Displaying 6 results from an estimated 6 matches for "2.0000e".
Did you mean:
0.0000e
2012 Nov 28
3
Speeding reading of large file
R 2.15.1
OS X and Windows
Colleagues,
I have a file that looks that this:
TABLE NO. 1
PTID TIME AMT FORM PERIOD IPRED CWRES EVID CP PRED RES WRES
2.0010E+03 3.9375E-01 5.0000E+03 2.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 1.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00 0.0000E+00
2.0010E+03 8.9583E-01
2008 Sep 05
0
help for color parameter
Dear all:
I attached the dataset with this email and post codes as below.
My questions is related to the 'col=temp.col' for the line and pch in my code, I have 4 IDs, 10 DIDs and each ID include different DIDs, for example, first ID has 3 DIDs, then the color is the first three colors(black, red, green) in the first plot, but in the second plot, why the color change to pink which is
2012 Oct 18
4
speeding read.table
R 2.15.1
OS X
Colleagues,
I am reading a 1 GB file into R using read.table. The file consists of 100 tables, each of which is headed by two lines of characters.
The first of these lines is:
TABLE NO. 1
The second is a list of column headers.
For example:
TABLE NO. 1
COL1 COL2 COL3 COL4 COL5 COL6 COL7 COL8 COL9 COL10
2012 Feb 16
2
help with e+01 number abbreviations
Dear List,
I will appreciate any advice regarding how to convert the following numbers
[I got in return by taxondive()] in numeric integers without the e.g.
6.4836e+01
abbreviations.
Thank you very much in advance,
Gian
> taxa_dive
Species Delta Delta* Lambda+ Delta+ S
Delta+
Nat1 5.0000e+00 6.4836e+01 9.5412e+01 6.7753e+02 8.7398e+01
436.99
Nat2
2012 Mar 28
2
Data extraction
Dear ReXperts,
I have the below text file output. I need to extract the T, QC, QO, QO-QC
and WT columns for
the data between T = 10 and T=150.
Any ideas?
Thanks in advance.
========================================================================================
1 D C ---CAT-- T THETA QC QO
QO-QC QC/QO WT FSD
8 1 0 1.0000E+01
2008 Aug 01
5
drop1() seems to give unexpected results compare to anova()
Dear all,
I have been trying to investigate the behaviour of different weights in
weighted regression for a dataset with lots of missing data. As a start
I simulated some data using the following:
library(MASS)
N <- 200
sigma <- matrix(c(1, .5, .5, 1), nrow = 2)
sim.set <- as.data.frame(mvrnorm(N, c(0, 0), sigma))
colnames(sim.set) <- c('x1', 'x2') # x1 & x2 are