Python_ANOVA/Repeated measures ANOVA using R and afex.ipynb

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    "## How to Carry out Repeated Measures ANOVA using R and afex\n",
    "\n",
    "Here you will learn how to carry out an one-way and a two-way ANOVA for repeated measures using the R-package afex. \n",
    "This notebook is, furthermore, for the YouTube video on how to carry out a [within-subjects ANOVA in Python and R](https://youtu.be/_X3g-dvlMF0). Make sure to watch that Python statsmodels tutorial to learn more about R vs Python when it comes to repeated measures ANOVA."
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     "text": [
      "Loading required package: lme4\n",
      "Loading required package: Matrix\n",
      "************\n",
      "Welcome to afex. For support visit: http://afex.singmann.science/\n",
      "- Functions for ANOVAs: aov_car(), aov_ez(), and aov_4()\n",
      "- Methods for calculating p-values with mixed(): 'KR', 'S', 'LRT', and 'PB'\n",
      "- 'afex_aov' and 'mixed' objects can be passed to emmeans() for follow-up tests\n",
      "- NEWS: library('emmeans') now needs to be called explicitly!\n",
      "- Get and set global package options with: afex_options()\n",
      "- Set orthogonal sum-to-zero contrasts globally: set_sum_contrasts()\n",
      "- For example analyses see: browseVignettes(\"afex\")\n",
      "************\n",
      "\n",
      "Attaching package: 'afex'\n",
      "\n",
      "The following object is masked from 'package:lme4':\n",
      "\n",
      "    lmer\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(afex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "help(aov_ez)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df <- read.csv('rmAOV1way.csv')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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      "text/plain": [
       "Anova Table (Type 3 tests)\n",
       "\n",
       "Response: rt\n",
       "  Effect    df     MSE          F ges p.value\n",
       "1   cond 1, 59 7753.97 499.15 *** .77  <.0001\n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aov <- aov_ez('Sub_id', 'rt', df, within = 'cond')\n",
    "aov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Anova Table (Type 3 tests)\n",
       "\n",
       "Response: rt\n",
       "   Effect           df      MSE           F ges p.value\n",
       "1     iv1        1, 59 10958.55 2207.02 *** .87  <.0001\n",
       "2     iv2 1.98, 116.79  8871.20  275.41 *** .58  <.0001\n",
       "3 iv1:iv2 1.97, 116.37 10668.36        1.87 .01     .16\n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1\n",
       "\n",
       "Sphericity correction method: GG "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2way <- read.csv('rmAOV2way.csv')\n",
    "\n",
    "aov2way <- aov_ez('Sub_id', 'rt', df2way, within = c('iv1', 'iv2'))\n",
    "aov2way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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