	 node	 mean	 sd	 MCerror	2.5%	median	97.5%	start	sample
	y.pred[1]	-0.04062	1.496	0.01647	-2.986	-0.0505	2.898	10001	10000
	y.pred[2]	-0.2859	1.486	0.01487	-3.164	-0.2848	2.655	10001	10000
	y.pred[3]	-0.3346	1.477	0.01525	-3.287	-0.3274	2.546	10001	10000
	y.pred[4]	-0.01764	1.494	0.01595	-2.891	-0.0145	2.896	10001	10000
	y.pred[5]	0.486	1.487	0.01617	-2.414	0.4857	3.423	10001	10000
	y.pred[6]	0.7301	1.491	0.01555	-2.193	0.726	3.624	10001	10000
	y.pred[7]	0.5214	1.498	0.01307	-2.435	0.5369	3.482	10001	10000
	y.pred[8]	0.1776	1.489	0.0157	-2.74	0.1914	3.114	10001	10000
	y.pred[9]	0.05559	1.497	0.01612	-2.86	0.0579	2.993	10001	10000
	y.pred[10]	0.2304	1.498	0.01515	-2.735	0.2288	3.198	10001	10000
	y.pred[11]	0.558	1.497	0.01689	-2.353	0.5478	3.537	10001	10000
	y.pred[12]	0.701	1.503	0.01523	-2.233	0.6865	3.672	10001	10000
	y.pred[13]	0.4862	1.474	0.01417	-2.419	0.5126	3.355	10001	10000
	y.pred[14]	0.2186	1.505	0.01463	-2.703	0.2099	3.203	10001	10000
	y.pred[15]	0.1592	1.512	0.01446	-2.801	0.1643	3.092	10001	10000
	y.pred[16]	0.5209	1.51	0.01538	-2.419	0.5337	3.472	10001	10000
	y.pred[17]	1.012	1.502	0.01374	-1.929	1.028	3.965	10001	10000
	y.pred[18]	1.225	1.497	0.01405	-1.678	1.209	4.151	10001	10000
	y.pred[19]	1.07	1.491	0.01501	-1.882	1.074	4.003	10001	10000
	y.pred[20]	0.6934	1.488	0.01605	-2.256	0.6914	3.598	10001	10000
	y.pred[21]	0.5244	1.502	0.01449	-2.421	0.5132	3.502	10001	10000
	y.pred[22]	0.7552	1.512	0.01435	-2.186	0.7528	3.794	10001	10000
	y.pred[23]	1.066	1.498	0.01725	-1.843	1.084	3.994	10001	10000
	y.pred[24]	1.184	1.498	0.01465	-1.806	1.18	4.081	10001	10000
	y.pred[25]	0.9962	1.502	0.01405	-1.912	0.9783	3.938	10001	10000
	y.pred[26]	0.7009	1.486	0.01675	-2.207	0.7117	3.62	10001	10000
	y.pred[27]	0.6644	1.496	0.01699	-2.282	0.6591	3.613	10001	10000
	y.pred[28]	1.013	1.499	0.01821	-1.955	1.006	3.957	10001	10000
	y.pred[29]	1.459	1.507	0.01255	-1.511	1.483	4.435	10001	10000
	y.pred[30]	1.708	1.505	0.01495	-1.253	1.717	4.617	10001	10000
	y.pred[31]	1.551	1.491	0.01529	-1.397	1.561	4.457	10001	10000
	y.pred[32]	1.202	1.503	0.01447	-1.745	1.229	4.148	10001	10000
	y.pred[33]	1.061	1.51	0.01297	-1.931	1.071	4.002	10001	10000
	y.pred[34]	1.242	1.507	0.01617	-1.701	1.242	4.255	10001	10000
	y.pred[35]	1.595	1.486	0.01363	-1.321	1.596	4.463	10001	10000
	y.pred[36]	1.752	1.486	0.01385	-1.121	1.744	4.685	10001	10000
	y.pred[37]	1.508	1.499	0.01402	-1.407	1.502	4.466	10001	10000
	y.pred[38]	1.225	1.503	0.01397	-1.672	1.22	4.188	10001	10000
	y.pred[39]	1.185	1.495	0.0153	-1.734	1.174	4.13	10001	10000
	y.pred[40]	1.515	1.492	0.01711	-1.462	1.524	4.443	10001	10000
	y.pred[41]	2.008	1.491	0.01286	-0.9662	2.015	4.907	10001	10000
	y.pred[42]	2.24	1.474	0.0144	-0.634	2.25	5.158	10001	10000
	y.pred[43]	2.063	1.505	0.01386	-0.9072	2.06	5.035	10001	10000
	y.pred[44]	1.698	1.502	0.01521	-1.232	1.697	4.674	10001	10000
	y.pred[45]	1.569	1.507	0.01458	-1.279	1.564	4.561	10001	10000
	y.pred[46]	1.78	1.497	0.01445	-1.154	1.797	4.736	10001	10000
	y.pred[47]	2.101	1.492	0.01332	-0.796	2.105	5.07	10001	10000
	y.pred[48]	2.228	1.485	0.0134	-0.6666	2.227	5.177	10001	10000
	y.pred[49]	2.027	1.478	0.01477	-0.9383	2.026	4.888	10001	10000
	y.pred[50]	1.724	1.494	0.01496	-1.235	1.748	4.65	10001	10000
	y.pred[51]	1.681	1.494	0.01575	-1.305	1.684	4.629	10001	10000
	y.pred[52]	2.044	1.502	0.01602	-0.934	2.034	4.973	10001	10000
	y.pred[53]	2.484	1.503	0.01576	-0.4948	2.494	5.417	10001	10000
	y.pred[54]	2.754	1.503	0.01542	-0.174	2.762	5.691	10001	10000
	y.pred[55]	2.572	1.491	0.01509	-0.3263	2.566	5.494	10001	10000
	y.pred[56]	2.246	1.497	0.01336	-0.6616	2.26	5.17	10001	10000
	y.pred[57]	2.063	1.487	0.01646	-0.8783	2.064	4.953	10001	10000
	y.pred[58]	2.238	1.512	0.01428	-0.7211	2.238	5.202	10001	10000
	y.pred[59]	2.598	1.497	0.01551	-0.3081	2.602	5.568	10001	10000
	y.pred[60]	2.703	1.497	0.01593	-0.2411	2.708	5.622	10001	10000
	y.pred[61]	2.517	1.496	0.01507	-0.4262	2.496	5.456	10001	10000
	y.pred[62]	2.22	1.496	0.0153	-0.7661	2.21	5.141	10001	10000
	y.pred[63]	2.188	1.514	0.01598	-0.7747	2.196	5.164	10001	10000
	y.pred[64]	2.552	1.491	0.01537	-0.4223	2.549	5.492	10001	10000
	y.pred[65]	3.039	1.488	0.01413	0.1027	3.032	5.9	10001	10000
	y.pred[66]	3.255	1.501	0.01673	0.2852	3.244	6.162	10001	10000
	y.pred[67]	3.059	1.498	0.01611	0.1025	3.076	5.972	10001	10000
	y.pred[68]	2.74	1.5	0.01678	-0.2565	2.755	5.616	10001	10000
	y.pred[69]	2.586	1.51	0.01457	-0.3495	2.591	5.601	10001	10000
	y.pred[70]	2.784	1.514	0.01472	-0.191	2.798	5.698	10001	10000
	y.pred[71]	3.125	1.523	0.01714	0.1969	3.106	6.153	10001	10000
	y.pred[72]	3.233	1.489	0.01434	0.3476	3.203	6.208	10001	10000
	y.pred[73]	3.015	1.493	0.01575	0.09119	3.023	5.955	10001	10000
	y.pred[74]	2.754	1.501	0.01472	-0.1842	2.757	5.668	10001	10000
	y.pred[75]	2.702	1.517	0.01561	-0.266	2.705	5.724	10001	10000
	y.pred[76]	3.072	1.482	0.01673	0.1855	3.069	6.009	10001	10000
	y.pred[77]	3.529	1.511	0.01426	0.5098	3.536	6.519	10001	10000
	y.pred[78]	3.795	1.507	0.01551	0.8326	3.8	6.727	10001	10000
	y.pred[79]	3.585	1.517	0.01666	0.5803	3.575	6.592	10001	10000
	y.pred[80]	3.24	1.507	0.01377	0.2746	3.244	6.198	10001	10000
	y.pred[81]	3.091	1.512	0.0152	0.1483	3.096	6.094	10001	10000
	y.pred[82]	3.286	1.517	0.01543	0.3208	3.278	6.276	10001	10000
	y.pred[83]	3.602	1.51	0.01388	0.5942	3.617	6.535	10001	10000
	y.pred[84]	3.745	1.492	0.01632	0.7302	3.754	6.642	10001	10000
	y.pred[85]	3.546	1.501	0.0143	0.5959	3.557	6.459	10001	10000
	y.pred[86]	3.268	1.508	0.01433	0.3327	3.257	6.263	10001	10000
	y.pred[87]	3.21	1.507	0.01485	0.2451	3.204	6.177	10001	10000
	y.pred[88]	3.574	1.509	0.01615	0.6402	3.576	6.504	10001	10000
	y.pred[89]	4.036	1.517	0.01497	1.065	4.04	7.027	10001	10000
	y.pred[90]	4.268	1.49	0.01602	1.312	4.264	7.184	10001	10000
	y.pred[91]	4.121	1.509	0.01653	1.122	4.117	7.041	10001	10000
	y.pred[92]	3.753	1.497	0.01641	0.7427	3.779	6.671	10001	10000
	y.pred[93]	3.578	1.492	0.0165	0.6289	3.575	6.519	10001	10000
	y.pred[94]	3.809	1.514	0.01332	0.8625	3.813	6.791	10001	10000
	y.pred[95]	4.159	1.513	0.01559	1.208	4.164	7.153	10001	10000
	y.pred[96]	4.235	1.504	0.01812	1.284	4.242	7.16	10001	10000
	y.pred[97]	4.066	1.497	0.01474	1.139	4.072	6.983	10001	10000
	y.pred[98]	3.752	1.508	0.01626	0.7402	3.769	6.658	10001	10000
	y.pred[99]	3.754	1.517	0.01623	0.6897	3.778	6.732	10001	10000
	y.pred[100]	4.077	1.509	0.01821	1.098	4.081	7.057	10001	10000
	y.pred[101]	4.54	1.492	0.01511	1.588	4.533	7.46	10001	10000
	y.pred[102]	4.767	1.508	0.01565	1.837	4.763	7.778	10001	10000
	y.pred[103]	4.619	1.513	0.01469	1.703	4.611	7.644	10001	10000
	y.pred[104]	4.277	1.516	0.01566	1.272	4.269	7.267	10001	10000
	y.pred[105]	4.112	1.51	0.01588	1.136	4.105	7.058	10001	10000
	y.pred[106]	4.315	1.505	0.01443	1.353	4.289	7.327	10001	10000
	y.pred[107]	4.651	1.5	0.01517	1.688	4.654	7.64	10001	10000
	y.pred[108]	4.741	1.517	0.01331	1.782	4.725	7.733	10001	10000
	y.pred[109]	4.582	1.513	0.01562	1.617	4.579	7.546	10001	10000
	y.pred[110]	4.26	1.52	0.0147	1.32	4.257	7.228	10001	10000
	y.pred[111]	4.233	1.529	0.01627	1.184	4.248	7.235	10001	10000
	y.pred[112]	4.602	1.511	0.01841	1.64	4.614	7.573	10001	10000
	y.pred[113]	5.074	1.487	0.01626	2.162	5.08	8.064	10001	10000
	y.pred[114]	5.295	1.496	0.01621	2.336	5.293	8.212	10001	10000
	y.pred[115]	5.102	1.516	0.01691	2.142	5.093	8.077	10001	10000
	y.pred[116]	4.787	1.503	0.01582	1.87	4.789	7.762	10001	10000
	y.pred[117]	4.607	1.519	0.01526	1.572	4.607	7.611	10001	10000
	y.pred[118]	4.826	1.491	0.01522	1.896	4.829	7.749	10001	10000
	y.pred[119]	5.156	1.519	0.01515	2.185	5.181	8.19	10001	10000
	y.pred[120]	5.266	1.517	0.0165	2.298	5.276	8.215	10001	10000
