search for: pdxgary163

Dear R users, I have a city map in shape file (polygon). I also have some points that I hope to plot them to the city map. The only information I have about those points are their relative longitude and latitude to the city center by miles. Is there a way that R can help me to do this? Thanks. Gary [[alternative HTML version deleted]]

Dear R users, I'm estimating a two-level regresion model but having difficuly in finding a good R syntax example. Let me use an example to explain what I'm doing. The dependent variable is math score (mathscore). Predictors are at two levels. At the student level, they are household income (hinc) and IQ (IQ). At the school level, there is a dummy variable indicating if the school is a

Dear R community, I'm running "a mlogit" function with a very large data set. Since the data size is so large, I got the Error: Error: cannot allocate vector of size 5.4 Mb In addition: Warning messages: 1: In mapply("*", X, P, SIMPLIFY = FALSE) : Reached total allocation of 16340Mb: see help(memory.size) 2: In mapply("*", X, P, SIMPLIFY = FALSE) : Reached

Dear R users, I have created a Loess surface in R, in which x is relative longitude by miles, y is relative latitude by miles, and z is population density at the neighborhood level. The purpose is to identify some population centers in the region. I'm wondering if there is a way to determine the coordinates (x,y) of each center, so I can know exactly where they are. Let me use the

Dear R users, I'm plotting housing prices in City A over past 30 years in ggplot2. The Xs are years since 1980. I have two housing price variables: new home prices and old home prices, both of them measured by $/sqft. I have searched related threads on multiple Y axes in ggplot2 and I understand that multiple Y axes in different scales are not possible. I'm wondering if it is possible to

Dear R users, I'm plotting housing prices in City A over past 30 years in ggplot2. The Xs are years since 1980. I have two housing price variables: new home prices and old home prices, both of them measured by $/sqft. I have searched related threads on multiple Y axes in ggplot2 and I understand that multiple Y axes in different scales are not possible. I'm wondering if it is possible to

Dear R users, I'm applying a correlation structure in a mixed model (nmle function) to control for spatial correlation between land parcels that are adjacent to each other. I generated X,Y coordinates in ArcGIS for each land parcel and used them in the correlation form like this: test.exp<-corExp(1, form = ~ X + Y) test.exp<- Initialize(test.exp,dataset) However, the correlation

Dear R users, I'm trying to determine local population centers in a region through kernel smoothing. What I have is population density in each neighborhood, which can be presented as point density. So in my data set “pop”, I have three columns for about 1000 neighborhoods: x (x coordinate), y (y coordinate), and z (population density at xy). I searched R documentations and found that the

Dear R users, I'm estimating a mixed effects model in which the spatial correlation is controlled for by the "corGaus" structure. I'm wondering if there is a document or paper that explains how the spatial correlation structure (such as "corExp" or "corGaus") works. Let me use the example and data posted on UCLA's R FAQ webpage to explain my problems.

Dear R users, I'm wondering if the gls function reports pseudo R. I do not see it by summary(). If the package does not report, can I calculate it in this way? Adjusted pseudo R squared = 1 - [(Loglik(beta) - k ) / Loglik(null)] where k is the number of IVs. Thanks! Gary [[alternative HTML version deleted]]

Hi, R users, I am doing Lagrange Multiplier Test Statistics for Spatial Autocorrelation with "spdep" and got this warning message: "Spatial weights matrix not row standardized". It is a warning, not an error. I am wondering if this is a problem. Thanks! Gary [[alternative HTML version deleted]]

Dear R community, I'm having hard time to understand the kde function in "ks" package. Let me use a 3-dimensional kernel smooth example to explain my question using the elevation data in geoR. ### here is what I did ### library(ks) require(geoR) data(elevation) elev.df <- data.frame(x = elevation$coords[,"x"], y = elevation$coords[,"y"], z = elevation$data)