Description Usage Arguments Value Author(s) References
This function finds the sum of uniquenesses score for a specified bandwidth for GWFA. It can be used to construct the bandwidth function across all possible bandwidths and compared to that found automatically via bw_gwfa function.
1 2 | gwfa_uniquenesses_sum(bw, x, dp.locat, k, robust, kernel, adaptive,
p, theta, longlat, dMat, vars, n.obs, fm, rotate, scores, oblique.scores, timeout, foreach)
|
bw |
bandwidth used in the weighting function;fixed (distance) or adaptive bandwidth(number of nearest neighbours) Description from GWmodel::gwpca.cv |
x |
Same as GWmodel::gwpca. A Spatial*DataFrame, i.e. SpatialPointsDataFrame or SpatialPolygonsDataFrame as defined in package sp. |
dp.locat |
Same as GWmodel::gwpca.cv. A two-column numeric array of observation coordinates |
k |
Same as GWmodel::gwpca.cv. The number of retained components; k must be less than the number of variables |
robust |
Same as GWmodel::gwpca.cv. If TRUE, robust GWPCA will be applied; otherwise basic GWPCA will be applied |
kernel |
Same as GWmodel::gwpca.cv. Function chosen as follows: gaussian: wgt = exp(-.5*(vdist/bw)^2); exponential: wgt = exp(-vdist/bw); bisquare: wgt = (1-(vdist/bw)^2)^2 if vdist < bw, wgt=0 otherwise; tricube: wgt = (1-(vdist/bw)^3)^3 if vdist < bw, wgt=0 otherwise; boxcar: wgt=1 if dist < bw, wgt=0 otherwise |
adaptive |
Same as GWmodel::gwpca.cv. If TRUE calculate an adaptive kernel where the bandwidth (bw) corresponds to the number of nearest neighbours (i.e. adaptive distance); default is FALSE, where a fixed kernel is found (bandwidth is a fixed distance) |
p |
Same as GWmodel::gwpca. The power of the Minkowski distance, default is 2, i.e. the Euclidean distance. |
theta |
Same as GWmodel::gwpca. An angle in radians to rotate the coordinate system, default is 0. |
longlat |
Same as GWmodel::gwpca. If TRUE, great circle distances will be calculated. |
dMat |
Same as GWmodel::gwpca. A pre-specified distance matrix, it can be calculated by the function gw.dist . |
vars |
Same as GWmodel::gwpca. The number of retained components; k must be less than the number of variables. |
n.obs |
Same as psych::fa. Number of observations used to find the correlation matrix if using a correlation matrix. Used for finding the goodness of fit statistics. Must be specified if using a correlaton matrix and finding confidence intervals. |
fm |
Same as psych::fa. Factoring method fm="minres" will do a minimum residual as will fm="uls". Both of these use a first derivative. fm="ols" differs very slightly from "minres" in that it minimizes the entire residual matrix using an OLS procedure but uses the empirical first derivative. This will be slower. fm="wls" will do a weighted least squares (WLS) solution, fm="gls" does a generalized weighted least squares (GLS), fm="pa" will do the principal factor solution, fm="ml" will do a maximum likelihood factor analysis. fm="minchi" will minimize the sample size weighted chi square when treating pairwise correlations with different number of subjects per pair. fm ="minrank" will do a minimum rank factor analysis. "old.min" will do minimal residual the way it was done prior to April, 2017 (see discussion below). fm="alpha" will do alpha factor analysis as described in Kaiser and Coffey (1965). |
rotate |
Same as psych::fa. "none", "varimax", "quartimax", "bentlerT", "equamax", "varimin", "geominT" and "bifactor" are orthogonal rotations. "Promax", "promax", "oblimin", "simplimax", "bentlerQ, "geominQ" and "biquartimin" and "cluster" are possible oblique transformations of the solution. The default is to do a oblimin transformation, although versions prior to 2009 defaulted to varimax. SPSS seems to do a Kaiser normalization before doing Promax, this is done here by the call to "promax" which does the normalization before calling Promax in GPArotation. |
scores |
the default="regression" finds factor scores using regression. Alternatives for estimating factor scores include simple regression ("Thurstone"), correlaton preserving ("tenBerge") as well as "Anderson" and "Bartlett" using the appropriate algorithms ( factor.scores). Although scores="tenBerge" is probably preferred for most solutions, it will lead to problems with some improper correlation matrices. |
oblique.scores |
When factor scores are found, should they be based on the structure matrix (default) or the pattern matrix (oblique.scores=TRUE). Now it is always false. If you want oblique factor scores, use tenBerge. (See ?psych::fa) |
timeout |
A numeric specifying the maximum number of seconds the expression is allowed to run before being interrupted by the timeout. (See ?R.utils::wituTimeout) |
foreach |
default:FALSE. If TRUE, foreach function works to implement calculation using multicores. |
Returns the summed factor uniquenesses.
N. Tsutsumida,...
Isabella Gollini, Binbin Lu, Martin Charlton, Christopher Brunsdon, Paul Harris (2015). GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models. Journal of Statistical Software, 63(17), 1-50. URL http://www.jstatsoft.org/v63/i17/.
Binbin Lu, Paul Harris, Martin Charlton, Christopher Brunsdon (2014). The GWmodel R package: further topics for exploring spatial heterogeneity using geographically weighted models. Geo-spatial Information Science, 17(2), 85-101. URL http://dx.doi.org/10.1080/10095020.2014.917453
Revelle, W. (2017) psych: Procedures for Personality and Psychological Research, Northwestern University, Evanston, Illinois, USA, https://CRAN.R-project.org/package=psych Version = 1.7.8.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.