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About This Analysis: The goal of this analysis is to fit a Linear Mixed Effects Model to the cleaned and filtered data set.
data <- read.csv("data/screening_data/filtered_screening_long.csv")library(ggstatsplot)
library(ggplot2)
library(ggeffects)
library(dplyr)
library(viridis)
library(lme4)data$collection_period <- factor(data$collection_period, levels = c("Summer", "Winter", "6Weeks"))
data$reflectance_metric <- as.factor(data$reflectance_metric)model <- lmer(reflectance_value ~ collection_period * reflectance_metric + (1 | participant_centre_id), data = data)
# Summarize the model
summary(model)Linear mixed model fit by REML ['lmerMod']
Formula: reflectance_value ~ collection_period * reflectance_metric +
(1 | participant_centre_id)
Data: data
REML criterion at convergence: 121836.4
Scaled residuals:
Min 1Q Median 3Q Max
-7.1287 -0.4258 0.0110 0.3332 16.0660
Random effects:
Groups Name Variance Std.Dev.
participant_centre_id (Intercept) 37.92 6.158
Residual 148.43 12.183
Number of obs: 15521, groups: participant_centre_id, 103
Fixed effects:
Estimate Std. Error t value
(Intercept) 12.95029 0.92115 14.059
collection_periodWinter -0.05200 1.03620 -0.050
collection_period6Weeks 3.19209 1.44957 2.202
reflectance_metrica_2 0.01120 0.98017 0.011
reflectance_metrica_3 0.04799 0.98017 0.049
reflectance_metricb_1 -0.57414 0.98017 -0.586
reflectance_metricb_2 -0.79777 0.98017 -0.814
reflectance_metricb_3 -0.73641 0.98017 -0.751
reflectance_metricb1 25.06414 0.99739 25.130
reflectance_metricb2 25.57189 0.99646 25.663
reflectance_metricb3 25.65120 0.99646 25.742
reflectance_metrice1 5.07537 0.98017 5.178
reflectance_metrice2 5.17583 0.98017 5.281
reflectance_metrice3 5.40709 0.98017 5.516
reflectance_metricg1 29.76310 0.99739 29.841
reflectance_metricg2 30.18223 0.99646 30.289
reflectance_metricg3 30.33051 0.99646 30.438
reflectance_metricl_1 9.48605 0.98017 9.678
reflectance_metricl_2 9.36401 0.98017 9.553
reflectance_metricl_3 9.46256 0.98017 9.654
reflectance_metricm1 54.40453 0.98017 55.505
reflectance_metricm2 55.50084 0.98017 56.624
reflectance_metricm3 54.87540 0.98017 55.985
reflectance_metricr1 51.62816 0.99739 51.763
reflectance_metricr2 51.95809 0.99646 52.142
reflectance_metricr3 52.17189 0.99646 52.357
collection_periodWinter:reflectance_metrica_2 -0.17449 1.46246 -0.119
collection_period6Weeks:reflectance_metrica_2 0.18117 2.03786 0.089
collection_periodWinter:reflectance_metrica_3 -0.12355 1.46246 -0.084
collection_period6Weeks:reflectance_metrica_3 0.12846 2.03786 0.063
collection_periodWinter:reflectance_metricb_1 -0.86125 1.46246 -0.589
collection_period6Weeks:reflectance_metricb_1 -1.85521 2.03786 -0.910
collection_periodWinter:reflectance_metricb_2 -0.59588 1.46246 -0.407
collection_period6Weeks:reflectance_metricb_2 -1.50458 2.04213 -0.737
collection_periodWinter:reflectance_metricb_3 -0.62510 1.46246 -0.427
collection_period6Weeks:reflectance_metricb_3 -1.52435 2.03786 -0.748
collection_periodWinter:reflectance_metricb1 1.54094 1.47405 1.045
collection_period6Weeks:reflectance_metricb1 -8.06726 2.04620 -3.943
collection_periodWinter:reflectance_metricb2 0.93399 1.47343 0.634
collection_period6Weeks:reflectance_metricb2 -8.31694 2.04575 -4.065
collection_periodWinter:reflectance_metricb3 1.69595 1.47343 1.151
collection_period6Weeks:reflectance_metricb3 -7.64356 2.04575 -3.736
collection_periodWinter:reflectance_metrice1 -0.92117 1.46246 -0.630
collection_period6Weeks:reflectance_metrice1 2.39699 2.03786 1.176
collection_periodWinter:reflectance_metrice2 -1.11519 1.46246 -0.763
collection_period6Weeks:reflectance_metrice2 2.79514 2.03786 1.372
collection_periodWinter:reflectance_metrice3 -1.46356 1.46246 -1.001
collection_period6Weeks:reflectance_metrice3 1.93302 2.03786 0.949
collection_periodWinter:reflectance_metricg1 1.91340 1.47405 1.298
collection_period6Weeks:reflectance_metricg1 -9.16407 2.04620 -4.479
collection_periodWinter:reflectance_metricg2 1.44666 1.47343 0.982
collection_period6Weeks:reflectance_metricg2 -9.46492 2.04575 -4.627
collection_periodWinter:reflectance_metricg3 2.15553 1.47343 1.463
collection_period6Weeks:reflectance_metricg3 -8.77448 2.04575 -4.289
collection_periodWinter:reflectance_metricl_1 -4.20196 1.46246 -2.873
collection_period6Weeks:reflectance_metricl_1 -8.44390 2.03786 -4.144
collection_periodWinter:reflectance_metricl_2 -4.15508 1.46246 -2.841
collection_period6Weeks:reflectance_metricl_2 -8.22778 2.03786 -4.037
collection_periodWinter:reflectance_metricl_3 -3.89141 1.46246 -2.661
collection_period6Weeks:reflectance_metricl_3 -7.92976 2.03786 -3.891
collection_periodWinter:reflectance_metricm1 -9.07104 1.46246 -6.203
collection_period6Weeks:reflectance_metricm1 4.70386 2.03786 2.308
collection_periodWinter:reflectance_metricm2 -10.23612 1.46246 -6.999
collection_period6Weeks:reflectance_metricm2 3.48206 2.03786 1.709
collection_periodWinter:reflectance_metricm3 -10.26160 1.46246 -7.017
collection_period6Weeks:reflectance_metricm3 3.11169 2.03786 1.527
collection_periodWinter:reflectance_metricr1 2.38168 1.47405 1.616
collection_period6Weeks:reflectance_metricr1 -12.94310 2.04620 -6.325
collection_periodWinter:reflectance_metricr2 1.79381 1.47343 1.217
collection_period6Weeks:reflectance_metricr2 -12.77841 2.04575 -6.246
collection_periodWinter:reflectance_metricr3 2.60780 1.47343 1.770
collection_period6Weeks:reflectance_metricr3 -12.14275 2.04575 -5.936
Correlation matrix not shown by default, as p = 72 > 12.
Use print(x, correlation=TRUE) or
vcov(x) if you need itpredicted_values <- ggpredict(model, terms = c("collection_period", "reflectance_metric"))
# Ensure all levels of collection_period are included in the predictions
predicted_values$x <- factor(predicted_values$x, levels = c("Summer", "Winter", "6Weeks"))# Define the custom color palette
color_palette <- c(
"e1" = "#FF9AAB", # Light Pink
"e2" = "#FF6F91", # Pink
"e3" = "#D9385A", # Deep Pink
"m1" = "#FFD5A1", # Light Tan
"m2" = "#C89A5B", # Tan
"m3" = "#7D4F26", # Dark Brown
"r1" = "#FFB2B2", # Light Coral
"r2" = "#FF6B6B", # Red
"r3" = "#D52B2B", # Dark Red
"g1" = "#B2FFB2", # Light Green
"g2" = "#6BFF6B", # Green
"g3" = "#2B5D2B", # Dark Green
"b1" = "#A7C6E7", # Light Blue
"b2" = "#3A8EDB", # Blue
"b3" = "#1A3F78", # Dark Blue
"l_1" = "#D3D3D3", # Light Grey
"l_2" = "#A9A9A9", # Grey
"l_3" = "#7B7B7B", # Dark Grey
"a_1" = "#FFEB91", # Light Yellow
"a_2" = "#FFD300", # Yellow
"a_3" = "#A57B00", # Goldenrod
"b_1" = "#E0BBFF", # Lavender
"b_2" = "#A35CC3", # Purple
"b_3" = "#5E1B94" # Dark Violet
)
p <- ggplot(predicted_values, aes(x = x, y = predicted, group = group, color = group)) +
geom_line(size = 1) +
geom_point(size = 2) +
labs(title = "Predicted Reflectance Value by Collection Period and Reflectance Metric",
x = "Collection Period",
y = "Predicted Reflectance Value") +
scale_color_manual(values = color_palette, name = "Reflectance Metric") +
theme_minimal()Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.ggsave("output/predicted_reflectance_values.pdf", plot = p, width = 8, height = 6, device = "pdf")
sessionInfo()R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.6.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/Detroit
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lme4_1.1-35.5 Matrix_1.7-0 viridis_0.6.5 viridisLite_0.4.2
[5] dplyr_1.1.4 ggeffects_1.7.1 ggplot2_3.5.1 ggstatsplot_0.12.4
loaded via a namespace (and not attached):
[1] gtable_0.3.5 xfun_0.47 bslib_0.8.0
[4] bayestestR_0.14.0 insight_0.20.5 lattice_0.22-6
[7] paletteer_1.6.0 vctrs_0.6.5 tools_4.4.1
[10] generics_0.1.3 datawizard_0.13.0 tibble_3.2.1
[13] fansi_1.0.6 pkgconfig_2.0.3 correlation_0.8.5
[16] lifecycle_1.0.4 compiler_4.4.1 farver_2.1.2
[19] stringr_1.5.1 git2r_0.33.0 textshaping_0.4.0
[22] munsell_0.5.1 httpuv_1.6.15 htmltools_0.5.8.1
[25] sass_0.4.9 yaml_2.3.10 crayon_1.5.3
[28] nloptr_2.1.1 later_1.3.2 pillar_1.9.0
[31] jquerylib_0.1.4 whisker_0.4.1 MASS_7.3-60.2
[34] cachem_1.1.0 statsExpressions_1.6.0 boot_1.3-30
[37] nlme_3.1-164 tidyselect_1.2.1 digest_0.6.37
[40] mvtnorm_1.3-1 stringi_1.8.4 purrr_1.0.2
[43] rematch2_2.1.2 labeling_0.4.3 forcats_1.0.0
[46] splines_4.4.1 rprojroot_2.0.4 fastmap_1.2.0
[49] grid_4.4.1 colorspace_2.1-1 cli_3.6.3
[52] magrittr_2.0.3 patchwork_1.3.0 utf8_1.2.4
[55] withr_3.0.1 scales_1.3.0 promises_1.3.0
[58] estimability_1.5.1 rmarkdown_2.28 emmeans_1.10.4
[61] gridExtra_2.3 workflowr_1.7.1 ragg_1.3.3
[64] hms_1.1.3 coda_0.19-4.1 evaluate_1.0.0
[67] haven_2.5.4 knitr_1.48 parameters_0.22.2
[70] rlang_1.1.4 Rcpp_1.0.13 zeallot_0.1.0
[73] xtable_1.8-4 glue_1.7.0 minqa_1.2.8
[76] rstudioapi_0.16.0 jsonlite_1.8.9 effectsize_0.8.9
[79] R6_2.5.1 systemfonts_1.1.0 fs_1.6.4