GitHub - ericward-noaa/ggrowth: fit gompertz growth models

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<title>Fitting weight or length at age data with ggrowth</title>



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<h1 class="title toc-ignore">Fitting weight or length at age data with ggrowth</h1>
<h4 class="author"><em>Eric Ward</em></h4>
<h4 class="date"><em>December 01, 2016</em></h4>



<!-- README.md is generated from README.Rmd. Please edit that file -->
<div id="ggrowth" class="section level1">
<h1>ggrowth</h1>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(ggrowth)</code></pre></div>
<p>Load the dataset that comes with the package (for illustration, this includes data for 2 individuals).</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">data</span>(<span class="st">&quot;ggrowthData&quot;</span>) <span class="co"># load data</span>
growthData$ID =<span class="st"> </span><span class="kw">as.factor</span>(growthData$ID)</code></pre></div>
<p>Fit the gompertz model to the dataset</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">fit =<span class="st"> </span><span class="kw">fit_gompertz</span>(growthData)</code></pre></div>
<p>Make a plot of the predicted values<br />
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAASAAAAEgCAYAAAAUg66AAAAEDWlDQ1BJQ0MgUHJvZmlsZQAAOI2NVV1oHFUUPrtzZyMkzlNsNIV0qD8NJQ2TVjShtLp/3d02bpZJNtoi6GT27s6Yyc44M7v9oU9FUHwx6psUxL+3gCAo9Q/bPrQvlQol2tQgKD60+INQ6Ium65k7M5lpurHeZe58853vnnvuuWfvBei5qliWkRQBFpquLRcy4nOHj4g9K5CEh6AXBqFXUR0rXalMAjZPC3e1W99Dwntf2dXd/p+tt0YdFSBxH2Kz5qgLiI8B8KdVy3YBevqRHz/qWh72Yui3MUDEL3q44WPXw3M+fo1pZuQs4tOIBVVTaoiXEI/MxfhGDPsxsNZfoE1q66ro5aJim3XdoLFw72H+n23BaIXzbcOnz5mfPoTvYVz7KzUl5+FRxEuqkp9G/Ajia219thzg25abkRE/BpDc3pqvphHvRFys2weqvp+krbWKIX7nhDbzLOItiM8358pTwdirqpPFnMF2xLc1WvLyOwTAibpbmvHHcvttU57y5+XqNZrLe3lE/Pq8eUj2fXKfOe3pfOjzhJYtB/yll5SDFcSDiH+hRkH25+L+sdxKEAMZahrlSX8ukqMOWy/jXW2m6M9LDBc31B9LFuv6gVKg/0Szi3KAr1kGq1GMjU/aLbnq6/lRxc4XfJ98hTargX++DbMJBSiYMIe9Ck1YAxFkKEAG3xbYaKmDDgYyFK0UGYpfoWYXG+fAPPI6tJnNwb7ClP7IyF+D+bjOtCpkhz6CFrIa/I6sFtNl8auFXGMTP34sNwI/JhkgEtmDz14ySfaRcTIBInmKPE32kxyyE2Tv+thKbEVePDfW/byMM1Kmm0XdObS7oGD/MypMXFPXrCwOtoYjyyn7BV29/MZfsVzpLDdRtuIZnbpXzvlf+ev8MvYr/Gqk4H/kV/G3csdazLuyTMPsbFhzd1UabQbjFvDRmcWJxR3zcfHkVw9GfpbJmeev9F08WW8uDkaslwX6avlWGU6NRKz0g/SHtCy9J30o/ca9zX3Kfc19zn3BXQKRO8ud477hLnAfc1/G9mrzGlrfexZ5GLdn6ZZrrEohI2wVHhZywjbhUWEy8icMCGNCUdiBlq3r+xafL549HQ5jH+an+1y+LlYBifuxAvRN/lVVVOlwlCkdVm9NOL5BE4wkQ2SMlDZU97hX86EilU/lUmkQUztTE6mx1EEPh7OmdqBtAvv8HdWpbrJS6tJj3n0CWdM6busNzRV3S9KTYhqvNiqWmuroiKgYhshMjmhTh9ptWhsF7970j/SbMrsPE1suR5z7DMC+P/Hs+y7ijrQAlhyAgccjbhjPygfeBTjzhNqy28EdkUh8C+DU9+z2v/oyeH791OncxHOs5y2AtTc7nb/f73TWPkD/qwBnjX8BoJ98VVBg/m8AAEAASURBVHgB7V0HnFNV1j/JJFMYeu+9I11AQCkqRZoiVlgVlcVtunZFP1dxdS2ssupiX8FlEUVEmqAgvUgH6b1KGzoMU5JM8p3/hRfezCQhmXmZvCTn/H4zeXnlvnP/972Te0+1eJhISBAQBASBCCBgjcA95ZaCgCAgCCgERADJgyAICAIRQ0AEUMSglxsLAoKACCB5BgQBQSBiCIgAihj0cmNBQBAQASTPgCAgCEQMAVvE7mzQjc+ePUtut9tna0lJSZSTk0Mul8vn8YLstNvt5HQ6C3Kpz2uSk5NVe+DTCLJarWSxWFS/jWgPbQHH7OxsMspjIyEhQbXlb9xC5RvtYVyysrJCvdTv+UaPc9myZf3eK54PRL0Awovh70EuVaoUZWZmqpfHiEHGy629jEa0h5cbDyaEKPphBIE/vJBGtYcXsVixYnTx4kXDBHlqaqpqy+FwGNFlxR94PHfunCHtoRH8MKDPRgldwxiLsYZkCRZjAyrdEQSiCQERQNE0WsKrIBBjCIgAirEBle4IAtGEgCXaY8EyMjIIuhlfFA4ltM1mM0wXAp6ha4AuxJ8ey1e/Au0TJXQgdII/ZrQSGuMslB+BqFdCX7hwwe/LW7FiRWUZwTlGEF7u4sWL0/nz541oTlmrKleuTBCiUJYbQZoSGm0aQXgR0Sb6bJQ1EUpoWBKNVELD4HDmzBkjuqzaQHvos1FK6CpVqhjGWyw15HvqEEs9lL4IAoKAaRGI+hmQaZEVxuICgYT9+yhpymSyXEwnd/UalHn/g8R+EHHRdyM6KTMgI1CUNuISAWtaGqV88iElpB0nK/sMJezeRcXeH02sE4hLPArSaRFABUFNrhEEGIHkr8YTW3G8WFjYm91y7izZtmz27pONwAiIAAqMjxwVBPwiYPHhyQ2BZMkyxqDg98YxdEAEUAwNpnSlaBFwXtOcPHlcQCwcUuOqW69oGYniu4kAiuLBE9Yji4Cjdx9ycyyfh2P63Ckp5GGXhYwHHiJPufKRZSyK7i5WsCgaLGHVZAjw7Cfj6ecpYddOtezKqVGLPKVLm4xJc7MjAsjc4yPcRQECOQ0aRgGX5mRRlmDmHBfhShCICwREAMXFMEsnBQFzIiACyJzjIlwJAnGBgAiguBhm6aQgYE4ERACZc1yEK0EgLhAQARQXwyydFATMiYAIIHOOi3AlCMQFAiKA4mKYpZOCgDkREAFkznERrgSBuEBABFBcDLN0UhAwJwIigMw5LsKVIBAXCIgAiothlk4KAuZEQASQOcdFuBIE4gIBEUBxMczSSUHAnAiIADLnuAhXgkBcICACKC6GWToZFgQ4CX3C1i2UPOG/ZD12NCy3iPVGJSFZrI+w9M9wBKyHDpJ93Vqy/bqerFyB1l2+AlnS0w2/Tzw0KAIoHkZZ+lhoBCynT5N9wzoleKwnT5C7WDFytWxNzjZtyV2jZqHbj9cGRADF68hLv6+OQGYm2Tf+Srb1aylh314im41cTZpSVp9+lNOosVRAvTqCVz1DBNBVIZIT4goB6HV2bCf35o2UysKHXC7KqVOXsm+/k5wtWnA1wpS4giPcnRUBFG6Epf2oQCCvXocqVSLHjTeTs3Ub8pQpGxV9iEYmRQBF46gJz4YgoPQ6vLyy85/15Elyp6Z69ToluOig8/x58uhKLxtyU2kkFwIigHLBIV9iHgHodTZtJNu6NZSwf59Or9Nf9DoRGPyICaC0tDRat24dNWrUiOrUqZOr6zt27KADBw5QmzZtqHx5qTKZCxz5EjoCl/U6mOnY2G+H+LvodUKHMRxXREQAzZw5k7799lu64YYbaPz48TR48GDq27ev6t/o0aNpy5Yt1KBBAxozZgx98MEHVLOmmDnDMfix3mZevQ78dRw39RC9jokGvsgFENbUc+bMoZEjR1Lt2rWpZcuWSshAAO3fv5+WLFlCkydPJiuXvf36669pwoQJNGLECBNBJqyYGQGfep1W7K/TWvx1zDhuRS6ALBYLvf/++woLp9NJy5YtU4IIO/bu3Ust2NQJ4QPCEuyHH35Q29q/HEyf+U9IEPAiwN7ITl5epSyYn1uv03cA5TRsJP46XqDMt1HkAkiD4DxbGIYMGUKZrBT87LPP1O6jR49SqVKltFOoZMmSdOrUKe93bIwaNYrGjh3r3Td//nyqVq2a93vejeLFixP+jKRUtpYYSaVLlyb8GUl6HI1ot0KFCkY0Y1gbHvjnsJ+Oc/lSylm/jrL5eyI7B9ofHEa29h3Iwp7KhaViBrRRWB5i/fqICSAIl+nTp9PixYvpkUceoWnTplFCQkKu2Y2LH6qUlNyOX7fccgvVrVvXOy645uzZs97v+g3cw+FwUFZWln53gbcxM0tKSlJCs8CN5LkQgufixYuE2aARZLfbCbNM9NsIAr4lSpSgCxcu5BqbwrQNDDGLxfiGTAf2k3X1KrKwFcvCuHkqViIreyandO1G5212Ur1G3wvZfzx3eG6MMsMb/QMTMm4mvaDIBRBeDFi/rrvuOvWidO3aVSmboXjGr+zGjRu9UJ3m+JsqVap4v2MDOiP8aXT8+HG/AgEvDh5yzLKMIAggvJBGtQdBAYLwMapNt9ttKI8QaMARL2OBBIYP4IEj+hyskLScPsW+OhyHpfPXcbZs5dXrYKZi5ZlzJs+gjaLExEQ1JiKAjELUdztFLoDwQH/88cdKz9O+fXuCyR3LrFq1aqnZxXvvvUeHDh1SgmfGjBmEc4TiEAHlr8NxWBx1fsVfpxlliV4nph6GIhdA+NV//PHHlRD69NNPldCBRUzTMQwfPpyGDRtGZcuWVUIJJnqhOEGAl2WIw1KpLrbp/HUGcRxWc4nDisWnoMgFEEBs1aqVEkDpnEMlr4K4X79+1KtXL8rOzs53LBYHIJb7ZGMlsZ2VxKxIUXFVKoLcR4etBw+o5ZXt1w0qv04OL8XFX8cHUDG4KyICSMMxr/DR9mOZhj+h6EXAvmgBJc++4kKRMH4cZfe6hZw3dL3UKV52J65akUuv4xJ/negd8AJyHlEBVECe5TKzI5CVmUv4gF0LGwOSfppNrPwjC4dDJO7ZfTkOS/Q6Zh/OcPInAiic6MZp25ZsB3nY1G7hZXQughCaMY2ofgNy3XUvZTVtKvl1cgEUf19EAMXfmIe9xx52/PSkFs8ngOB0kP7M81SsZi1yw++pkL46Ye+I3CDsCEhVjLBDHIc3YF+prAG3qY57+L/Hnkgetn6mPzuCPOUku0EcPhF+uywzIL/QyIGCIpCwayelTPwf5XD0ufO6juQpxom+GjchktCGgkIas9eJAIrZoY1Mx2xr11Dyd5Mop3YdyrxvKHEsTWQYkbtGBQIigKJimKKDycR5cylp7k/kZHN61h13KytXdHAuXEYKARFAkUI+lu7LHsxJU7+jRA4Sze7WnRy9+rDd/VKcWyx1U/piPAIigIzHNL5aZFN7Cpcmht4na+AgcnboGF/9l94WCgERQIWCL74vtnBOp5Rxn5P1xEnKfOBBymnMfj1CgkAICIgACgEsOfUKAtbjxyhl7H+4cJ+TMh75I7mr17hyULYEgSAREAEUJFBy2hUEEvbuoZT/jiVP8RKUMfwP5Clb7spB2RIEQkBABFAIYMmpbNjasJ6Sv/2acmrUoMz7HxLfHnkoCoWACKBCwRdfFydyhHsiR7i7ODdP1t2cp8kmj098PQHG91aeIOMxjbkWPZzm1f7dt2T7ZRk5ru9C2X37i5k95kY5Mh0SARQZ3KPnrhwwmvXeu5TAycKy+t9Gzs7XRw/vwqnpERABZPohihyDlvQLlPjlWMo5dpQcbGZ3ipk9coMRo3cOOhq+R48ehBSqeWncuHGqtHLe/fI9uhGwnDhBxT78gFCRIuX5F8l9DedkFhIEDEYg4Axo5cqVNHfuXHXLpUuX0ptvvknJycleFlDbaerUqaqOu3enbEQ9AqhCoczsHEjqeOwJKsEJxIgFkpAgYDQCAQVQ/fr16cknn1QJ4lHHCWWSURdLI+Rtrs313V944QVtl3xGOQK2Tb9S8jcTyV2lKmUOfZhsBldsjXJ4hH2DEQgogMqVK6dqt+OeAwYMoK+++koqVRg8AGZqzr5kESXNmkmuJk0p697fEVcGMBN7wksMIhBQAOn7izLKoCNHjtC5c+f0h1QN90D12XOdLF/MhwCb2ZN+mE6Jy5aSo1Nnyu53q0oebz5GhaNYQyBoAYTl15AhQ/IJHwBy55130qRJk2INm7jpT9K078m+8hfK4hrrzi7d4qbf0tHIIxC0AHrkkUdUwcBHH32Uqlevnovz1NTUXN/lS/QgYF+8kBIhfDiHs7OT+PhEz8jFBqdBCaDznHbh8OHD9O6775IstWJj4NEL25bNlMShFQ52LhThEzvjGk09CcoPqGTJksradfDgwWjqm/AaAAHrb79R8tcTCOWSs/sOCHCmHBIEwodAwBnQqlWr6OLFi+ru0P9A1/Piiy9SvXr1cpVOrlixIjVr1ix8XErLhiJgOXuWUr78D7m5akUmrF1crVRIEIgEAgEF0EMPPURbtmzJxdef/vSnXN/xRZTQ+SAx747sLM5iyInEOGcz/HyIK5gKCQKRQiCgAFq7di15PCgtF5j0zomBz5SjkUYgefK3ZD11ijL+8GfylCoVaXbk/nGOQEABlKT7ddy6dSsh9CIvWXn6XpxL8VauXJl/TOXXNC8+ZvpuX76U7OzpnMl12d3VqpmJNeElThEIKID0mHTq1MmnD5B2TgrHDb388sv03HPPabvk00QIWA8dZGfDGeRo14FcbdqaiDNhJZ4RCFoAjRw5kj799FP661//qvyBfmMryuzZs+njjz+madOm0Z49e1TcWMOGDWngwIFFhikEn4X1Gb4I+xGvhhmaEWR0expPmDkatYxFO5iV4s9LGWxImDiBqHIVsg/+XS4DgvccPxtaO8W4rLKbPaaNoMTERE6maCN8GkEYY5BR44y20Kb4twGJ8FJQAggPHmY3U6ZMoRtvvFFxVKtWLercubNSUs+ZM4cgoHbt2kU///xzkQogByfM8kd4abBszObaVUYQXka84Ea1pwlOl8tlWJt4cRacOUtHL2ZQ3ZRkaluiONnHfUHWi+nk4OoVyG7IN/PCYd2xnWyTJ5ElM4MQgOpk3RB30nscggIEnH0twb0nhrABHNFnBDgbQRqORo0LeAKO6HMwOtBg+lCiRIlgTou7c4ISQFlZWZSZmUk1a9bMB1DVqlXp6NGjaj+Ob9++Pd854dyBlyLQLzOOGfWg48XBbMWo9rQXB30wqs37t2ynHRksTNh4kOn20N2OLHp96xbKHHI/uUqVJr6Rdzish3+jpM8/IQuEEpP1wH6yv/M2Zfz1yXymeQgM/BlBmPkYKYC0GZBRGKKP2nNjlAAyArdYbEM3T/ffPcwkoAN6+umnaceOHd4T161bR+PGjaPrr79e6Ye++eYbuummm7zHZaNoEZh4/AStu5BOF3PcSvjg7lMS7PRz9x4qkXxebpJ5WaYJHxyzsCC0cKCxjQWWkCBQFAgEJYDAyGeffaaWWI0bN1Ze0ZjtXHvttXTvvffSfffdRyNGjFDCCWk7hCKDwLLzF/Ld2MmztlVNr8m3HzssOb5mNOx2EWBZ67Mh2SkIFBCBoJZgaBvJyVavXk3Lly+n9eu5NhRnRsTMp3Xr1urWSFw2atQoUdwVcCCMuKy83UbQ3uidJeysiC+d6Duvj7N5S0pcujjXLMjKy+2cevWNYEfaEASuikBAAYSUrLAsIMxi0aJFat0OvUWbNm1Uw6dPn6Z58+ZRpUqV6JprfP/KXpUDOcEwBB6tVoVmnT6rFM1uHicMLjQ+gytW8HkPR+8+ZNu+jaxpx4nYmsiKI67x/pA4KPpES3aGA4GAAujhhx+mFi1aqEyIt956q18/IAnFCMfQhN5mObbcrKhWkZ5evZaOlK9ITdk59OkaVSmBhZFP4uVZxpPPUMK+vUQ883FztVOUWxYSBIoKgYACCEstzVKTlpbmlyfNV8TvCXKgaBBga1YJLpv8qcNJ6b17BV25NKdO3aLhT+4SNgRef/11QuWa9u3bK6s0fPY0gusI0iu3atWKOnbsqO02xWdAAaSZN8GpUU5jpuh1jDKBnM4WdhB1w4wuZZNjdJR9d+u1116jUhzbBwGEtMmvvPKK2oYKBf5McBw+dOgQde3alWbMmEGwbJuBgraCgVn4An399df06quvqk6uWbMmoA+OGToYLzxYTp2kpLk/kbsTZzUUJXK8DHvAfmIWBB3tkiVLaN++fQSHYbyzzzzzTMDrivJg0AJo27Zt1KRJExo6dChB2h4/fpxeeuklZQk7duxYUfIs9/KBQPKUyeSB53d/cYPwAY/sYgQQxYB398MPP1QZTs0AStACCApprB+hC6rBykoQnBCh/5k4caIZ+hK3PNjWrCLbnt2UddsgomS2ZvkhFBxM/eebVPzl/6Ni77ylFM9+TpXdMYqApgPavHmzKXoYlADKYNd+ZEfE0gvpWTWC+R3BqbNmzdJ2yafBCCAU4L3fjlD3XzdTp/Wb6NX9h3LFJ1kuXKBkjnJ3Nm9BOU39Z6W0sMtEsY/HkPXkSbJwUjJ8po56M1dcmMGsS3MmRKBly5YqnlEf0RBJNoMSQAhIxExHS8+qZxgZE8UKpkfE2O0n9+yncRxicdaVw+EVbvbzOUNvHjrsvUnS9KlqO3tA4AwEyVO/I31qOQsLNovTQfa1q71tyUbsI5Cenq6CiqGwNgMFJYBgAevZsyc98cQTyhsajGNWhEqpH330EfXu3dsMfYk5Hi5wbNbCc+dz9SubBcf0k6cpnY8lcMwWEoxl9+lPHn20NRsLENPFT9qVa9nPJ583EIJQdcGpV06WrVhFQAsWR2SDGSigGV7PIDTqyPMDMx98g7p3764iuO+55x567LHH9KfKtkEIOFlApPDMEzMfPeGbMyuTkqdOIRdbvJzt2qvDDj7vwLSp1GDJQkqyJpCdZzjpz79IntJlVBKyBE5KhpmPRhaObs9p3ET7Kp9xgMAbb7xBdevWpeuuu84UvQ1aACEObO7cubRhwwaVcgOzIjg24U8oPAiUZc/mBpzTZxPn9tHEBmYxDhYilefOIQsnGsu6/Y+0NzOLppw8RVnr19ErK5dSEmY+l2c/qf98i9JffJmc13WiBFZU2zdtJDdbyxAFr1KzVqocHual1YgjgGwVqOmHNCWo6/fdd9+pJIL4NCoBXmE7GbQAgu/AgQMHVLqN2267TS27kAdaKLwI/LtBXeqyYTOl8kzIxjPPqkmJNDbZRu7vVtDkvrfSpJNnaX36YSqeYKUfdm2jFP2yi1nz2Oxk27WTXC1aUtaQ+8lx9AhBce3msfOUNIceILwIxm/rqGoDgo4WpbMQOA5fIKxezEJBCyBozSFRZ86cqVJzoDwPlmMQRnfccYea1pmlU7HERwl2o1/XpgXt4lkOCMumMQsW0LRb76LzLFxa8mxoZO0a1LNMaSq7dL6PrmPupM2fON6Usx5SFR+nya6oRgBOwhq1bds2l6VU22/Gz6CU0GAceh90DKlZsRz75ZdfVF5fJKF//vnnzdi3mOEJ2B9md/rXD/5Gd+3aS9MrVqZ+JUvQ5KaNaFzjBjSgXFlK5l85Ryf2gs5DSK/haiR6njywyFeTIBD0DOjEiRO0gH9558+fr/52796tIuWfeuopNQMySX9ijg1Yu95ms/uMU2eoJef1+eeqZXRTrZpEHS8pnvUdRrULV/oFss2aSZ7EJHKzz1bmw7+X4oN6kGTbVAgELYBQ7QIKrdtvv105JMKtG+tKofAhsJ59Nl7cd5DO8LLrjZLFaNCUb1kDnU0XH7y0tvd1Z0+NmuS+/U7K4oRxOc04RxMv4YQEAbMikMBRs68EwxycERFVi+A2+BIgET2SqSMpfSQj5eEc6S9xOMqqIPk5+DaCsBRCX42qvoD2EK2MpP/6hO9O1ut8eOQYjTzwG9XkJPjjPU7qNH4sWbHO52NJ838mR+cbULohV7eSueSybeF8suzcTokb1pP1RJpSPuc6KcQvsJYgchp+X4GS/4fSLDBEW0ZV2UDWBmTohJOdUYT2jBpn8CRVMXyPTNA6ICSkxxLsFJf1Re4RPDzQ/ZQtW1aKEfrGtkB797Ewun/7Lhp3LI2GValEX1arRLW/Gu/134ES2sOCK3lS7vg7++pVZOfshhYuuWO5LHCRXN62cUOB+JCLogMBfz++0cE9Z40JlVH8UuOXAb+IWJLh10eke6go+j7/m7STNJrjviqwrueLRvWpZfFUStixnTz2RBU2oV0FZ0Lbvr3aV/WZsOtKtRLtAKpcwPfH1UJ8tTRMYu0Ts2i8j/oZtFF9xMwX7YeTghZA8KCECR55oqH76d+/P40ZM0aF+GO6KlRwBM6zUH+crVvLuarFwPJl6enqVanYZd2NB9iyR3M+yvNgeFjh7OFrIHQ0wndJsaqhEbufRtaVK2qUghZAiHiHA9O//vUvVY4n3JKxqIGI1P3SsrLpbo50T+NfsXfr1abupXM7ByZwFjuQXrh42OSe8RBbt3SUfWMPsq9aqfRh8HLGORBGjhtv1p0lm4KAuRAIWgAhq5qQsQjsZaXyves2UjYLDNCUE6eoa6mSZNVmNyyUEhfMI1er1uRm3x/oczyslM7u258TyLMpXk88XU5/aSQVm/wNWVlP56pYibL63ypWMD1Gsm06BIIWQKbjPMoZOs3xObdvya23WcpLsNG/HaWnarC3MlPiUq7ZxTXbs3v0Ik+58jybuSlwr1kflzP0YSV0slhHJyQImB2BoK1gZu9INPF3gXP7PLRjt0+WJ6WduLSf3QsSFy8kZ/sOSvj4PFl2CgJRjoAIoCIewCPZDrpz6w46xJ+JPu4NdfPcM2cpif152FmGZz09fJwluwSB2EAg4BLsJKftDMa8BytY6dKlYwORMPbiGPvnDNi8zVs62YdtS9199vE0GvjLMnJcfwNHrF9JgRtG1qRpQSBoBM5xsrvFixcrS3jQF/k5MaAA6tatGyHl6tVIKqNeDaFLx4du3+0VPv6ugNdFiePH2EOLPc+7mCdtgj9+ZX90IGDhHzTrtO8v5QDnIoU5nKiOOJtCqAT/PyQhhIc8XHEKSwGXYEhchARk+BsxYgQhHuz777+nvXvZZ2X5chUZj1AMJKYXCoxADjsPngoi/SkSZzy3iEMturLwYcuWkCBQWAQsa1aTdfIkNmhwql5YXNlKmvDuP9USP5S2UUkDSe3Pnj0bymUBzw04A2rUqJG6GO7eN998M40fP96b/7lOnTqqTA+coEaPHk2dO3cOeKO8B8+cOUNr166lZs2aUZUquRPUIPcQkp+1adOGypcvn/fSqPz++dHj5GLOMcO5kp3nSleQcKwSot23baIKnE71YmeO9RISBEJAwMpluX2RZeUKlUdKO6b8xHiWbf3PJ0ScrjcvuVu3JarfIO9uusCJ7L788ksVBzp27Nh8xwuyI6AA0hpEsiOs+xo0yM8USvMsW7ZMOzWoz2nTptHkyZOpS5cu6rNx48b0+OOPq2shzLDsw73gaf3BBx9QzZp5fF6Cuot5TtrMKVU/YwF0B3s5T+aE8gAdwkhPEExjOeK96pqVlH0rV7jggE0hQSAUBCy7dvo+nScJ+Yj3oYw3cZqdvGSpU8/nj6RWUwwrI6MoKAGEmBDcHGlZIflQ0gNCaeHChfTmm2+qahnBMoQZE2ZSo0aNIsyihgwZQnfddRc98MADSsjB4RHCCWkkUQZ6woQJavkXbPtmOw8J5ZFSoy7ndn62RjV6ksMs7tm6kw5eDhjV+M3hIf9p3XoaysG9zvbXabvlUxAIGoGcF/7m81wrl2SihQu8Ac04CfGErhEvcSHLyIZRBSWAwPAXX3yh0q8iDqxatWpqGoY0Er///e9DqooB5RWmcUiVAUIbWq0i6JZatGjhrTOGJdgPP/ygztP+TZ06VSVE075DN+XPAodwkST2HDYqATfaQ1oSf/fTeNJ//nP7ToL1a1r7NlSBU294uIx1TVsCCyD9WZxD3u0hN9d3T7j1dirNSsKCEvoKPo1KkaLVfEPAsVGR18AQ42JUeg9tfEMZl6vhiyBrtGdUn692v3Aed3PNuIQ9e4gOHlDlu5HGJeexJyIufNDnoAVQvXr1VHVUVEjduHGjqpDarl07atq0acjYacIHD+B7772n9ErQ9SDHkL5gGqqwIv2HnqCFP81VPjXCA6K9JNo+7RMvIv78HdfOC/Yz1PYWcKWKCYeP0v81rEdNWODmjHmfPLt30V1VqtHSth1z3RbyqLfLQQkdriMLz/4KSqHyeLX7oD0QMDTqZTRyTMCbnkd8N4I0Ho3qsxE8FbgNHrucp54l4oIE/ItPVK26aZb4QQsgdD4lJYW6du2q/goMxuULkUIApZ4xwC++yCZBJvySYYmmEXyQcE89DR48mPCn0fHjx3MJJG0/PjFbwwwLyjMjCC8hEoghDcnVCFkMn+JQi2tLpNLtnFYj+6UXOEaLyyJzf/vs3U07kovRmGYtqYydh4DP/Wjej1S2bz86U0gLgzbjg6A2gjATwI8DdIDB+IQFc0/8AKFUjFGJ4qAiwA+X/ocpGD4CnYP2MM5GCaC8hpZA9w7bMRQkMBmF9FMLvQ/0MhAcR44coTVr1hRoGo2XAwnOMK3/+9//7l0uVKhQIddDhAfKFANXgEH7+4FDlO1x099r16RiH37AtdhP5FqDP7F1Iy2d+wNNqlmN5i9fQK1TiwWs7V4AFuQSQSAsCAwaNEil5jGi8aAF0LZt26hJkyY0dOhQeu211wgzj5deeomuv/56OsZ6jVAIlTVg4kdGRW39juuxpIOvwSFOQYFf2xkzZqjSP6G0bYZzp7Gla8HZ8zSiZnWqwcUCbahI6oOxSunnqeHunVSCrRHZvfv4OEN2CQKxjUDQAujhhx9WlrC0tDSqUaOGQmXcuHFKNzBxYu70oIEggyBbsWIFffPNN8oMD1M8/jS90vDhw2nYsGF03333qaWTfrkVqF2zHDvMS0tUsUCdrj5ly1DCPlb++SD4AiG1houtE+7GTSmnbj0fZ8kuQSC2EQhKB4QlE5TPsF5BMawRfIDgBY268U88wVr1IAizqEC5hfr160e9evVSaSahb4k2GsPJ5FHP/UVeWoHc7OjlYasPcjlrBOHj5nw9Lo50t86cTu4HOYWGkCAQhwgENQOC2RQKWFSgyEtwGjTKyqS1DcVnNAqfE6xYnXvmHN1bsTyVZMxAju43KeGDDIUgldmQPzP+9CjZefZjg89P9UszSnWC/BME4giBoAQQfEp69uypZjmoigrCrOirr76ijz76yBueEUe4+ezqeK5k4WYr12FOtfELJxdTxMusC6+/pfL6uOrWJ8d1nejCq69T4vJlZMngnD+D7vTZluwUBIJFAC4D4fgL9v6FOS+oJRhugGXWwIEDlVIYnUV+aJhSERn72GOPFYaHmLj2hMNJ47mqBST696dOq7/HqlWmBytXUhkKs28bdKWfLLwTFy8g17XtyFq5MnF035VjsiUIhIAA3ATgqpLXXSWEJiJ6atACCFHvUB5Df4PChJgVtWrVSv1FtAcmuTlqeYE41thLnx9Noy7sT1KPwzD0lLhogfL9cdzcS79btgWBkBHAZCAuyvL06NFDpeLQrFYaUrCEzZkzRy3HtH3x+HnSR6oNOz8csIrpBZCFndsSly0hZ8fO5GHhJCQIFBaBmC3Lgxpgc+fOVfgsXbpUBZ7qa4Ch44jN8hUlX1hQo+n6VazvuWLjusL5OcanEs8U9ZS44OdLS7JuN/r0DdKfK9uCQKwjEHAJVr9+fXryySfVFA/6HgSG6h0HYa2qXbs2vfDCC7GOU8D+fcW6n6qcy+cI64GsHN/mZosXPgfardSo2JVQEguHhKCEsqNLN+Jo3IBtykFBIB4QCCiAynFUtpbrZ8CAAWqZhTgerDtBsIQhDiee6RAvsRafO09jTx2n5ksW0qjmrelsYhLd8tsB6sN/GVWrsM9PRQWRfckiRE6Ss/MN8QyZ9F0Q8CIQlBkeZ3/yySf06KOPKmdEfIdPEPL5IIGYUUGKaDfaaCLPflITrNR+1zYqxSWUX1u3kv69YjH1ZeGDvM4JO7Zd6hLH0SWu+OVSmR2Z/UTbMAu/YUIgaAEET+f9+/dTp06dFCsw+6FM8/vvv0/Tp08PE3vmbjaddTyI+xpYvhxZk68stbxcY6bIy1RQ4orlbPniCHAsv4QEAUFAIRBwCaZhhLQJP/30k0qVCnM8CN7P9957r4rXgkPi7bffrp0eN58QPlms67mnQnnK7tWHkz7tvpT0mxHwsPCxsN7MeW17ZXK3L1uqSix7Skn5orh5QGKwoyc4heu8efPouuuuo9q1axe6h0HNgKDzgRL68OHD+W6InClG5mHJdwOT7oDHM5Zf3UuXoqpJieSuXl2FV3jY6oUgUwSXolY7lmH29WvJmn5BZj8mHct4YGsmP6vXr1xLrZavors2bCIn/3CGSlDDdOvWTWWsQJC4EQ7IQc2AYO268cYbVfoM+P1o0fDIBwQdUDyW5VnEiufDPDP8e50rcVxujulKf/UfuceVBZV98UJyNWxE7sq5q3/kPlG+CQLhQWAez9RH7NpDTn4WQdu4SMLgjVtoUstrvAalq90ZLjdvvPEGzZo1S2VBRRpl6ICRkgd5vApKQQkgNA7Bg0JkqFCBiHgsy5BtEJIQpvp4ownHT1BjNrG3vkrEfsK2rZTA09YMzvUsJAiEE4G397Hhwwd9xRVZNOGDw9jewULoCfber5qclO+Km8uVpTYlS+TaD/cbLWUODsAIhSyZWBkVhoIWQGW5WgPCMFCzC4UKoQNq3rx5gXJCF4ZhM1y7IyOT1qZf5GyHV2Y//vhCzFdO1WqUU7+Bv1NkvyBgCAI/cg5yX+TwsdyCEFrFs/jk9PxamIbsWpNXAKFdLRUPcrlj1XP//feTphP2dd9g9gUUQPCERloMFA9ctGiR19yOXMsgJJHHH/ICXXPNNcHcLybO+SrtBJVj3U4vTjoWiKxchcDGlsPMe4cEOk2OCQKGIDC/XRuf7fx9zz76mmdBObqjWIzNvrYVlbqcNkZ3KOAmVj0opYUgWNTtKywFFEDIgogyObBy3XrrrWrK5euG8VQb/iQvPWefPkvDqlSkRPbtsXK6VQ+7JLhr1c4HTeKiheQuU4ZczVvmOyY7BIGiQmBE3dq0lPNU/cbCoziXhLJZrDS+edOQhQ+KO0ANgwgJKKT1UREF7UtAAbR+/XqvkgqpWP2R0QnJ/N3HDPu/4jI7oMGcyyf1k39fMrezIHJzxYGMR7m6Ky9NQZaTJ8m2dTNl9xvg3acOyD9BoIgRsLEV+yee7fzKAiQzx03XcJWW4iHOfMAyUu+0bduW3nnnHcN6EFAABVuKBQGqRhaFM6x3BjQEc+VbnON5ESeZT+CBzOap591JNqry2UeqdXY1VGTlWtuJP80mxy191fdEhF0wLs527AckJAiYAIGWXIWmoIREhLCAzZ49Wzkga+1ANYPCFAWlgAIINn+kXL0axfISrPuvW5SzoX793Corm9wsXKw8pdXIwoLKvnGDEkAWNlHa166+5PfDcWFCgkC0I4CKNUbVSNNjEVAAoQg9lE4gVLHA97feeotatmypSvHAO/qzzz6LWT+gJWwlgLVAL3yAxSf2JOKFVT5CvmeQ/Zdl6hM5f4QEAUHAPwIBBRBqd4Eg+W6++WYaP368N/8znJA6duyoKpnCGbFz59h72RDrlcTLLgf3X09nrSxoeA2twi0uH0PS+ay7B3MWegclsgBytrmWPIWY8urvJ9uCQKwikN8JwEdPUREV+iBficdggo/VUIw2rKzLK3wSWSB14lpfFsaEJbNCy51anDIfeIjcNWqqpRfnKSHHDV19ICm7BAFBQI9AUAIIOX8w23nmmWe8pngIJSik3nzzTerb95LiVd9wLGwjm+HoerW9XcFsqPmJNBq1cglZeHakKaC5PjXlsPCBQErkoNOcxk3IUwj3dO8NZUMQiHEEghJAwOCLL76gXbt2EZwQ69atS/CM7tOnj/ozIijNrDh3LFWSnq1+KQPAi+z5/M3SearyRS5+WRJZz57h3D/bVQ14x/Vdch2WL4KAIOAbgYA6IP0l9erVU9VRUSFViwmBZrxp06b602Jyez3HvTTgyha3ct4fKsapVM/lLqNj5SWXhz3Gk2bNpBwusyNhFzH5GJiyU9DPYoUSrZlJgxZAQB9JyLp27ar+TDkaYWAKVjAUGUTOH0VDHyJ6712C9kdbgmX37K10QrbduyhLCg2GYRSkSX8IaOmR/R03+/6QBBD0PtOmTaOdO3fSsGHD6MiRI9SmTRvDSzObCbS1F9Ipnb1Hu3LeH5ClUWO68MzzlMROhxwKzI6GHSinaTNKmjKZ3PxL5GzlOx7HTH0SXgQBsyAQtADatm0b3XLLLcr/B9GwiAlBLhBYx6ZMmUKVUeEzBgke0Ag8baarbuEpV56yBt93pbe8RLOvW3PJ8dBuv7JftgQBQSAgAkEroRGYCksYYsK0hGTIEYQ4sIkTJwa8STQfXMQCtkvpkt6YOF99SVy1gkuiusnJdd+FBAFBIHgEghJAKL8D5fOrr77qzQmCW8AHCHlBECMSi7ST8/4c5Vpf3dgS5pfYHA/PZ1eLluThRG1CgoAgEDwCQS3BbLwEwUwHWdDyEmLFIhkNj3sHUsThWEHTBixm5TN8fzpy3p8Evg/uhT99ewkbfyUr58VGtQv9/rw4+fqu8Z23TV/nBrvPF4/BXuvrPK1P+DQqFshoHjUcNV599SPUfdpzY1SfQ71/vJwflABKZIe8nj17EkrzvP322wobzIqQJ+ijjz5SuaIjBRgi8fFA+yI8RMhnjWKKBaElLICuL1uGyl0OqUB7EMb69jzLlhDVrUcp7HxYUEriJPZGvTxoB3z6wyRUHrV2YAGF7s8IwpiATzxXRhDGBKQfl8K2izaj1bRd2L4X5fVBCSAw9PHHH9OgQYOoffv26gHv3r27ygeLHCGRdESEIPT3YkA4IXc1EimFSifZwrWRLWB/q1WdUPkDhJcRGSK93znjYeoBzng45D5yXT4nlPtAUOClgXURf0aQJsyAixEEYQHhgyTkRhWgRJ+RSxhjYwRBUKDf2rgY0WapUqXUc2PUDMhI4WhE/8zSRtACCDMgZMUvwxn+tm/frn69WrVqRfiLRVrM1i9QN3KT5dQp8nCZ6ryUuHwpuflBdTWNn3S0eTGQ74JAYRAISgDh13nPnj1KB9ClSxfCX6zTojNnadzqZVRj+mEV42XhX+uLI18nngKprlsunCcb638cPXoRrydiHQ7pnyAQFgSCEkCYgiPo9Nlnn1XFCREVj30alS9fnpo0KbgORGvHLJ+ZrOv4/czvqd2pEyroFHzB8zn5I07B+n8vKzbtXOed12Rc6/069V3+CQKCQOgIBCWA0OzIkSOV0+Ff/vKXfHeJtYyIK1n53OtkGtl0SleEXVjZJ4iOHeWYlGJkX7niUqnlAiq484EoOwSBOEQgaAGEssz+FHKaFSJW8IP3c7cEGyW68yhJXVyEjWc9tk0bVanlTMl4GCtDLv2IEAK+7dd5mIHFAj5AsAD5+oO1KVYIQnYxp2Jdx6V0PJfNu+gbsh+y+Y8sXF4ZyuecmrXIXa16rHRb+iEIRASBgAIIgufPf/6z8n6G1zNStC5btiwijBbVTTdxydrTLhc5e/chZ8vW6rYQRK6GjSnj5VfJw3XAEtj87uhU8EoARdUXuY8gYHYEAi7BUHzs008/peeee46qV6+ufIHg94PyzLHqpLWIZz/FE6zUpkRxyr7zbsoeOOjSGLIQUk55CxeQm2eCrmuam31shT9BwPQIBBRA06dPp0ceeYRee+011RHU/0E9+LVr19INN9xg+s5djUEst14/+Bv9yJVOkfu5HQudY9kO6swxXXYsuUC6ZRhxMUJas4qcyPes33/pTPkvCAgCISIQcAl2kqt7NmzY0Nsk6r9X4FzHBw4c8O6L1o0cFji9N26l706epouIZOfvq9nzeW92top+99Uv+6qVxGVAOOq9o6/Dsk8QEARCRCCgAIKrfN54nXLsEWyUm3+IvBp6+sM7dlMa63r0BCGUyAKm09wfybZurf6QSrdh+2U5UavWHPV+KTlZ7hPkmyAgCISKQEABFGpj0XQ+lM2+yMpCKGH7NkqZNJGSJ/7Pe4pKOH/mNFHX7t59siEICAKFQyCgDghNIwXroUOHvHc5ceIETZ06NdcyDHohKKejiVLYnwdLr1zEwieLdTtVMy8JJxtngUzYu4dyONodpnc3m+ATGvCStACBp7nuI18EAUFAIRBQAMHyBYsX/jQqyQpaBKPiTyOkZY02ATSgXBmafOIUsWvhJcLMh//WTP1G26OyzltOnyJLiZKUsGsnOW6/kyTq6wo8siUIFBaBgALoxx9/LGz7pr3+6RrV6Bj7OS1gr2c7c9mAg0u/+vkHKs46II0srJBG/ufEFez7xM6WrjZttUPyKQgIAgYgEFAAGdC+aZuwspn93Xp1KPuyBezuNeuV8PEgoRcLIXy6mregnGrVKOXLL8h5bXtijbxp+yOMCQLRiEDcCiBtsJJYF/QrB58esdlp2fMv0bU827FcTKcc1vW4WrQi+wq2fGVlkYMTzl/2DNIulU9BQBAoJAJxL4CAH7yfy7Dy+RpOPu/gEAw92dn0nsNhGB5OOSICSI+MbAsChUcgbs3weuiW8wyoc8kShGWZnmABSzh+jOO+pNyOHhfZFgSMQiDuBVAal93Zn5VNHUpeynSoBxbldtxlyqoZkH6/bAsCgoAxCMS9AFrBsx9QB54B6cly/hzZtmxm3Q+HXbCeSEgQEASMRyDu36yVXDGjbnISVeDqD3pCxkOVchXWLyFBQBAICwJxL4AQgNr+ct0vL8JshrdzuWVXS674ISlXvbDIhiBgNAJxLYAOsHn9hNOl0nDogbVt2URWnhk5JOWqHhbZFgQMRyCuBdDqCxeVab1tidyVU5XpvXoNcvOfkCAgCIQPgTgXQOnUkMsLldIlF7Ny1Qvbvr0y+wnfMyctCwJeBOJeACELop7sy9n0zqV+lf5Hf0C2BQFBwHAE4lYA7cnMojOckKydfvmVlUn29eu42GAHSblq+KMmDQoC+RGIWwG05ehRGrJnB12/dRPHfnGuZyb7mtVEXPvLyXFfQoKAIBB+BOIyFgx6niGfjqEsjnhP2rSeLNOn0sXHn6JEjvtyNWlKntJlwo+83EEQEAQo/mZAvOxK/dc7auiT2d/HwjmBQCmff0LWUyfJKfW+FB7yTxAoCgTiTgBhueVKSsqHrSUjg3I4+VhO/Qb5jskOQUAQCA8CERVA6enpPiutIgXsnDlzCGWBjCYPh1148uaC5ptYkJisg5TbMRpvaU8QCIRAxARQFnshv/LKKyrpvZ7B0aNH06hRo2j9+vX08MMP08GDB/WHC7W9KzOTNjpz6Mv2nfO1gwyIzg5s/RISBASBIkMgIkrovXv30gsvvEClSpVSf1pv9+/fT0uWLKHJkyerMshff/01TZgwgUaMGKGdUqBPNyebf2rPflV4EDl/LlStQVmD7qFHtm7kQoNusu3fS8627YiSkgvUvlwkCAgCBUMgIgIIhQ1ffPFFOnXqFM2aNcvLOQRTixYtLtVg571t2rShH374wXscGzt37sxVEgjVWpN86HS0ixJ4ZvPMvoOEpGMov6zRF/Zk+t3Dw6n46pVk2ckVPrp2C9gOrkNteLQX6H5a+6F82tgT26g27RzVDz6Nag+8gVCgEn03gtCmhX8I8GcEaTwa1WfwpI0zyncLhQ+BiAggCA3QwoUL1af27yj75mBWpBFKAEFI6WnKlCk0duxY76758+dT2bJlvd/zbqRwqAUi3vXCB+fgJd3LkWDXsund0rQZlWXze7CUzBUyjKTixXN7YxvRdqrBUfz6cTGCv3C0Eeg5KMj9jBRoBbl/PFwTEQHkD1j86uSwaVwjF5vMIUD09Je//IUefPBB7y78Qh0/ftz7Xb9RnvM4Q9eUjGWX/gBvZ7HXcxbn/HEfPEBZ9z9I5/20ob8MQgsv9gWOlDeCMAOoWLEioa4a+DSCtJlKJuu7jCDMqPBi44cA42EEFeNQFye7P+DPCMIzgh8rf89BQe6B9jDORs2AKlWqVBA2Yv4aUwmgChUq0MaNrJe5TKdPn6YqVapoX9UnZgv6GQMeOrcPq5Z2ER6gP1SpRG8dOuydBVn5fEysO347kdzcnrNxE1X7Xbsm0CfaC3S/QNfmPaYtQYxsE20Z2Z7WV3xq23n7Eep3o3lEeyCj+ENbGoZa29gnZDwCEbOC+epKu3btaPPmzaoUNH5tZ8yYQe3bFz4j4e0VylFfj4ss/KDaeYbV4/AhWjtt0qUqF1yOh9djvtiRfYKAIBBmBEw1A8K0d/jw4TRs2DA17a9VqxYNHjy40BD8bttO2udhIcMCyMnC5hDPeuAFDfLkScVa6JtJA4KAIBA0AhEVQN26dSP86alfv37Uq1cvyuayyPqllv6cULa/OXKMtmSwPgQWl8tWlz0lStHXderT3fv3SNqNUMCUcwUBgxGIqADy1xcoPvFnBG24XPVC31Y2m4HXl6tAd1g85Li5p/6QbAsCgkARIhDzyo+qHHqRdHnmo+FqY4VqZV6KZQ7/o7ZLPgUBQSACCMS8ABpeozpls+5Hc3mz87aLhc/vG9SNANxyS0FAENAjEPMCKCnBSmsb1qG79+2mjmnH6L6d22jjlK/I06CRHgfZFgQEgQggYEodkKE4sLWr1Ouv0mu6RuE14vpmImU9cMWhUXdYNgUBQaCIEIj5GRAdP0Y5eRTaWI7Zdu0oIojlNoKAIOAPgZgWQCezHXQ/R8G3v2Ugte9/J+0tUdKLg8WgsAJvg7IhCAgCISMQswIomy1dLecvohUuN53jaPnTHEB6S8/+tJuFEJZgjlatQwZLLhAEBAFjEYhZATTpxElKZPO7W2eCz+Htd69pTR72A8q5prmxSEprgoAgEDICMSuA0hxOb/CpFxUWQCc56ZiVl185tWp7d8uGICAIRAaBmBVAh1n/44tKcQqIjOF/Io9OH+TrPNknCAgC4UcgZs3wKez/44tqNWtKOXVr+zok+wQBQaCIEfD9lhYxE+G43YByvrMk9q4siaHCgbe0KQgUBIGYFUCVOYdxMR0iCWwV+2D9SmqUaEyQq65p2RQEBIECIhCTAugCez/ftmU7ZehAcbP34a/FOA/Qd5N0e2VTEBAEIolATAqgdZyEvoQWfXoZXY/FSlNr1iXb5k2RxFvuLQgIAjoEYlIAJXK0u/VSmmBdVznzKlwQuQ6YkCAgCJgDgZgUQB1KFKdMN6dc5dQbGkEH9NL6VeRq1kzbJZ+CgCAQYQRi0gz/n6PHyWHNXUQPoqiCw0E5depGGHK5vSAgCGgIxOQMaOG581r/vJ8JPBtaW7Y8+wDV8+6TDUFAEIgsAjEngFDHaSuS0OuWX4AYMWGpXP3UXTl3nbHIwi93FwTiG4GYE0Cb0i9SMipu6oJQMcQIRL1786/xPdrSe0HAZAjEnABypx2nZFY456VUFkoJfExIEBAEzINAzAmgZhwDluriGZBuCZbM0e83HzlEJF7Q5nnyhBNBgBGIOQFkL1+BpsybrZZgxZ0OKsYzn16HD9I/Vy+n7Jt7yaALAoKAiRCIOTO8xZFNZVnobJ/8P9rHKTdQgrnGxXSVBTFhx3ZySSZEEz1+wkq8IxBzAshTvASbvHIIHWtw/px3fFVkRp7k9N6DsiEICAIRQSDmlmDEUfD+yNVM0rD6w0b2CwKRQCD2BBBQ5LSrvshd1neOIF/nyj5BQBAIPwIxKYCye/ZWiec1+DzsA+SqXZs8FSpou+RTEBAETIBAzOmAgKmz8/XkYZN7ypwfFcSO1m3J0buPCeAWFgQBQUCPQEwKIHTQ1a4DpfTtT5mZmeS4cEHfZ9kWBAQBkyBg4dipKzkrTMJUKGxkZGSQlfP/+KIkLkiYw2Z4l4FVUG1cU8zI9pK5YKKDo/TdPry3ffXpavuAhYWXnOi3EYS2gGN2djb7dhrzqCQkJKi2jOoz2rOzhTMrK8uILqs20J4TIT0GEcZZKD8CUT8DusCzG38PcsWKFdVDiXOMILzcxYsXp/Pn80fbF6R9vNyVK1cmCFHM1IwgCAu8kGjTCMKLiDbRZ6MEb2pqqnq5IXiNoGLFilGpUqXozJkzRjSn2kB76LNRQrdKFQmC9jU4vqcOvs6UfYKAICAIGIyACCCDAZXmBAFBIHgERAAFj5WcKQgIAgYjIALIYEClOUFAEAgegai3gkHx6E8JvXXrVipTpgwZpQCE0hhWMKOsI7BUbdq0iWrVqqX4DH7Y/J8JRTn+jFIYQ5m9c+dOatiwIUHZawQBQ4yZv3EL9R6nT5+mgwcPUsuWLZUFMNTrfZ2fyCE9RinJ0X65cuV83Ub2wQwfq9SlSxfP22+/bdrusXXOwy+25/vvvzctj2vXrlU8btmyxbQ8Tpw4UfHIPwym5VEY842ALMHkN0gQEAQihoAIoIhBLzcWBASBhFeYYhUGOPm1adOGKlWqZMouQqdUvXp1uvbaa5UjnRmZhC6kXr16Sr9iVm9e6KaaccFJ/AFToehBIOqV0NEDtXAqCAgCeRGQJVheROS7ICAIFBkCMSuATp48ST/99BPt2LGjyMD0dSMESC5atIjWrFmTzzTuj0cEfi5btoyWL19umMnfF2/aPtxv6dKl6p55XQyA35w5cwi86qmoeURc1s8//0xHjx7Vs6G2/fHoD998DciOiCEQkzqg9evX0+OPP06lS5emTz75hKC7aNKkSZGDfOLECXrooYcIwZe//PKLeoF69uyp+PDHI4JSH3zwQbp48aISCAsWLKBevXqFTbcBP58//vGPKnj1t99+ozFjxtCAAQOUv9Po0aNp6tSpyh8G+zt37qx0VUXN4/Tp0+n111+nEiVK0NixY5U/Uv369RWO/nj0h2+RPwRyw8AI+LbOR/fe+++/37NhwwbViWPHjnn69evn4V/sIu8U/FMmTJig7suOgZ4+ffp49u/fr77745FfMA+/VF5ehw8f7mHh5f1u9AZ4/Mc//uFt9osvvvD8+uuvnn379nkGDhzoYWdJdUx/XlHzeO+993rYqVTxwbMaz0033eThmWVAHv3h6+2obJgCgZhbgsEDGL/kLVq0UJIXFjBYSQ4fPhxYEofh6D333EODBw9WLeMXGZ7PFTgtbCAed+/erSx3Gjuw4sGjO1y0atUqNbNZsWKFmnE98MADCru9e/eqT3hVg/R8FCWP8HRPS0tTntjgAx7FmAlhPP3xGAhftCFkHgRiTgDhYcWSR2+ORW4XuOtHil5++WV64okniH+VlTAMxCPP2KhkyZJeVrF96tQp73ejN7BM5NkNzZo1i2bOnEm/+93vVG4i6FqAm0Z6PoqSR4TSwA1g4cKFihUIcuh2wJ8/HgPhq/VHPs2BQNQnJMsLI5Jx5c0GiF/ESPqwjBw5kngpRU899ZSKS2vatKlfHvPyD95TUlLydtOw78CqQYMG9OSTT6o2X3jhBaU0D8RHoGOGMaZr6JFHHqF///vf9Omnn3rxg3D0x0fe/Wgq0s+ArjuyqUMg5mZAmKJDgQsrjUaY/VStWlX7WmSfHD/lnb1Uq1aNunXrpqxhgXgsX758rtlauHlH1kgIRI0QdIolH5aK+lkjtrWg3qLmsXXr1sS6Kfrss8/oX//6F509e5aApz8eA+Gr9VM+zYFAzAkgRFp36NCBYDkBLV68WEWaYypf1LR69Wr6/PPP1W1hjoclDNabQDzecMMNNHv2bJVKFksNmOLxAoaLOGBXWecQmY7ob5hFJIyKAAADs0lEQVTjoT9r164dbd68mQ4dOqRmDzNmzKD27dsrNoqaR8weIcyxDIR7AgQgxtMfj4HwDReO0m7BEIhJT+gDBw7Qs88+q6boUKL+7W9/8yoxCwZTwa5KT0+nt956SylMsdTBiwsTO5YI/njEUgFLNrz84B2K7DvvvLNgDARxFfx+wCNmPZg1wtSuLcegE/rggw+oLBd0RMqQ1157TQnPouYRPlRYgmEZDewwnlp4jT8e/eEbBCRyShEiEJMCSMMPU3X4AkWa4GuDl0ezKOn58ccjEulD94Nf86Ig8Aj+8urKIKAgmJCMPy8VNY8Q6L74CMSjP3zz9kW+RwaBmBZAkYFU7ioICALBIhBzOqBgOy7nCQKCQOQREAEU+TEQDgSBuEVABFDcDr10XBCIPAIigCI/BsKBIBC3CIgAituhl44LApFHoGhsvJHvZ1xx8OqrrwasaQ6/IvglweER/lJCgkCkEBAzfKSQD+N9e/To4a25tWfPHhVN3rFjR+8dERyLjAHImYTATSFBIFIIiACKFPJFdN8RI0bQ5MmTadeuXUV0R7mNIBA8AqIDCh6rmDpz5cqVagaETiEX0BtvvKFi1YYOHUp33HEHIfYLIRfY379/f3rnnXdyBafiuv/+97901113qQyKyEyI84UEgVAQEAEUCloxdC6Sio0fP171CLMjCBgEfbZq1YrsdjsNGjSIevfuTZxZUqWEff/991XMmAbBX//6V5VeBKk8OnXqRFyBVgku7bh8CgLBICBK6GBQioNzkPQMyeeR+RCxVdOmTVN5opEIHsT5O+nDDz9UQgi14hEc+r///Y84Xao6jlkThBES8Hft2lXtk3+CwNUQEAF0NYTi5DiCXlu2bKl6ixlQjRo11AxI6z5y72gVKRCdDoGEdCOcP1o7RQWK4pgIIC8ksnEVBEQAXQWgeDmsZRjU+ouUtvrUsPoUt4gwh8BKSkrKlfr20UcfVdVJtTbkUxC4GgIigK6GkBzPhwCSqmGZBuU09D8g+BV9+eWXEcm7lI9B2RE1CIgSOmqGyjyMdu/enRo1aqQSgyFTIbI9vvLKK/Tcc8/lmjWZh2PhxKwIiAAy68iYmC/oiDQldfPmzVWK1Hnz5imrGtKlCgkCwSIgjojBIiXn+UTg3Llzyv8HieCFBIFQERABFCpicr4gIAgYhoAswQyDUhoSBASBUBEQARQqYnK+ICAIGIaACCDDoJSGBAFBIFQERACFipicLwgIAoYhIALIMCilIUFAEAgVgf8HIoavYs6AodMAAAAASUVORK5CYII=" /><!-- --></p>
<div id="adding-new-data" class="section level3">
<h3>Adding new data</h3>
<p>New data can be added in as a new data frame, or rbind() to the existing data frame. The format is</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">head</span>(growthData)
<span class="co">#&gt;   Weight ID Month Year Day</span>
<span class="co">#&gt; 1 6.2500  1     1 2012   2</span>
<span class="co">#&gt; 2 5.9375  1     1 2012   3</span>
<span class="co">#&gt; 3 5.6250  1     1 2012   4</span>
<span class="co">#&gt; 4 5.8750  1     1 2012   7</span>
<span class="co">#&gt; 5 6.3125  1     1 2012   9</span>
<span class="co">#&gt; 6 6.8750  1     1 2012  13</span></code></pre></div>
</div>
</div>



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