search for: univariately

How to test the statistical significance of the difference of two univariate Linear Regression betas? Hi all, There are two samples of data: D1 and D2. On data D1 we do a univariate Linear Regression and get the coefficient beta1. On data D2 we do a univariate Linear Regression and get the coefficient beta2. How do I test the statistical significance of (beta1-beta2)? Could you please

Hey, Does anyone know of a package in R that provides univariate kernel density estimation with boundary correction ? or how to easily extend an existing bivariate kernel density estimation function (e.g. lambdahat in the spatialkernel package) with boundary corrections to allow univariate density estimation? Thanks a lot, Steve B. -- View this message in context:

Dear R users, I use the excelent Anova function of the library car because the easy way to get sphericity correction. Unless I use the scan function. I have not been able to access the values ​​of sum squares and degrees of freedom for each effect in the univariate summary table. Example of the car library for Anova function: library(car) phase <- factor(rep(c("pretest",

This seems like a FAQ, but I can't figure it out. I have a mv ts object: R > tsp(pg) [1] 1982 2003 1 R > dim(pg) [1] 22 12 and a univariate ts: R > tsp(rw) [1] 1690 1996 1 Yet, when I try to intersect them: R > tsp(ts.intersect(rw, pg)) [1] 1982 2176 1 the process goes awry. How to I get rw and pg to be one ts that runs from 1982 to 1996 and has 13 univariate time

Hello! I want to realise an univariate time series analysis in R, can someone help me for the first steps? Thanks -- View this message in context: http://r.789695.n4.nabble.com/Univariate-Time-Series-Analysis-in-R-tp4638538.html Sent from the R help mailing list archive at Nabble.com.

Dear colleagues, In checking a function I am adding to an R package, I get the following warning pair: ... Bad \usage lines found in documentation object 'nominal': "\\method{print}{nominal}"(x, max.print = 10, posthoc = "std.pearson.residuals.sign", assoc = ifelse("univariate" list(c("N", "alpha.X2",

Dear R Helpers, Currently I'm working with the ts package of R and created a TimeSerie from pixels extracted from satellite imagery(S10 NDVI data, 10 daily composites). I'm trying to decompose this signal in different signals (seasonal and trend). When testing out the STL method is says => Only univariate timeseries are allowed, but the current Timeserie I'm using is univariate!

Hi the list *** New package *** R to LaTeX : Univariate analyses r2lUniv *URL:*http://www.r-project.org, http://christophe.genolini.free.fr/r2lUniv *** Description *** r2lUniv performs some basic analyses, then generates a code to be included in a LaTeX document to print the analyses in a (so nice!) LaTeX way. r2lUniv performs the analyses automatically according to the classical statistics

Hi the list *** New package *** R to LaTeX : Univariate analyses r2lUniv *URL:*http://www.r-project.org, http://christophe.genolini.free.fr/r2lUniv *** Description *** r2lUniv performs some basic analyses, then generates a code to be included in a LaTeX document to print the analyses in a (so nice!) LaTeX way. r2lUniv performs the analyses automatically according to the classical statistics

Hi, Does anyone know if R has a Lagrange multiplier (LM) test for ARCH effects for univariant time series? Thanks! -- Tom [[alternative HTML version deleted]]

hi everyone! i want to check the autocorrelation function for a univariate time series (streamflow) in a data frame as below: < DF <- read.table("D:/file path....") < DF year jan feb mar apr ...... dec 1966 0.504 0.406 0.740 0.241 0.429 1967 0.683 0.529 0.780 0.443 0.503 . . . . what i first tried is: acf (DF, plot = TRUE)

Hallo everyone I have two univariate time series (class ts) describing the same variable. They have the same resolution, but span different periods in time with a big gap in between. I need to append one to the other such that they are one object, with the gap filled with NAs. The method ts.union produces a multivariate time series where the time axis is correct, but the individual time series

Hello, I have a concrete statistical question: I have a sample of an univariate mixture of an unknown number (k) of normal distributions, each time with an unknown mean `m_i' and a standard deviation `k * m_i', where k is known factor constant for all the normal distributions. (The `i' is a subscript.) Is there a function in R that can estimate the number of normal distributions k

This is a cryptographically signed message in MIME format. --------------ms010908060700000604050108 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: 7bit Sorry. You're right about the univariate numeric operators. My bad. However, I was expecting !x to return a time series, just like the binary logical operators do. For example: > b <-

Hi, I got the same problem like http://tolstoy.newcastle.edu.au/R/devel/04/11/1204.html R crashes when I use the em function from the mclust package on univariate data and on a special case on bivariate data (when the matrix is not provided as written in the manual). It seems as if the problem is the format of the data to be analyzed. Operating System: Windows XP (SP2) R version: R-2.0.1 The

Hi everyone. I'm having some troubles with STL function to decompose some data. My issue is that I have monthly data from September 2005 up to August 2010 i. e. 60 observations. I define it in the following way: *u<-read.csv("C:/CELEBREX.csv",header = TRUE) u.ts<-ts(u, start=c(2005,9), frequency=12) * The issue is that when I try to use stl(u.ts, 'per') Then the

In ggplot2, I would like to make a boxplot that has the following properties: (1) Contrary to default, the meaningful axis should be the horizontal axis. Lattice does this, for instance, by library(lattice);bwplot(~mtcars$mpg) (2) It is *univariate*, i.e., of a single vector, say mtcars$mpg. I do not wish to make separate plots for the different values of mtcars$cyl. (3) Nothing on the

Hi, I am interested in a applying wavelets as a smoothing tool for my (1-dimensional) data. I looked into wavetresh and waveslim packages but could not quite figure out an obvious way to do this after running dwt or wt functions. Would someone be able to point me in the right direction on how to denoise univariate data using one of wavelet packages available in R? Thank you very much Jane ps

Hi, I am trying to use the stl function in the ts package. It requires that the data is a univariant time series at the moment my data is in a vector. I have coerced it to a time series using.... crimets <- ts(crimeData) However, this does not work. Does anyone have any suggestions? Cheers, Sam. p.s. I am fairly new to R so apologies if this is a stupid posting.

just a short beg for the next R 2.3 version: I know it is easy to add the sd into summary() in the source bowels of R---but everytime R is updated, my change disappears. :-(. I do not believe that R has an easy extension mechanism for univariate summaries, short of a function rewrite here. Could this please be added into R 2.3? Aside, a logical ordering might also be: mean sd min q1 med q3