similar to: Q: combine 2 data frames with missing values

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Hi all, I have a dataset with several variables, each of which is a separate column. For each variable, I want to produce a boxplot and include the name of the variable (ie, column name) on each plot. I have included a sample dataset below. Can someone tell me where I am going wrong? Thank you for your help, Shawn Morrison # Generate a sample dataset var1 = rnorm(1000) var2 = rnorm(1000)

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If I do > example(lm) ... > mycoef <- function(object, ...) UseMethod("coef", object) > mycoef(lm.D9) Error in mycoef(lm.D9) : no applicable method for "coef" which is pretty surprising, as coef has a default method. After a bit of digging, this comes from do_usemethod having defenv = environment where the generic was defined */ defenv =

Hi, I'm using R to do a time series analysis. In the model, I use the lags of some variables. such the lags of the variables have different length, I just can't use them directly in the lm function. Intuitively, I feel that "subset" might be useful, but I do not know how to use it. Anyone can give me an example syntax? Thanks. Harry [[alternative HTML version deleted]]

Hi all, I´m re-starting (as my name indicates) my little knowlegde of CRAN R. I made this function time before but I don´t know where is the error because nothing appears as an error but the histogram plot doesn´t appear. Should I install some special library to run sapply? pru<-function(){ randz<-matrix(rnorm(200000),100,2000) H<-matrix(0,100,2000) for (j in 2:2000){ for (i in

Any better (more efficient, built-in) ideas for computing coef[1]+coef[2]*x+coef[3]*x^2+ ... than polynom <- function(coef,x) { n <- length(coef) sum(coef*apply(matrix(c(rep(x,n),seq(0,n-1)),ncol=2),1,function(z)z[1]^z[2])) } ? Ben -- 318 Carr Hall bolker at zoo.ufl.edu Zoology Department, University of Florida http://www.zoo.ufl.edu/bolker

Hi all, I want to make the following: I want to run a linear regression on each column of a matrix "estima" on the correspondent column on the matrix "estima2". You see I want to regress estima[,1] on estima2[,1] this way to all columns.... At the same time I want to make a regression adding each time a new observation. You see, the first regression will regress only one

Hello, I'm trying to create a custom function that "mean-centers" data and can be applied across many columns. Here is an example dataset, which is similar to my dataset: *Location,TimePeriod,Units,AveragePrice* Los Angeles,5/1/11,61,5.42 Los Angeles,5/8/11,49,4.69 Los Angeles,5/15/11,40,5.05 New York,5/1/11,259,6.4 New York,5/8/11,187,5.3 New York,5/15/11,177,5.7

Hi all, I want to make multiple least squares estimation on two matrix (without intercept) imagine matrix "a" and matrix "b", I want to "regress" each colum on matrix "a" (dependent variable) on each column of matrix b, I mean, regress first colum on a to first column on b. Second column on a to second column on b. Imagine "a" and "b"

Hi, Is there a way in sieve to get the current date, preferably in YYYYMMDD format? To what I've found it's hard to achieve. I have tried including a :global sieve script in which I would set the current date to a certain variable and overwrite this included script every day. But to my disappointement variables set in an included script do not exist in the original script... In fact what

Hi all I am looking for an add-in. I am currently working on something and I use daily data of closing stock prices. As not all companies are traded daily (e.g. on monday, then on thursday etc) at the stock exchange, there is satistically a problem. There are some papers which explain the approach to handle infrequent trading of a stock or non synchronous data and beta estimation (Dimson, 1979;

Hello List I'm trying to calculate the median period (in months) of a set of time intervals (between two interventions). I have been playing with the lubridate package to create the intervals but I can't think of the right approach to get the median timeperiod. Toy code: library(lubridate) test <- c('08-04-22', '08-07-28', '09-03-02', '09-03-03',

Hello all, I'm going to tackle learning C this week, and start writing my first * add-on/contribution; assuming it's actually worthy of contributing once it's done.. I think I've chosen a hefty project for my first go round here... I'd like to get some feedback from everyone on a FindMe/FollowMe spec I've put together. Before you read on, let me say, I don't want

Hi list members I have a vector of strings x=c("w","ex","ee") And I want to get a logical vector showing the positions where my search string "e" matches the elements partially, i.e. is at least the left-hand part of the target strings, i.e. I want to get a vector FALSE TRUE TRUE. Any ideas? Thanks Steve Powell proMENTE social research research |

Does anyone know where i can find out how powerful a machine has to be to handle a certain amount of call volume? Eg, 2Ghz is enough processing power to maintain 100 calls at a time. 4Ghz is engouh to process 250 calls etc etc. Thanks Dan -------------- next part -------------- An HTML attachment was scrubbed... URL:

Hello! I'd like to create an assets-based economic indicator using data from a national household survey. The economic indicator is to be the first principal component from a principal components analysis, which (given the source of the data) I believe should take in consideration the sampling weights of the observations. After running the PCA with svyprcomp(), from the survey package, I

I'm having trouble understanding how functions in the subset argument for lmList search for the objects they need. This trivial example produces "Error in rownames(fakedf) : Object "fakedf" not found": library(nlme) fitbyID <- function() { fakedf <- data.frame(ID = gl(5, 10, 50), A = sample(1:100, 50), B = rnorm(50))

Hi, I've a data frame like this: > as.data.frame(cbind(rnorm(1:12),rnorm(1:12))) V1 V2 1 -1.30849402 -0.52094136 2 0.96157302 0.76217871 3 -0.44223351 -1.72630871 4 -0.10432438 -1.04732942 5 -1.38748914 0.95877311 6 -0.63965975 0.65494811 7 -0.24058318 0.19496830 8 -0.11172988 1.01680655 9 0.08065333 0.22168589 10 0.25196536 0.84619914 11

Hello, I calculate splines from messured points(x,y) of an unknown function f(x). e.g. x <- c(0.004115, 0.012345, 0.037037, 0.111110, 0.333330, 1.000000) y <- c(37, 50, 45, 60, 50, 66) w <- c(0.8540541, 0.8320000, 0.8822222, 0.7983333, 0.8220000, 0.8151515) as weights f <- smooth.spline(x,y,w) Now I have y-values and want to calculate the x-value(s) from this spline. There is no