lf_araujo lf-araujo

Correcting non-positive definite matrices and misspecification related to starting values

In OpenMx, as model complexity grows, the higher the chance of not setting starting values that will help the optimizer reach a solution. This is particularly true for longitudinal models. This post aims at defining a set of rules that can help you define good starting points and also present what is current available. Starting values can lead to time-consuming, annoying errors and thinking about them during model

Wrap no more

The ggplot2 plus syntax is a much better fit for what I was attempting. For model building, summing models makes more sense than piping them. And also, the code needed to implement this is much shorter. So for something like:

mb <- mxModel("bivariate Heterogeneity Path Specification", 
    type = "RAM",
    manifestVars = c('X','Y')) +
    mxPath( from=c('X','Y'), arrows=2, free=T, values=1, lbound=.01 ) +

Lunarvim R-IDE

Lunarvim settings for R statistics with neovim as an IDE.

--[[
lvim is the global options object

Linters should be

Mx.wrap(): Using R-base pipes to create OpenMx models

TLDR: Create OpenMx models using pipes.

Pipes (|) were introduced in bash in order to facilitate I/O redirection. Together with the UNIX tools, manipulation of data becomes very simple and short to write. In R the concept of the pipe came much more recently, with the magrittr package within the context of dplyr and tidy tools manifesto. One fundamental decision was to name the dplyr tools so that they can be read as verbs, which helps with memorizing what each tool does, in contrast to the less obvious naming of the UNIX tools.

	<?xml version="1.0"?>
	<labwc_config>
	<core>
	<gap>5</gap>
	</core>

	<theme>
	<name>OpenboxSur-Dark</name>
	<cornerRadius>8</cornerRadius>
	<font name="sans" size="10"/>

	#!/usr/bin/env bash
	# lsfz: LocalSearch + fzf with compact labels and Enter-to-open
	# Deps: localsearch, fzf, gio (or xdg-open), bash

	set -euo pipefail

	# How many results to fetch
	LIMIT="${LIMIT:-200}"

	# What to search (e.g. --files \| --documents \| --images)

	# npsol.nim
	# Compile & run:
	# nim c -r -d:release -d:opencl -d:blas=openblas --mm:refc npsol.nim
	import std/[math, strformat, sequtils, os]
	import arraymancer
	import arraymancer/tensor/tensor_opencl
	import arraymancer/tensor/backend/opencl_backend
	import numericalnim
	import opencl
	import datamancer

	library(purrr)
	library(dplyr)
	library(future)
	library(progress)

	# Set up parallel processing
	plan(multicore, workers = 23)

	# Define the parameters
	params <- list(

	#+OPTIONS: org-html-head-include-default-style:nil
	#+OPTIONS: num:nil
	#+OPTIONS: html-style:nil
	#+OPTIONS: html-scripts:nil
	#+OPTIONS: html-postamble:nil
	#+OPTIONS: broken-links:mark
	#+HTML_HEAD: <style type="text/css">
	#+HTML_HEAD: @charset "utf-8";
	#+HTML_HEAD: @import url('https://fonts.googleapis.com/css?family=Roboto:300,400,500');
	#+HTML_HEAD: @import url('https://fonts.googleapis.com/css?family=Roboto Slab:300,400,500');