Is there any packages or commands allow multiple weighted matrices in a spatial lagged X (SLX) model?
I want to include two different weighted matrices with one dependent variable, but I cannot find any packages for it?
Theoretically, in spatial analysis, including multiple W matrices are not appropriate? If it is possible, how can I conduct analysis with W1 and W2? Do I have to do it by hand?(I meant, once create the lagged variable by multiplying W matrix and the key DV, and and run a OLS regression with the variables. Is it the right way applying multiple weighted matrices?
Thanks!
Dongjin
Related
I wanna calculate the correlation of features, every feature consists of a 50*100 matrix. My question is how can I use R to calculate the correlation between features, instead of correlation of columns inside the matrix.
I created a phylogenetic NJ tree in R using the ape package. My data contains metric measures from multiple individuals belonging to known groups. Thus, I decided to calculate the Mahalanobis distance between these groups in order to incorporate the covariance structure in my analyses. Creating the nj tree thus was not a problem.
require(ape)
lda <- lda(y, as.factor(ynames))
dist <- dist(as.matrix(predict(lda,lda$mean)$x),upper=T,diag=T)
plot(nj(dist))
However, now I'd like to calculate some bootstrap values for branch splits. I'd use the boot.phylo function, but here I have no idea how I can deal with the FUN (function) command, and thus with the correct calculation of Mahalanobis distances for the bootstrapped data set.
I wish to use the function gls in the R package nlme to analyse a set of nested spatial samples, in which many samples overlap in at least some spatial coordinates. I want to account for non-independence in the response variable (the thing I'm measuring in each spatial sample) using either a corStruct or pdMat object, but I'm confused about how to do this.
I have generated a covariance matrix that should encode all the information about non-independence between spatial samples. Each row/column is a distinct spatial sample, the diagonal contains the total number of sampling units captured by each spatial sample, and the off-diagonal elements contain counts of sampling units shared between spatial samples.
I think I should use the nlme function gls while specifying a correlation structure, possibly using a corSymm or pdMat object. But I've only seen examples where the correlation structure in gls is specified via a formula. How can I use the covariance matrix that I've created?
I discovered that you can pass the nlme function gls a positive-definite correlation matrix by using the general correlation structure provided by corSymm.
# convert your variance covariance matrix into a correlation matrix
CM <- cov2cor(vcv_matrix)
# if your correlation matrix contains zeros, as mine did, you need to convert it to a positive-definite matrix that substitutes very small numbers for those zeros
CM <- nearPD(CM)$mat
# convert into a corStruct object using general correlation structure provided by corSymm
C <- corSymm(CM[lower.tri(CM)], fixed = T)
# correlation structure can now be included in a gls model
gls(y ~ x, correlation = C, method = "ML")
I have two variables that I'd like to analyze with a 2x2 table, which is easy enough.
datatable=table(data$Q1data1, data$Q1data2) summary(datatable)
However, I need to weight each variable separately using two frequency weighting variables that I have. So far, I've only found the wtd.chi.sq function in the weights package, which only allows you to weight both variables by the same weighting variable.
In addition, I need to perform this 2x2 chi-square 1000 times using bootstrapping or some resampling method, so that I can eventually peek at the distribution of p-values.
What are some proven methods for finding groupings of highly correlated variables within a large, high-dimensional binary dataset (think 200,000+ rows and 150+ fields) that can be easily implemented in R? I want to find groupings of variables which lends itself to interpretation so I don't think PCA would be the best method.
library(Hmisc)
mtc <- mtcars[,2:8]
mtcn <- data.matrix(mtc)
clust <- varclus(mtcn)
clust
plot(clust)
?varclus :Does a hierarchical cluster analysis on variables, using the Hoeffding D statistic, squared Pearson or Spearman correlations, or proportion of observations for which two variables are both positive as similarity measures. Variable clustering is used for assessing collinearity, redundancy, and for separating variables into clusters that can be scored as a single variable, thus resulting in data reduction.
For Binary Vraibles:
library(cluster)
data(animals)
ma <- mona(animals)
ma
plot(ma)
?mona : Returns a list representing a divisive hierarchical clustering of a dataset with binary variables only.