similar to: linear regression in a data.frame using recast

Ha! -- A fortunes candidate? -- Bert > > If this is really a time series, then you will have serious validity > problems due to auto-correlation among non-independent units. (But if you > are just searching for a way to pull the wool over the eyes of the > statistically uninformed, then I guess there's no stopping you.) > > -- > > David Winsemius, MD > West

Dear Help-Rs,   I have data similar to the following:   DF <- structure(list(X = 1:22, RESULT = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("NEG", "POS"), class = "factor"), YR_MO = c(201011L, 201012L, 201101L, 201102L, 201103L, 201104L, 201105L, 201106L, 201107L, 201108L, 201109L, 201011L,

Hi! I'm failing to understand the value of the intercept value in a multiple linear regression with categorical values. Taking the "warpbreaks" data set as an example, when I do: > lm(breaks ~ wool, data=warpbreaks) Call: lm(formula = breaks ~ wool, data = warpbreaks) Coefficients: (Intercept) woolB 31.037 -5.778 I'm able to understand that the value of

Dear all, model.frame behaves in a way I don't expect when both its formula and subset argument are passed through a function call. This works as expected: model.frame(~wool, warpbreaks, breaks < 15) #> wool #> 14 A #> 23 A #> 29 B #> 50 B fun1 <- function(y) model.frame(~wool, warpbreaks, y) fun1(with(warpbreaks, breaks < 15)) #> wool #> 14

Hi, I'd like to select one row in a data frame per subset which is maximal for a particular value. I'm pretty close to the solution in the sense that I can easily select the maximal values per subset using "aggregate", but I can't really figure out how to select the rows in the original data frame that are associated with these maximal values. library(stats) # this

Is there a way to do this in R? I have data in the form: 57_input 57_output 58_input 58_output etc. can i use a for loop (i in 57:n) that plots only the outputs? I want this to be robust so im not specifying a column id but rather something like c++ code, %s_input, i is that doable in R? Thanks, justin

is there a way to look for value changes in a column? set.seed(144) df<-data.frame(state=sample(rep(1:5,200),1000)) any of the five states are acceptable. however if, for example, states 4 or 5 follow state 3, i want to overwrite them with 3. changes from 1 to any value and 2 to any value are acceptable as are changes from any value to 1 or 2. By way of an example: the sequence 1 3 3 5 5 3

Hi all, I'm demonstrating a block entry regression using R for my regression class. For each block, I get the R**2 and the associated F. I do this with separate regressions adding the next block in and then get the results by writing separate summary() statements for each regression. Is there a more convenient way to do this and also to get the change in R**2 and associated F test for

Happy weekend helpeRs! As usual, I'm stumped by R... My plan was to take an integer number, convert it to binary and wind up with a data.frame where each column is either 1 or 0 so I can see which bits are changing: bb<-function(i) ifelse(i, paste(bb(i %/% 2), i %% 2, sep=""), "") my.dat<-c(36,40,10,4) my.binary.dat<-bb(my.dat)

I think need to do something like this: dat<-data.frame(state=sample(id=rep(1:5,each=200),1:3, 1000, replace=T,prob=c(0.7,0.05,0.25)),V1=runif(1,10,1000),V2=rnorm(1000)) rle.dat<-rle(dat$state) temp<-1 out<-data.frame(id=1:length(rle.dat$length)) for(i in 1:length(rle.dat$length)){ temp2<-temp+rle.dat$length[[i]] out$V1[i]<-mean(dat$V1[temp:temp2])

Hi all, I tended to use rbind, or cbind to force a vector be be deemed as a column or row vector. This is very important if I want to do things like u' * A * u, where u' is a row vector and u is a column vector, regardless of what originall format the "u" is... I want to recast it to column vector or row vector... How can I do that?

One problem I've been having is the special case in which only one row/column remains and the variable gets converted into a vector when entries are removed by logical masking. This is a problem because subsequent code may rely on matrix operations (apply, colsums, dim, etc) For example: > a <- matrix(c(1, 2, 3, 4), nrow = 2) > a [,1] [,2] [1,] 1 3 [2,] 2 4 >

Am 07.10.2010 um 19:50 schrieb Chris Lattner: > > On Oct 6, 2010, at 6:16 PM, Gabor Greif wrote: > >> Hi all, >> >> I am slowly working on a SwitchInst optimizer (http://llvm.org/ >> PR8125) >> and now I am running into a deficiency of the x86 >> peephole optimizer (or jump-threader?). Here is what I get: >> >> >> andl $3,

Quick question, as I attempt to learn R. For post-hoc tests 1) Is there an easy function that will take, say the results of tukeyHSD and create a grouping table. e.g., if I have treatments 1, 2, and 3, with 1 and 2 being statistically the same and 3 being different from both Group Treatment A 1 A 2 B 3 2) I've been stumbling over the proper syntax for simple effects for a tukeyHSD

I have a specific question and a general question. Specific Question: I want to do an analysis on a data frame by 2 or more class variables (i.e., use 2 or more columns in a dataframe to do statistical classing). Coming from SAS, I'm used to being able to take a data set and have the output of the analysis in a dataset for further manipulation. I have a data set with vote totals, with one

I am trying to extract the shape and scale parameters of a wind speed distribution for different sites. I can do this in a clunky way, but I was hoping to find a way using data.table or plyr. However, when I try I am met with the following: set.seed(144) weib.dist<-rweibull(10000,shape=3,scale=8) weib.test<-data.table(cbind(1:10,weib.dist))

On Oct 13, 2010, at 11:22 AM, Gabor Greif wrote: > Hi Chris, > > I had a look into MachineCSE, but it looks like MBB-oriented. > The above problem is an inter-block one. Also MCSE seems > to perform value numbering on virtual/physical registers, which > does not map very well to status register bits that are implicitly > defined. > Any chance to recast this issue as a

I need to run a regression analysis with a large number of samples. Each sample (identified in the first file column) has its own x and y values. I will use the same model in all samples. How can I run the model for each sample? In SAS code I would use the "BY SAMPLE" statement. Alvaro [[alternative HTML version deleted]]

Hi, I used to use reshape and moved to reshape2 (R 2.15.1). Now I tried some of my older scripts and was surprised that my cast function wasn't working like before. What I did/want to do: 1) Melt a dataframe based on a vector specifying column names as measure.vars. Thats working so far: dfm <- melt(df, measure.vars=n, variable_name = "species", na.rm = FALSE) 2) Recast the

Hi All, I have two questions: I am computing a linear regression model with 0 as Intercept. Well, I would like the sum of my predicted values be equal to a constant and therefore analyze if my coefficients are significatively different using or not this constraint. Does anyone know how I can constrain my model in a such way? Here is the code: data<-read.table ("input.txt",