The Health and Retirement Study (HRS) is a large-scale longitudinal project that follows a representative sample of about 26,000 Americans over the age of 50 every two years. Many thanks to graduate students Kyoung Jo Oh and Yeong Joon Yoon who developed much of the content used in this tool.įor questions or comments please contact and Retirement Study (HRS)
Meps codebook archive#
Archive of Data on Disability to Enable Policy and research (ADDEP)Īcknowledgements: This tool was developed through the efforts of William Erickson and Arun Karpur, and web designers Jason Criss and Jeff Trondsen at Cornell University.Other CLDR supported resources and collaborative opportunities: The CLDR is funded by NIH - National Institute of Child Health and Human Development, through the National Center for Medical Rehabilitation Research, the National Institute for Neurological Disorders and Stroke, and the National Institute of Biomedical Imaging and Bioengineering. The Center for Large Data Research and Data Sharing in Rehabilitation involves a consortium of investigators from the University of Texas Medical Branch, Cornell University's Yang Tan Institute (YTI), and the University of Michigan. The Rehabilitation Research Cross-dataset Variable Catalog has been developed through the Center for Large Data Research & Data Sharing in Rehabilitation (CLDR). # Error in var.Have a question about rehabilitation datasets? Contact our researchers for technical assistance, log in or register. #populate var.vecc with the SOM output maps Var.som <- som(t2m.scaled, grid = somgrid(2,2, "rectangular"))#, keep.data=TRUE)) T2m.scaled <- apply(t2m.vec, 2, scale, scale=FALSE, center=TRUE) # remove the column mean from each column of data (i.e. T2m.vec <- matrix(t2m.ens, nrow=20, ncol=Nlat*Nlon, byrow=TRUE)
# Generate Nlon * Nlat random numbers for Nens ensembles An error occurs fails accessing the SOM data vector codes. # An R script that provides an example of using a Self Organizing Map to calucate a SOM from latitude/longitude
Meps codebook how to#
Help and guidance for how to access the var.som$codes one codebook vector at a time is appreciated. In the code, I indicate the location of where the error occurs and what the error message is.
Meps codebook generator#
A random number generator replaces the accessing of the weather model data. In the R script below, I have reduced the process to only the essential steps. var.som$codes, and var.som$codes] but they do not solve the problem. I have tried different permutations to access the list data but none work.
Meps codebook code#
"num ", suggesting two dimensions, which is why I think my code should work. The attribute data for var.som indicates the size of the list var.som$codes is I loop from N = 1:Nsom, where Nsom is the number of "maps" specified in the call to the som function. 'Error in var.som$codes : incorrect number of dimensions.' In this case, var.som is the Kohonen object. I am, however, receiving an error message at this step. I then seek to access the som "codebook vector" output and transform it back to the latitude longitude grid. I convert a 20 x Nlat x Nlon matrix to a 20 x Nlat*Nlon matrix, present it to the Kohonen package som function. To do so, access a 20 ensemble global weather forecast model over a specific geographic domain. I am working to use SOMs to help analyze variability of weather forecast model ensembles.
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