forked from ldecicco-USGS/GLPF_manuscript
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Workflow_Detection_level_model_testing.R
222 lines (175 loc) · 7.86 KB
/
Workflow_Detection_level_model_testing.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
# Workflow to test the impact the choice to use the detection level for censored values.
###
#Outline of workflow for the three different spatial scales
#
#1. Data processing:
# a. MRL determination for optical data
# -Question: can we determine a common MRL for all data sets or separate?
# i. Locate blanks for each data set
# ii. Compute MRLs
# iii. Apply MRLs to each raw data set
#
# b. Compute summary optical data parameters
# i. Develop common set of parameters needed
# - determine frequency of censored values for each parameter for each data set
# - decide which parameters to keep
# - modify Optical summary definitions
# ii. Use HydroOpt routines to compute
# iii. Result: summary optical data sets
# c. Combine summary optical data sets with bacteria data
# i. Use GR numbers form CA lab and FT numbers from UWM lab. These are
# all joined already from previous data tasks
# ii. Use optical parameters from the final GLPF data set to define
# optical parameters for all three spatial scales
#
#2. Data description
# a. Generate plot (figure 2) with concentration and occurrence of HB
# i. Done: script = Figure 2.R, Results.Rmd
# b. Determine numbers of samples and such for adding to text
# ii. Began this task: script = Results.Rmd
#
#3. Modeling
# a. Large watersheds
# i. Begin with LME modeling of common parameters
# ii. Choose groups of sites where this works
# iii. Explore additional parameters for watersheds where that doesn't work
# iv. Develop summary table of models
# b. Subwatersheds
# i. LME modeling with common parameters
# ii. Include in modeling table with Large watersheds
# c. Small
#
##########################################
# Project Setup
##########################################
# Packages in all scripts included here
library(tidyverse)
library(USGSHydroOpt)
library(scales)
library(USGSHydroTools)
library(lme4)
library(smwrBase)
## Don't run again. Files have been saved ##
# C. Functions for adjusting raw data to include MRLs
source(file = file.path("process","src","applyMRLs_detection_level_test.R"))
source(file = file.path("process","src","optMRLAdjust.R"))
# Apply MRLs
apply_MRLs(multiplier = 0.1)
#Add summary variables with modified censored values (0.5 * detection)
source(file.path("process", "src","get_summaries_0.5_dl.R"))
get_summaries(multiplier = 0.1)
GLRI_formulas <- readRDS(file.path("process","out","GLRI_formulas.rds"))
names(GLRI_formulas)
#Read GLRI data
glri <- readRDS(file.path("process","out","glri_summary.rds"))
glri_0.1 <- readRDS(file.path("process","out","glri_summary_0.1_dl.rds"))
glri_1 <- readRDS(file.path("process","out","glri_summary_1_dl.rds"))
# * Transform seasonal variables
glri$sinDate <- fourier(glri$psdate)[,1]
glri$cosDate <- fourier(glri$psdate)[,2]
glri_0.1$sinDate <- fourier(glri_0.1$psdate)[,1]
glri_0.1$cosDate <- fourier(glri_0.1$psdate)[,2]
glri_1$sinDate <- fourier(glri_1$psdate)[,1]
glri_1$cosDate <- fourier(glri_1$psdate)[,2]
#test GLRI ag models for all organisms
response <- c("BACHUM.cn.100mls", "Lachno.2.cn.100ml","ENTERO.cn.100mls", "Entero.CFUs.100ml","E..coli.CFUs.100ml")
ag_models <- c("Turb_F_T","Turb_F_T","Turb_F","Turb_F","Turb_F")
bact_DLs <- c(225,225,225,1,1)
names(ag_models) <- response
names(bact_DLs) <- response
############ Explore different substitutions for optical (original, 0.1, and 1) #############################
for(i in 1:length(response)) {
glri$log_response <- log10(glri[,response[i]])
glri_0.1$log_response <- log10(glri_0.1[,response[i]])
glri_1$log_response <- log10(glri_1[,response[i]])
sites <- c("MA","PO","RM")
form <- formula(as.character(GLRI_formulas[ag_models[response[i]]]))
m <- lmer(form, data = (glri %>% filter(abbrev %in% sites)))
m_0.1 <- lmer(form, data = (glri_0.1 %>% filter(abbrev %in% sites)))
m_1 <- lmer(form, data = (glri_1 %>% filter(abbrev %in% sites)))
plot(predict(m_0.1),predict(m_1))
summary(predict(m_0.1)/predict(m_1))
summary(predict(m)/predict(m_1))
summary(predict(m)/predict(m_0.1))
if(i == 1) {
df_predict <- data.frame(response = response[i],
model = "Ag",
predict_orig = predict(m),
predict_0.1 = predict(m_0.1),
predict_1 = predict(m_1))
} else {
df_predict <- bind_rows(df_predict,data.frame(response = response[i],
model = "Ag",
predict_orig = predict(m),
predict_0.1 = predict(m_0.1),
predict_1 = predict(m_1)))
}
}
df_predict_optical_summary <- df_predict %>%
mutate(ratio_0.1 = predict_0.1/predict_orig,
ratio_1 = predict_1/predict_orig,
diff_0.1 = predict_0.1 - predict_orig,
diff_1 = predict_1 - predict_orig) %>%
group_by(model,response) %>%
summarise(mean_0.1 = mean(ratio_0.1),
median_0.1 = median(ratio_0.1),
stdev_0.01 = sd(ratio_0.1),
mean_1 = mean(ratio_1),
median_1 = median(ratio_1),
stdev_1 = sd(ratio_1),
mean_diff0.1 = mean(diff_0.1),
median_diff_0.1 = median(diff_0.1),
stdev_diff_1 = sd(diff_1),
median_diff_1 = median(diff_1),
stdev_diff_1 = sd(diff_1))
##############################################################################
######## Now try 0.1 * LOD vs LOD for bacteria ####################################
for(i in 1:length(response)) {
glri$response <- glri[,response[i]]
censored <- glri$response <= bact_DLs[response[i]]
glri_0.1 <- glri
multiplier <- 0.1
glri$log_response_0.1 <- log10(ifelse(censored,bact_DLs[response[i]]*multiplier,glri$response))
multiplier <- 1
glri$log_response_1 <- log10(ifelse(censored,bact_DLs[response[i]]*multiplier,glri$response))
sites <- c("MA","PO","RM")
form <- formula(as.character(GLRI_formulas[ag_models[response[i]]]))
glri$log_response <- log10(glri[,response[i]])
m <- lmer(form, data = (glri %>% filter(abbrev %in% sites)))
glri$log_response <- glri$log_response_0.1
m_0.1 <- lmer(form, data = (glri %>% filter(abbrev %in% sites)))
glri$log_response <- glri$log_response_1
m_1 <- lmer(form, data = (glri %>% filter(abbrev %in% sites)))
plot(predict(m_0.1),predict(m_1))
summary(predict(m_0.1)/predict(m_1))
summary(predict(m)/predict(m_1))
summary(predict(m)/predict(m_0.1))
if(i == 1) {
df_predict <- data.frame(response = response[i],
model = "Ag",
predict_orig = predict(m),
predict_0.1 = predict(m_0.1),
predict_1 = predict(m_1))
} else {
df_predict <- bind_rows(df_predict,data.frame(response = response[i],
model = "Ag",
predict_orig = predict(m),
predict_0.1 = predict(m_0.1),
predict_1 = predict(m_1)))
}
}
df_predict_bact_summary <- df_predict %>%
mutate(ratio_0.1 = predict_0.1/predict_orig,
ratio_1 = predict_1/predict_orig,
diff_0.1 = predict_0.1 - predict_1) %>%
group_by(model,response) %>%
summarise(mean_0.1 = mean(ratio_0.1),
median_0.1 = median(ratio_0.1),
stdev_0.01 = sd(ratio_0.1),
mean_1 = mean(ratio_1),
median_1 = median(ratio_1),
stdev_1 = sd(ratio_1),
mean_diff0.1 = mean(diff_0.1),
median_diff_0.1 = median(diff_0.1))
glri$log_response <- log10(glri$response)
df_log_response <- glri[,grep("response",names(glri),value = TRUE)]