diff --git a/simulations.ipynb b/simulations.ipynb index 7ce5be2..f0613e1 100644 --- a/simulations.ipynb +++ b/simulations.ipynb @@ -28,6 +28,37 @@ "Q = 1.96" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# helper functions\n", + "def plot_simulation(coverages: np.ndarray, mu_txs: np.ndarray, ci_lowers: np.ndarray, ci_uppers: np.ndarray,\n", + " truth: float, transform: str, line_width=0.75, cap_size=2, marker_size=3, fig_size=(17, 6)):\n", + " \"\"\"\n", + " creates plot of CI coverage simulation\n", + " \"\"\"\n", + " plt.figure(figsize=fig_size)\n", + " for i in range(NREP):\n", + " if coverages[i] == 1:\n", + " plt.errorbar(i, mu_txs[i], elinewidth=line_width,\n", + " yerr=[[mu_txs[i]- ci_lowers[i]], [ci_uppers[i] - mu_txs[i]]],\n", + " fmt=\"o\", color=\"blue\", ecolor=\"blue\", capsize=cap_size, markersize=marker_size)\n", + " else:\n", + " plt.errorbar(i, mu_txs[i], elinewidth=line_width,\n", + " yerr=[[mu_txs[i] - ci_lowers[i]], [ci_uppers[i] - mu_txs[i]]],\n", + " fmt=\"o\", color=\"red\", ecolor=\"red\", capsize=cap_size, markersize=marker_size)\n", + "\n", + " plt.axhline(y=truth, color='gray', linestyle='--')\n", + " plt.xlabel(\"replication no.\")\n", + " plt.ylabel(\"Mean\")\n", + " plt.title(\"95% CIs using Delta Method SE for \" + transform)\n", + " plt.legend([\"True Mean\"], loc=\"upper right\")\n", + " plt.show()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -37,18 +68,18 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/55/53rfb8cd7bd3rvs9lvgv5zyw0000gn/T/ipykernel_12398/746050651.py:15: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + "/var/folders/55/53rfb8cd7bd3rvs9lvgv5zyw0000gn/T/ipykernel_19434/1300529156.py:15: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", " mu_txs[i], sigma_tx = transform_data([x_bar], [sigma_hat], \"log\", \"delta\")\n", - "/var/folders/55/53rfb8cd7bd3rvs9lvgv5zyw0000gn/T/ipykernel_12398/746050651.py:16: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + "/var/folders/55/53rfb8cd7bd3rvs9lvgv5zyw0000gn/T/ipykernel_19434/1300529156.py:16: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", " ci_uppers[i] = mu_txs[i] + Q * sigma_tx / np.sqrt(N)\n", - "/var/folders/55/53rfb8cd7bd3rvs9lvgv5zyw0000gn/T/ipykernel_12398/746050651.py:17: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + "/var/folders/55/53rfb8cd7bd3rvs9lvgv5zyw0000gn/T/ipykernel_19434/1300529156.py:17: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", " ci_lowers[i] = mu_txs[i] - Q * sigma_tx / np.sqrt(N)\n" ] }, @@ -56,12 +87,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "CI coverage rate: 288\n" + "CI coverage rate: 0.9466666666666667\n" ] }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -91,30 +122,11 @@ " if (ci_lowers[i] < LOG_MU and LOG_MU < ci_uppers[i]):\n", " coverages[i] = 1\n", "\n", - "coverage_pct = (coverages == 1).sum()\n", + "coverage_pct = (coverages == 1).sum() / NREP\n", "print(\"CI coverage rate: \", coverage_pct)\n", "\n", "# plotting\n", - "line_width = 0.75\n", - "cap_size = 2\n", - "marker_size = 3\n", - "plt.figure(figsize=(17, 6))\n", - "for i in range(NREP):\n", - " if coverages[i] == 1:\n", - " plt.errorbar(i, mu_txs[i], elinewidth=line_width,\n", - " yerr=[[mu_txs[i]- ci_lowers[i]], [ci_uppers[i] - mu_txs[i]]],\n", - " fmt=\"o\", color=\"blue\", ecolor=\"blue\", capsize=cap_size, markersize=marker_size)\n", - " else:\n", - " plt.errorbar(i, mu_txs[i], elinewidth=line_width,\n", - " yerr=[[mu_txs[i] - ci_lowers[i]], [ci_uppers[i] - mu_txs[i]]],\n", - " fmt=\"o\", color=\"red\", ecolor=\"red\", capsize=cap_size, markersize=marker_size)\n", - "\n", - "plt.axhline(y=LOG_MU, color='gray', linestyle='--')\n", - "plt.xlabel('SampleID')\n", - "plt.ylabel(\"Mean\")\n", - "plt.title(\"95% CIs using Delta Method SE for log transform\")\n", - "plt.legend([\"True Mean\"], loc=\"upper right\")\n", - "plt.show()" + "plot_simulation(coverages, mu_txs, ci_lowers, ci_uppers, LOG_MU, \"log transform\")" ] }, { @@ -139,48 +151,52 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "True percentage change in proportions: 0.04081632653061229\n", - "10 replications of incidence counts in year 1: [473 507 492 479 491 475 465 509 504]\n" + "True percentage change in proportions: 0.050000000000000044\n", + "10 replications of incidence counts in year 1: [398 403 399 407 424 389 378 406 397]\n" ] } ], "source": [ "# parameters for population sizes and prevalence\n", - "Y1_POP, Y1_PREV = 1000, 0.49\n", + "Y1_POP, Y1_PREV = 1000, 0.4\n", "Y2_POP, Y2_PREV = 1050, (Y1_PREV + 0.02)\n", "\n", "# simulate incidence from 2 years w/differing aforementioned parameters\n", "y1_incid = np.random.binomial(Y1_POP, Y1_PREV, size=NREP)\n", "y2_incid = np.random.binomial(Y2_POP, Y2_PREV, size=NREP)\n", "\n", + "# y1_incid = np.random.poisson(300, size=NREP)\n", + "# y2_incid = np.random.poisson(350, size=NREP)\n", + "\n", "# true difference in prevalence\n", "TRUE_DIFF = (Y2_PREV / Y1_PREV) - 1\n", + "# TRUE_DIFF = (350 / 300) - 1\n", "print(\"True percentage change in proportions: \", TRUE_DIFF)\n", "print(\"10 replications of incidence counts in year 1: \", y1_incid[0:9])" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CI coverage rate: 0.95\n" + "CI coverage rate: 0.9433333333333334\n" ] }, { "data": { - "image/png": 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" ] @@ -190,6 +206,7 @@ } ], "source": [ + "# TODO: document coverage with graphs in the tails\n", "coverages = np.zeros(NREP)\n", "mu_txs = np.zeros(NREP)\n", "ci_uppers = np.zeros(NREP)\n", @@ -198,39 +215,25 @@ "y2_rep1 = y2_incid[0]\n", "\n", "# print(transform_percentage_change_counts1(y1_rep1, Y1_POP, y2_rep1, Y2_POP))\n", + "# print(np.var(y1_incid, ddof=1))\n", "# print(transform_percentage_change_counts2(y1_rep1, Y1_POP, y2_rep1, Y2_POP))\n", - "\n", + "# raise KeyboardInterrupt\n", + "# TODO: ask Kelsey for function interface options\n", + "# TODO: get some slides together for high level issue and consequence descriptoin\n", "for i in range(NREP):\n", - " mu_txs[i], sigma_hat = transform_percentage_change_counts2(y1_incid[i], Y1_POP, y2_incid[i], Y2_POP)\n", + " mu_txs[i], sigma_hat = transform_percentage_change_counts1(y1_incid[i], Y1_POP, y2_incid[i], Y2_POP)\n", " # print(delta_hat, sigma_hat)\n", " ci_uppers[i] = mu_txs[i] + Q * sigma_hat / np.sqrt(Y1_POP)\n", " ci_lowers[i] = mu_txs[i] - Q * sigma_hat / np.sqrt(Y1_POP)\n", + " # ci_uppers[i] = mu_txs[i] + Q * sigma_hat\n", + " # ci_lowers[i] = mu_txs[i] - Q * sigma_hat\n", " if (ci_lowers[i] < TRUE_DIFF and TRUE_DIFF < ci_uppers[i]):\n", " coverages[i] = 1\n", "\n", "coverage_pct = (coverages == 1).sum() / NREP\n", "print(\"CI coverage rate: \", coverage_pct)\n", "\n", - "line_width = 0.75\n", - "cap_size = 2\n", - "marker_size = 3\n", - "plt.figure(figsize=(17, 6))\n", - "for i in range(NREP):\n", - " if coverages[i] == 1:\n", - " plt.errorbar(i, mu_txs[i], elinewidth=line_width,\n", - " yerr=[[mu_txs[i]- ci_lowers[i]], [ci_uppers[i] - mu_txs[i]]],\n", - " fmt=\"o\", color=\"blue\", ecolor=\"blue\", capsize=cap_size, markersize=marker_size)\n", - " else:\n", - " plt.errorbar(i, mu_txs[i], elinewidth=line_width,\n", - " yerr=[[mu_txs[i] - ci_lowers[i]], [ci_uppers[i] - mu_txs[i]]],\n", - " fmt=\"o\", color=\"red\", ecolor=\"red\", capsize=cap_size, markersize=marker_size)\n", - "\n", - "plt.axhline(y=TRUE_DIFF, color='gray', linestyle='--')\n", - "plt.xlabel('SampleID')\n", - "plt.ylabel(\"Mean\")\n", - "plt.title(\"95% CIs using Delta Method SE for percentage change\")\n", - "plt.legend([\"True Mean\"], loc=\"upper right\")\n", - "plt.show()" + "plot_simulation(coverages, mu_txs, ci_lowers, ci_uppers, TRUE_DIFF, \"percent change transform\")" ] }, { @@ -239,6 +242,176 @@ "source": [ "It seems as though the CI length is wider than it should be at more extreme values of prevalence (i.e. those near 0 or 1). This is probably due to the fact that when computing the CI, the standard error was divided by the sample size of the first year. Unclear as to what it should be instead, any suggestions?" ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.01 0.06 0.11 0.16 0.21 0.26 0.31 0.36 0.41 0.46 0.51 0.56 0.61 0.66\n", + " 0.71 0.76 0.81 0.86 0.91 0.96] [0.9466666666666667, 0.9466666666666667, 0.94, 0.9766666666666667, 0.9633333333333334, 0.96, 0.9466666666666667, 0.94, 0.95, 0.9466666666666667, 0.9766666666666667, 0.97, 0.9533333333333334, 0.9533333333333334, 0.9433333333333334, 0.9333333333333333, 0.9433333333333334, 0.95, 0.9466666666666667, 0.94]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y1_prevalences = np.arange(0.01, 1, 0.05)\n", + "all_coverages = []\n", + "for y1_prev in y1_prevalences:\n", + " coverages = np.zeros(NREP)\n", + " mu_txs = np.zeros(NREP)\n", + " ci_uppers = np.zeros(NREP)\n", + " ci_lowers = np.zeros(NREP)\n", + " Y1_POP, Y1_PREV = 1000, y1_prev\n", + " Y2_POP, Y2_PREV = 1050, (Y1_PREV + 0.02)\n", + " TRUE_DIFF = (Y2_PREV / Y1_PREV) - 1\n", + "\n", + " # simulate incidence from 2 years w/differing aforementioned parameters\n", + " y1_incid = np.random.binomial(Y1_POP, Y1_PREV, size=NREP)\n", + " y2_incid = np.random.binomial(Y2_POP, Y2_PREV, size=NREP)\n", + " for i in range(NREP):\n", + " mu_txs[i], sigma_hat = transform_percentage_change_counts1(y1_incid[i], Y1_POP, y2_incid[i], Y2_POP)\n", + " # print(delta_hat, sigma_hat)\n", + " ci_uppers[i] = mu_txs[i] + Q * sigma_hat / np.sqrt(Y1_POP)\n", + " ci_lowers[i] = mu_txs[i] - Q * sigma_hat / np.sqrt(Y1_POP)\n", + " # ci_uppers[i] = mu_txs[i] + Q * sigma_hat\n", + " # ci_lowers[i] = mu_txs[i] - Q * sigma_hat\n", + " if (ci_lowers[i] < TRUE_DIFF and TRUE_DIFF < ci_uppers[i]):\n", + " coverages[i] = 1\n", + " all_coverages.append((coverages == 1).sum() / NREP)\n", + "print(y1_prevalences, all_coverages)\n", + "plt.xlabel(\"y1 prevalence\")\n", + "plt.ylabel(\"coverage percentage\")\n", + "plt.title(\"coverage vs y1 prevalence\")\n", + "plt.scatter(y1_prevalences, all_coverages)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0. 1. 2. 3. 4. 5. 6. 7. 8.] [0.96, 0.9366666666666666, 0.9666666666666667, 0.94, 0.93, 0.9433333333333334, 0.9166666666666666, 0.96, 0.9533333333333334]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y2_prevalence_incs = np.arange(0, 0.9, 0.1)\n", + "# print(y2_prevalence_incs)\n", + "all_coverages = []\n", + "for y2_prev_inc in y2_prevalence_incs:\n", + " coverages = np.zeros(NREP)\n", + " mu_txs = np.zeros(NREP)\n", + " ci_uppers = np.zeros(NREP)\n", + " ci_lowers = np.zeros(NREP)\n", + " Y1_POP, Y1_PREV = 1000, 0.1\n", + " Y2_POP, Y2_PREV = 1050, (Y1_PREV + y2_prev_inc)\n", + " TRUE_DIFF = (Y2_PREV / Y1_PREV) - 1\n", + "\n", + " # simulate incidence from 2 years w/differing aforementioned parameters\n", + " y1_incid = np.random.binomial(Y1_POP, Y1_PREV, size=NREP)\n", + " y2_incid = np.random.binomial(Y2_POP, Y2_PREV, size=NREP)\n", + " for i in range(NREP):\n", + " mu_txs[i], sigma_hat = transform_percentage_change_counts1(y1_incid[i], Y1_POP, y2_incid[i], Y2_POP)\n", + " # print(delta_hat, sigma_hat)\n", + " ci_uppers[i] = mu_txs[i] + Q * sigma_hat / np.sqrt(Y1_POP)\n", + " ci_lowers[i] = mu_txs[i] - Q * sigma_hat / np.sqrt(Y1_POP)\n", + " # ci_uppers[i] = mu_txs[i] + Q * sigma_hat\n", + " # ci_lowers[i] = mu_txs[i] - Q * sigma_hat\n", + " if (ci_lowers[i] < TRUE_DIFF and TRUE_DIFF < ci_uppers[i]):\n", + " coverages[i] = 1\n", + " all_coverages.append((coverages == 1).sum() / NREP)\n", + "print(((y2_prevalence_incs + 0.1) / 0.1) - 1, all_coverages)\n", + "plt.xlabel(\"true delta\")\n", + "plt.ylabel(\"coverage percentage\")\n", + "plt.title(\"coverage vs true delta\")\n", + "plt.scatter(((y2_prevalence_incs + 0.1) / 0.1) - 1, all_coverages)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "unsupported operand type(s) for /: 'list' and 'int'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mtransform_percentage_change_counts1\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m200\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1050\u001b[39;49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(transform_percentage_change_counts1(\u001b[38;5;241m300\u001b[39m, \u001b[38;5;241m1000\u001b[39m, \u001b[38;5;241m400\u001b[39m, \u001b[38;5;241m1050\u001b[39m))\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(transform_percentage_change_counts1(np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m100\u001b[39m, \u001b[38;5;241m300\u001b[39m]), np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m1000\u001b[39m, \u001b[38;5;241m1000\u001b[39m]), np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m200\u001b[39m, \u001b[38;5;241m400\u001b[39m]), np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m1050\u001b[39m, \u001b[38;5;241m1050\u001b[39m])))\n", + "File \u001b[0;32m~/Documents/UW Grad/IHME/MSCA/distrx/src/distrx/transforms.py:325\u001b[0m, in \u001b[0;36mtransform_percentage_change_counts1\u001b[0;34m(c_x, n_x, c_y, n_y)\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"alternative percentage change transformation with only counts provided\u001b[39;00m\n\u001b[1;32m 306\u001b[0m \n\u001b[1;32m 307\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 321\u001b[0m \u001b[38;5;124;03m standard errors in the transform space\u001b[39;00m\n\u001b[1;32m 322\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 324\u001b[0m mu_x \u001b[38;5;241m=\u001b[39m c_x \u001b[38;5;241m/\u001b[39m n_x\n\u001b[0;32m--> 325\u001b[0m mu_y \u001b[38;5;241m=\u001b[39m \u001b[43mc_y\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mn_y\u001b[49m\n\u001b[1;32m 326\u001b[0m \u001b[38;5;66;03m# typical sample var calc\u001b[39;00m\n\u001b[1;32m 327\u001b[0m sigma2_x \u001b[38;5;241m=\u001b[39m (c_x \u001b[38;5;241m*\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m mu_x) \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m+\u001b[39m (n_x \u001b[38;5;241m-\u001b[39m c_x) \u001b[38;5;241m*\u001b[39m mu_x\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m) \u001b[38;5;241m/\u001b[39m (n_x \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)\n", + "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'list' and 'int'" + ] + } + ], + "source": [ + "print(transform_percentage_change_counts1([100], 1000, 200, 1050))\n", + "print(transform_percentage_change_counts1(300, 1000, 400, 1050))\n", + "print(transform_percentage_change_counts1(np.array([100, 300]), np.array([1000, 1000]), np.array([200, 400]), np.array([1050, 1050])))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.00990990990990991 0.009909909909909911\n", + "1000.0000000001432 1000\n" + ] + } + ], + "source": [ + "x_bar = 0.000693\n", + "s = 2.88e-7\n", + "temp = np.zeros(Y1_POP)\n", + "y1_rep1 = 10\n", + "temp[0:y1_rep1] = 1\n", + "tempvar = np.var(temp, ddof=1)\n", + "c_x = y1_rep1\n", + "n_x = Y1_POP\n", + "mu_x = c_x / n_x\n", + "sigma2_x = (c_x * (1 - mu_x) ** 2 + (n_x - c_x) * mu_x**2) / (n_x - 1)\n", + "print(tempvar, sigma2_x)\n", + "\n", + "s = np.sqrt(0.00990990990990991)\n", + "x_bar = y1_rep1 / Y1_POP\n", + "print(s**2 / (s**2 - x_bar * (1 - x_bar) * ((1 - x_bar) + x_bar)), Y1_POP)" + ] } ], "metadata": { diff --git a/src/distrx/transforms.py b/src/distrx/transforms.py index 0139c92..39b3fe2 100644 --- a/src/distrx/transforms.py +++ b/src/distrx/transforms.py @@ -15,6 +15,7 @@ import numpy as np import numpy.typing as npt +from mrtool import MRBRT, LinearCovModel, MRData class FirstOrder: @@ -291,8 +292,15 @@ def transform_percentage_change( return p_hat, np.sqrt(sigma_trans) +def handle_input_pct(c_x, n_x, c_y, n_y): + return np.array([c_x]), np.array([n_x]), np.array([c_y]), np.array([n_y]) + + def transform_percentage_change_counts1( - c_x: int, n_x: int, c_y: int, n_y: int + c_x: np.ndarray, + n_x: np.ndarray, + c_y: np.ndarray, + n_y: np.ndarray, ) -> float: """alternative percentage change transformation with only counts provided @@ -312,18 +320,31 @@ def transform_percentage_change_counts1( sigma_trans: array_like standard errors in the transform space """ + c_x, n_x, c_y, n_y = handle_input_pct(c_x, n_x, c_y, n_y) + mu_x = c_x / n_x mu_y = c_y / n_y - # sigma2_x = (c_x * (1 - mu_x) ** 2 + (n_x - c_x) * mu_x**2) / (n_x - 1) - # sigma2_y = (c_y * (1 - mu_y) ** 2 + (n_y - c_y) * mu_y**2) / (n_y - 1) - sigma2_x = n_x * mu_x * (1 - mu_x) - sigma2_y = n_y * mu_y * (1 - mu_y) + # typical sample var calc + sigma2_x = (c_x * (1 - mu_x) ** 2 + (n_x - c_x) * mu_x**2) / (n_x - 1) + sigma2_y = (c_y * (1 - mu_y) ** 2 + (n_y - c_y) * mu_y**2) / (n_y - 1) - # sigma_trans = (sigma2_y / mu_x**2) + (mu_y**2 * sigma2_x / (mu_x**4)) - sigma_trans = (sigma2_y / c_x**2) + (c_y**2 * sigma2_x / (c_x**4)) - print(sigma2_x, sigma2_y) + # var for p assuming binomial model + # sigma2_x = mu_x * (1 - mu_x) / n_x + # sigma2_y = mu_y * (1 - mu_y) / n_y - return ((c_y / c_x) - 1), np.sqrt(sigma_trans) + # var for p assuming beta prior + # sigma2_x = ( + # (mu_x * c_x + 1) * (c_x - mu_x * c_x + 1) / ((c_x + 2) ** 2 * (c_x + 3)) + # ) + # sigma2_y = ( + # (mu_y * c_y + 1) * (c_y - mu_y * c_y + 1) / ((c_y + 2) ** 2 * (c_y + 3)) + # ) + + sigma_trans = (sigma2_y / mu_x**2) + (mu_y**2 * sigma2_x / (mu_x**4)) + # sigma_trans = (sigma2_y / c_x**2) + (c_y**2 * sigma2_x / (c_x**4)) + # print(sigma2_x, sigma2_y) + + return ((mu_y / mu_x) - 1), np.sqrt(sigma_trans) def transform_percentage_change_counts2( @@ -349,14 +370,30 @@ def transform_percentage_change_counts2( """ mu_x = c_x / n_x mu_y = c_y / n_y - sigma2_x = (c_x * (1 - mu_x) ** 2 + (n_x - c_x) * mu_x**2) / (n_x - 1) - sigma2_y = (c_y * (1 - mu_y) ** 2 + (n_y - c_y) * mu_y**2) / (n_y - 1) - # print("look", sigma2_x, sigma2_y) + rat = mu_y / (mu_x + mu_y) + Rl = np.log(rat / (1 - rat)) + # variance of p assuming binomial model, somewhat reasonable coverage w/o scaling CI + sigma2_x = mu_x * (1 - mu_x) / n_x + sigma2_y = mu_y * (1 - mu_y) / n_y - sigma_trans = (sigma2_y / mu_x**2) + (mu_y**2 * sigma2_x / (mu_x**4)) + # standard sample var calculation, extreme overcoverage + # sigma2_x = (c_x * (1 - mu_x) ** 2 + (n_x - c_x) * mu_x**2) / (n_x - 1) + # sigma2_y = (c_y * (1 - mu_y) ** 2 + (n_y - c_y) * mu_y**2) / (n_y - 1) + + # assumed beta prior sample var calculation, extreme overcoverage + # sigma2_x = ( + # (mu_x * c_x + 1) * (c_x - mu_x * c_x + 1) / ((c_x + 2) ** 2 * (c_x + 3)) + # ) + # sigma2_y = ( + # (mu_y * c_y + 1) * (c_y - mu_y * c_y + 1) / ((c_y + 2) ** 2 * (c_y + 3)) + # ) + + # sigma_trans = (sigma2_y / mu_x**2) + (mu_y**2 * sigma2_x / (mu_x**4)) # sigma_trans = (sigma2_y / c_x**2) + (c_y**2 * sigma2_x / (c_x**4)) + sigma2_Rl = (sigma2_x / (mu_x**2)) + (sigma2_y / (mu_y**2)) + sigma_tx = sigma2_Rl * np.exp(2 * Rl) - return ((mu_y / mu_x) - 1), np.sqrt(sigma_trans) + return ((mu_y / mu_x) - 1), np.sqrt(sigma_tx) def _check_input( diff --git a/tests/test_transforms.py b/tests/test_transforms.py index 50f6f41..2de2e86 100644 --- a/tests/test_transforms.py +++ b/tests/test_transforms.py @@ -1,13 +1,14 @@ """Tests for transforms.py module.""" import numpy as np +import pandas as pd import pytest from distrx.transforms import ( delta_method, transform_data, transform_percentage_change, - transform_percentage_change_counts2, + transform_percentage_change_counts1, ) TRANSFORM_DICT = { @@ -107,8 +108,31 @@ def test_percentage_change(): def test_percentage_change_counts(): x = np.random.choice([0, 1], size=1000, p=[0.1, 0.9]) y = np.random.choice([0, 1], size=1100, p=[0.2, 0.8]) - mu, sigma = transform_percentage_change_counts2( + mu, sigma = transform_percentage_change_counts1( (x == 1).sum(), len(x), (y == 1).sum(), len(y) ) - assert -1 <= mu and mu < np.infty + assert -1 <= mu and mu < np.inf assert 0 < sigma and sigma < 1 + + +def test_percentage_change_input(): + # scalar input + c_x, n_x = 100, 1000 + c_y, n_y = 200, 1050 + # with pytest.raises(TypeError): + transform_percentage_change_counts1(c_x, n_x, c_y, n_y) + + # base list input + c_x = [100, 200] + n_x = [1000, 1000] + c_y = [300, 400] + n_y = [1050, 1050] + # with pytest.raises(TypeError): + transform_percentage_change_counts1(c_x, n_x, c_y, n_y) + + # dataframe input + df = pd.DataFrame({"c_x": c_x, "n_x": n_x, "c_y": c_y, "n_y": n_y}) + # with pytest.raises(TypeError): + transform_percentage_change_counts1( + df["c_x"], df["n_x"], df["c_y"], df["n_y"] + )