago I came across a reference to LM package, http://www.econ.uiuc.edu/~anovo/LM.html, by Roger Koenker and Alvaro Novo. So I thought here it is at last, but to my surprise this project hasn't made to the recommended