simple_regressions_trunc

###load libraries and read truncated indices data####
suppressPackageStartupMessages(library(tidyverse))

## Warning: package 'tidyverse' was built under R version 3.3.3

## Warning: package 'purrr' was built under R version 3.3.3

library(broom)
library(readr)
library(knitr)

setwd ("P:/MTM2 Oranga Taiao/EMP Data/EMP Data/Biotic Index calculator/Index calculation/MTM new Data")
source('theme_javier.R')
theme_set(theme_javier(8))

all_ind<- read_csv('data_multiple_regression.csv',col_types =  cols(Date = 'D')) %>% 
  mutate(Type = factor(Type),
         logTOC = log(TOC+1))
options(digits = 3)

dat_simple <- 
  all_ind %>%
  gather(index, value,AMBI:MEDOCC,BQI:TBI,logN,S) 

###Mud simple regression
simple_mud <- 
  dat_simple %>%
  group_by(index) %>%
  do(fit_index2 = lm(value ~ sqrtmud,data = .,weights = sqrt(n)))

coef_mud <- tidy(simple_mud, fit_index2,conf.int = T)
summ_mud <- glance(simple_mud, fit_index2)
kable(coef_mud)

index	term	estimate	std.error	statistic	p.value	conf.low	conf.high
AMBI	(Intercept)	1.325	0.076	17.357	0.000	1.175	1.476
AMBI	sqrtmud	0.140	0.018	7.791	0.000	0.105	0.176
AMBI_S	(Intercept)	1.426	0.054	26.593	0.000	1.321	1.532
AMBI_S	sqrtmud	0.133	0.013	10.513	0.000	0.108	0.158
BENTIX	(Intercept)	5.207	0.123	42.472	0.000	4.965	5.449
BENTIX	sqrtmud	-0.156	0.029	-5.386	0.000	-0.213	-0.099
BQI	(Intercept)	6.010	0.192	31.332	0.000	5.632	6.388
BQI	sqrtmud	-0.321	0.045	-7.095	0.000	-0.410	-0.232
ITI	(Intercept)	33.011	1.678	19.676	0.000	29.706	36.316
ITI	sqrtmud	-0.132	0.395	-0.333	0.739	-0.911	0.647
logN	(Intercept)	4.628	0.129	35.808	0.000	4.373	4.883
logN	sqrtmud	-0.112	0.030	-3.677	0.000	-0.172	-0.052
M_AMBI	(Intercept)	0.631	0.012	51.840	0.000	0.607	0.655
M_AMBI	sqrtmud	-0.025	0.003	-8.748	0.000	-0.031	-0.019
MEDOCC	(Intercept)	1.779	0.100	17.802	0.000	1.582	1.976
MEDOCC	sqrtmud	0.172	0.024	7.327	0.000	0.126	0.219
S	(Intercept)	14.122	0.539	26.208	0.000	13.060	15.183
S	sqrtmud	-0.922	0.127	-7.264	0.000	-1.172	-0.672
TBI	(Intercept)	0.350	0.014	24.707	0.000	0.322	0.378
TBI	sqrtmud	-0.021	0.003	-6.322	0.000	-0.028	-0.015

kable(summ_mud)

index	r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual
AMBI	0.205	0.202	0.916	60.705	0.000	2	-204	414	424	196.98	235
AMBI_S	0.320	0.317	0.643	110.519	0.000	2	-120	246	257	97.17	235
BENTIX	0.110	0.106	1.470	29.014	0.000	2	-316	638	648	507.81	235
BQI	0.176	0.173	2.300	50.337	0.000	2	-422	850	861	1242.98	235
ITI	0.000	-0.004	20.116	0.111	0.739	2	-936	1878	1889	95092.31	235
logN	0.054	0.050	1.550	13.519	0.000	2	-329	663	674	564.34	235
M_AMBI	0.246	0.242	0.146	76.526	0.000	2	232	-457	-447	5.00	235
MEDOCC	0.186	0.182	1.198	53.679	0.000	2	-268	541	552	337.28	235
S	0.183	0.180	6.461	52.770	0.000	2	-667	1340	1350	9809.30	235
TBI	0.145	0.142	0.170	39.964	0.000	2	196	-385	-375	6.77	235

###Metals simple regression
simple_metals <- 
  dat_simple %>%
  group_by(index) %>%
  do(fit_index1 = lm(value ~ metals,data = .,weights = sqrt(n)))

coef_metals <- tidy(simple_metals, fit_index1,conf.int = T)
summ_metals <- glance(simple_metals, fit_index1)
kable(coef_metals)

index	term	estimate	std.error	statistic	p.value	conf.low	conf.high
AMBI	(Intercept)	1.865	0.040	46.89	0.000	1.787	1.943
AMBI	metals	0.065	0.015	4.19	0.000	0.034	0.095
AMBI_S	(Intercept)	1.940	0.029	66.72	0.000	1.882	1.997
AMBI_S	metals	0.069	0.011	6.12	0.000	0.047	0.091
BENTIX	(Intercept)	4.613	0.062	74.48	0.000	4.491	4.736
BENTIX	metals	-0.052	0.024	-2.15	0.033	-0.099	-0.004
BQI	(Intercept)	4.757	0.095	50.06	0.000	4.570	4.945
BQI	metals	-0.216	0.037	-5.86	0.000	-0.289	-0.144
ITI	(Intercept)	32.844	0.782	42.03	0.000	31.304	34.383
ITI	metals	1.215	0.304	4.00	0.000	0.617	1.813
logN	(Intercept)	4.186	0.062	67.49	0.000	4.064	4.308
logN	metals	-0.093	0.024	-3.88	0.000	-0.141	-0.046
M_AMBI	(Intercept)	0.533	0.006	85.13	0.000	0.521	0.546
M_AMBI	metals	-0.015	0.002	-6.10	0.000	-0.020	-0.010
MEDOCC	(Intercept)	2.439	0.052	46.85	0.000	2.337	2.542
MEDOCC	metals	0.068	0.020	3.36	0.001	0.028	0.108
S	(Intercept)	10.528	0.269	39.08	0.000	9.997	11.059
S	metals	-0.589	0.105	-5.63	0.000	-0.795	-0.383
TBI	(Intercept)	0.267	0.007	39.06	0.000	0.253	0.280
TBI	metals	-0.016	0.003	-6.21	0.000	-0.022	-0.011

kable(summ_metals)

index	r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual
AMBI	0.069	0.065	0.991	17.53	0.000	2	-223	451	461	230.65	235
AMBI_S	0.137	0.134	0.724	37.42	0.000	2	-148	303	313	123.24	235
BENTIX	0.019	0.015	1.543	4.61	0.033	2	-328	661	671	559.53	235
BQI	0.127	0.124	2.367	34.32	0.000	2	-429	864	874	1316.88	235
ITI	0.064	0.060	19.468	16.02	0.000	2	-928	1863	1873	89067.46	235
logN	0.060	0.056	1.545	15.02	0.000	2	-328	662	672	560.95	235
M_AMBI	0.137	0.133	0.156	37.18	0.000	2	216	-425	-415	5.72	235
MEDOCC	0.046	0.042	1.297	11.28	0.001	2	-286	579	589	395.36	235
S	0.119	0.115	6.712	31.66	0.000	2	-676	1358	1368	10586.03	235
TBI	0.141	0.137	0.170	38.59	0.000	2	195	-384	-374	6.81	235

###sqrtTP simple regression
simple_sqrtTP<- 
  dat_simple %>%
  group_by(index) %>%
  do(fit_index3 = lm(value ~ sqrtTP,data = .,weights = sqrt(n)))

coef_sqrtTP <- tidy(simple_sqrtTP, fit_index3,conf.int = T)
summ_sqrtTP <-  glance(simple_sqrtTP, fit_index3)
kable(coef_sqrtTP)

index	term	estimate	std.error	statistic	p.value	conf.low	conf.high
AMBI	(Intercept)	1.037	0.167	6.21	0.000	0.708	1.366
AMBI	sqrtTP	0.044	0.009	5.00	0.000	0.027	0.061
AMBI_S	(Intercept)	1.106	0.122	9.10	0.000	0.866	1.345
AMBI_S	sqrtTP	0.044	0.006	6.90	0.000	0.032	0.057
BENTIX	(Intercept)	5.404	0.261	20.69	0.000	4.889	5.919
BENTIX	sqrtTP	-0.042	0.014	-3.06	0.002	-0.069	-0.015
BQI	(Intercept)	6.390	0.420	15.21	0.000	5.562	7.217
BQI	sqrtTP	-0.085	0.022	-3.85	0.000	-0.129	-0.042
ITI	(Intercept)	25.808	3.410	7.57	0.000	19.089	32.527
ITI	sqrtTP	0.364	0.180	2.02	0.044	0.010	0.718
logN	(Intercept)	4.619	0.271	17.04	0.000	4.084	5.153
logN	sqrtTP	-0.022	0.014	-1.55	0.123	-0.050	0.006
M_AMBI	(Intercept)	0.673	0.027	24.70	0.000	0.619	0.726
M_AMBI	sqrtTP	-0.007	0.001	-5.12	0.000	-0.010	-0.005
MEDOCC	(Intercept)	1.475	0.218	6.76	0.000	1.045	1.904
MEDOCC	sqrtTP	0.051	0.011	4.47	0.000	0.029	0.074
S	(Intercept)	14.909	1.189	12.54	0.000	12.566	17.252
S	sqrtTP	-0.229	0.063	-3.65	0.000	-0.352	-0.106
TBI	(Intercept)	0.375	0.031	12.25	0.000	0.315	0.435
TBI	sqrtTP	-0.006	0.002	-3.49	0.001	-0.009	-0.002

kable(summ_sqrtTP)

index	r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual
AMBI	0.096	0.092	0.976	24.95	0.000	2	-219	444	455	224.07	235
AMBI_S	0.168	0.165	0.711	47.59	0.000	2	-144	294	304	118.81	235
BENTIX	0.038	0.034	1.528	9.35	0.002	2	-325	656	667	548.67	235
BQI	0.059	0.055	2.458	14.85	0.000	2	-438	882	892	1419.52	235
ITI	0.017	0.013	19.948	4.10	0.044	2	-934	1874	1885	93507.20	235
logN	0.010	0.006	1.586	2.39	0.123	2	-334	674	684	590.79	235
M_AMBI	0.100	0.096	0.159	26.20	0.000	2	211	-415	-405	5.96	235
MEDOCC	0.078	0.074	1.275	19.96	0.000	2	-282	571	581	381.89	235
S	0.054	0.050	6.955	13.35	0.000	2	-684	1375	1385	11366.50	235
TBI	0.049	0.045	0.179	12.18	0.001	2	183	-360	-349	7.53	235

###Region simple regression
simple_region<- 
  dat_simple %>%
  group_by(index) %>%
  do(fit_index4 = lm(value ~ Region,data = .,weights = sqrt(n)))

coef_region <- tidy(simple_region, fit_index4,conf.int = T)
summ_region <- glance(simple_region, fit_index4)
kable(coef_region)

index	term	estimate	std.error	statistic	p.value	conf.low	conf.high
AMBI	(Intercept)	2.070	0.097	21.236	0.000	1.878	2.262
AMBI	RegionEastern North Island	-0.210	0.129	-1.630	0.104	-0.463	0.044
AMBI	RegionNorth Eastern	-0.232	0.124	-1.876	0.062	-0.477	0.012
AMBI	RegionSouthern	-0.378	0.121	-3.139	0.002	-0.616	-0.141
AMBI	RegionWestern North Island	0.522	0.325	1.605	0.110	-0.119	1.163
AMBI_S	(Intercept)	2.050	0.074	27.800	0.000	1.905	2.195
AMBI_S	RegionEastern North Island	-0.055	0.097	-0.570	0.569	-0.247	0.136
AMBI_S	RegionNorth Eastern	-0.168	0.094	-1.792	0.074	-0.353	0.017
AMBI_S	RegionSouthern	-0.250	0.091	-2.740	0.007	-0.430	-0.070
AMBI_S	RegionWestern North Island	0.518	0.246	2.105	0.036	0.033	1.003
BENTIX	(Intercept)	4.375	0.147	29.789	0.000	4.086	4.665
BENTIX	RegionEastern North Island	0.576	0.194	2.973	0.003	0.194	0.958
BENTIX	RegionNorth Eastern	0.151	0.187	0.810	0.419	-0.217	0.519
BENTIX	RegionSouthern	0.311	0.182	1.714	0.088	-0.047	0.669
BENTIX	RegionWestern North Island	-1.117	0.490	-2.280	0.024	-2.083	-0.152
BQI	(Intercept)	5.102	0.196	26.029	0.000	4.715	5.488
BQI	RegionEastern North Island	-1.762	0.259	-6.809	0.000	-2.272	-1.252
BQI	RegionNorth Eastern	0.921	0.249	3.698	0.000	0.431	1.412
BQI	RegionSouthern	-0.432	0.242	-1.781	0.076	-0.909	0.046
BQI	RegionWestern North Island	-0.140	0.654	-0.214	0.831	-1.428	1.149
ITI	(Intercept)	30.490	1.747	17.454	0.000	27.048	33.932
ITI	RegionEastern North Island	12.459	2.306	5.403	0.000	7.916	17.003
ITI	RegionNorth Eastern	-1.680	2.221	-0.756	0.450	-6.056	2.696
ITI	RegionSouthern	-0.636	2.160	-0.295	0.769	-4.892	3.620
ITI	RegionWestern North Island	-7.671	5.828	-1.316	0.189	-19.153	3.812
logN	(Intercept)	3.762	0.118	31.923	0.000	3.530	3.994
logN	RegionEastern North Island	-0.524	0.156	-3.367	0.001	-0.830	-0.217
logN	RegionNorth Eastern	1.120	0.150	7.477	0.000	0.825	1.415
logN	RegionSouthern	0.805	0.146	5.525	0.000	0.518	1.092
logN	RegionWestern North Island	0.410	0.393	1.042	0.298	-0.365	1.184
M_AMBI	(Intercept)	0.551	0.015	36.933	0.000	0.521	0.580
M_AMBI	RegionEastern North Island	-0.082	0.020	-4.165	0.000	-0.121	-0.043
M_AMBI	RegionNorth Eastern	0.040	0.019	2.124	0.035	0.003	0.078
M_AMBI	RegionSouthern	-0.016	0.018	-0.863	0.389	-0.052	0.020
M_AMBI	RegionWestern North Island	-0.055	0.050	-1.099	0.273	-0.153	0.043
MEDOCC	(Intercept)	2.738	0.126	21.760	0.000	2.490	2.986
MEDOCC	RegionEastern North Island	-0.391	0.166	-2.355	0.019	-0.718	-0.064
MEDOCC	RegionNorth Eastern	-0.295	0.160	-1.843	0.067	-0.610	0.020
MEDOCC	RegionSouthern	-0.500	0.156	-3.211	0.002	-0.806	-0.193
MEDOCC	RegionWestern North Island	0.643	0.420	1.532	0.127	-0.184	1.470
S	(Intercept)	10.878	0.556	19.569	0.000	9.782	11.973
S	RegionEastern North Island	-4.389	0.734	-5.981	0.000	-5.834	-2.943
S	RegionNorth Eastern	3.191	0.707	4.515	0.000	1.799	4.583
S	RegionSouthern	-0.160	0.687	-0.233	0.816	-1.514	1.194
S	RegionWestern North Island	-1.128	1.854	-0.608	0.544	-4.781	2.526
TBI	(Intercept)	0.291	0.013	21.730	0.000	0.265	0.318
TBI	RegionEastern North Island	-0.128	0.018	-7.236	0.000	-0.163	-0.093
TBI	RegionNorth Eastern	0.081	0.017	4.755	0.000	0.047	0.115
TBI	RegionSouthern	-0.038	0.017	-2.322	0.021	-0.071	-0.006
TBI	RegionWestern North Island	-0.065	0.045	-1.445	0.150	-0.153	0.024

kable(summ_region)

index	r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual
AMBI	0.064	0.048	1.000	3.96	0.004	5	-223	458	479	232.01	232
AMBI_S	0.070	0.054	0.757	4.38	0.002	5	-157	326	347	132.83	232
BENTIX	0.076	0.060	1.507	4.79	0.001	5	-320	653	674	526.98	232
BQI	0.378	0.368	2.011	35.29	0.000	5	-389	790	810	938.29	232
ITI	0.217	0.203	17.924	16.03	0.000	5	-907	1826	1847	74536.73	232
logN	0.432	0.422	1.209	44.05	0.000	5	-268	548	569	339.19	232
M_AMBI	0.181	0.166	0.153	12.78	0.000	5	222	-431	-411	5.43	232
MEDOCC	0.067	0.051	1.291	4.15	0.003	5	-284	580	600	386.67	232
S	0.372	0.361	5.703	34.32	0.000	5	-636	1284	1305	7546.78	232
TBI	0.446	0.436	0.138	46.65	0.000	5	247	-482	-461	4.39	232

###AFDW simple regression
simple_logAFDW<- 
  dat_simple %>%
  group_by(index) %>%
  do(fit_index5 = lm(value ~ logAFDW,data = .,weights = sqrt(n)))

coef_logAFDW <- tidy(simple_logAFDW, fit_index5,conf.int = T)
summ_logAFDW <- glance(simple_logAFDW, fit_index5)
kable(coef_logAFDW)

index	term	estimate	std.error	statistic	p.value	conf.low	conf.high
AMBI	(Intercept)	1.629	0.065	25.032	0.000	1.500	1.757
AMBI	logAFDW	0.309	0.074	4.200	0.000	0.164	0.454
AMBI_S	(Intercept)	1.717	0.047	36.733	0.000	1.624	1.809
AMBI_S	logAFDW	0.282	0.053	5.328	0.000	0.177	0.386
BENTIX	(Intercept)	4.873	0.098	49.804	0.000	4.680	5.066
BENTIX	logAFDW	-0.406	0.111	-3.666	0.000	-0.624	-0.187
BQI	(Intercept)	4.852	0.161	30.068	0.000	4.534	5.171
BQI	logAFDW	0.145	0.183	0.792	0.430	-0.216	0.505
ITI	(Intercept)	33.028	1.354	24.385	0.000	30.357	35.700
ITI	logAFDW	-0.901	1.533	-0.588	0.558	-3.924	2.123
logN	(Intercept)	4.139	0.096	43.313	0.000	3.951	4.328
logN	logAFDW	0.158	0.108	1.465	0.145	-0.055	0.372
M_AMBI	(Intercept)	0.555	0.011	52.560	0.000	0.534	0.576
M_AMBI	logAFDW	-0.016	0.012	-1.302	0.195	-0.039	0.008
MEDOCC	(Intercept)	2.150	0.085	25.425	0.000	1.983	2.316
MEDOCC	logAFDW	0.381	0.096	3.986	0.000	0.193	0.570
S	(Intercept)	10.808	0.453	23.848	0.000	9.914	11.702
S	logAFDW	0.378	0.513	0.736	0.463	-0.634	1.389
TBI	(Intercept)	0.269	0.012	22.683	0.000	0.246	0.293
TBI	logAFDW	0.022	0.013	1.612	0.109	-0.005	0.048

kable(summ_logAFDW)

index	r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual
AMBI	0.085	0.081	1.008	17.641	0.000	2	-186	379	388	191.94	189
AMBI_S	0.131	0.126	0.724	28.386	0.000	2	-123	252	262	99.04	189
BENTIX	0.066	0.061	1.516	13.438	0.000	2	-264	535	544	434.14	189
BQI	0.003	-0.002	2.500	0.627	0.430	2	-360	726	735	1181.00	189
ITI	0.002	-0.003	20.980	0.345	0.558	2	-766	1538	1548	83193.74	189
logN	0.011	0.006	1.480	2.145	0.145	2	-260	526	535	414.20	189
M_AMBI	0.009	0.004	0.163	1.694	0.195	2	161	-316	-306	5.05	189
MEDOCC	0.078	0.073	1.310	15.889	0.000	2	-236	479	488	324.17	189
S	0.003	-0.002	7.020	0.542	0.463	2	-557	1120	1130	9314.76	189
TBI	0.014	0.008	0.184	2.599	0.109	2	138	-271	-261	6.40	189

###logPb simple regression
simple_logPb<- 
  dat_simple %>%
  group_by(index) %>%
  do(fit_index6 = lm(value ~ logPb,data = .,weights = sqrt(n)))

coef_logPb <- tidy(simple_logPb, fit_index6,conf.int = T)
summ_logPb <- glance(simple_logPb, fit_index6)
kable(coef_logPb)

index	term	estimate	std.error	statistic	p.value	conf.low	conf.high
AMBI	(Intercept)	1.486	0.102	14.55	0.000	1.285	1.688
AMBI	logPb	0.213	0.056	3.84	0.000	0.104	0.323
AMBI_S	(Intercept)	1.539	0.075	20.45	0.000	1.390	1.687
AMBI_S	logPb	0.226	0.041	5.52	0.000	0.145	0.306
BENTIX	(Intercept)	4.830	0.159	30.35	0.000	4.516	5.143
BENTIX	logPb	-0.119	0.086	-1.38	0.168	-0.290	0.051
BQI	(Intercept)	6.179	0.241	25.61	0.000	5.704	6.655
BQI	logPb	-0.805	0.131	-6.14	0.000	-1.063	-0.547
ITI	(Intercept)	22.947	1.949	11.78	0.000	19.108	26.787
ITI	logPb	5.649	1.059	5.33	0.000	3.562	7.736
logN	(Intercept)	5.000	0.154	32.56	0.000	4.698	5.303
logN	logPb	-0.466	0.083	-5.58	0.000	-0.630	-0.301
M_AMBI	(Intercept)	0.629	0.016	39.39	0.000	0.597	0.660
M_AMBI	logPb	-0.054	0.009	-6.22	0.000	-0.071	-0.037
MEDOCC	(Intercept)	2.052	0.134	15.36	0.000	1.789	2.315
MEDOCC	logPb	0.218	0.073	3.00	0.003	0.075	0.361
S	(Intercept)	14.864	0.671	22.16	0.000	13.543	16.186
S	logPb	-2.466	0.365	-6.76	0.000	-3.184	-1.748
TBI	(Intercept)	0.386	0.017	22.70	0.000	0.352	0.419
TBI	logPb	-0.068	0.009	-7.32	0.000	-0.086	-0.049

kable(summ_logPb)

index	r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual
AMBI	0.059	0.055	0.996	14.75	0.000	2	-224	454	464	233.22	235
AMBI_S	0.115	0.111	0.734	30.50	0.000	2	-151	309	319	126.45	235
BENTIX	0.008	0.004	1.552	1.91	0.168	2	-329	664	674	565.91	235
BQI	0.138	0.135	2.353	37.70	0.000	2	-427	861	871	1300.58	235
ITI	0.108	0.104	19.004	28.44	0.000	2	-923	1851	1862	84867.09	235
logN	0.117	0.113	1.497	31.14	0.000	2	-320	647	657	526.96	235
M_AMBI	0.141	0.138	0.156	38.70	0.000	2	216	-426	-416	5.69	235
MEDOCC	0.037	0.033	1.303	9.00	0.003	2	-287	581	591	399.04	235
S	0.163	0.159	6.541	45.75	0.000	2	-670	1346	1356	10054.72	235
TBI	0.186	0.182	0.166	53.57	0.000	2	201	-397	-386	6.45	235

###logCu simple regression#####
simple_logCu<- 
  dat_simple %>%
  group_by(index) %>%
  do(fit_index7 = lm(value ~ logCu,data = .,weights = sqrt(n)))

coef_logCu <- tidy(simple_logCu, fit_index7,conf.int = T)
summ_logCu <- glance(simple_logCu, fit_index7)
kable(coef_logCu)

index	term	estimate	std.error	statistic	p.value	conf.low	conf.high
AMBI	(Intercept)	1.558	0.077	20.11	0.000	1.406	1.711
AMBI	logCu	0.176	0.040	4.34	0.000	0.096	0.255
AMBI_S	(Intercept)	1.623	0.057	28.58	0.000	1.511	1.734
AMBI_S	logCu	0.181	0.030	6.11	0.000	0.123	0.240
BENTIX	(Intercept)	4.958	0.120	41.48	0.000	4.723	5.194
BENTIX	logCu	-0.201	0.062	-3.22	0.001	-0.324	-0.078
BQI	(Intercept)	5.851	0.182	32.06	0.000	5.492	6.211
BQI	logCu	-0.629	0.095	-6.60	0.000	-0.816	-0.441
ITI	(Intercept)	29.915	1.565	19.11	0.000	26.832	32.998
ITI	logCu	1.581	0.817	1.93	0.054	-0.029	3.191
logN	(Intercept)	4.634	0.121	38.37	0.000	4.396	4.871
logN	logCu	-0.257	0.063	-4.07	0.000	-0.381	-0.132
M_AMBI	(Intercept)	0.603	0.012	49.56	0.000	0.579	0.627
M_AMBI	logCu	-0.040	0.006	-6.31	0.000	-0.053	-0.028
MEDOCC	(Intercept)	2.087	0.101	20.67	0.000	1.888	2.286
MEDOCC	logCu	0.203	0.053	3.84	0.000	0.099	0.307
S	(Intercept)	13.368	0.522	25.60	0.000	12.339	14.397
S	logCu	-1.628	0.273	-5.97	0.000	-2.165	-1.091
TBI	(Intercept)	0.346	0.013	26.20	0.000	0.320	0.372
TBI	logCu	-0.046	0.007	-6.61	0.000	-0.059	-0.032

kable(summ_logCu)

index	r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual
AMBI	0.074	0.070	0.988	18.85	0.000	2	-222	450	460	229.46	235
AMBI_S	0.137	0.133	0.724	37.33	0.000	2	-148	303	313	123.28	235
BENTIX	0.042	0.038	1.525	10.35	0.001	2	-325	655	666	546.45	235
BQI	0.156	0.153	2.328	43.52	0.000	2	-425	856	866	1273.40	235
ITI	0.016	0.011	19.962	3.74	0.054	2	-934	1875	1885	93646.40	235
logN	0.066	0.062	1.540	16.55	0.000	2	-327	660	671	557.54	235
M_AMBI	0.145	0.141	0.155	39.85	0.000	2	217	-427	-417	5.66	235
MEDOCC	0.059	0.055	1.288	14.78	0.000	2	-285	575	586	389.82	235
S	0.132	0.128	6.662	35.64	0.000	2	-674	1354	1365	10430.35	235
TBI	0.157	0.153	0.169	43.66	0.000	2	197	-388	-378	6.68	235

###sqrtZn simple regression####
simple_sqrtZn<- 
  dat_simple %>%
  group_by(index) %>%
  do(fit_index8 = lm(value ~ sqrtZn,data = .,weights = sqrt(n)))

coef_sqrtZn <- tidy(simple_sqrtZn, fit_index8,conf.int = T)
summ_sqrtZn<- glance(simple_sqrtZn, fit_index8)
kable(coef_sqrtZn)

index	term	estimate	std.error	statistic	p.value	conf.low	conf.high
AMBI	(Intercept)	1.451	0.107	13.54	0.000	1.240	1.662
AMBI	sqrtZn	0.066	0.017	3.98	0.000	0.033	0.099
AMBI_S	(Intercept)	1.495	0.079	19.03	0.000	1.341	1.650
AMBI_S	sqrtZn	0.071	0.012	5.83	0.000	0.047	0.095
BENTIX	(Intercept)	4.933	0.167	29.60	0.000	4.604	5.261
BENTIX	sqrtZn	-0.051	0.026	-1.97	0.050	-0.102	0.000
BQI	(Intercept)	6.115	0.258	23.74	0.000	5.608	6.622
BQI	sqrtZn	-0.217	0.040	-5.43	0.000	-0.296	-0.138
ITI	(Intercept)	24.783	2.101	11.80	0.000	20.645	28.922
ITI	sqrtZn	1.291	0.326	3.96	0.000	0.650	1.933
logN	(Intercept)	4.760	0.167	28.42	0.000	4.430	5.090
logN	sqrtZn	-0.092	0.026	-3.53	0.001	-0.143	-0.040
M_AMBI	(Intercept)	0.627	0.017	36.99	0.000	0.594	0.661
M_AMBI	sqrtZn	-0.015	0.003	-5.73	0.000	-0.020	-0.010
MEDOCC	(Intercept)	2.010	0.140	14.33	0.000	1.733	2.286
MEDOCC	sqrtZn	0.069	0.022	3.16	0.002	0.026	0.112
S	(Intercept)	14.211	0.730	19.46	0.000	12.773	15.650
S	sqrtZn	-0.589	0.113	-5.20	0.000	-0.812	-0.365
TBI	(Intercept)	0.370	0.019	19.96	0.000	0.334	0.407
TBI	sqrtZn	-0.017	0.003	-5.74	0.000	-0.022	-0.011

kable(summ_sqrtZn)

index	r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual
AMBI	0.063	0.059	0.994	15.86	0.000	2	-223	453	463	232.19	235
AMBI_S	0.126	0.123	0.729	34.01	0.000	2	-150	306	316	124.81	235
BENTIX	0.016	0.012	1.545	3.89	0.050	2	-328	662	672	561.21	235
BQI	0.112	0.108	2.389	29.52	0.000	2	-431	868	879	1340.80	235
ITI	0.063	0.059	19.480	15.72	0.000	2	-928	1863	1873	89172.16	235
logN	0.050	0.046	1.553	12.45	0.001	2	-329	664	675	566.78	235
M_AMBI	0.123	0.119	0.157	32.83	0.000	2	214	-421	-411	5.81	235
MEDOCC	0.041	0.037	1.300	9.98	0.002	2	-287	580	590	397.44	235
S	0.103	0.099	6.771	27.03	0.000	2	-678	1362	1372	10772.96	235
TBI	0.123	0.119	0.172	32.97	0.000	2	193	-379	-369	6.95	235

###logTOC simple regression#####
simple_logTOC<- 
  dat_simple %>%
  group_by(index) %>%
  do(fit_index9 = lm(value ~ logTOC,data = .,weights = sqrt(n)))

coef_logTOC <- tidy(simple_logTOC, fit_index9,conf.int = T) 
summ_logTOC<- glance(simple_logTOC, fit_index9) 
kable(coef_logTOC)

index	term	estimate	std.error	statistic	p.value	conf.low	conf.high
AMBI	(Intercept)	1.681	0.122	13.818	0.000	1.436	1.925
AMBI	logTOC	0.573	0.221	2.594	0.013	0.128	1.018
AMBI_S	(Intercept)	1.680	0.100	16.862	0.000	1.480	1.881
AMBI_S	logTOC	0.786	0.181	4.343	0.000	0.422	1.150
BENTIX	(Intercept)	4.912	0.214	22.950	0.000	4.482	5.343
BENTIX	logTOC	-0.440	0.389	-1.130	0.264	-1.222	0.343
BQI	(Intercept)	5.021	0.377	13.317	0.000	4.262	5.780
BQI	logTOC	-1.698	0.685	-2.479	0.017	-3.077	-0.319
ITI	(Intercept)	34.938	2.803	12.464	0.000	29.296	40.581
ITI	logTOC	-0.257	5.093	-0.051	0.960	-10.508	9.994
logN	(Intercept)	3.923	0.306	12.824	0.000	3.307	4.539
logN	logTOC	0.236	0.556	0.424	0.673	-0.883	1.354
M_AMBI	(Intercept)	0.568	0.024	23.413	0.000	0.519	0.616
M_AMBI	logTOC	-0.141	0.044	-3.202	0.002	-0.230	-0.052
MEDOCC	(Intercept)	2.191	0.151	14.550	0.000	1.888	2.494
MEDOCC	logTOC	0.733	0.274	2.682	0.010	0.183	1.284
S	(Intercept)	11.384	1.061	10.728	0.000	9.248	13.520
S	logTOC	-4.961	1.928	-2.574	0.013	-8.842	-1.081
TBI	(Intercept)	0.272	0.023	11.935	0.000	0.226	0.318
TBI	logTOC	-0.107	0.041	-2.579	0.013	-0.190	-0.023

kable(summ_logTOC)

index	r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual
AMBI	0.128	0.109	0.798	6.727	0.013	2	-29.9	65.8	71.4	29.26	46
AMBI_S	0.291	0.275	0.653	18.858	0.000	2	-20.3	46.7	52.3	19.64	46
BENTIX	0.027	0.006	1.404	1.278	0.264	2	-57.0	120.1	125.7	90.62	46
BQI	0.118	0.099	2.472	6.146	0.017	2	-84.2	174.4	180.0	281.17	46
ITI	0.000	-0.022	18.382	0.003	0.960	2	-180.5	367.0	372.6	15543.02	46
logN	0.004	-0.018	2.006	0.180	0.673	2	-74.2	154.4	160.0	185.10	46
M_AMBI	0.182	0.164	0.159	10.251	0.002	2	47.5	-89.0	-83.4	1.16	46
MEDOCC	0.135	0.116	0.987	7.192	0.010	2	-40.1	86.3	91.9	44.83	46
S	0.126	0.107	6.958	6.623	0.013	2	-133.9	273.8	279.4	2227.34	46
TBI	0.126	0.107	0.150	6.651	0.013	2	50.4	-94.8	-89.2	1.03	46

###sqrtTN simple regression#####
simple_sqrtTN <- 
  dat_simple %>%
  group_by(index) %>%
  do(fit_index10 = lm(value ~ sqrtTN ,data = .,weights = sqrt(n)))

coef_sqrtTN  <- tidy(simple_sqrtTN , fit_index10,conf.int = T)
summ_sqrtTN <- glance(simple_sqrtTN , fit_index10)
kable(coef_sqrtTN)

index	term	estimate	std.error	statistic	p.value	conf.low	conf.high
AMBI	(Intercept)	1.617	0.050	32.064	0.000	1.518	1.716
AMBI	sqrtTN	0.018	0.003	6.894	0.000	0.013	0.023
AMBI_S	(Intercept)	1.707	0.036	46.950	0.000	1.635	1.778
AMBI_S	sqrtTN	0.016	0.002	8.858	0.000	0.013	0.020
BENTIX	(Intercept)	4.906	0.079	61.979	0.000	4.750	5.061
BENTIX	sqrtTN	-0.022	0.004	-5.336	0.000	-0.029	-0.014
BQI	(Intercept)	4.931	0.136	36.238	0.000	4.662	5.199
BQI	sqrtTN	-0.009	0.007	-1.236	0.218	-0.022	0.005
ITI	(Intercept)	32.333	1.083	29.847	0.000	30.198	34.467
ITI	sqrtTN	0.012	0.055	0.212	0.833	-0.097	0.120
logN	(Intercept)	4.133	0.085	48.357	0.000	3.965	4.301
logN	sqrtTN	0.006	0.004	1.405	0.161	-0.002	0.015
M_AMBI	(Intercept)	0.560	0.009	64.014	0.000	0.543	0.578
M_AMBI	sqrtTN	-0.002	0.000	-3.994	0.000	-0.003	-0.001
MEDOCC	(Intercept)	2.135	0.066	32.494	0.000	2.005	2.264
MEDOCC	sqrtTN	0.022	0.003	6.579	0.000	0.015	0.029
S	(Intercept)	10.963	0.384	28.535	0.000	10.206	11.720
S	sqrtTN	-0.021	0.020	-1.051	0.294	-0.059	0.018
TBI	(Intercept)	0.277	0.010	28.107	0.000	0.258	0.297
TBI	sqrtTN	0.000	0.001	-0.825	0.410	-0.001	0.001

kable(summ_sqrtTN)

index	r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual
AMBI	0.169	0.165	0.938	47.525	0.000	2	-209	423	434	205.79	234
AMBI_S	0.251	0.248	0.676	78.465	0.000	2	-131	269	279	106.95	234
BENTIX	0.108	0.105	1.472	28.475	0.000	2	-315	636	647	506.96	234
BQI	0.006	0.002	2.530	1.528	0.218	2	-443	892	902	1498.12	234
ITI	0.000	-0.004	20.145	0.045	0.833	2	-933	1871	1882	94965.37	234
logN	0.008	0.004	1.589	1.974	0.161	2	-333	672	683	591.12	234
M_AMBI	0.064	0.060	0.163	15.951	0.000	2	205	-403	-393	6.20	234
MEDOCC	0.156	0.153	1.222	43.288	0.000	2	-271	548	559	349.24	234
S	0.005	0.000	7.145	1.104	0.294	2	-688	1382	1392	11945.92	234
TBI	0.003	-0.001	0.183	0.681	0.410	2	176	-347	-336	7.87	234

###Type simple regression####
simple_type<- 
  dat_simple %>%
  group_by(index) %>%
  do(fit_index11 = lm(value ~ Region,data = .,weights = sqrt(n)))

coef_type<- tidy(simple_type, fit_index11,conf.int = T)
summ_type <- glance(simple_type, fit_index11)
kable(coef_type)

index	term	estimate	std.error	statistic	p.value	conf.low	conf.high
AMBI	(Intercept)	2.070	0.097	21.236	0.000	1.878	2.262
AMBI	RegionEastern North Island	-0.210	0.129	-1.630	0.104	-0.463	0.044
AMBI	RegionNorth Eastern	-0.232	0.124	-1.876	0.062	-0.477	0.012
AMBI	RegionSouthern	-0.378	0.121	-3.139	0.002	-0.616	-0.141
AMBI	RegionWestern North Island	0.522	0.325	1.605	0.110	-0.119	1.163
AMBI_S	(Intercept)	2.050	0.074	27.800	0.000	1.905	2.195
AMBI_S	RegionEastern North Island	-0.055	0.097	-0.570	0.569	-0.247	0.136
AMBI_S	RegionNorth Eastern	-0.168	0.094	-1.792	0.074	-0.353	0.017
AMBI_S	RegionSouthern	-0.250	0.091	-2.740	0.007	-0.430	-0.070
AMBI_S	RegionWestern North Island	0.518	0.246	2.105	0.036	0.033	1.003
BENTIX	(Intercept)	4.375	0.147	29.789	0.000	4.086	4.665
BENTIX	RegionEastern North Island	0.576	0.194	2.973	0.003	0.194	0.958
BENTIX	RegionNorth Eastern	0.151	0.187	0.810	0.419	-0.217	0.519
BENTIX	RegionSouthern	0.311	0.182	1.714	0.088	-0.047	0.669
BENTIX	RegionWestern North Island	-1.117	0.490	-2.280	0.024	-2.083	-0.152
BQI	(Intercept)	5.102	0.196	26.029	0.000	4.715	5.488
BQI	RegionEastern North Island	-1.762	0.259	-6.809	0.000	-2.272	-1.252
BQI	RegionNorth Eastern	0.921	0.249	3.698	0.000	0.431	1.412
BQI	RegionSouthern	-0.432	0.242	-1.781	0.076	-0.909	0.046
BQI	RegionWestern North Island	-0.140	0.654	-0.214	0.831	-1.428	1.149
ITI	(Intercept)	30.490	1.747	17.454	0.000	27.048	33.932
ITI	RegionEastern North Island	12.459	2.306	5.403	0.000	7.916	17.003
ITI	RegionNorth Eastern	-1.680	2.221	-0.756	0.450	-6.056	2.696
ITI	RegionSouthern	-0.636	2.160	-0.295	0.769	-4.892	3.620
ITI	RegionWestern North Island	-7.671	5.828	-1.316	0.189	-19.153	3.812
logN	(Intercept)	3.762	0.118	31.923	0.000	3.530	3.994
logN	RegionEastern North Island	-0.524	0.156	-3.367	0.001	-0.830	-0.217
logN	RegionNorth Eastern	1.120	0.150	7.477	0.000	0.825	1.415
logN	RegionSouthern	0.805	0.146	5.525	0.000	0.518	1.092
logN	RegionWestern North Island	0.410	0.393	1.042	0.298	-0.365	1.184
M_AMBI	(Intercept)	0.551	0.015	36.933	0.000	0.521	0.580
M_AMBI	RegionEastern North Island	-0.082	0.020	-4.165	0.000	-0.121	-0.043
M_AMBI	RegionNorth Eastern	0.040	0.019	2.124	0.035	0.003	0.078
M_AMBI	RegionSouthern	-0.016	0.018	-0.863	0.389	-0.052	0.020
M_AMBI	RegionWestern North Island	-0.055	0.050	-1.099	0.273	-0.153	0.043
MEDOCC	(Intercept)	2.738	0.126	21.760	0.000	2.490	2.986
MEDOCC	RegionEastern North Island	-0.391	0.166	-2.355	0.019	-0.718	-0.064
MEDOCC	RegionNorth Eastern	-0.295	0.160	-1.843	0.067	-0.610	0.020
MEDOCC	RegionSouthern	-0.500	0.156	-3.211	0.002	-0.806	-0.193
MEDOCC	RegionWestern North Island	0.643	0.420	1.532	0.127	-0.184	1.470
S	(Intercept)	10.878	0.556	19.569	0.000	9.782	11.973
S	RegionEastern North Island	-4.389	0.734	-5.981	0.000	-5.834	-2.943
S	RegionNorth Eastern	3.191	0.707	4.515	0.000	1.799	4.583
S	RegionSouthern	-0.160	0.687	-0.233	0.816	-1.514	1.194
S	RegionWestern North Island	-1.128	1.854	-0.608	0.544	-4.781	2.526
TBI	(Intercept)	0.291	0.013	21.730	0.000	0.265	0.318
TBI	RegionEastern North Island	-0.128	0.018	-7.236	0.000	-0.163	-0.093
TBI	RegionNorth Eastern	0.081	0.017	4.755	0.000	0.047	0.115
TBI	RegionSouthern	-0.038	0.017	-2.322	0.021	-0.071	-0.006
TBI	RegionWestern North Island	-0.065	0.045	-1.445	0.150	-0.153	0.024

kable(summ_type)

index	r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual
AMBI	0.064	0.048	1.000	3.96	0.004	5	-223	458	479	232.01	232
AMBI_S	0.070	0.054	0.757	4.38	0.002	5	-157	326	347	132.83	232
BENTIX	0.076	0.060	1.507	4.79	0.001	5	-320	653	674	526.98	232
BQI	0.378	0.368	2.011	35.29	0.000	5	-389	790	810	938.29	232
ITI	0.217	0.203	17.924	16.03	0.000	5	-907	1826	1847	74536.73	232
logN	0.432	0.422	1.209	44.05	0.000	5	-268	548	569	339.19	232
M_AMBI	0.181	0.166	0.153	12.78	0.000	5	222	-431	-411	5.43	232
MEDOCC	0.067	0.051	1.291	4.15	0.003	5	-284	580	600	386.67	232
S	0.372	0.361	5.703	34.32	0.000	5	-636	1284	1305	7546.78	232
TBI	0.446	0.436	0.138	46.65	0.000	5	247	-482	-461	4.39	232

simple_regressions_trunc_data.R

javiera

Mon May 29 10:53:01 2017