I wonder if anyone can help me with this query?
I am undertaking textual content analyses which result in time series
data (i.e., relative frequency counts of word categories in successive
segments of a larger text or in successive individual texts in a
temporal order). In regard to the content
I would like to apply network measures (such as betweenness centrality,
upper boundedness, etc.) to a weighted graph with non-integer weights,
defined by a euclidean distance matrix. The package sna provides the
measures that I want to use, but seems only to operate on binary graphs.
I have read
Two questions about principal components analysis in R:
Q.1) Hogenraad and McKenzie (1999) used Bruce Thompson's FACSTRAP
program to bootstrap the factor loadings and scores in a principal
components analysis. The input to the analysis was a word-word
correlation matrix derived from a frequency
When fitting a simple linear or polynomial regression using lm, R
provides a p-value for the whole model as well as for the individual
coefficients. When fitting the same models using gls (in order to
correct for autocorrelation), there doesn't seem to be a p-value
provided for the whole model,
I wonder if anyone could tell me how I can plot a Minimum Spanning Tree
using the functions provided in the graph and RBGL packages?
I am able to build the MST using the following set of commands:
library(graph)
library(RBGL)
x - read.table(h:/pole.tab,header=T,row.names=1)
y - dist(x)
Probably a very simple query:
When I try to plot a curve from a fitted polynomial, it comes out rather
jagged, not smooth like fitted curves in other stats software. Is there
a way of getting a smooth curve in R?
What I'm doing at the moment (for the sake of example) is:
x -
