Hello,
I used the gls function from the nlme package to run a generalized least
squares model. One of the predictor variables is a factor with 3 levels. Here
is a reproducible example:
library(nlme)
response <- c(rnorm(5,1,3), rnorm(5,6,1), rnorm(5,10,5))
foo <- data.frame(response = response
Hi,
I have a large dataframe that contains species names. I have a second
dataframe that contains species names and some additional info, called 'Class',
about each species. I would like match the species name is the first data
frame with the 'Class' information contained in the second. Sinc
This works:
expression(paste('Greek', italic('\uo3bc'))
thanks!
Jake
-Original Message-
From: David Winsemius [mailto:dwinsem...@comcast.net]
Sent: Thursday, August 22, 2013 2:01 AM
To: David Winsemius
Cc: Beaulieu, Jake; r-help@r-project.org
Subject: Re: [R] it
Hi,
I would like to include the greek letter mu, in italics, in an axis title. The
following gets close, but the mu isn't italicized.
x <- 1:10
y <- 1:10
plot(y ~x, ylab = expression(paste('Greek ', italic(mu
Anybody know how to accomplish this?
Thanks,
Jake
=
The reference below describes the utility of the two-dimensional
Kolmogorov-Smirnow (2DKS) test for detecting relationships in bivariate
data. If this test has been implemented in R I would love to know about
it!
Thanks,
jake
Garvey, J. E., E.A. Marschall, and R.A. Wright (1998). "From star
I measured nitrate concentration and primary production (PP) biweekly for
23 months in one headwater stream. I would like to use linear regression
to determine if PP is related to nitrate concentration. My dataframe is
called "data" and consists of the vectors Rdate, PP, and nitrate. Rdate
Hello,
I just upgraded to R version 2.13.1 and am running Rcmdr version 1.7-0.
Prior to the upgrade, Rcmdr returned descriptive error messages. However,
since the upgrade the only error message Rcmdr supplies is:
ERROR:
How can I convince Rcmdr to return useful error messages again?
Thanks,
Hello,
I would like to write a loop to 1) run 100 linear regressions, and 2)
compile the slopes of all regression into one vector. Sample input data
are:
y1<-rnorm(100, mean=0.01, sd=0.001)
y2<-rnorm(100, mean=0.1, sd=0.01)
x<-(c(10,400))
#I have gotten this far with the loop
for (i in 1:10
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