similar to: quantiles on rows of a matrix

I want to perform linear regression on groups of consecutive rows--say 5 to 10 such--of two matrices. There are many such potential groups because the matrices have thousands of rows. The matrices are both of the form: > shp[1:5,16:20] SL495B SL004C SL005C SL005A SL017A -2649 1.06 0.56 NA NA NA -2648 0.97 0.57 NA NA NA -2647 0.46 0.30 NA NA

Within a very large matrix composed of a mix of values and NAs, e.g, matrix A: [,1] [,2] [,3] [1,] 1 NA NA [2,] 3 NA NA [3,] 3 10 17 [4,] 4 12 18 [5,] 6 16 19 [6,] 6 22 20 [7,] 5 11 NA I need to be able to consecutively number, in new columns, the non-NA values within each column (i.e. A[1,1] A[3,2] and A[3,3] would all be set to one, and

Dan Nordlund wrote: "It would be necessary to see the code for your 'brief test' before anyone could meaningfully comment on your results. But your results for a single test could have been a valid "random" result." I've re-created what I did below. The problem appears to be with the weighting process: the unweighted sample came out much closer to the actual

I'm trying to run this simple random sample procedure and keep getting the error message shown. I don't understand this; I've designated x as a numeric vector, so what is going on here? Thanks. > x = as.vector(c(1:12));x [1] 1 2 3 4 5 6 7 8 9 10 11 12 > mode(x) [1] "numeric" > sample(x, 3) Error in sample(x, 3) : .Random.seed is not an integer vector

I continue to have great frustrations with NA values--in particular making summary calculations on rows or cols of a matrix containing them. For example, why does: > a = matrix(1:30,nrow=5) > is.na(a[c(1:2),c(3:4)]);a [,1] [,2] [,3] [,4] [,5] [,6] [1,] 1 6 NA NA 21 26 [2,] 2 7 NA NA 22 27 [3,] 3 8 13 18 23 28 [4,] 4 9 14 19 24 29

This seemingly should be quite simple but I can't solve it: I have a long character vector of geographic data (data frame column named "XY") whose elements vary in length (from 11 to 14 chars). Each element is structured as a set of digits, then an underscore, then more digits, e.g: > data.frame(head(as.character(XY))) head.as.character.XY.. 1 -448623_854854 2

I admit that I've not done a thorough search on this topic, but from the several instructional manuals and/or tutorials I've looked at, I don't see any mention of relational database capabilities in R? Have I missed something, and if so, can someone point me in the right direction to get started? Thanks! Jim Bouldin, PhD Research Ecologist Department of Plant Sciences, UC Davis

I can't seem to find info on how to unload packages that have been loaded. My goal in doing so is to gain access to functions that have been masked out by those packages. Or is there another way to do so? Thanks in advance. Jim Bouldin, PhD Research Ecologist Department of Plant Sciences, UC Davis Davis CA, 95616 530-554-1740

When I try to run the following non-linear regression with variables index1 and prl3: > beta = 4 > nls(index1~beta*(1/prl3),start = list(beta = 4)) I get this error message: Error in nls(index1 ~ beta * (1/prl3), start = list(beta = 4)) : REAL() can only be applied to a 'numeric', not a 'logical' I've got no clue as to the REAL() to which this is referring. Any

Is there a command for partial matching of character strings? Specifically, I'd like to be able to calculate the mean of the values in any columns in a data frame or matrix that have identity in part of their column names. For example, columns labeled "mpw06a" and "mpw06b" match on the first five characters; their mean would be taken whereas any columns beginning with

I just upgraded from 2.8.1 to 2.10 on Windows Vista. BIG MISTAKE apparently because now when I type: > help(functionname) or ?functionname I get only a small text window giving some very basic info on the topic, e.g.: base-package package:base R Documentation The R Base Package Description: Base R functions Details: This package contains the

When I try to select only those rows from the following data frame, called "data", in which X > Y X Y V3 2 2 1 8.062258 3 3 1 2.236068 4 4 1 6.324555 5 5 1 5.000000 6 1 2 8.062258 8 3 2 9.486833 9 4 2 2.236068 10 5 2 5.656854 11 1 3 2.236068 12 2 3 9.486833 14 4 3 8.062258 15 5 3 5.099020 16 1 4 6.324555 17 2 4 2.236068 18 3 4 8.062258 20 5 4 5.385165 21 1 5 5.000000

Hi everyone! #I need a loop or a function that creates a X2 variable that is X1 without the extreme values (or X1 winsorized) by industry and year. #My reproducible example: firm<-sort(rep(1:1000,10),decreasing=F) year<-rep(1998:2007,1000) industry<-rep(c(rep(1,10),rep(2,10),rep(3,10),rep(4,10),rep(5,10),rep(6,10),rep(7,10),rep(8,10),rep(9,10), rep(10,10)),1000) X1<-rnorm(10000)

Hello everybody, after searching around for quite some time, I haven't been able to find a package that provides a function to compute the Windorized mean and variance. Also I haven't found a function that computes the trimmed variance. Is there any such package around? thanks, Roberto

bradleyd wrote > Excuse the request from an R novice! I have a data frame (DATA) that has > two numeric columns (YEAR and DAY) and 4000 rows. For each YEAR I need to > determine the 10% and 90% quantiles of DAY. I'm sure this is easy enough, > but I am a new to this. > >> quantile(DATA$DAY,c(0.1,0.9)) > 10% 90% > 12 29 > > But this is for the entire

OK, what is the trick to extracting the overall p value from an lm object? It shows up in the summary(lm(model)) output but I can't seem to extract it: > test2 = apply(aa, 1, function(x) summary(lm(x[,1] ~ 0 + x[,3] + x[,6]))) > test2[[1]] Call: lm(formula = x[, 1] ~ 0 + x[, 3] + x[, 6]) [omitted summary output] F-statistic: 40.94 on 2 and 7 DF, p-value: 0.0001371 It does not seem

This has got to be incredibly simple but I nevertheless can't figure it out as I am apparently brain dead. I just want to convert the elements of a character vector to variable names, so as to then assign formulas to them, e.g: z = c("model1","model2"); I want to assign formulas, such as lm(y~x[,1]) and lm(y~x[,2]), to the variables "model1" and

I realize this should be simple, but even after reading over the several help pages several times, I still cannot decide between the myriad "apply" functions to address it. I simply want to apply a function to all the rows (or columns) of the same index from two (or more) identically sized arrays (or data frames). For example: > a=matrix(1:50,nrow=10) >

I realize this is probably pretty basic but I can't figure it out. I'm looping through an array, doing various calculations and producing a resulting data frame in each loop iteration. I need to give each data frame a different name. Although I can easily create a new character string for writing each frame to an output file, I cannot figure out how to convert such strings to

Hi All, I want to take a matrix (or data frame) and winsorize each variable. So I can, for example, correlate the winsorized variables. The code below will winsorize a single vector, but when applied to several vectors, each ends up sorted independently in ascending order so that a given observation is no longer on the same row for each vector. So I need to winsorize the variable but