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<h1>Comparing several means, iwhtin subject</h1>
<p><a href="mailto:christophe@pallier.org">christophe@pallier.org</a></p>
<p>This file was generated by this <img src="comparing-the-means-several-conditions.Rmd" alt=""/> </p>
<pre><code class="r">rm(list = ls())
require(ez)
</code></pre>
<pre><code>## Loading required package: ez
</code></pre>
<pre><code class="r">require(gplots)
</code></pre>
<pre><code>## Loading required package: gplots
## KernSmooth 2.23 loaded
## Copyright M. P. Wand 1997-2009
##
## Attaching package: 'gplots'
##
## The following object is masked from 'package:stats':
##
## lowess
</code></pre>
<pre><code class="r">require(lme4)
</code></pre>
<pre><code>## Loading required package: lme4
## Loading required package: lattice
## Loading required package: Matrix
</code></pre>
<h2>Creation of simulated data</h2>
<pre><code class="r">nsub <- 20 # number of subjects (statistical units)
nconds <- 5 # number of conditions
effects <- c(110, 110, 120, 140, 100)
sd_between_sub <- 10
sd_within_sub <- 4
ot <- data.frame(sub = factor(rep(paste("s", 1:nsub, sep = ""), each = nconds)),
cond = factor(rep(paste("cond", 1:nconds, sep = ""), nsub)), y = effects +
rep(rnorm(nsub, sd = sd_between_sub), each = nconds) + rnorm(nsub *
nconds, sd = sd_within_sub))
</code></pre>
<h3>Exploration plots</h3>
<pre><code class="r">with(ot, interaction.plot(cond, sub, y, main = "Cond * Subject plot", legend = FALSE))
</code></pre>
<p><img 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J+3qRHW5WrTvEuTDH2uLiFtcUTKm3Z32pd7Fu0pNhqOL+vqfWohEL47MDfP1Ain2gnqzYdYdt+XS4p5A6szFNGPgNBIdYFVRuQ7eXWX7lIHAuG7Ad5KC1MjGjWa2sRr+JO02iiyXgUiDnB1KmTOwN/3iQGOL2I/A4lTs7p6qxoIhO8GmBtzqWIMagtTb8qQCm9JVURKbEshICz7wsgWhQtZ3CxrKpL4242E8NjVdfle1RAI3wsY0aKtfSUKAI+6UWjDqSAfm2+cnS1lqXuANMgGPEEfi44bOVP3NaUTCN9lzDcaHI0wqUGbT4VUuJA2JMARqe+j6Q0SXOZmiGuNL3W0DA98FdJDN6oDgfBdRaDSRJOh+SVa/VuESOzuBhBSmUH+KnA++khMiSqE6UFoa8G76GNgqZGT+XdXkQyB8F3FSxMTdO0jhkN1Q/pIJLXLM+oox71mBbg0LfYW+iM/gb9kG+lAK26y6/ytMiEQvoeRdY0RX+FZt+8KwLj2VCqexrGI2h2wyK5F8UrTm8C6zYiSMD27e+5LIHzXYKLciejccaZe/uDxPnDIL9D3tpivgpX7x2DDvZj01yw3UveYWNKp+zSAQPguIdh4wVlpfprO1w3hkPJ6Bv1NUkhG4diQX4hqqg1NOQ7GICqDuvtGDSAQviswazJKIbNnKcyvnpEwoCjHRv2VGIg/E69YWoKoGAY9BSzav7UN74w0VvomsFs6FQmE7wqS5xs76lCrY2gb9M3W9AZtusU8pMXLL3oCxGShfwDnZiiRFKhoIg9ems592pBuKYgkEL7H4NEcz/wa/V5rUxojUWqnaECklOwsiJvKu4J1666VaOMU+SO7ViOd5i8ndsPdCYTvPIwXoQtoi9L5PuDregaPdrAC2KIOploF8MweUo7VoO1gKo46dS/3mT2NHOONrhPe4czXKtU3OQbq5fee8Oa1XkaORrTp6PfT5/7qPvP73RWMx7NPATYEEK9itbtEtT7dcY/c6DLhpdXXwszNQ7Kr9A987wl/faqRg3ENTEVPcvcnV6sZdhzZUYOiWKMtYGAJ3oig3HPRBUbO7tT1YqldJrz1N6SYamlQuun7TngLY1G1SbVMDij+6OejGV+dg/bt1tUCl82FlHHokZ1AQp5ZPbXv+hTu06/puuG26zO+5lqYmVlwdq3+ge874Y1hZQnT9jZUeUg6fpr2+77ogXravyW1LFi9/wx9Xokmvzi0cufOSJu7XBCpG9b47G9Uqt9esNff//0mvK2xfPbtDxkxUhb5LQFgqWk6OPk6DGRZIwZDXALS4j8JRpt03uzb73JfYekG7mP8IEj1nQLxeDL3wawrjCV40o8/Q7r6qWTyiyTdQS6Gxb7M0YHkK1QOztg104hl+lrqJRE3cncWJrqcSdc9hBcPN5Q2vteE33SE85DoEiOK2rngVRWSgcJJsc3S0Xo2AWLdJ+7d/IYhDoThgcl0wOWoe912v4boHsLb/MXGYN/3mvBjOUvWKArf1mwTqa3xlpjrV5GRM9lkWPXK5Trjxw1cSf5gmbfzDfTnJLOo3R0jpatPdtFw22XCW/1DRUH/wPea8JyweqX1noVWHdpMbhBIXEu4wDbcGkn7FuSRoSMBm3Mt/4YWhhW0ZcdFyV1IKeJB12606zM+8uPFhM6Mn15J4quKrp65v4LINZBk1XCq1xjZ5VkNw16QdWhtA8TrrK2pVTnmEnO4opzRj1BGemnOIW1umPol2b6F+y4e8i2Iz45uYPX2L8+5/KBY/VZOd7lXyxj15vj2VFE5pYrfmeywTF3JxJUZQyt5QiZPinfhT6ungKPrJX8IRTIeXTZB0sTt73fjFv34oDvWeEnGB//8ARF+SDmX3Sy4OZzecsh/5EZ5XwBmfKpxqpjplKUV3X2L2qhwwp+e1PjACgI2raCHjM/rpps2QPcId4mZhs6k7y3hrbhyF4e3qfU0HG0xbAS5OTa7WTvJw6Yxx2sYeQoVrjFBnIQZw7GFYKNZJh+NAU5s6UpBJEGP7zZMbKIbQwZVZpnD/AokCBCTFK5n3tKMCOf4pYyK49kasx2L6gs/s4EDaqbi2cBtlj/0Fuch0xAI30HsWMdxILmaSnGWZTREAswpUAAhdcuQgrYwjodOZdJtdCGFoe8iRU/HlP/xKca3Xdx9Cqyau5BEKRC+YxjJ1WQolSpyAsOUmmiL28ZaiK+4T51oZIvSCUIo6gZsWIpF9fPKwZCtNs3JWpw4z5Hc6eb03UB4iy9+UHp8KUeToQyqyIl1bgla5812AoygGoiv0vjR5BXM+bnwEcXC7Ue5vKWx+IhmhOBiWF6zKokdC9U7E7u1EIYGXZ/x8tfsZuvvJ+HZITpHFTmZR4bQejSMGy7xc8A7POo1Jj4RM4V+XDXFHyZUIkHAi4DABdR+WkrIXOLwSjP2KSM+Qx9GDplAN7D6DaNZd38fCe/Dvlt6hzTPez/Pwcs8URXtdYae3Q80lEnV4dhLKPu8YhmW+NNngnkDtb9g2kH8Z3qL7T1Nfq13Pbf0XmMqT5cTwhrfAcQVse42e46XaHH6a6pUIdzXdJWafkUzaCPjB/50VNbCPJCP2rnc4zDAuZHkHmspmVtVtDI7XivhZazivKOJnS6NJxCeP5zaWOeXzStclja6OZ00rKcxRfI6B3rDi2ng8qb9rWsq5ZB9cqY9+LpDeB51LHQQJsj4k88itbYeszcOwIWnnfXPCoTnjxTWNc29eTyAZU4Z6YAL3VbNrGOs8bHcYmRNebeSjrWVP/ndO/SecTlq6x14bS7Dfx77MUuizDrfDXevB4HwXUTA6xiAybi2BYJvzSnGehzOmhVh1zBq+qkiMXhlqU0zMXeaLeiSZ6C280LGcgYXLzLSoaKTurxAeL6I9GXbO+RNKDg/otx1+eN1Dmkr0h/RHvBY+Gzs5D2jpPAOo9bN1VxG4OZD6oC8schHsy+gmjNFz9JYNr0RCITniZGtbAttXOsAUVornqy+i8A9U+fY5u3qLY2dJUr5eBM1owMy1aQ0jwarFiTUz6HrWY85gV1zSesmFTGcgEe5c2SzjUV5c0MgPD8sfcEm2E1tdA6rzkCE8oCEiLBGHQ+5Zx2twsdgA54k9TbuSyG6QyZNwUGNmid5/rECpOjVCVUbdN0w4eUtkqcfaMN8zNsM3Z80nAzim3lBIDw/2LLx2hVljln1OFom8KEURC91M2fvjaI35o1aOxFsF+PyRsTt5XgPka2ZzKJ7S1ciTc8ciXdl9Ks1+ym2Du5P9v4lw5c3n6ks6KJzfnmB8DwwfjPr7u0FU0ih7iSr65QsaChxlAcV5y1TdyA4R/pfRVd2aUad2gtSbN37cyDMV4dTx2IFwS4cTjKNtS+7t2q5QHjTEGc8Zq07k3Un/7YruE8ja5Ct0yO+HK/rK4sb161aoN259TT5Z+NWzZ7t2RA7cwcS5yYUgbyJ5irEDmpDpmS0rAgt57zBMB4F0w0gEN4kRm9kY/OiS+XvT0O61ywcb0FkndY1q5rnPc5GioCePLgYn0iWpR26/JEIklys6tD+n8+ANDWd7y3Hn/NhXTPjx6c4c/EljXzbmjIgEN4EOFZQRcVPssxhYCWliy9hVjwxGymCzC+mk0QfeUa7fyJlojmn1eMml5jnOYLk6JHJAAcYXakUpB54f7DkTyO0O63aOKuWz8nkOsINgfBGobh0knW/w5efR8CBESxHxlVVZ0hEdXQJSnttjtvUcmxdNWOkO45ospYMhqB1Kc5IF3cvUxC71JrDQRyEOegenPuYwUiWHOW6S2Imv/8NEwLhjcGlfiHr/il/u+4yGqKpJcCflsEUs3PqvcEKp0um7iH3mDNyaiZWYy3Nqlw7sYNfz72C1u7mHJBdRq/Q45MbFu2mD4U8xp8XCd9PGO8JUREM3QeB8MZgzhrdbH3vT/NgidoaN0bpB+CaFA7BO2LVivdZykoToilVDtH1WBG3r9EG5bi1hVshHjALSYGXxxcADJvcEPhGbcWV09U0ql4zvLlUIhYrIjpc5FYgPCcs9rHbSVM++un8Wg0LFt91Aru6Z9tZInOitZuSFlw9Q9oQp9lj21w8Eg3B5roTTub3wwCSY26dVQdimynxp0I2fuVVxgnPzea61yGFpv8/uhAIz4WhrXPZdnu/ePQbRsnR5BvNBg2EJlIeVR/9NsEu0doMHIWS5AYb0IQ2m23mMfka0ghTwV9DP7KE9WTE+h8wBAm7ds6yuXls8oYxCITngOIZm1NGnN5+tHkhydGJgZs2EBkHwgzGmDXrZ1jZluL13b5J62MTl9chnhGJVYYDH4lkYXuKPcByHEgOaEbstkN8olmxcUYtI756ZQbX7Xpt5DrCAYHwrOAIb4hpSrv9MaIIgfStU9cWe99nq11wgjTZWGij7axeYSP+gBDtm+T0EjOF+Dp0qr1fH12XHQYTkPawIMqzUkPlR7hZ0brVQfd2MGogEDXdUtwQQyA8G0Y0s7V5tcxprE8tsYCZxU2UkvcWW0YDXZVS5KreIX2GV+6QZh/tmAc4830czpxflWs29nejLsPBGlsYeAc2tqjlinFOiK3LBkCFWx3DPaTTdF4XAzpWEEkgPAu2FrO54pJ+tVVx88OncvDiTJbFKCabTWzTGO1Ed3A1jNhGzYsAa3bdwuT1sQQ49GtErs/S6m3XztsFUOpBfK5RycMoLTFlc9wNxtlzOZ2waXxanGohEJ4F41nCWp2e1G01e/bRbI3ZPvKq4SAM0n9KaOMjrXHEdYTSUzsiu0SK1DhsITj6LeYsY34zyAwUjWYwdzcEatLed5Ei/GazRkaoLhIUOEtemrWYcRxhhUB4fYxi69dNpPzlNxnO+QyDzIwyVs+NI1mbOlRrpCcLVdpHa+a7b24cGVMfiz6iv6RWlMw5LkTW0XUgKQUoU9dFtHPC5rsTiStlnrWMF3H9buDAMu5gXBYIhNeFJPMJS5fPQZ9nbhabPWdUupJeYm8mcI8MyMzTmF32Yofc2HptZQuneOxzW4y1r3V0th3Yf7FvvINbkwSkXjD/E7X673TKDWmPL9HWFm11FRDVdSFvigGB8Lo4s9Zwn2VO2x4CbCoXm/55wk3d7+vxaq6YqXmXDqd4v0HC/br76E2I+koTels3zwwcT80Hp1KQHWiiVYqhx/DnAckVSzGzTOaIRx35/3BCILxJTPr5p0/Nwb1+nmaP/C6HucSyGa/wV9QBs7DqAWLSUzWSuK3I3r5pEFombolBtLhR++gd68M8Pa/UEXAavRBLlqt3jyNftdXrYFQe4yJ3OVPmr3hyHTGEQHgtZNks6plL6dWqDQSk1EVqdtmWc839Y+QBzfQMfoUksaQy9aJgWWtrURdPbUsefcEMqanPPgdwHKl9k0GuYRoWKfj9snCvQWtHvHasexNXya1RNzkOsEAgvAa+tSkG+0Rp7dXmCvAuTmc87EVxBuNoYNfbBF2Fa2ERLWyLYxC5n81CMxiJiMSLT4bTAyxxGdOYP9ZYQ9ZLIFosocjDkYqVFx8EP8Taz1+PAOdGRv2rtO3AgWcDjf8XGRAIr8E4wz5iA18XXogAIrWa1wMlyKk+Vy3tD8JWGtEJtRFw1mqAq7iqgh1a9qUVCfTupYtysEXvoztzYN31aNiUCnP2zMhRn7IGvW81Uy8CbNKGa4G4Sb+bkRqeHEncLBAIT8GKJT3KIufVwfYl4FXEnO6JNwwH0li9C51HE2Id1oCk9zS1CT0KvzoHDyN+jriC1d276jCct57JAP9kME6XDXkA1g0gaRHn05UNL2NuMf9d9LqI6xmhQHH5nfgf6kEgPIkIZYLBvonvfWm1ORhNd2bpmjUvOetGk7nwq9WZsr5KRCmXk2oN/B2k4G2+RsApHFht80Bd32RBEWII17GBJjd9FOyqDYSLw+HgdLtr1OGpovF+ICJ1gpg7jEvdi+e6ia1Gu2IxIBAeY3SFQWid89OiILTCer5gTndQZHDXmrw9lvHFCVc+O0KXjDA/gckx77EYYtD67nc0Q91bJOIltsMFY7F/0O+Xgu2kXHBzAy9tPL11OGnCxenZ1xkJWq5KLvnuyDyOA/rgR3izzR1QFGh8lwhvAFFa65XtbbGQWsM7nH1aLtL8Tqu/hUYBkX2Gnn4uWAFLqDYTYY/biLZMdXLEJDqW6gG+yHUkMlo2uMM5UlIQU21PcsnImqM3kErhwLACwbsMk44OLI1UudcBP8LL8/6reQ1nEg87vjOEH37OYFdYjdJu9Ss3j+c6092q2EjSKlxGS7oV/dQV83DaRAb1ZTZp8ItosIGla5BgX7bjGv0+xJXTcRVDsBjo9+lhcCzPgjlbIHoLWL4mZURHM1EZwP69+BbXvaO9mKyVq+RlMk/5ji+rlyc9+0dRUkdScr8jhBfvKdJ/VIqs+rgxYC9KrdFJhvOuM1Fxxl6dICMpXKBuHYgQhGeqT7OHDF/HC+Y+pP2ngzw0/hbSSvvBL5D4UGtLeBIypQgm5ZFHQmyjkB6grLNAb1IVYz2awC1j8gPvNd5t42vVZ39dZHqgGt8Rwg9Yry8OJbY9WT99Lbg/S9ddz+dz1SfEGISIHkRb2UV3l4D4KKW+E9Uks3ZuC5M/n4FEvuL45/TsGfVKG0fleBl9RP4H4t930mFHEhyeAnCCpMzsZRAqgdU7lqLtKGYfq8fDgQMjjdymFvwIL93x/n8VJCkg7lteJyXxnSC8YeCUU0GVp+XtHLPUWmZpCyCMpyaKqvxBnSQJlzYA7KbqIHoCSX/L2mHyF4gL2ENUNa0VDK9hLp1FWHJvzQRY3qxwLQVvZuzkWiQi2pHlkK8w6ot5cZa8rGULIjEAP8JbXJ5Grl5ms0yN1OI7QHjJkft6051IfT/lhHjyKvfnuuK7fylXQUsKm94B3+P09uyDoNhJbY6jih9JX0wHfySqb80JbaCpEtOgk14lx0ac0ALEDNavgXuh8BIv4WT2rT8p4tuRJht7JUNy28vViWrSGY4DOvghq3OyCv1yoaFVD1ehWQrz6ofp7J/caFy4d2nSkX6sK8gJ6kibAok7S6yWo78Z533UbWYUE/VifJ5jh73yE4CNn4mHX4I4bO85joP3bDaBw2YIKSLTaFbvZNx9C9fMzudT3PiHTHjQe3LyrMbBsc739js9yNILc5GZiGe7NdqqiX7aK7LA6hVV/GY/nRWRtQdGIyVcvtalkc57iGzSFywmYBVvaD1irsfnAs1dFLQ3lkBz+2k7frVE1T7an0y6BF3AD5bwZtn6cXUJ7ekV0oD3Zsyt15GbJDv3mTyZOxB0HN7sYhlU4uBKYrXaKbc12xap6OINYFun1hJSGcb2hZTIVoVjNwqXobexDXxmwga8nEdjjiS6hgscja4mLTNDmc7451xaBsGjK90PlfChjXrpEk63n+IgN8sQvekeWL3WlBVUPhX204nKU17K7EeR74DtdvpnKQ+5Xea9AAAgAElEQVSd6sagtXqJ4iUtb4czQ2VTn1Eu1xk47GLo/ziDxU/HW9aD5wvNCLTIX/GHAS/JLxcYvj+/Go47s39p4o7hh0v4Y7rpEkiom7Yh3PUIzGnQi7EI9gZTyFghonMro5rsrBrxCQI1ntNJL13QHos5IHk6g9oT1so4pfig1JvsMkaQYsGvXgF4FMFYgBekrdQFsyVrsEea32Pyju2UjNfy0HKOO8ox9Dzo4wdJeDP9eNSQyhOPxDD9zeSHWTqHRrIEYhkg+FW4Ot56vJtbECm+DVU/1qF11uZ+4FA9WZRPR28Et/pofztNO2dDcb2VCf+FPlpjFRdhJplUE1mGBhRZgesQuD+UHLac0UPcrJUlPhDDyXQNNB6Et/6Zl+2bjlfONE74dzqgGHY3hip1VHSQZ9SNsvIGpxtLGnSMH2annxlrFE6DqAy4RqnkXmNhQDO2DJzRqPRByggkgpk3DocL6oSY+4xkmENnyT+kS03SjCXICjSvtz+DCjfqK2SgV89NBE4nIJVuZVbG4FVzsjluijUCWAc8CC++RFekV3XIWm+c8PbZr0I7crZuRGqprjFmQnvqkZlgC/Z60x0mJvM53bJDtD3fuSEonEqX0TxT12Z/D0RnJPntP0jt8WfUzhBdocO1D5BJV/ZYThjxHprEhQGzD8Js0rAnxZaAaDztFL+gUuvDHzMuXqz7CncA/Fi91RNubyQXTLH6wGHGj/cYwnTUXMfb+bvRjvkN8xp0IqoshvI83dq7lBLg0BAe1IzUtaA6Dfu2VSahaepbhOY0NqpI7BSitmjwjIoKB58JMLDt7hq5G27jZElF0dXg5fsPcwB21Uhamd6hTeMAzimW0unx2YxKqYFVHPJdrHGDUx+v8XMOsgen9yDidLsqECmvp/usBOLSzcdZOinIk5oTgRdsbP3tFBBibfFm26I5A8bGuc3enb4Sxu+EqQVPfrP5gxaz4Pc/aD94sypaXFhZuW3WliRIzcpaA1EzIO1K8gywHAzvxsJucv3fgFeDcRfQ0qB0QzuvUgH0IsoaMM4KXlK+cetGhnyXxdGQSNZioiAST8I7Pvjnfz/iLp7NBh6EF6W0cZZy6hGIdj3XMdoEV2XtJp9cWqNu6NWSO/r5cfLdWTNh+2LYX1mZDQtyYUF7u9LCr3RmdWNBwSDx4eDjty78YjyMPbE4MiFhENg6g9zhyQalF0xAatr0ZxTDdFdqY/fM1Uu9+cxGM0sysELRgof94iLA1KsQDrOpkkZEZTCMjEb0Nk+hQ/GWaJOpwbKdg8DJnF0RKfAjvOjxbQ/32wUd6nPGS6o3Z+9u0VNYuoPJGZFQNwl8CWJt+KNcncfHJskeTI1yAAt9v7RlyyJ1LSq7SU4wiClUXcTxdvOLzGFMOSU7uDdoa1/aaKIt7/548Sl4ixQ7tuCCdTl/8gTJn23uhUreUOw+qFbijdTF+H0+f6ENiOWMGpoLT7D/V4l49v1q8Fzjv3ZCOsLXPAxCWvBV5wIKDaNbewZ69BzXnhqcR4B94e0mnXZRLvc3AF+coq16l5Om1iIx+CrjCb17FenkNnlmEFVDvVU2Ndo4Do/GGHpr1oFF29R2PEssvln853U0qbfHJEImXShpDeXuVUAdbWMYWMy4hfLOCck8Cf9NzxEehpTldKVlIl/IsnVMrw75BbNJeucWXdGZ7pPbWLjQLPbub2GfUmPP7UqqsbHUKTC54qZEDti1E9JAJdJZeWn1MI9GtbU1bgCIgkVzbGaTF4jBctulB0gO/NI62dKzTPMDNxzTtWpQFv31NKO+WZh2lC5GB3EcIMGT1RfmubnlPel+Vk8him9oaBfgqZsugZuALnAGsWRC4yTdga5sdWYC2JOTL1HPdmv2iidyiGLKjVNargIMRWx4QBMlktkwW1c8V8d0+DailWLm5sTxsma8HNjU4511aIbf2L3ybdDUOoqR4UDOo+LbdNKtVTNDKj7LWqsHYDBrnwQ1+Ap3Bf/9P4+5O9+xoUOWO1FVcg8TP4hZJC6oMmtwCbqgd+3zK0zjBLGa1VQ3wDDFhkTSEfJPyg1it/ytGcwjIz+othLjK7o0UVnXUl9tDSyt3Gjmhbm8rBAdX0lWTArEQsTdL23AvNFKqe0+sTYTyPiwJLXZbiFDdLNq64xu1F9MtjbHq7k7ZncZCp06hbKM+jhPOVq3pnz8vo5251W0hy1s2bKJ3UzikEEJW1PW7wGYyJT7QpR55tL7byOiVFNruRUjOD9G3SQcRM/pRXsw1Lsp8UIqwt2EB7ehV+P2jXPxUErfDvFyKK5Kv2ra79WTr5TxHi/rREXTfkN4JPN2qKBDhxBYz9QaRyjTJJmYHpbPruvaIuPZo+oeT2Pd7f4RucCPH2h1yqFYZ33w+sALZM/Rkq8oo1Ji7Bq0csPYOg3nzKTjMaUtstGPF5Ghmiew921JXgC4tIXeg/3qtdxjG2YTNgEF6rSKUIYDjqgwupqzo/8QHiH3WCfKMJuGZyWjyIV9/uMQUkYatK1RJ70xbArX7zmCLFeRttNR9fvmyaU6Nn27tlIRWI/B0bYU4axqtN6yqRWal23hNfXW2+sgewrprHepQR/LvkLMP3uRj5jhnAXSoDTlD+o37Dij2F5kJ/rS9CvCEwvaxvXAnTCBhTovbJt9++u7TNeWZFc1R6XY2RxPaFY9NmhF1x87l3JM54CifizYYRuL6PZSas98rRA+qlizIJu/0vA4i2bM00nKX0BPwOrzjwaD57ej98ETTalSj4s+SF+s3KomvHkjY2W/qCNg8AJPwu/ubicNB+Qd0hj5IPoVQxfxKcqxySWpcOTXuhWfUzdwqSxp7NlKYZW4P1Co8lyOHpcS/wit08F4KTlPpbso8hn1xqWaIC4HpnixazGITldj57sXFkcu/BSd/HZUGUzSVKx2UlIk14gS8xjFrO04SyJxgh/hLb+Z1GGhu7P++JS7Pp37ISs2l2llRllGw9BRZLyzRe5lpo1Nwp194s5R2swMSLPTs9QDt/UMKI2tYt/leGMPJfPDdq2TbztDDCtm+oQs0dmSZpIJWGPRxHfclI5eqgZHb7F2NZpRhWf/UqUmQ+85w/C1xnR4mB54Ev5jziL5nOh0IEZYIVdiWMchXakNkxyOhLrBWIIS3/hax+EaU8+p8dor2UsNOTSQKYziswv0j+wrFYcqsUyxhfKVy05opgxx/Jx2+jjoSg4L5+La1tjEE/IEfbi8LgBoWV6Kw2/UuBaP/xNPP1SfI4Ah34lqWbviGQFPVr9sZwcz57oUgWMG4o6VaeTAYAYXts596buMnD9ev3xfJ85y7UvOcAtZJUe9GWItWsTNyw8d26l34N5lcG3C51t4j1w7ZE81HeNEV7TvACzVC363LQbwf0NKe1jhTyw8CxB3/l6opX743JBPNEzgCOP1jXoMHQM/wtt0eyBGJFdRBwqDqjvXRo8J8Z4iLZ9CQp0c4vECObhtv+44I2pkMLvzkMjxb5aB4mWBAadY9KEECOwTmPacdMhJH2trW59nLMpRVfruHnNvxCQ+x0u1Fa5kY9kSBtB85xw81Kpqw3E5bOdvNRm5Zo0MPpyn2+bSJPpKqp9eqMxdZMRkY5l5r6s3UJCmmWADXuTYxJI9wC0fvmBmfJtnGxQdZ4ArWU4y+V4cSJ61ZesljxMHK8xGkNHSY2iJ/Rqj6CDjSTu1GPzXbaqxGkC+LYVIv3A6XZ0As27V2kzQet+kJXg2pGt7oSUd1/7etbljsTIdILzFDc5q6WwwTvidpdn7T9xvKUg1Vo65w8uLDjSCrmh927hASlmb+9fTzCHDG42F/nnqF5ynMcJ+XB7A5W/1K4UTj76yASl+/kPryFsXT9aEMMiuujJGXmTJeMzGLqOZWICLuYVWpvr2WiAaVruJGAS1bUcaqMXbWmbB7HG4iVF2kwd4En4/5vTKDukMJtZ4iV9CalZx+8c//lVzBkdfDfOmvR161RiQHtNGSQ9rQhrZARzSLL/8O914ryij5Yi5cM5sgyMQyv36+2995b6DtKEHN1JCxC1N/JNVOVPTZo2aca9EH8s+xpslPugiZ14Nh2WfH4Cd2hoXCtLMseepxh7lX6GVGsQccigHeCZNfjXiTlhqx14pfsKdzG/G8dpf/fV3DYcS/Aw0RtHKNl6ZnwYIqNE8LevcEt8BVDJ5VJ2OMD87CYzBmaODM6WJn6gxeCft3xt47iT+jXcbycaIAZqlwqGGaZhacZb5syC1c+0CXqVLcDm9EYgPBe1tkYG05cIoe0baHqmD+hVf0ew4yMhbH8nMojYJnsLdV1apUzqo03VIqo84Uv+rn332XkFWaoL+6teZ1qlrNY88+fUMmbkTnu6y/N8yT+1w9xpHUDoFRWUU+4EHg8Lvi7b+fqLebuJGY6w32bfAoYG6+hnNUmBXw5S8HEIYq7EorV59HTdM87B6bJAbg9eFbbviYWtuOMgYjHbyaDQTXv9EMx3MlIzlML8jZk9+hFd8sWtCfuwve47wGH6p+cqS3LMFlXUFWSlRGhNezhO2ctJGYKFNVPF+nmMLD0mVbOjP2nWUuCVjwSh2cDX+MRPXejd9aKA2y+pnUKY4q2pyOSFytYHUnswSKp7NzBUzbptWcV2NFbkGrJclIJ7gkPbmHbD4+LlH3GHt8Nxxj9Br/UB7hhkMmcVF2QH5jucaP+8Tu5f/2sH/tNBJPR4RvyE/dXBcalZBZVN+enIUYqhDyzmLt7IhvEn94ovT20YpKF4oy3qWxrDJOnU62mvhBNh4cv2f9YvhyE7kL5lAKgiKUqrn2FENuXy3MUcqarX1VaSUj34RbfsLwrzaD1uTcAdB0fr0FIBDxzKIRqbqd/YtsKxhvMJPGS7jbYwqiKbQr5w0FPxSC9ryU71p8a/pVW568sBg3tV3rn04ID4QXBHDjG1Kk0IMuaxHfrCEOWap0lQewpxdHAcSZR4ttX/V1/Osyh9lUO+CpJDiFFYaakc06QhdN7W2voimFdRfdRKlI2apjlgi9MWLhkXzQ4lD8zeyPQxH3CY3xEiuNWmVUO9yrSgiaeIf09APCY+BiP86N4W0qMn8EtJyf/TXD3LTEvw4Y79EnlKIKqj5/DD4usCE4bA1yepHH/gdejguKQyCnE7+tID54uw7ZdL178KeMuYaBRaPFjUbaCE3b1yqJD2vRB5Fkn1L1YeimnTsglqvC0x+oX4jbtMqiAdm89KX6O5ETdYABXvvroQLJXPdGNa7cVgt9Y9u0XqO9jPe6dF5pv5jGvAkvGfV7yIvdsxZ3uWkSYr4tI3E7sKp5PTc6veL9cQ/80Gz3CG2pup2FCxjtluf96Nk8HCIgJHhcPrTD17NgQtzYHUEDLYhePAOH65eXuuiYMHWVsP82eQnddRydI4y4XofUU/DuHodyWI46+P2VMt+j3Dc/blC9HUxYjmXE1uapb5ll+hEWTWcEsHijHbZldczhNSHvFdFnsGWxdl/92m8x6rfOJz5WqX6JscgTa9bsmUR8d9oiO+N/tpFJWeQ4t+Krek5gyG8+NwWWxCPlcTpqEheL5BQF082jRFnlJITy1wOQwbAhsVlJTPg0lR4NxpGOoAVq0vAsp7DNW8GCyNu/t0gt6pkRbET9Rh3Uaa07dq4ixSd5+KlFsLNslg7BEfcRR+uWFOUNJtD2N2De8Rwtzk0mGH8WTpfrvReue+5ds9ERuakZyNfJwfP8Opf+v3FxvXnbFK9tPpamLl5SHaV/oHuSpMWDSSJj7S60PpMfAfW+/OqJsXtanrd3ozFP4tkR98jOtNYnP46DiZSMlRoTQuzfM3qOrrOVaATLIuGG5PhVgKciIHpruClOccjfVWNxqK9sCL3K8M5New1TcW1V8mpse26eoZM0326Ck/6FY5o05E5ACbQrPs5XkWOz0B8IwXdxHTbJm/Z4FeXwEm7xtkEQuRLW52nXcAQNXfyzQngR3jrr7z/YuPwK7YoCetvyJfY8gv9A92ZHy8emPrkk9vJAW++rkkAyzHe9HNA4t/tG2U/q8Tin/ZNj2nGBerW45sVpze160RUsTZnC7CH5BC4OAbujSLORRLLY8GfNfjC4yKs+d9RejudK4Y0xVAep4UPyLNvvqUmEw66ZoAoiFefST8AYBcdkx+OdXmH2jPovYhCv7U+mr0Miq54bmY4ixRusMQhb8hT7bvg9UrLimUtPGOh+RFeXLT2r/5599lkK2nNtTAzs+DsWv0D3UV4h5gFo8C8vPzqW6kF7+cm+8RrD22eSkwzp8W/0soKUvyzyin1hyjKyh5SmbpG6wWU7DUaaE7BzXzZOh/iRJToaazoZoR4hxdE0sTzPuwalPhnfZ+Oe9OkT2jX7NQikmdMzle/XJtv674/u6nm4hNY2lOat9Ly5gof9BFhhsTzjesBKp3q5RBflOXMCKrzzkMfo3IKGS2Ldy/XbidydEXTB0/hzqNK9dfL7D4Th+xvVKrfXjCwened8NPTc5eC9PaJtRrZRhyVVviHX24hXR9vr4MInQnlMnpVVmFt+1qRs4MFfozi9GpmTsygmlW8ooiWaahlKYE4Z/G+MEntUOmLQYf3Q/Kvj+uPTh/x8RtqGR9dSRpx52vW430XdSfKlAJ8favL99nifZbQYkHcBfw5cGAoKFJwfcQTcxdAdTvxkGHEwtabZU2DirXLm6KRIXcX8ks/73/qnBg9v10PazaCaNWMSBapQrzgTeF7DUX2DpxkvEOu48EVZ5uZ+vZsXgW4B3CUvJERjgTx5qyeHujqKG+op4z2kZRwPeeh+r3x0IvRkFWQ/5lRJgINgvHv311ChMOguQCDXWobbZJeF45npkYmIfHxnOw1w56WcEG77V3PK+GJH+GJw70QbGk5OAbdT00V0qXC2Vp80nCE6XnivTseKnOT2XxrLpT1SpReuveh9n5Hm6g/rIZDEzvhxc2esOz98/q7bwYV0KkawW3kmjLBmaa7eLLeUBscnG8Tz31pmg8MwjS0UCqKZRbNYsiNPZuZQbQ3EkqG+HoZnb4m1pHJ7h4y/n8HdHL4uMBTqv/dFM75FfojVYEVuP5Nfz9/wntPWb0Sqa/FOTNNDo2UDiFFp9VtCyRR6c+bc5O16pLlImnYE2kkqW0FVaQS2tlpkfOUXzFv+St2PmmP+Kuk4ZGef95VBi9eUZvuSvI5Liqk13fpff2KFE8QL5/dEs997ee0N6oKmym24kaxu9+Cwbddqw/Cqs9CI/SmcVC5n5QRmuhZoz2ueMOnkAFPwn/C6Z+Rvt7meusq0TnCjz2QnwOw49AyXs1+xru5X1AHk1tnYi0NE7/l5aKB+ZYh1WaeKSIxNQtE6WVbNzN+mMGRVmgAL/YyGDHXRDNL3znfosvxwpqd0mkObN9A6oxv3aXpblWhH4SJYyvn5BozgY2lC5FPwTUuZUrz4WssGkVQZnZhHMjfPPZixnHsRVpLQpYjM1n6XcY0n8lg/Jzgy+rf5nL8WH9lDeYfhDMIH59L4iOu9F0SKc9r0H90zFgvvhm486LlugGfkbIF553dX9l7n91T1JyTzDBfBbzadPOaJt3Agn+wBXvBbzNf+dtNJyfCMd34iYHNfg31FB+0rCYjaqY8p7mvVcVC3VPA/Itg1AmMUUxbcOuw03XNTFF5aOZwxGm8yi447/jULJ/hV5qP/kvER0OVjPdIwqxZ/cJYQwUaPC13WZxrvPnPkeg8VemrJby1H4mbRuM+HTtk/102HRbQ/MtZKt/ublvn7ZhjP1y5k/ZPoplf3kgTX5Re7p+gdWtMUXL41Vnu6SLr7swrAxavbBsW8Vx396TA5g+p2SAvJRmF2QS1t/QdfZfuoFrvR5vABKxo8WACbRr0rREhNjFTmptiZ/XzZStO6Q0/8dmaKm0TRBh9WbvtzxFDwgRPA87X3Cnsqf93XyI6+9v/0d/fbQacxVthqLVcQqR4yksGmt/2kieouQ8xn6GZW046Vluxf4zP04NMWT4rz+RMU4PdczMjMG9WSFW6FFJ17LjDhsHCDyl5XlJIun4nFNCHrA2uaN88v56ndImxAp/AZTEEwYnxsCbcu9je9mjm+haGG3//CKQ1fJC5jRmfdI8hnpzQ5ziG4GnAOZ7MXeHQZ4IIiDEGfszuIXzy2bGD5DdjJc8jpMmsSsUYJn+2nHL82bgiZogY/xRczzcsq1n88y8CYVrjvrstetTcFLStkeJBRB5VLWMVHYvl2jAY9HB2jDWvApF0h9BFuFO4uMkawsaSifk31152/mZwOkNscMUqRPxyMGe4l12rtVZJqzaTMTN9FVdvEmFuNkc3ProXT2z2BFsjnMsh94FOoG5wqybMyemeiSgbJszrIw32RdeWesPAFa2P1+o5Ysd4weIPaLtAFpm9NFrdIyZQqV+ThrjFN7/pJLVEiMkleu4hcH03KhIWRfg/f+n77NkdZkqsHRZcXGA4s/PgNkZRh2SdYGI29DcDjpMDrB4N16YH7JtdHMAv+mKodvW08AUzzbs+S78esVEs19cl5ennPTYVNXz8yNlA/pz6KnxiPb2gvEuuveNr6UkxuMlHb/CAD1/zjSKxb6Um7XhsiyBwDqVXKdLo4M6Kk35/mMd8oUbhaN566xJm5r6klmGmLzZgO3roJ4RH5JoxBrLnwf5xEGMd5Wp3o0NF9fzXYzYdda2C6VeL60g0uD6bd9/vv/TBp1m7mlgC7xPLo1vqaSddKumJi6+j6W7RZBDjPGUK/zrAYynWJiXX85nHAE6VjIQKl5AnCnh92IXplccDDs4ofv0uI9g3jmGmDzUl33WZ8BZf0IVu9Q/wJHzMMNi9DA7Mg3janZIQLdrb0cha6faWJICl2tZCxNqOzHaA2Ud0vk5v+c3nqZDsde48m/LhG6EsofnDgkd4jope0JZGg1ctKMm1peP5prAeR4IQG3HI9jNIiIQHQ1YPK7v0mvEGWUxCvOS5y7epTEP0bcZ/+qRBOqcuuj7j5a/1rZMUjBPePwJWr4E962BqJDBe2mlzeHfMY4JIePE50+HqU5bZsYRxSy07tzJ3+vJXRwJga+BwVtFw0SKwvkEvp1NeYi4etZw+NFEvw1G6uzLcUNQzBmuKXFJN16HJP8GRW2EPDxHvLXiXYUiQnUQfJZ4JXszga5cOyHfdwOo3sFenNE74pERw0meBM3fCiE6ltsDYw0j6kpzSuGDTjLWIM4T/bnpDHp9Z2/oeVeNqY+k21sGxkw7soktRx9Vjp79VMx0LllSq96wXbhFdMpabZwhxI2WCXkYu6DfQdLZwh/lz4HE4LP8sVqk3OhlJjRHM0KPNjJixxcfAGPrJGo8Eplvg0ZlcOcXCYnWg07jG/XgChncolQjBrpl0egZLBtXm5N2zwAzI/U7OQ9Z83qWx4LuB7kka0Yz16EHqaO41D3SEOBl+smN4tQJjYCbt+F2MrTkjcdp0+zDbwRBxd2dUa2w7830+gS4xBKzLmAqHmBl4/YojgIxCfyC8Aoa/JOSdy4gXle/U/l9FC91Auq+uow2Qn00A7xV3W29Y+V+oTKJ4fnDrUtahG+9umJv0iLrVoDYsigxsoS+38K6OgDi0bgbSB40+e1bQzCedZDsvkTy3eB/IbeFZ4ou9NWNPMgYuRHcqcYD38pgFGkfe1G4P4UoJIdHnhHdSeBZ3svCNecoRw53Xt3f0DbJOhCUFSFMfHryLNgatPCxhv6WkawduqjOg3ZR4roVqYm5tdbS+RYVuOHW5cysXgiVp6Yu9hT6GRYRch6F510b/3fYBcwjiL54PYcpHD5mM8iZf00XfEj7U0+xBZ3LjSKyuWNu1PGogF/XP27B7U+SWekUtVSa/5mjPqoC442HV1EXtSanNh24wQxxncQDKPEw3MjKEHcVBppInxImxCV97VDnCy5EBz24m+TAGXkdnL3MiVk5g/tqhlquzvB76kPDDR8CxTlTmI+GY1vGG9gYYGA1HsxJnIw4hX9O4Qr3X+/e3OJKzt24LAu8maum3rItHnx7+1DfR5d2McVaXqIbf+ezqjgmEkAUcwIbsYOiK37K2ird2wPBrsti/OtQxBnpZIH6Pw3CmMzJtYAPTH20EfUX48cmQwpEWbxr2dyqWd7xXig7IRR2xRX80b0W7mzZoaL1QGcf1G8W6Zfa1VPCbvAznu/vSfF5yh+l6G95MzX4ejQpZcZOyDR0gI2vtEUOcXDcGrRtlkQ+rUz5iih5+5iBXEhA9aTtjp6jSeJEZNfqK8CN49Hdih9dIkHdUbteF1YxgyXXK/O7Y5B1y2SdRszwPjCnneKOszs9zAPMqyskmfoJ7icjDqFeXKNTR2QZR/pypDztZltmTygiyIZ39QbivdK2VWRCMvaSw/nQis63o4uUAp8fClvXNTM9zdB6vq/S5cNdBTCkp7ICD0xDi+MyaSo1lUF6xKTpVG6dsfrKey1J8oHFN9SjJU8o2TtzC+rJLnVooYGhZiYvVF6rg7Q/mwE6SneTFIDnS6cldgArPVV/NadcrouLiAeZt8d8y37GrvNLkv1OER/Sa2pW1feAGu+CjidoVXLrhk0uM801+c28Vy6/QurQXHP0aRsAVOv/lKDbgONVTNjn7u1px3jr3Ki1vWpqOH+SGlKIjKSL44QCrkjh0jYScI1nVGcx4K1+0Vtkq4EC0juJmX8dnGfzuEF6RUprXhYXdAS/qb+moeoE/3sVUty49Z9W6w/cT7pYW0iAfyKBjDt7BVhnHBopTeDEjfOZodOp77BWv+WEpFal5lgzsOT8eYMFMf7SOV4Y2tI9nsiRnJNotJKOKJzN1o1R2k6MuvjuEj0jttPJmNeMdSNTzqROHG+wYZjViODjdZhGLYhtuD0N8uxY9ps20cXT1TTwhl1Mq34BWdXMZcI7R/syhYzZjPZhRrQfc4vGnJ17WI4L/7AJTTq17WOd8RVd0sGgmb5NZHE5UwUNF/k4Q3mZTS8fLM6sRid+VuU8AACAASURBVBd1/QzXwXmhscy541eewZJfZXfWwh7xBC/SczKPziBLfoH4jh2dORzQromBWdim9Y3NvAFdwjIqWWKeRpxZufVyFRLynAN+vbiEoahLTiFGNQIUd+AoU9yPumv6Et8Jwt9c3gnXJsbAzSthUaK+Wi5OrsjxB683mkBcWVZZQKB+GRGzJSFWOGhavKUeMXVp+nPKYDe5Ev/NiqcGDdIYIlad1F5mUE2Hi0nrQkQFFXlT3r5tOBfSsomAGcdmf363jhlo/RZ6sdFL9ijc6g/MWX7JdJnL/k54z73XTA9ixxK8qBuWS5PHTj3hiUOtNDHIjlVv++gb3mSXmzdTlJy6HgkGE2tpljOqwRrJcHR9myEay4mOo9GxrdO6qh6ukzaFLHTtTetwrEid15bP999jcj8Z2mkJI67DTGaYhp1p+a6fE35FyWyeJkgdmE06M068lM1AJPOgu1HNUUtfZr4SO5ihWxRn/lgxJbKJr+GTOD/IoeWLITgL2aqaCvRJqKZlQ69nOunuWTy7kxrDavLdCXqGP20aRGDWInp8HOYcgq3nq5m1ForMIRwRH2lw4Uxfwcp3TF2gHxPe71DHfVsY1juKS/eOYH1ffE/X0XNTE1mT2BAPC3REsaDJMItaWgh3IhKXoqtUiwi+zYgeFpVUyv3sKvptSNYxoMCCzryq+hiVR/65Tr5aexBHD4G1DkA0OvziswdRDF0+AJGbmutbzzJISVSasnH1W8LbluaP73gfQtns+y7OEzhs7UOflKnn4jTa7e10+4I17fymMfHlbfXsD3qFLbEDyzQ9Ktya8ZJeRImBuHkoBU8dP/zsvA7fNRueUXdBBstb4G7TJSJYYbNg//ZPMp2ZiR8DJXABvXeJVhBayXheg0zVgO6fhI+eAUQn5KPhF5TpXAYe0bR507T8nF4CE2tHgM1VrX7kuwziNMJ+0ssQnG/5UCM12SljcL4MPWAnKeyJ0vSKmtgFdk83rSAqP+IxyfUWoAVmd/z9ebdETU7N7cOYj2ZXPETnIq6zC9wLmYUIz3H2VqLQHwk/p/Ymj+QvfTiI4nI53KlIlJs8+qA2li+Qmu8+ZrboBbBXx9PLZojitObbqWR8x+RarSRlUYtWUtFLqgLuHEol8H+1VZctOepnXnQRg7T+9bCU7ddmL9kV33h/5THdmAMk3ombFOINTHOudb3x4N7+RnhilAQGd1x5k88rfsX9xJ0PNjITHu2b8X9auuclmrvT1DWonbPqmC42+WHEyD2fZWlbPcnLZqGPQEocSL9K0XuyXrlVSSnnu9dhUG2AZpOa3bw1AJmzlbbTxc1Wn79fk8SIE4kNg/WrkTQ4ktGoCCOFq0Yjhf5FeJs9zVm8a1hq4XGuYacRG/7xVzN0pmUB9mIk1JFz2Z9km+Jki5CxjArgb2OPqjitgpFcI7u9Hr0sT0kFTnSBzNIaaqgtX2CP2OoUjpK8ZSgOwgGp0hycK0cTELRmy7mmqihGVHDwErCupDbzxjGSpYlSH2Nn70eEF1lCwPyOk935bStfIwmx0cXj9OtAkRaYPLz3BmkaC3R33sGUxMOqd0hx/lQq43UZ0rIOPf4npLovycfli2SZ5T76F4u9pL+nC7BtIhl6Gemkw61nTyS6wOGEFvvWDy6tXqwzlDQjycDRrJRRaGCwsSYM/YfwfgfrOxGwopj/4oWRdsTieakj9f+D6+MBQgGryXLA5p2k4js6qo+VOByJc5bZNxiWe3nWKzRGRNcrHUmuCVtXGFw3gld9B95YS0o648gCLFKlBbi/vIE0t9S05cqPzBkvs+deUqOTVKPNBL6RV/2H8AcndKKJ/MS2nUb8EbY72vYZ+tdHiDyfkSmF3k0iCAmQrdM17pk14ij5uUqm6T66NRUT2Z2c73IsAMrZqsk5tBgp3dN5VJJccOk7AGOsJSC93GLzuzqQaqMSCDSza9GKlTMBfI9EfJcIH37+SSfO772nymjjEmfL2lRDlTAiTvR2K1mk0B5kgbBrkO7hINKz7VWYwTi1LKsCm0iIC2RIlEUJGhLXxGIMl7p3tPebSVBmeZJEYiRGiu8MtoLMgg2tn61SMOvxWMM2xPr9n3Xk1P2A8HNuxnU4SMliRUXhDKMv97EStknp1jakfh85g5YfOlqvXz7A/GiJP66PV8U04A5tIac7DFqJP20r0YK0rdAVDHHVaBx7p1BDcqMKipGgOZ6/cwtIm3/ktObnOlJNNTiWA5C9L0+KL/IzgPQt4YkxtwxKAvP4VYxo3BZjlTuJqWWzWM0osroZcnJtcB8DS8cZvG4LsUweW5XCOCDLqiL1Kfti8kE51+F8MdZzrzOZkt5xUE1HEyhdvs4BZL7NIvDYs6by9QPYpSUw0mju027CiWZv8buPPiW8xets/SoCpjHgQFu2Ud+TdEXDYbYZiTDjU1KAdITywwbHFHfwj6xz85i/jWimRPuBrfHk98vRVlfYS2iNKu0OE70+qMC9JNIpMCML4OZVNO9tfrz7s99ZTGIIFMNgPG6NPtUdh2I91S+3y4o+I7wokVcdPn3ElBTMNEp2h9SA9Rbsh7zeu4CeoySpVGlo1BK5SXEIXYqSWXhSnN5AZV7PaCKXbxcZjG2cZPBbDOJV5/plmYCcZD5z9pJfql1hCO5eImpNq/3sHZBqpeELXgS2JCWeBZi93Z5XWGJfEX5FfUaH00zEU3IlzgZ18bXwmrYz3/YeZ5TjxPfuE+AliU6PPGXA5GNrcBT7gCcZTC4eUp9OzmLCZjNpSwxXOkEse7UuM+OtzLoIUQNpqUvMAXi2xAdxypa7uV9Bkk4dLSnWSRscgOBpMu4rwjt2WHkLOvreKS492UW64pJn/t13J3EnE/iYRVhLJA737WxZ7Bprn3jiLuOVTBlfnN5IXc78yXLy79Cm9ZyOj5t8Syh2FNJH+HMxVRf3qRSGvcAv9q3at7+1lTCUiKkeDrhEzixsME6G5eyJ6zroB1I9H0hhwTNWFi9XTDtdXv/QO9F4eyFJen10+4l2/FymTdA/mETO4uHVTKEOgmqp6Q7eflRmS0JjEWdlyq7FVhrFVaw4ihs1t0a+tjZ/Tn/vCRBav0B8IhSoa0BlgU2zEb5I47tAePGkO0WGDEI6ePHx2IPlQ8ZGmdZfIuu3DGh5NFWMVmvDdAybDOyju53HrMQhSm+iZ/+kZldQxCyTxL3YmWDwUxoTu9wAmRvuNfjTlrq5RX5gk4tj/099k/UPR7FOCcnJpLxKjZtm2nTc/wk/4GTbYb0gKknYO+M3V55fM4ynZX//U69T+6hwqVQ9z598D9nLI6VWJ704oCadZi9bixblNVScXijn0BMw/JQda9PUMVBsu4b8r8bkIa2uHU0Cm9eJj24zR41YKWpGHIrAqZabR4PpZNR+Tni7hcToSczZ7vv2laq65LjpHXjUPrPrsoA0x8tO6L8po1qWIybq+zSDyTWINCWW8C1GbrrxmzrxEH80wq2KOzPGorFzIWIdwnIqaP9JKLidxYpH5t29/yOCbZpUA0UCHMD7980DcOJzP/2Z8OJJD2s1OU0W0cuSZtavjp4b0jF92f1WQ5Y/jCdDL2z1hB4bItIRC3VFOl5177IMm+Hrw8yeZa1fSj+dyDYjCXsZnY/55wVPUmOYQLKgweihluMwf/sftX/+DkxgsEJrS7ywO5agDyIM3ExVsO7HhJ/bcIgiiPWC5NG1pSdXdcYUfvobjQQUo2+0cWjEE2akrlAnTf24tLH8xCJL3BVaXbHKy4idcB7/Mnadg6IF2yW2Uwn89yMg+QDeyHq94jdo0dOE4li8BA9MeazXKJCEf3oR27m06KeEd972GBQEyObsuV97OnhdmOlfsMA7s61kL2buQWRlGB9dT91gUjdzvH1FIwFbjU7La/ttlrsP9XWlP0VSudGKcZM7mw3NHyuw/caymaRxwCQQkZ0yHD9+/Ys0GK/tf+IB0w4wfmTZZrwMS38kvHlK2Ys1kxd51OZ6v53Y6UopEPtqtRX5a5dGNA0ydZOHrc8+9UI8MaWG4v72E/csgn1H5u3W5EFJr+RQa4pnnbG5YxvQySSfDkBMUncPfRsySCE7DJ55/5O/kT47NYaLlPiOk/EKv9IW3I1Lvv2P8J7DbpxW1j3az24a5QvrKo/htfQpbuAgRD074UocQxVSjoR3J6nt/cYX+yYjtu9enKW23DlV0NKFY5thcWMtbJt5tnnrOmzqyD+KYpCQbblca5b/72I6/prEA2kmFu/C76CPsSYNC/2K8L7zTn30xwuH5pgeaRSiifcTwfFWttZ4ad+m41CzfoinqTzj5ZL0J41PvEW0qpbaprWI3OMsiKJzpRe8ihB0Ga7YIUOH718eCz6f4eXlyvuPKwBeMIb5kHENRZikhKkcqv5BeMJ3uuWNaruHH7eldj0qXVR1IgDGNKjrGW+aDnY6QfoSF7Mw8JqV/03y5IzpWquN08MsTfTFIrpRp9l547WUj2zp8t3yQjSexaIaUprwqADZb7B84vll3H/qNBRdbV6MPUUjsHfO55aJU/Y54V2nOByors3bZjF6K0R2rbYN4oOukIV96s5SPw0RBxKLmHUBYUzrErjS+ORNoY/OT+c2a+e4zznqRQltNV7eYEgXs6H54yEWPfZSPuwzk2A6+Wwv7/vyfYC1GrfgzBAXraprSpfvQ8Kbx6w+VzFk10FnVwk4b2260bF60/ows4IT9ZWkACTa1Kqxs0VNtWBMWum8I5/9/vl4IFIbdJ3WDo+0dng7jVp/Ri8wSw8DjfsHuhMhZA0HaiVyrgJ4iUV87z9u/XQCjGbexUCSKfhrB3Oirwi/7MWE7SeWDqElz43PF3WBxZuP3DwZcsaDK2XiG6bUJLuBe5vnBcrm5TMn87mzx80J2MEZVpqmawWa/UZb6D5KyTMoyKaZX2Gx7sNJKg4ZTeY0sqZn/bnZ/8m0Zi+ZeiIe/23GDzPTx+i5+orwAzTScNz1+125zOLBokfHkxmzel+VtlGXrGYporD3CFjRmL91rA247UbvhiLrha4pyL4gV1tlZVo1ldxu8cBoWDoSFdx77NGxAU8RNzpxIkZk+Qf8kof9eci3n4gGa9qPOg0KId1F6012vOrzNd7t4Pt5E+3M6JZlYiIiCmEA+CVgmMlXpSKMh4R0DHubI1kIC2EJ2dfOybnlp79vLLhkv60SocLJpRz/3QpDGQYV8bCak2WN96j0FvMj5N+JtXom1plvtAH9hN06ann3bzIVOnnOhGmsm/F2Mvq4EE9u714I+8jyh58eb/pTJGM5l9m9wlPAgmwvH9svixhvRUSqbnXc3FpYUFCwBlZiUl6ydcKUzZoJszChd5jbYrqnxsKwZIQksRn5OgyAAVEz4mPqIyw2jHGys9M3mDrTRkzCP/noJ8fhrZHqOKzEVvystDUOaNgV5GrVPvPHK+mt+BAwjmXdmTTDA9bYOuNNFTuzapLYPcUb0SV1P0YvqqYXQuDVJaSeQb68rLH/GvTZjCfirtauMDqCHe4Kx9LG2xyhOKKZL5+N3H8ycZXZwLq8zWNuaLm1DcTa6tQ4oJHQzAiy8/KnnLOEaRNxdGUXa6p2GFvw06Jfx3dWwC1Sk/vJ2cLdlUSJ5l7sFbU8T9dXhF/+OqfDqdAOk/aEWeR7Epxy4LDG/S5xZ+reu1Hc9Hx3wtx47RFvMmk4vExPqLPMvcIIUUtUUq0mrJ+YLgRcasQ/3zOQYpODOTnRwbxZNuL3eGPNJ61fbmSOSqBM3ItN2p/6ivDeHcyOtNxl7/9072TuXxGLW7+sqd8P0THkGMepp3/xKt8qkpwLMWTei2WOfon0ca+TGd/SCimNzr3OIDhLH/J5Hbv77sM96r+wbircJ2WZokeX5kWHz1MbQIi7QCbJexaz/1yLPjfgmIZD2qMfDbBM4HKC2UxKu/SqNvXJ5tQBjIgN4kS7L3hIj9fU5KzKvYcnyxR9oc4i9zEz1+3iAfoK5qYDpS8uNjmkB7Ab3652kSOTqI4onZuWK8cwLMeVpFfOZKJKvya81ShpWJ1iwHz2e/SYtPlSdQGM3ZkUub7+mF4wucVeOrtNOjR/fu0Dh+WlWXqZdmN1prvHBMqLI9rAY+227lBnqW7DNNwL5z5lvvNMgY/w/9D22003Pl/CaEi0NYUs2BcYw3ICJvor4aVhMKGxJJMtNYLwHrfmeOE0yNyzMBqv0B7D16bqETXwqNo75fgIE1L8zhfVTY+WKrTBp+a5hczgSi+6Majt83Wm723YC9NjegTlvgCRVKidRGm5jOzT/vSDzB/tWe6pqQa42r2ZV4BAPyS8NGJ1tmzoYZbbt45Zcvi2LOr5iVWjNOzYY8yzaIOB/rOoBmFyRytsfqVqHHhEDiw6FJ0ahWWA2EbmdIcVgbRW+DLe9P2590w2NA+EYI29kKLYkl1wDzuMh/yq/cF1sGeEhl2gBLvhhr9nor8RPmwPTD+Xoq9Emw1KemcRZLw4sSpOZwGWLqjMNDzHDjVRJzXGo0+r7JtummN2S7Or6lddrmAK5fJrOR2I41PUGHPQ9zyiqUai4kbb82QbtOOfrLgDEw5rVp/db1HV0Kq+MxE4UScqXhDuuiq02H/qFlenytLzaVNZGlCmNe5gSxgaGFpI/j18FbP2eUp9F1uscvfNt1JOzfehvqprXYqP8ipctMtE784eBY67pLvazj0U+Aqbnj0+fV583wEK1cvYgAEUxZMOGj1TvyC8axzMvwSDRzAVdJe4ybCitvr6O1w5koMfDo9m6TSmKLVzxJ19xVRtem+dGgcYsqxnmAHIh2++0zhh0mgp3KNyzR1K0/jc62Sj1Rh6Grnx6GlR1daJjVCHSyEoPvzZuAc2sFvDCq3sSYleXG70TH1F+DBfcPTzcwbbcNjW+HgN44jzCBdRfm3tg8PGYq9kixdMY1NYZs8EOziOFzx/XK1fnF6lL4APVaYypIfhR9Jf3d08HK/wa3l1bBtT3LnGiN0ED9yopoIudibzIw3MY18NqAqyGVWvdiYnHKrhcaa+IvykmbC+/JPXcKQaEioryx1cSxyCb2Tll7d8WXZswfjRzmBnpAuh054fHWQRsNK2w2hqt/VZ0lirW+MAQ5qlV6rK+6xswPxTFQN2zeNFUPPAnkya4YF0XzLchMR97wDcRl5SXDUn/xzcVDvpCNtcI1XA1Ogrwt++By+++Ow5TDgDI05k7lh787OGF4VL18ZJkvwhdXdWMqxfC7sqK6/CtFxILLh10drlkIVj6rJkBxgc6ty4yVXXhCcdCCuP0V2pty2CWS24dJFejQOMiKZUnTfBvojS4iRnT26yf15+ZJYbGIVZXWdaCHY7XlLyzoRr6z7E5osXv5nT2Ogl10SNmK+6YvocfUV4X0oWsdl+tayhdU/CyMGp1rZHs7Ji4HIc3K5pfwd2boK0gmv7YdjbMGj5urUS80VSi7RNqUHERzXmlworN8DetXC4veoCJBxdNfpm6Va53SxCERXuJ/GyBdIjl1KrHwgpTi/TdcQPo1tKOpaQ1XAsx+y4Yfdgi4jTF0Dc69E0eH5AL/M0enJXBE/APsJJZT9edfkoWFerTY8X6003be0rwgesyHpaX2HlPHMgd66r3M7XAxxGgG9qaqq1c5at26OSzz8f9mAkUdJWuQ7WJ8O89PTNN4ZumBS0fuM6ue1mc5dj2blDQyuw5danMEOffiFVutMd5jfQ89eP4exzj3Mqa7yxljVkbed+tr29jEZLIOopwX3UHZ8W9B4Qyj8PbUNLULZGz9ykX9bJEH1F+FnLhpnO4daDy9nGtVpTnq27LPbDlTMHhC6EYVkXcq28y6x9KysrSUuGLKNYP19UnF7up7ODsFtPvRqGjiwieOmmoVVbbPRytkLv9XjSDA8syUCqHK2qFU+uX4v+pD2seSdlFR1rh3GAoxgMA/1CneOF+L2hY7TfYj3lJZy5wCOq9YU6CKpJ002xN7tP1TWWXTzEfhKZa2h+Y/F+bSijTycKdPUAREopiGh/k7tV8QQsbcb+zrcZsf/8clpC3ehjsg5On6lzCQkJsVFRQ/yQTmdnZ1JUli/2ytTGXr81Bnb7cI61PP1QP+9KlF6nF1vh5U+ZNCVlRtM3bCeOXdC4InqEAncLCDQ2sveA18YA2gnvuuISTpi7vuPICRxqv1bdzjY+x9RZ+orws7OyEH/OzS0oKHiGGLSyvb3hdfubmvb2psb29qrKyhJ04Hpu7kU0bG/64Yov7q1dkpw8D70tcWeORi2O9PO0s7NjV8BmNhg0EguoStezyU5uUUfI8unwPuRkVd3F2M52vu4JiJXUfyisSIQjcQbO+DLs1YHBMLyBfjmvvzFlZ+qfrN7Kzs4e8YKQqKioMQmTr7Qfnb8ICXjbj6cXV2flnETvSx56L0rRC9NaWVlXV1nZiDbRnwq0t+qE/vpGpDXoRWoRO59RPGYl7/+9eHBvx1Jzw/Y6YvI0k7y9ZCFukZH08cnrb70Av6W0mhu821T99P5JeAaGvKLiH52TIdlU1LAYsQGD5FWvYv3pDrcOkuu9RX5qt9xir+MOWqYqKW3Ip35DM5rci+79IubJo2ikH9NxjOFVJs7Rvwlvu+OSA+bFfmdEUZ2rO0Gk1uqnw/hOp7W4oyzNJb8TCHwBsIFe5a8krklDkmjbT/c9nOgL4ofp1O41LSbyy/sz4YMvVi6QKAi/WgubzvaQ9ixK1/fxeDSYipzu/4jGdTIoru5z9RbeOvT005F5cjcYDRYkfxM5mjBB91vCExMDDsYOdrEpN2FFNYrUOoMgqeXB1ANLW2kw+ruFzWvpjbIMpJh4lxeklw67h3T5KWRjA/BaazyGrJ8S3tx3x5GNMZJTXekWD+5FGfr/eVnuOXKX+e2d/cEY02mETgGzFHr7ftYANLu95M0zzqJ1/3RkHpkp4FVjvNhmvyR8wOnG2/NhimmDs1GkNA/T3+VeQzUXgTET9Q99t2DTJoPBtNSTlb0NCfbR53O3NTiuAqnYoYTc7VJi9AxdJ7zDma9Vqm9yDAywJgw4LAE1FMRhJZ9fJpy7OiGdHmQZegGuUrGnfHT3fo60jf+/vTOPi+q64viZhZ1hlWXYRFAgYhRUqCYohojiWqMMUXFBRVQU1youRFHrAgquNUHUuEVZxASXUTLIvig1H42tttolta0xmk9qrPXTNklD37w3GmSY++DNPIZ5c75/MBfnvcN1fnPXd8854FDHFJ2uea6mhsZPmxqnrc+dAKEad3PL3xKl1Vt4iyv5vW1tQ3ZrLR9Y9upLo+YmaDt3eY+f9bv5hnj0ObFW290+hYlfIUrvZLc3PrBMeelPBZmf+w+lpnOH9i5p9GsQe6SBgl6u7Cf6A+ktvMMD2gnR/l7rN1i7+qBl5+pyHVqMwr03dz+5IN5R9x3txrVgu/azVf9s+m9Zneqk+CX8c5npFu1vyasoIZrONi5MW67uSR2GEm+j0b/FV+T3trEJ3q3lrNeeMV4aaV9aIRtsDTYQqpSOv3RlokEONk2o1U7S4FKi2XK17RrPWvSmlxts0TiFTonaGUeNY00Zi66pv9vFLfxndWKAMX73g+bmh/u1xs12T+6k+TXVd4dYho06aphzyy4FrZ1mKF6vZR7KDDVEh9IliH0f3Ko05QthKoB+lWMbMxLfnaROJp87g3SrGqPP6qf1sctxd5y86c7tJKlBVljjrrYRXlxRSW9kid47zCGDaRdF2QtyNAdSdyoPRAKUuy1fVONEtR5/kVVDT/LNRhZ+Z5x4qoM6etQFK3AK/1l9yZIwDmkHX2IdPHx2/nHtObtEPoMZ8g+mar1nuoSvB3kSU7T5p7s7wJCpwxp2jAZ/yBwGEWyPIYy1nJO6Qs50JqFetPLQiyHJa/KB1BHJHU7r4xkxadnusxUVF95fM62NA6b2F148quKQk75rE6ZxH5u/OpdaxeQUrEstk9ZJpO1wDOJhORdMBzBJKSe6Fq7UDEL2S2cMenVV55Ycu/jk7PZEvPMaoEjfU1SlOp+XrogK0NmH+/RkVnYT9AuS2gWxBN9LTElyp38GQFZTYMPhN5bNBf9ogJPkj5CH5VywguaDbJ33vCRw1uDEtgTrmVLQMHCwjj0ei4DhKduPl6lURdtTxg1gPbk3ooLuV8S/PCCc4V1D5tvwIgL/xcyAXuAyZ3XG8hLbK+CSDuBBTkfF33IugXU8jSkpIHlxzywJXDmuxaGsVxt4O6PEi1YVMgczEuewXGmCeNaIpaeZojgw5AzA/utO9WcI55ZbwN9yjkV4a6viLNYtuvB1ZZVbgsakZOa1u4G34vAGk34Yw8LmSXBE4yA9b1RhKMRU+2xcfQQSrCGfbUOEv1k9Wfi5NWzJRTUN/ErdkbtNI+O5RYUPGMdMFBN2sVxooti4QJAmh/Q7X8ScAKizc6pT+b0bDtFsqyNjCd9NV0OUUiO4VgOXrVdeXN/hKFng20h/uyTZ5GS0ps1HzAFwye19pX6wpNBj68ad6t/dWZQ3lvBaaBp4nUrHCG47YsqQ0nGWHdnSXdybmc75GC1GFf9skoVqVuwzKiOcwbU4w7W6xnXAcljMEvXf2MK32cB14fHaqPqP03TlGW2F9fFt9JfeV8iDPEzeAAM0Er6Zd8IXet2QZGetE1e7SFg6Ob2Ft7vXzND6DbLwVr1ikjYcvlxxpSA7bVxYBx6Qe049PGHmHPY5nlclE2t2+jnBreJaIqpy63uCKS4qi10M/W6M9ipvsEnYAC46E2PS6N/irZri2vx3svAL8zKmR/fgqInv7L45+VN0HuWgyWP2tHKyjRrIgH/C/UDzjK5nCPg5wZqzsH9PqjgabLYT7zNAV5/adu7iDo7xHUTUZ0lpnfdIXXF6k1doCvxl++06OGQxrzYzkreB/Ax4VjZIwZ/lJmOP8foxr8QlR9HG1r7PFnqEG8gtX52JoQClP10YvbOgxtmiQQL7D08G4sIjNQAABppJREFUJcs2jmkLT9F3+fnaTPErqwD3ckbw5LPkXUuBUBgxjMknKqk7nb4eNhWLfMvrRGzbdyYvPIU0pHtZljz6hfhhmqSBKzbr84jXdOipBBXj2Ld2wcf1dl73RkJe4UiIJTskCEF4Gu9tVZfXqH1F46v1Ooxvgqx2jptGF2TyI4lWoDwPfqrL1OqOeJNghKdwHD8qoSSGOXPxpmBOVLaL/sz61nXQ8OM2Dpe94cjFQXCe3+PVOul84dW4MIGuUouMHJascwmYwGTKDs+G1OlwYyMElhdBJHHvzljC/0J1ZhfAhGVJbwHIA1zZY7Z0iPA1gt6ua41Fk/U0TdH2cqB0yYcAJ6pYHoIZS3grZ3WYlj6K5ESAGXkFewBWqsoPUgu0vOw0gMGKEf2oWSpfVRMci5bKGRdKuzE5v5oF1GcXUpFPvqWrdfVOAf1DAeLSt60CWKMqP6D+WbyVWqymzxtDdWkDAtmzN791icsfNmksKkR19NJVek1eXSc9VAxw+io5hEdXE74tnGQAQcPjRwNM3X1sB0DW9c+OACzPy1oAEK2I7UN90Z1/6tqXnuI/o3sXZGoG/ZIRv+VoUr+/dofeNVnE601B+LZxDugfDDAsJX02wKoiVQ5A7vXyHBAnmtXw/hJJA+1c7Lm1W1W9+CbVPIpvELevTFf4ttCOgGMuBO6d/SLcas848ZS/SCGskZhlRVjCmzHnQ4PprUurlTDoA6vj4wE+IToJovACYWDBPCYc0kJYrYyeuQUgghgSA4UXCgOs0jQl60Yf3xK2y1F44dCDSb5TIE67FBOzkuViFF4wRMY20j5zme9Y/ro84iHfZ+50gsJ3Ms4Na+jQTu4XYa4y6j5LensUXjisXdhAN/mxAAHn3rtFvhiFFw6ypA3MyVoH8M93/IYcGgGFFxISRuxSW3H19kPacYBagsILidez6KiWXiKIvx1orMiWKHznM3EXnUMcIsWiqyG3iBmJUHhBUR5F79NuGgNjbs0ZT7oShRcUw1bQ6UXll0C03XwOWyIU++g0i4UeACpiqgIUXmC8pnxRquA3lq1OUHijsO8C7S4Y5wBDcXJnTvjU0Am1E7TzZ74KCi80dn3SrpDAKLzQsA5fpX4ZHU2+DIUXHkHqmJZe58kXofDCo4iOjVJEdqVB4YVH8q1g6udArVRMr4DCCw/pzdPsF6HwAiSyTL1pF0vMUoHCCxFvtRONJdHNEIUXJLUsKYVReIFSv4XtChRekPT5lC3bFgovTArJTtIovGCpYokggcILlPi95PdReIHi+JQc9wuFFyq55JSDKLxQsXtIjCGHwguWWUmkd1F4MwWFN1NQeDMFhTdTUHgzBYU3U1B4MwWFN1NQeDMFhTdTUHgzBYU3U1B4M4U/4Ufeu450Xf7gzpfwCIIgCIIgCIIgCIIgCIIgCIIgCIIgCIIYC8cnbOGZuphhk6swf4b1gq6V7ef8GFb8+fvrITzYHX//x/tjDWxXo0/4NwZXiTIsvdXc3NyOALedCFUrycrf/I8Xw72+HSZZdtPwdmVfjZL8/EtivAluhgG63f2BD+Ht/x5kb29taMPtwv3Cf/6WKOpW+PxfBa5UVRb/8d9rRW4l3z3a/NRR/HbiM14MDzkmBvljw9u1jpXKFF/YGN4wWJQu5N4vEwz3ePan5jI5V8P6ID6zyzL6H17Fx+3tjhWJHX/IkEQ/cSopdnb46EdH9XeSJ8NgebCIo2GiXc/mZpbMH5wMi9bvc+EuPMFwWG2Ex6lzEq6W9UD2yA/A2emxP4D/I5njUzkltvfXPQB6PNNPeLLhUbd3WXI0TLQLFvPvce05CYZH1NrqMRMj1xh6fE2Of8YPDo99QNTb/XF3gO6PZer/HVWrx1St/PUUnmRYknMtlLNhgt2gXAvw4DwJIxje2ayGLXEYB8ORbwD4finjaFgfJModllGPvEo+tLM7WiJmauVcXOQkO6FnV08yPOSOm729PUfDBLueT+LE8z7j2pUQPwl91l4Ew2Mf9LPZf0zE1bI+eJd/91UiuBU9f17YDZhaOapnHqu+1U94kuFMugHxUGHF77+/ybkvIX4S+ghPMCxe+vC/hU5cDSMIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgnQMfQ6HIiYMCm+moPACR7cborFrhvAJwSHT2FVD+ITgkGnsqiF8QnDINHbVED4hOGQau2oIr+h2QzR2zRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQZCux/8BMo2+dodEK2gAAAAASUVORK5CYII=" alt="plot of chunk exploration_plots"/> </p>
<pre><code class="r">
ot$ycorr <- ot$y + mean(ot$y) - tapply(ot$y, ot$sub, mean)[ot$sub]
with(ot, interaction.plot(cond, sub, ycorr, main = "Cond * Sub after removing Sub main effect",
legend = FALSE))
</code></pre>
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" alt="plot of chunk exploration_plots"/> </p>
<h3>Classical analysis of variance model:</h3>
<pre><code class="r">require(ez)
# summary(aov_model <- aov(y ~ cond + Error(sub/cond), data=ot))
ez_model <- ezANOVA(data = ot, dv = y, wid = sub, within = cond)
print(ez_model)
</code></pre>
<pre><code>## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 cond 4 76 245.7 1.22e-42 * 0.6725
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 2 cond 0.5862 0.412
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05 HFe p[HF] p[HF]<.05
## 2 cond 0.8109 5.088e-35 * 0.9976 1.519e-42 *
</code></pre>
<pre><code class="r">
ezPlot(data = ot, dv = y, wid = sub, within = cond, x = cond)
</code></pre>
<p><img 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" alt="plot of chunk anova"/> </p>
<pre><code class="r">require(lme4)
summary(lmer_model <- lmer(y ~ cond + (1 | sub), data = ot))
</code></pre>
<pre><code>## Linear mixed model fit by REML ['lmerMod']
## Formula: y ~ cond + (1 | sub)
## Data: ot
##
## REML criterion at convergence: 616.4
##
## Random effects:
## Groups Name Variance Std.Dev.
## sub (Intercept) 72.1 8.49
## Residual 17.9 4.23
## Number of obs: 100, groups: sub, 20
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 109.23 2.12 51.5
## condcond2 1.28 1.34 1.0
## condcond3 12.12 1.34 9.1
## condcond4 29.58 1.34 22.1
## condcond5 -9.12 1.34 -6.8
##
## Correlation of Fixed Effects:
## (Intr) cndcn2 cndcn3 cndcn4
## condcond2 -0.315
## condcond3 -0.315 0.500
## condcond4 -0.315 0.500 0.500
## condcond5 -0.315 0.500 0.500 0.500
</code></pre>
<pre><code class="r">anova(lmer_model)
</code></pre>
<pre><code>## Analysis of Variance Table
## Df Sum Sq Mean Sq F value
## cond 4 17555 4389 246
</code></pre>
<pre><code class="r">require(car)
</code></pre>
<pre><code>## Loading required package: car
</code></pre>
<pre><code class="r">Anova(lmer_model)
</code></pre>
<pre><code>## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: y
## Chisq Df Pr(>Chisq)
## cond 983 4 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
</code></pre>
<pre><code class="r"># plotmeans(y ~ cond, data=ot, gap=0.1)
plotmeans(ycorr ~ cond, data = ot, gap = 0.1)
</code></pre>
<pre><code>## Warning: "gap" is not a graphical parameter
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-2"/> </p>
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