Displaying 5 results from an estimated 5 matches for "sqdist".
Did you mean:
sdist
2007 Mar 12
2
Lmer Mcmc Summary and p values
...9
Treatment 4 -0.2483 0.8661 -0.287 0.774
Correlation of fixed effects
Intra T2 T3
T2 -0.989
T3 -0.745 0.737
T4 -0.577 0.570 0.430
> The p-values from mcmc are:
>
mcmcpvalue<-function(samp)
{
std<-backsolve(chol(var(samp)),
cbind(0,t(samp))-colMeans(samp),
transpose=TRUE)
sqdist<-colSums(std*std)
sum(sqdist[-1]>sqdist[1]/nrow(samp)
}
fitSI<-mcmcsamp(fit,50000)
library(coda)
HPDinterval(fitSI)
lower upper
Intercept -4.0778905 -3.1366836
Treatment2 3.4455972 4.3196598
Treatment 3 0.399302 1.287747
Treatment 4 -1.7898933 -0.2980325
log...
2009 Feb 24
2
lmer, estimation of p-values and mcmcsamp
...t yet been successful. This is my code:
lnmass <- lmer(log.mass ~ treatment + (1|block), data=exp1)
summary(lnmass)
samp <- rnorm(n=10000)
mcmcpvalue <- function(samp)
{std <- backsolve(chol(var(samp)),
cbind(0,t(samp)) - colMeans(samp),
transpose = TRUE)
sqdist <- colSums(std*std)
sum(sqdist[-1] > sqdist[1]/nrow(samp) }
markov1 <- mcmcsamp(lnmass, 10000)
HPDinterval(markov1)
mcmcpvalue(as.matrix(markov1[,1]))
mcmcpvalue(as.matrix(markov1[,2]))
mcmcpvalue(as.matrix(markov1[,3]))
mcmcpvalue(as.matrix(markov1[,4]))
mcmcpvalue(as.matrix(markov...
2007 Feb 12
1
lmer and estimation of p-values: error with mcmcpvalue()
...How can I proceed in estimating the p-values, then?
I very much acknowledge any suggestions.
Best regards
Christoph.
##
mcmcpvalue <- function(samp)
{ std <- backsolve(chol(var(samp)),
cbind(0, t(samp)) - colMeans(samp),
transpose = TRUE)
sqdist <- colSums(std * std)
sum(sqdist[-1] > sqdist[1])/nrow(samp) }
m1<-lmer(number_pollinators~logpatch+loghab+landscape_diversity+(1|site),quasipoisson)
Generalized linear mixed model fit using Laplace
Formula: number_pollinators ~ logpatch + loghab + landscape_diversity +
(1 | si...
2007 Mar 13
1
lme4 and mcmcamp
...9 0.719 Treatment 4 -0.2483 0.8661 -0.287 0.774
Correlation of fixed effects
Intra T2 T3
T2 -0.989
T3 -0.745 0.737
T4 -0.577 0.570 0.430
> The p-values from mcmc are:
>
mcmcpvalue<-function(samp)
{
std<-backsolve(chol(var(samp)),
cbind(0,t(samp))-colMeans(samp),
transpose=TRUE)
sqdist<-colSums(std*std)
sum(sqdist[-1]>sqdist[1]/nrow(samp)
}
fitSI<-mcmcsamp(fit,50000)
library(coda)
HPDinterval(fitSI)
lower upper
Intercept -4.0778905 -3.1366836
Treatment2 3.4455972 4.3196598
Treatment 3 0.399302 1.287747
Treatment 4 -1.7898933 -0.2980325
log(T...
2008 Sep 16
1
Spatial join – optimizing code
Hi,
Few days ago I have asked about spatial join on the minimum distance between 2 sets of points with coordinates and attributes in 2 different data frames.
Simon Knapp sent code to do it when calculating distance on a sphere using lat, long coordinates and I've change his code to use Euclidian distances since my data had UTM coordinates.
Typically one data frame has around 30 000 points