-
Notifications
You must be signed in to change notification settings - Fork 0
/
modeling.R
733 lines (654 loc) · 31.2 KB
/
modeling.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
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
# install.packages("XYZ")
require(tidyverse)
require(ggplot2)
require(ggpubr)
require(dplyr)
require(tidyverse)
require(caret)
require(leaps)
require(MASS)
require(glmnet)
require(effects)
require(viridis)
require(car)
require(stargazer)
require(effectsize)
library(effsize)
###############################
####PREPARATION: COMMENTED OUT#
###############################
## LOAD AND MERGE MICROCLIMATE
# microclimate_hourly <- read.csv("FILEPATH/Microclimate/hourly_summary_all_temps/all_plots_hourly_summary_all_temps.csv")
# microclimate_hourly <- rename(microclimate_hourly, las_name = plot)
# just in case these are needed
# ecology_metrics <- read.csv("FILEPATH/figures/temp_vs_metric/other_metrics_sorted.csv")
# pad_metrics <- read.csv("FILEPATH/figures/temp_vs_metric/total.pad.stat.summarized.csv")
# enl_metrics <- read.csv("FILEPATH/ENL/summary_metrics/summary_ENL.csv")
# LOAD OLD SUMMARY DF
# stats_plus_micro <- read.csv("FILEPATH/figures/temp_vs_metric/stats_plus_micro_v2.csv")
# # DELETE TEMP ROWS (IRRELEVANT BECAUSE WE DONT GO OFF SUMMARY METRICS NOW (?)
# stats_plus_micro <- stats_plus_micro[,-c(1,3)]
# stats_plus_micro <- stats_plus_micro[,-c(2:15)]
# stats_plus_micro_unique <- unique(stats_plus_micro)
# # FOR SOME REASON LAS 17 IS TWO TIMES WITHIN THIS DF! INVESTIGATE PS:
# # METRICS LOOK FINE, ROW 17 IS TO BE REMOVED, POSSIBLE REASON: ROUNDING ERROR?
# stats_plus_micro_unique <- stats_plus_micro_unique[-17,]
# model_df <- merge(microclimate_hourly, stats_plus_micro_unique, by='las_name', all.x=TRUE)
# write.csv(model_df, "FILEPATH/modeling/model_df.csv",row.names = F)
# ADAPT ERA5 DATA TO NEW FORMAT AND THEN ROWBIND THEM INTO COMMON DATAFRAME
# las_name = "las_17"
# era5 <- read.csv(paste("FILEPATH/Macroclimate/ERA5_corrected_temperature/", las_name, "_AT2M_corrected.csv", sep = ""))
# era5 <- era5[,c(1,2)]
# era5$las_name <- las_name
# era5 <- rename(era5,
# t_air_ERA5 = AT2m_corrected,
# hourofday = TIMESTAMP)
# write.csv(era5, paste("FILEPATH/modeling/era5/", las_name, "_era5_for_model_df.csv", sep = ""), row.names = F)
# # APPLY THEM INTO A LIST
# my_files <- list.files("FILEPATH/modeling/era5/", pattern = "\\.csv$")
# # CHANGE WD FOR LAPPLY
# setwd("FILEPATH/modeling/era5/")
# # READ ALL FILES
# sum_metric_list <- lapply(my_files, read.csv)
# # COMBINE FILES
# all_sum_metrics <- bind_rows(sum_metric_list)
# #all_sum_metrics <- all_sum_metrics[,-1]
# write.csv(all_sum_metrics, "FILEPATH/modeling/era5/all_era5_for_model.csv", row.names = F)
# CONVERT DATETIME TO POSIXCT JUST IN CASE IT MATTERS FOR DPLYR FILTER
# all_sum_metrics$hourofday <- as.POSIXct(all_sum_metrics$hourofday)
# model_df$hourofday <- as.POSIXct(model_df$hourofday)
# EVERYTHING BEFORE 13.06 IS NONSENSE DATA
# model_df_filtered <- model_df %>% filter(hourofday >= "2019-06-13" & hourofday < "2020-02-05")
# era5_filtered <- all_sum_metrics %>% filter(hourofday >= "2019-06-13" & hourofday < "2020-02-05")
# WRITE
# write.csv(model_df_filtered, "FILEPATH/modeling/filtered/model_df_filtered.csv", row.names = F)
# write.csv(era5_filtered, "FILEPATH/modeling/filtered/era5_filtered.csv", row.names = F)
# READ MICROCLIMATE AND ERA5 DATA
model_df <- read.csv("FILEPATH/modeling/filtered/model_df_filtered.csv")
era5_df <- read.csv("FILEPATH/modeling/filtered/era5_filtered.csv")
data_merge1 <- merge(model_df, era5_df, by = c("las_name", "hourofday"))
# TEST IF COLUMN-BIND LEADS TO CORRECT VALUE TO VALUE PLACEMENT (IT DOES)
# test <- cbind(era5_df, model_df)
# tester<- ifelse(test[,1]==test[,5],"Yes","No")
# any(tester== "No")
# tester<- ifelse(test[,3]==test[,4],"Yes","No")
# any(tester== "No")
model_df <- cbind(era5_df, model_df)
model_df <- model_df[,-c(1,3)]
model_df <- model_df %>% relocate(t_air_ERA5, .after = t_air)
model_df$t_air_offset <- model_df$t_air_ERA5 - model_df$t_air
model_df <- model_df %>% relocate(t_air_offset, .after = t_air_ERA5)
write.csv(model_df, "FILEPATH/Modeling/model_df_2_1.csv", row.names = F)
plot(model_df$t_air_offset ~ model_df$ENL1_mean)
abline(model)
####################
#####MODELING#######
###################
########
###what about data normalization?
###what about independence of observations, most of these
###values are pad/CR derived iirc: check for correlation
###normality?
model <- lm(t_air_offset ~ ENL1_mean, data = model_df)
summary(model)
plot(model_df$t_air_offset ~ model_df$ENL1_mean)
abline(model)
plot(model)
# ggplot(data=model_df, aes(x=RH95_mean, y=t_air_offset)) +
# geom_smooth(method="lm") +
# geom_point() +
# stat_regline_equation(label.x=30, label.y=310)
###YE OLDE DAYNIGHT EXTRACTION###
# THIS TIME WITHOUT A FUNCTION
# extract daytime
# model_df[,2] <- as.POSIXct(model_df[,2], tz = "UTC")
# model_df$hms <- hms::as_hms(model_df[,2])
# model_df_day <- model_df %>% dplyr::select(everything()) %>%
# dplyr::filter(
# hms >= hms::as_hms('05:59:00'),
# hms <= hms::as_hms('18:01:00'))
# # extract nighttime
# model_df_nighttime1 <- model_df %>% dplyr::select(everything()) %>%
# dplyr::filter(hms >= hms::as_hms('18:01:00'))
# model_df_nighttime2 <- model_df %>% dplyr::select(everything()) %>%
# dplyr::filter(hms <= hms::as_hms('05:59:00'))
# model_df_nighttime_bind <- rbind(model_df_nighttime1, model_df_nighttime2)
# model_df_night <- model_df_nighttime_bind %>% dplyr::select(everything()) %>%
# arrange(model_df_nighttime_bind$hourofday)
#
# write.csv(model_df_night, "FILEPATH/modeling/model_df_night.csv", row.names = F)
# write.csv(model_df_day, "FILEPATH/modeling/model_df_day.csv", row.names = F)
model_df_day <- read.csv("FILEPATH/modeling/model_df_day.csv")
model_df_night <- read.csv("FILEPATH/modeling/model_df_night.csv")
model_day <- lm(t_air_offset ~ ENL1_mean, data = model_df_day)
summary(model_day)
plot(model_df_day$t_air_offset ~ model_df_day$ENL1_mean,
ylim= c(-25, 15))
abline(model_day)
plot(model_day)
model_night <- lm(t_air_offset ~ ENL1_mean, data = model_df_night)
summary(model_night)
plot(model_df_night$t_air_offset ~ model_df_night$ENL1_mean,
ylim= c(-25, 15))
abline(model_night)
plot(model_night)
#############
results_df <- read.csv("FILEPATH/Modeling/corrmat/results_df.csv")
unique(results_df[,c(1,2,33,39,77)])
##################################
##########CORRMAT#################
##################################
# remove columns (trim df for cormat)
results_corrmat <- dplyr::select(results_df, -c("las_name", "hourofday",
"t_soil_min","t_soil_max","t_air_min","t_air_max",
#"t_air_mean", "t_air_med",
#"t_ERA5_mean", "t_ERA5_med",
"pad_n", "pad_min", "pad_sd", "pad_n",
"cc_class", "als_elevation",
"veg_type", "veg_class",
"RH98_mean", "RH98_med", "RH98_sd", "RH98_min", "RH98_max",
"RH75_mean", "RH75_med", "RH75_sd", "RH75_min", "RH75_max",
"CR_min", "FHD_min", "pai_min", "RH25_min", "RH50_min", "RH95_min",
"FHD2_mean", "FHD2_max", "FHD2_sd",
"ENL1_mean", "ENL1_max", "ENL1_sd",
"CR_sd", "RH25_sd", "pai_med", "pad_med", "ENL2_sd",
"pai_sd", "RH25_med", "RH50_med", "RH50_sd","RH95_med", "FHD_med",
"FHD_sd", "RH95_sd", "CR_med"))
corr_renamed <- rename(results_corrmat,
sor = t_soil_range,
air = t_air_range,
cc = cc_percent,
elev = gps_elevation,
pad_mn = pad_mean,
pad_mx = pad_max,
FHD_mn = FHD_mean,
FHD_mx = FHD_max,
CR_mn = CR_mean,
CR_mx = CR_max,
pai_mn = pai_mean,
pai_mx = pai_max,
RH25_mn = RH25_mean,
RH25_mx = RH25_max,
RH50_mn = RH50_mean,
RH50_mx = RH50_max,
RH95_mn = RH95_mean,
RH95_mx = RH95_max,
ENL2_mn = ENL2_mean,
ENL2_mx = ENL2_max
)
res_var <- corr_renamed$air
corr_renamed <- select(corr_renamed,
-c("sor", "offd",
"offn", "pad_mn", "pad_mx"))
center_scale <- function(x) {
scale(x, scale = FALSE)
}
# apply it
center_corrmat <- as.data.frame(center_scale(corr_renamed))
set.seed(123)
# Set up repeated k-fold cross-validation
train.control <- trainControl(method = "cv", number = 10)
# Train the model
step.model <- train(air ~., data = corr_renamed,
method = "leapBackward",
tuneGrid = data.frame(nvmax = 1:5),
trControl = train.control
)
step.model$results
step.model$bestTune
summary(step.model$finalModel)
coef(step.model$finalModel, 5)
##SET RESOLUTION VARIABLE AND PREDICTION VARIABLE
res_var <- corr_renamed$air
pred_var <- data.matrix(corr_renamed[, c('cc', 'elev', 'FHD_mn', 'FHD_mx',
'CR_mn', 'CR_mx', 'pai_mn', 'pai_mx',
'RH25_mn', 'RH25_mx', 'RH50_mn', 'RH50_mx',
'RH95_mn', 'RH95_mx', 'ENL2_mn', "ENL2_mx")])
##LASSO REGRESSION
cv_model <- cv.glmnet(pred_var, res_var, alpha = 1)
#find optimal lambda value that minimizes test MSE
best_lambda <- cv_model$lambda.min
best_lambda
#produce plot of test MSE by lambda value
plot(cv_model)
best_model <- glmnet(pred_var, res_var, alpha = 1, lambda = best_lambda)
coef(best_model)
best_model$dev.ratio
#MULTIPLE LINEAR REGRESSION
res_var <- corr_renamed$air
pred_var <- data.matrix(corr_renamed[, c('cc', 'elev', 'FHD_mn',
'CR_mn', 'pai_mn',
'RH25_mn', 'ENL2_mn')])
pred_var <- data.matrix(corr_renamed[, c('cc', 'elev', 'FHD_mn', 'FHD_mx',
'CR_mn', 'CR_mx', 'pai_mn', 'pai_mx',
'RH25_mn', 'RH25_mx', 'RH50_mn', 'RH50_mx',
'RH95_mn', 'RH95_mx', 'ENL2_mn', "ENL2_mx")])
#MODEL WITH ALL VARIABLES
all_var_lm <- lm(formula = air ~
elev +
cc +
CR_mn +
CR_mx +
pai_mn +
pai_mx +
FHD_mn +
FHD_mx +
ENL2_mn +
ENL2_mx +
RH25_mn +
RH25_mx +
RH50_mn +
RH50_mx +
RH95_mn +
RH95_mx
,
data = corr_renamed)
summary(all_var_lm)
vif(all_var_lm)
plot(corr_renamed$CR_mn ~ corr_renamed$pai_mn)
lm_cc <- lm(formula = offn_max ~ RH95_mean, data = results_df)
summary(lm_cc)
vif(lm_cc)
# MODEL WITH ONLY CC RS = 0.27
lm_cc <- lm(formula = air ~ elev*cc + ENL2_mn + CR_mn*pai_mn, data = corr_renamed)
summary(lm_cc)
vif(lm_cc)
lm_cc <- lm(formula = air ~ elev*cc + ENL2_mn + CR_mn*pai_mn, data = corr_renamed)
lm_cc2 <- lm(formula = air ~ elev*cc + ENL2_mn + CR_mn*pai_mn + slope, data = corr_renamed)
lm_cc3 <- lm(formula = air ~ elev*cc + ENL2_mn + CR_mn*pai_mn + slope + aspect, data = corr_renamed)
stargazer(lm_cc,lm_cc2,lm_cc3, single.row = T, column.sep.width = "0.5pt", font.size = "scriptsize")
lm_cc2 <- lm(formula = air ~ elev + ENL2_mn + CR_mn + cc*elev, data = corr_renamed)
summary(lm_cc2)
lm_cc3 <- lm(formula = air ~ elev + ENL2_mn + CR_mn*pai_mn + cc*elev, data = corr_renamed)
summary(lm_cc3)
stargazer(lm_cc3,lm_cc2,lm_cc, single.row = T, column.sep.width = "0.5pt", font.size = "scriptsize")
lm_cc1 <- lm(formula = air ~ cc + elev, data = corr_renamed)
summary(lm_cc4)
lm_cc2 <- lm(formula = air ~ ENL2_mn + elev, data = corr_renamed)
summary(lm_cc4)
lm_cc3 <- lm(formula = air ~ FHD_mn + elev, data = corr_renamed)
summary(lm_cc4)
stargazer(lm_cc1, lm_cc2, lm_cc3, single.row = T)
# MODEL WITH ALL MEAN VALUES
lm_all_var <- lm(formula = res_var ~ cc + elev + CR_mn + FHD_mn + CR_mn+elev + pai_mn + RH25_mn + ENL2_mn, data = corr_renamed)
summary(lm_all_var)
vif(lm_all_var)
# RS = 0.5 but high colinearity (coeffs dont tell much)
#MODEL WITH ALL STRUCTURAL METRICS (NO CC, NO ELEV)
lm_struc <- lm(formula = res_var ~ CR_mn + FHD_mn +pai_mn + RH25_mn + ENL2_mn, data = corr_renamed)
summary(lm_struc)
vif(lm_struc)
# RS = 0.37 but high colinearity between FHD/ENL2/PAI, RH25 is statistically insignificant
#MODEL EXCLUDING RH25 (statistically insigifnicant)
lm_struc_norh <- lm(formula = res_var ~ CR_mn + FHD_mn +pai_mn + ENL2_mn, data = corr_renamed)
summary(lm_struc_norh)
vif(lm_struc_norh)
# RH = 0.37 but high colinearity between FHD/ENL2
# MODEL USING PAI AND CR
lm_pai_cr <- lm(formula = res_var ~ elev + CR_mn + pai_mn, data = corr_renamed)
summary(lm_pai_cr)
vif(lm_pai_cr)
# RH 0.25, CR doesnt really add much, but with elevation R2 increases to 0.39
###ADDING CANOPY COVER TO THE MODEL DOESNT REALLY INCREASE R2, AFFECT COEFFICIENT (ITS NOT VERY SIGNIFICANT)
lm_cr_fhd <- lm(formula = res_var ~ elev + CR_mn + FHD_mn + FHD_mx, data = corr_renamed)
summary(lm_cr_fhd)
vif(lm_cr_fhd)
plot(lm_cr_fhd)
# mean-center and standardize variables
d2 <- data.frame(scale(corr_renamed))
lm_test <- lm(formula = air ~ CR_mn*elev + pai_mn + elev + cc*elev, data = d2)
lm_test2 <- lm(formula = air ~ CR_mn*elev + FHD_mn + elev + cc*elev, data = d2)
lm_test3 <- lm(formula = air ~ CR_mn*elev + ENL2_mn + elev + cc*elev, data = d2)
stargazer(lm_test,lm_test2,lm_test3, single.row = T, font.size = "footnotesize")
lm_plot <- lm(formula = res_var ~ cc + CR_mn + ENL2_mn + elev + cc*elev + ENL2_mn*elev, data = corr_renamed)
lm_plot2 <- lm(formula = sor ~ cc + CR_mn + ENL2_mn + elev + cc*elev + ENL2_mn*elev, data = corr_renamed)
round(coef(lm_test), 2)
summary(lm_test)
vif(lm_test)
######################################
####INTERACTION PLOTS USING BASE R####
######################################
air_temp <- plot(effect(term="ENL2_mn:elev", mod=lm_plot, xlevels=8), multiline = T, colors=colorRampPalette(c("#A89100","white","#2D6993"))(8), lwd=2, main="Air Temperature", sub="",
xlab="CC (%)", ylab="Daily Temperature Range (°C)")
air_temp
soil_temp <- plot(effect(term="cc:elev", mod=lm_plot2, xlevels=8), multiline = T, colors=colorRampPalette(c("#A89100","white","#2D6993"))(8), lwd=2, main="Soil Temperature", sub="",
xlab="CC (%)", ylab="")
plot.new()
grid(nx = 0, ny = NULL, col = "lightgray", lty = "dotted",
lwd = par("lwd"), equilogs = TRUE)
soil_temp
fig1 <- ggarrange(air_temp, soil_temp,
labels = c("", ""),
ncol = 2, nrow = 1,
align = "hv"
)
fig1
######################################
####INTERACTION PLOTS USING GGPLOT####
######################################
#########################################################
####INTERACTION PLOTS OF CC/ELEV AND CR/PAI FOR RESULTS##
#########################################################
lm_plot <- lm(formula = res_var ~ cc + ENL2_mn + elev + cc*elev + CR_mn*pai_mn, data = corr_renamed)
summary(lm_plot)
effect_ccair <- as.data.frame(effect(term="cc:elev", mod=lm_plot, xlevels=8))
ccair <- ggplot(effect_ccair) +
aes(x = cc, y = fit, color = factor(elev), group = elev) +
geom_point(aes(x = cc, y = air), data = corr_renamed, color = "gray", alpha = 0.2)+
geom_line(size = 0.5)+
geom_ribbon(aes(ymin=lower, ymax=upper, fill=factor(elev)), color = NA, alpha = 0.25)+
labs(x = "CC (%)", y = "", title = "")+
scale_color_viridis_d("Elevation\n(m.a.s.l.)\nand SE")+
scale_fill_viridis_d("Elevation\n(m.a.s.l.)\nand SE")+
ylim(c(-1,30))+
# scale_color_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
# scale_fill_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
theme_bw()+
theme(axis.text = element_text(face="bold"),
legend.position = "bottom")
lm_plot2 <- lm(formula = res_var ~ cc + ENL2_mn + elev + cc*elev + CR_mn*pai_mn, data = corr_renamed)
effect_crpai <- as.data.frame(effect(term="CR_mn:pai_mn", mod=lm_plot2, xlevels=8))
crpai <- ggplot(effect_crpai) +
aes(x = CR_mn, y = fit, color = factor(pai_mn), group = pai_mn) +
geom_point(aes(x = CR_mn, y = air), data = corr_renamed, color = "gray", alpha = 0.2)+
geom_line(size = 0.5)+
geom_ribbon(aes(ymin=lower, ymax=upper, fill=factor(pai_mn)), alpha = 0.25, color = NA)+
labs(x = "Mean CR", y = "", title = "")+
scale_color_viridis_d(expression(atop(paste("PAI", " (",m^2,m^-2,")"),
paste("and SE"))))+
scale_fill_viridis_d(expression(atop(paste("PAI"," (",m^2,m^-2,")"),
paste("and SE"))))+
ylim(c(-1,30))+
# scale_color_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
# scale_fill_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
theme_bw()+
theme(axis.text = element_text(face="bold"),
legend.position = "bottom")
fig1 <- ggarrange(ccair, crpai,
labels = c("A", "B"),
ncol = 2, nrow = 1,
align = "hv"
)
annotate_figure(
fig1,
top = text_grob("", color = "black", face = "bold", size = 14),
#bottom = text_grob("Mean PAI", color = "black"),
left = text_grob(bquote(""~DTR[a]~"- Daily air temperature range (°C)"),
color = "black", rot = 90),
#right = text_grob("Contains modified Copernicus Climate Change Service Information [2019]",
#size = 8, rot = 270, face = "italic")
# fig.lab = "Figure 1", fig.lab.face = "bold"
)
fig1
ggsave("FILEPATH/figures/interaction_plots/cc_elev_cr_pai.pdf",
width = 20,
height = 10,
units = "cm")
######################################
####INTERACTION PLOTS OF CC AND ELEV##
######################################
lm_plot <- lm(formula = res_var ~ cc*elev, data = corr_renamed)
summary(lm_plot)
effect_ccair <- as.data.frame(effect(term="cc:elev", mod=lm_plot, xlevels=8))
ccair <- ggplot(effect_ccair) +
aes(x = cc, y = fit, color = factor(elev), group = elev) +
geom_point(aes(x = cc, y = air), data = corr_renamed, color = "gray", alpha = 0.2)+
geom_line(size = 0.5)+
geom_ribbon(aes(ymin=lower, ymax=upper, fill=factor(elev)), color = NA, alpha = 0.25)+
labs(x = "CC (%)", y = "Daily Temperature Range (°C)", title = "Air Temperature")+
scale_color_viridis_d("Elevation\nm .a.s.l.")+
scale_fill_viridis_d("Elevation\nm .a.s.l.")+
ylim(c(-1,30))+
# scale_color_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
# scale_fill_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
theme_bw()+
theme(axis.text = element_text(face="bold"),
legend.position = "bottom")
legend <- get_legend(ccair)
ccair <- ggplot(effect_ccair) +
aes(x = cc, y = fit, color = factor(elev), group = elev) +
geom_point(aes(x = cc, y = air), data = corr_renamed, color = "gray", alpha = 0.2)+
geom_line(size = 0.5)+
geom_ribbon(aes(ymin=lower, ymax=upper, fill=factor(elev)), color = NA, alpha = 0.25)+
labs(x = "CC (%)", y = "Daily Temperature Range (°C)", title = "Air Temperature")+
scale_color_viridis_d("Elevation\nm .a.s.l.")+
scale_fill_viridis_d("Elevation\nm .a.s.l.")+
ylim(c(-1,30))+
# scale_color_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
# scale_fill_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
theme(axis.text = element_text(face="bold"),
legend.position = "none")+
theme_bw()
lm_plot2 <- lm(formula = sor ~ cc + CR_mn + ENL2_mn + elev + cc*elev + CR_mn*pai_mn, data = corr_renamed)
effect_ccsoil <- as.data.frame(effect(term="cc:elev", mod=lm_plot2, xlevels=8))
ccsoil <- ggplot(effect_ccsoil) +
aes(x = cc, y = fit, color = factor(elev), group = elev) +
geom_point(aes(x = cc, y = sor), data = corr_renamed, color = "gray", alpha = 0.2)+
geom_line(size = 0.5)+
geom_ribbon(aes(ymin=lower, ymax=upper, fill=factor(elev)), alpha = 0.25, color = NA)+
labs(x = "CC (%)", y = "Daily Temperature Range (°C)", title = "Soil Temperature")+
scale_color_viridis_d("Elevation\nm .a.s.l.")+
scale_fill_viridis_d("Elevation\nm .a.s.l.")+
ylim(c(-1,30))+
# scale_color_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
# scale_fill_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
theme(axis.text = element_text(face="bold"),
legend.position = "none")+
theme_bw()
fig1 <- ggarrange(ccair, ccsoil,
labels = c("", ""),
ncol = 2, nrow = 1,
align = "hv",
common.legend = T,
legend.grob = legend,
legend = "bottom"
)
fig1
ggsave("FILEPATH/figures/interaction_plots/cc_elev_complex.png",
width = 20,
height = 10,
units = "cm")
########################################
####INTERACTION PLOTS OF ENL2 AND ELEV##
########################################
lm_plot <- lm(formula = res_var ~ cc + ENL2_mn + elev + cc*elev + CR_mn*pai_mn, data = corr_renamed)
summary(lm_plot)
effect_ccair <- as.data.frame(effect(term="cc:elev", mod=lm_plot, xlevels=8))
ccair <- ggplot(effect_ccair) +
aes(x = cc, y = fit, color = factor(elev), group = elev) +
geom_point(aes(x = cc, y = air), data = corr_renamed, color = "gray", alpha = 0.2)+
geom_line(size = 0.5)+
geom_ribbon(aes(ymin=lower, ymax=upper, fill=factor(elev)), color = NA, alpha = 0.25)+
labs(x = "CC (%)", y = "", title = "")+
scale_color_viridis_d("Elevation\n(m.a.s.l.)\nand SE")+
scale_fill_viridis_d("Elevation\n(m.a.s.l.)\nand SE")+
ylim(c(-1,30))+
# scale_color_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
# scale_fill_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
theme_bw()+
theme(axis.text = element_text(face="bold"),
legend.position = "bottom")
lm_plot2 <- lm(formula = res_var ~ cc + ENL2_mn + elev + cc*elev + CR_mn*pai_mn, data = corr_renamed)
effect_crpai <- as.data.frame(effect(term="CR_mn:pai_mn", mod=lm_plot2, xlevels=8))
crpai <- ggplot(effect_crpai) +
aes(x = CR_mn, y = fit, color = factor(pai_mn), group = pai_mn) +
geom_point(aes(x = CR_mn, y = air), data = corr_renamed, color = "gray", alpha = 0.2)+
geom_line(size = 0.5)+
geom_ribbon(aes(ymin=lower, ymax=upper, fill=factor(pai_mn)), alpha = 0.25, color = NA)+
labs(x = "Mean CR", y = "", title = "")+
scale_color_viridis_d(expression(atop(paste("PAI", " (",m^2,m^-2,")"),
paste("and SE"))))+
scale_fill_viridis_d(expression(atop(paste("PAI"," (",m^2,m^-2,")"),
paste("and SE"))))+
ylim(c(-1,30))+
# scale_color_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
# scale_fill_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
theme_bw()+
theme(axis.text = element_text(face="bold"),
legend.position = "bottom")
lm_plot <- lm(formula = res_var ~ ENL2_mn*elev + cc*elev + CR_mn*pai_mn+ slope, data = corr_renamed)
effect_ENLair <- as.data.frame(effect(term="ENL2_mn:elev", mod=lm_plot, xlevels=8))
ENLair <- ggplot(effect_ENLair) +
aes(x = ENL2_mn, y = fit, color = factor(elev), group = elev) +
geom_point(aes(x = ENL2_mn, y = air), data = corr_renamed, color = "gray", alpha = 0.2)+
geom_line(size = 0.5)+
geom_ribbon(aes(ymin=lower, ymax=upper, fill=factor(elev)), color = NA, alpha = 0.25)+
labs(x = "Mean ENL2", y = "", title = "")+
scale_color_viridis_d("Elevation\n(m.a.s.l.)\nand SE")+
scale_fill_viridis_d("Elevation\n(m.a.s.l.)\nand SE")+
ylim(c(-1,30))+
# scale_color_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
# scale_fill_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
theme_bw()+
theme(axis.text = element_text(face="bold"),
legend.position = "bottom")
ENLair
legend <- get_legend(ENLair)
ENLair <- ggplot(effect_ENLair) +
aes(x = ENL2_mn, y = fit, color = factor(elev), group = elev) +
geom_point(aes(x = ENL2_mn, y = air), data = corr_renamed, color = "gray", alpha = 0.2)+
geom_line(size = 0.5)+
geom_ribbon(aes(ymin=lower, ymax=upper, fill=factor(elev)), color = NA, alpha = 0.25)+
labs(x = "Mean ENL2", y = "", title = "")+
scale_color_viridis_d("Elevation\n(m.a.s.l.)\nand SE")+
scale_fill_viridis_d("Elevation\n(m.a.s.l.)\nand SE")+
ylim(c(-1,30))+
# scale_color_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
# scale_fill_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
theme(axis.text = element_text(face="bold"),
legend.position = "none")+
theme_bw()
lm_plot2 <- lm(formula = air ~ FHD_mn + FHD_mn*elev + cc + elev + slope , data = corr_renamed)
summary(lm_plot2)
effect_ENLsoil <- as.data.frame(effect(term="FHD_mn:elev", mod=lm_plot2, xlevels=8))
ENLsoil <- ggplot(effect_ENLsoil) +
aes(x = FHD_mn, y = fit, color = factor(elev), group = elev) +
geom_point(aes(x = FHD_mn, y = air), data = corr_renamed, color = "gray", alpha = 0.2)+
geom_line(size = 0.5)+
geom_ribbon(aes(ymin=lower, ymax=upper, fill=factor(elev)), alpha = 0.25, color = NA)+
labs(x = "Mean FHD", y = "", title = "")+
scale_color_viridis_d("Elevation\n(m.a.s.l.)\nand SE")+
scale_fill_viridis_d("Elevation\n(m.a.s.l.)\nand SE")+
ylim(c(-1,30))+
# scale_color_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
# scale_fill_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
theme(axis.text = element_text(face="bold"),
legend.position = "none")+
theme_bw()
ENLsoil
fig1 <- ggarrange(ccair, crpai,
labels = c("A", "B"),
ncol = 2, nrow = 1,
align = "hv"
)
fig1
fig2 <- ggarrange(ENLair, ENLsoil,
labels = c("C", "D"),
ncol = 2, nrow = 1,
align = "hv",
common.legend = T,
legend.grob = legend,
legend = "bottom"
)
fig2
fig3 <- ggarrange(fig1, fig2,
labels = c("", ""),
ncol = 1, nrow = 2,
align = "hv"
)
fig3
annotate_figure(
fig3,
top = text_grob("", color = "black", face = "bold", size = 14),
#bottom = text_grob("Mean PAI", color = "black"),
left = text_grob(bquote(""~DTR[a]~"- Daily air temperature range (°C)"),
color = "black", rot = 90),
#right = text_grob("Contains modified Copernicus Climate Change Service Information [2019]",
#size = 8, rot = 270, face = "italic")
# fig.lab = "Figure 1", fig.lab.face = "bold"
)
fig1
ggsave("FILEPATH/figures/interaction_plots/interactions.pdf",
width = 20,
height = 20,
units = "cm")
ggsave("FILEPATH/figures/interaction_plots/ENL_FHD_elev_complex_v2.pdf",
width = 20,
height = 10,
units = "cm")
########################################
####INTERACTION PLOTS OF FHD AND ELEV##
########################################
lm_plot <- lm(formula = air ~ cc + FHD_mn + CR_mn + elev + FHD_mn*elev, data = corr_renamed)
summary(lm_plot)
effect_FHDair <- as.data.frame(effect(term="FHD_mn:elev", mod=lm_plot, xlevels=8))
FHDair <- ggplot(effect_FHDair) +
aes(x = FHD_mn, y = fit, color = factor(elev), group = elev) +
geom_point(aes(x = FHD_mn, y = air), data = corr_renamed, color = "gray", alpha = 0.2)+
geom_line(size = 0.5)+
geom_ribbon(aes(ymin=lower, ymax=upper, fill=factor(elev)), color = NA, alpha = 0.25)+
labs(x = "FHD", y = "Daily Temperature Range (°C)", title = "Air Temperature")+
scale_color_viridis_d("Elevation\nm .a.s.l.")+
scale_fill_viridis_d("Elevation\nm .a.s.l.")+
ylim(c(-1,30))+
# scale_color_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
# scale_fill_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
theme_bw()+
theme(axis.text = element_text(face="bold"),
legend.position = "bottom")
FHDair
legend <- get_legend(FHDair)
FHDair <- ggplot(effect_FHDair) +
aes(x = FHD_mn, y = fit, color = factor(elev), group = elev) +
geom_point(aes(x = FHD_mn, y = air), data = corr_renamed, color = "gray", alpha = 0.2)+
geom_line(size = 0.5)+
geom_ribbon(aes(ymin=lower, ymax=upper, fill=factor(elev)), color = NA, alpha = 0.25)+
labs(x = "FHD", y = "Daily Temperature Range (°C)", title = "Air Temperature")+
scale_color_viridis_d("Elevation\nm .a.s.l.")+
scale_fill_viridis_d("Elevation\nm .a.s.l.")+
ylim(c(-1,30))+
# scale_color_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
# scale_fill_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
theme_bw()+
theme(axis.text = element_text(face="bold"),
legend.position = "none")
lm_plot2 <- lm(formula = sor ~ cc + FHD_mn + CR_mn + elev + cc*elev + FHD_mn*elev, data = corr_renamed)
effect_FHDsoil <- as.data.frame(effect(term="FHD_mn:elev", mod=lm_plot2, xlevels=8))
FHDsoil <- ggplot(effect_FHDsoil) +
aes(x = FHD_mn, y = fit, color = factor(elev), group = elev) +
geom_point(aes(x = FHD_mn, y = sor), data = corr_renamed, color = "gray", alpha = 0.2)+
geom_line(size = 0.5)+
geom_ribbon(aes(ymin=lower, ymax=upper, fill=factor(elev)), alpha = 0.25, color = NA)+
labs(x = "FHD", y = "Daily Temperature Range (°C)", title = "Soil Temperature")+
scale_color_viridis_d("Elevation\nm .a.s.l.")+
scale_fill_viridis_d("Elevation\nm .a.s.l.")+
ylim(c(-1,30))+
# scale_color_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
# scale_fill_manual(values = colorRampPalette(c("#A89100","white","#2D6993"))(8))+
theme(axis.text = element_text(face="bold"),
legend.position = "none")+
theme_bw()
fig1 <- ggarrange(FHDair, FHDsoil,
labels = c("", ""),
ncol = 2, nrow = 1,
align = "hv",
common.legend = T,
legend.grob = legend,
legend = "bottom"
)
fig1
ggsave("FILEPATH/figures/interaction_plots/FHD_elev_complex.pdf",
width = 20,
height = 10,
units = "cm")
plot(x = corr_renamed$FHD_mn, y = corr_renamed$cc)
#########################
####TAKEAWAY FOR TODAY:
#THE INTERACTION PLOTS CHANGE STRONGLY IN MORE COMPLEX MODELS VERSUS SIMPLE LINEAR MODELS WITH ELEVATION
#AL DIFFERENCES? SEE CHAT WITH IRIS
#ADDING AN INTERACTION TERM FOR A SECOND PREDICTOR POSSIBLE?
#WHAT DOES THAT MEAN FOR THE MODELS?
###########OFFSET
lm_cc <- lm(formula = offd_min ~ ENL2_mn + elev, data = corr_renamed)
summary(lm_cc)
vif(lm_cc)