suppressing_edges_in_semplot_figures.ipynb

{
 "worksheets": [
  {
   "cells": [
    {
     "metadata": {},
     "cell_type": "heading",
     "source": "Suppressing edges in semplot figures",
     "level": 1
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "I have a set of SEM models, and I want to make figures of the structural models prior to fitting. \n\nProblem is that even when I use 'residuals=FALSE' flag, semplot is still showing me lines between latent variable nodes. \n\nLooking for solutions!\n\n\n---"
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "*Importage*"
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "from os.path import join\nfrom os import system\nimport pandas as pd\nfrom IPython.display import Image, clear_output",
     "prompt_number": 1,
     "outputs": [],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "*Load data*"
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "# Data file\ndf_all = pd.read_pickle(join(thesis_dir,'Chapter2','data','all_ROIplusbehav&SEMpreds_df.pkl'))\n\n# Replace column names with abbreviated versions\nthesetracts = ['AtrL', 'AtrR', 'Fmaj', 'Fmin', 'CcgL', 'CcgR', 'ChL','CtL','ChR','CtR',\n               'SlfL', 'SlfR', 'SlftpL', 'SlftpR', 'IffL', 'IffR', 'UfL', 'UfR']\ndf_all_FA = df_all[[a + '_FA_thr_10_mean__contrSexEd_z' for a in thesetracts] + ['AgeGroup']]\ndf_all_FA.rename(columns={c : c.split('_')[0] for c in df_all_FA.columns}, inplace=True)",
     "prompt_number": 2,
     "outputs": [],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "*Load notebook Rmagic extension*"
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "#%install_ext rmagic\n%load_ext rmagic\n%R library(lavaan)\n%R library(semPlot)",
     "prompt_number": 3,
     "outputs": [
      {
       "text": "Loading required package: MASS\nLoading required package: boot\nLoading required package: mnormt\nLoading required package: pbivnorm\nLoading required package: quadprog\nThis is lavaan 0.5-14\nlavaan is BETA software! Please report any bugs.\n",
       "output_type": "display_data",
       "metadata": {}
      },
      {
       "text": "This is semPlot 0.3.3\nsemPlot is BETA software! Please report any bugs.\n",
       "output_type": "display_data",
       "metadata": {}
      }
     ],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "*Model string*\n"
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "m_str =  \"\"\"\\\n\n# Measurement model\nLimb =~ CcgL + CcgR + UfL + UfR\nAss  =~ SlfL + SlfR + Fmaj + Fmin\n\n# Residual correlations\nCcgL ~~ CcgR\nSlfL ~~ SlfR\n\"\"\"",
     "prompt_number": 20,
     "outputs": [],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "Fit model with lavaan"
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "%R -i m_str,df_all_FA fit <- sem(m_str, data= df_all_FA);\n#, std.lv=T, estimator=\"MLM\", se=\"robust\", test=\"satorra.bentler\")\n#%R -i m_str,df_all_FA fit <- sem(m_str, data= df_all_FA, std.lv=T, estimator=\"MLM\", se=\"robust\", test=\"satorra.bentler\")\n#%R summary(fit)    ",
     "prompt_number": 5,
     "outputs": [],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "Make figure with semplot"
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "figfile = '/tmp/semplot_tester.png'\nsystem('rm %s' %figfile) # (to make sure no mistakes, since we are suppressing output of this cell)\n%R Graph <- semPaths(fit,title=FALSE, whatLabels='omit', nCharNodes=10, residuals=FALSE);\n%R -i figfile png(height=500, width=800, res=15000, filename=figfile, antialias = \"subpixel\", bg=\"transparent\"); plot(Graph); dev.off();\nclear_output()",
     "prompt_number": 6,
     "outputs": [],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "Image(figfile, width=\"100%\")",
     "prompt_number": 7,
     "outputs": [
      {
       "png": 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C2lhoQraRi9vXBFIgkzDf9U6MhdWCC2wd5CI31lhMUbjVol4WF4WjmEonV7Nd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\nAAwgEADA8H9YTrqA87q1pAAAAABJRU5ErkJggg==\n",
       "text": "<IPython.core.display.Image at 0x2eadcd0>",
       "output_type": "pyout",
       "metadata": {
        "png": {
         "width": "100%"
        }
       },
       "prompt_number": 7
      }
     ],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "%%R \nsemPaths(fit, residuals=FALSE)",
     "prompt_number": 21,
     "outputs": [
      {
       "png": 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B3mGUNXafSEeYlXCLGrODaCljXA5SJ8dEk5nAjL4IamTMsA2yLsOJCMz6ktWM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\n",
       "output_type": "display_data",
       "metadata": {}
      }
     ],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "%#,whatLabels='omit', nCharNodes=10);",
     "outputs": [],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "***The problem: why is there an arrow between 'Limb' and 'Ass'?***"
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "---"
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "...Actually, get same problem for one of the semplot examples. So it isn't to do with my syntax usage.\n\nClearly something I don't understand about the latent variables. "
    },
    {
     "metadata": {
      "run_control": {
       "breakpoint": false
      }
     },
     "cell_type": "code",
     "input": "%%R\n\n# Thresholds  -----------------------------------------------------\n## Lavaan\n\n# Example 5.8 from mplus user guide:\nData <- read.table(\"http://www.statmodel.com/usersguide/chap5/ex5.2.dat\")\nnames(Data) <- c(\"u1\",\"u2\",\"u3\",\"u4\",\"u5\",\"u6\")\nData <- as.data.frame(lapply(Data, ordered))\n\n# Lavaan model:\nmodel <- ' f1 =~ u1 + u2 + u3; f2 =~ u4 + u5 + u6 '\n\n# Run Lavaan:\nfit <- lavaan::cfa(model, data=Data)\n\n# Plot path diagram:\nsemPaths(fit,intercepts=FALSE, residuals=FALSE)",
     "prompt_number": 22,
     "outputs": [
      {
       "png": 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