similar to: apply lm() to each row of a matrix

Hi, is there a way to replicate a data.frame like you can replicate the entries of a vector (with the repeat-function)? I want to do this: x <- data.frame(x, x) (where x is a data.frame). but n times. And it should be as cpu / memory efficient as possible, since n is pretty big in my case. thanks for any suggestions!

Hi, I have a data.frame which I want to aggregate. There are some grouping variables and some continuous variables for which I would like to have the mean. However there are also some factor-variables in the data-frame that are not grouping variables and I actually would like to aggregate these variables with the unique() function. Is that possible with the standard aggregate-function? If I

Hi, I wanted to use the predict.lm() function to compare the empirical data with the predicted values. The problem is that I have NAs in my data. I wanted to cbind my data.frame with the empirical values with the vector I get from predict.lm. But they don't have the same length because predict.lm just skip NA-predictions. Is there a way to get a vector with predicted values of the same

I have a large matrix, each row of which needs lm applied. I am certain than I read an article in R-news about this within the last year or two that discussed the application of lm to matrices but I'll be darned if I can find it with Google. Probably using the wrong search terms. Can someone steer me to this article of just tell me if this is possible and, if so, how to do it? My simplistic

Hi, I am trying to use xyplot to plot the relationship between size and day (y~x) by a food factor that has two levels, low and high. I have 3 reps per factor/day. I want the plots from each food treatment on the same axiss, so I used this code: xyplot(Size ~ Day, groups = Food, data = louis.data.means,col=1, pch=c(1,17), panel=function(x,y,groups,...){ panel.superpose(x,y,groups,...)

Hi, I need to aggregate rows of a data.frame by computing the mean for rows with the same factor-level on one factor-variable; here is the sample code: x <- data.frame(rep(letters,2), rnorm(52), rnorm(52), rnorm(52)) aggregate(x, list(x[,1]), mean) Now my problem is, that the actual data-set is much bigger (120 rows and approximately 100.000 columns) ? and it takes very very long

Hi, I'm quite new to R (1 month full time use so far). I have to run loop regressions VERY often in my work, so I would appreciate some new methodology that I'm not considering. #--------------------------------------------------------------------------------------------- y<-matrix(rnorm(100),ncol=10,nrow=10) x<-matrix(rnorm(50),ncol=5,nrow=10) #Suppose I want to run the

Hello: I have a 4-column dataset: Crime, Education, Urbanization, Age. I want to construct a multiple linear regression to find the effect of Education, Urbanization, and Age on Crime" lm(Crime ~ Education + Urbanization + Age) If I use + in above statement, does it mean it will build a model to find the relationship between Crime and Education when Urbanization and Age are held constant?

All but one of the summaries of multiple linear regressions in this analysis set present the residuals by min, 1Q, median, 3Q, and max. Example: lm(formula = TDS ~ Cond + Ca + Cl + Mg + Na + SO4, data = snow.cast) Residuals: Min 1Q Median 3Q Max -277.351 -32.551 -2.621 40.812 245.272 The one that doesn't has only a small number of rows (23) and presents the

Dear all, I was reading last night the lm and the Formula manual page, and 'I have to admit that I had tough time to understand their syntax. Is there a simpler guide for the dummies like me to start with? I would like to thank you in advance for your help Regards Alex [[alternative HTML version deleted]]

Hi, I currently running regression models on an experimental dataset. The model contains one independent continuous variable and two independent experimental conditions (one with two factors, the other with three factors) and several covariates. Now I get different results for a covariate in this model when I run aov(modell) vs. lm(modell). In the ancova model, one of the covariates seems to

Hi, When using the aggregate function to aggregate a data.frame by one or more grouping variables I often have the problem, that I want the mean for some numeric variables but the unique value for factor variables. So for example in this data-frame: data <- data.frame(x = rnorm(10,1,2), group = c(rep(1,5), rep(2,5)), gender =c(rep('m',5), rep('f',5))) aggregate(data,

Hi, I am reading the book "Mixed Effects Models in S and S-Plus" and come across an example with the Rail data. I tried to use lm(travel~Rail,data=Rail) and got the following result: Call: lm(formula = travel ~ Rail, data = Rail) Residuals: Min 1Q Median 3Q Max -6.6667 -1.0000 0.1667 1.0000 6.3333 Coefficients: Estimate Std. Error t value Pr(>|t|)

Hi, I have the following problem: I have two vectors: i <- c('a','c','g','h','b','d','f','k','l','e','i') j <- c('a', 'b', 'c') now I would like to generate a vector with the length of i that has zeros where i[x] != any element of j and 1 where i[x] == any element of j.

Hi, is there a function or an easy way to convert a variable with continuous values into a categorial variable (with x levels)? here is what I mean: I want to transform x: x <- c(3.2, 1.5, 6.8, 6.9, 8.5, 9.6, 1.1, 0.6) into a 'categorial'-variable with four levels so that I get: [1] 2 2 3 3 4 4 1 1 so each element is converted into its rank- value / categorial-value (in

Hello, I am doing a simple regression using lm(Y~X). As my response and my predictor seemed to be skewed and I can't meet the model assumptions. Therefore I need to transform my variables. I wanted to ask what is the preferred way to find out if predictor and/or response needs to be transformed and if yes how (log-transform?). I found a procedure in "A modern approach to Regressoin in

Hello dear all, I am unable to understand why when I run the following three lines: set.seed(4254) > a <- data.frame(y = rnorm(40), x=ordered(sample(1:5, 40, T))) > summary(lm(y ~ x, a)) The output I get includes factor levels which are not relevant to what I am actually using: Call: > lm(formula = y ~ x, data = a) > Residuals: > Min 1Q Median 3Q Max >

Hi, I get a lot of error messages with this command, but I don't understand why; plot(c(),c(), xlim=c(1,10), ylim=c(0,10000), log="y") thanks for any help! [[alternative HTML version deleted]]

Hi I want to do a series of linear models, and would like to define the input arguments for lm() as variables. I managed easily to define the formula arguments in a variable, but I also would like to have the "subset" in a variable. My reasoning is, that I have the subset in the results object. So I wiould like to add a line like: subs <- dead==FALSE & recTreat==FALSE which

Hi, I have multiple three dimensional arrays. Like this: x1 <- array(rnorm(1000, 1, 2), dim=c(10, 10, 10)) x2 <- array(rnorm(1000, 1, 2), dim=c(10, 10, 10)) x3 <- array(rnorm(1000, 1, 2), dim=c(10, 10, 10)) Now I would like to compute the mean for each corresponding cell. As a result I want to get one 3D array (10 x 10 x 10) in which at position x, y, z is the mean of the