library(tidyverse)
library(ggpubr)
library(ggtext)
library(scales)
library(lme4)
library(afex)
library(emmeans)
library(gridExtra)CFUs
Data Input and Selection
File Paths
# Folder paths
input_path <- "data/input_data/CFUs/"
metadata_path <- "data/metadata/CFUs"
# Create subfolders for output files
dataframes_folder <- "data/dataframes"
if (!file.exists("data/dataframes")) {
dir.create("data/dataframes", recursive = TRUE)
}
# Load data and metadata
input_data <- read_csv(file.path(input_path, "HNOBac_CFUs_0130_2024.csv")) %>% mutate_if(is.character, factor)
input_data$Time <- as.factor(input_data$Time)
Bac_order <- read_csv(file.path(metadata_path, "Order_Bacteria_CFUs.csv"))Data clean-up
# Setting zero values to the limit of detection
CFU_data <- input_data %>%
mutate(
LOD = CFUs == 0,
NewCFU = ifelse(LOD, 3.75, CFUs)
)
# Factor Ordering and Styling
CFU_data <- CFU_data %>%
mutate(Combined = interaction(bacteria, Time))
CFU_data <- merge(CFU_data, Bac_order, by = "Combined")
CFU_data$bacteria_label <- factor(CFU_data$bacteria_label, levels = Bac_order$bacteria_label)
CFU_data$Line <- fct_recode(CFU_data$Line, "HNO918" = "A", "HNO204" = "B", "HNO919" = "C") Saving files
# Save data frame as CSV files in the output folder
write_csv(CFU_data, file.path(dataframes_folder, "CFU_values.csv"))
# Save data frame as R objects in the output folder
saveRDS(CFU_data, file.path(dataframes_folder, "CFU_values.rds"))
# Cleaning-up all objects from the environment
rm(list = ls())
# Use this to read the final objects
CFU_data <- readRDS("data/dataframes/CFU_values.rds")Stats and Plots
File Paths
# Folder paths
dataframes_path <- "data/dataframes"
metadata_path <- "data/metadata/CFUs"
# Create subfolders for output files
figures_folder <- "data/outputs/CFUs/figures"
if (!file.exists("data/outputs/CFUs/figures")) {
dir.create("data/outputs/CFUs/figures", recursive = TRUE)
}
stats_folder <- "data/outputs/CFUs/stats"
if (!file.exists("data/outputs/CFUs/stats")) {
dir.create("data/outputs/CFUs/stats", recursive = TRUE)
}
# Load data and metadata
CFU_data <- readRDS("data/dataframes/CFU_values.rds")
Bac_order <- read_csv(file.path(metadata_path, "Order_Bacteria_CFUs.csv"))Function for each temp condition
# Function to analyze each temp condition
analysis_function <- function(data, each_temp, cutoff_pvalue, cutoff_FC) {
# Subset the data to the selected temp
data_subset <- data %>%
filter(Temp == each_temp)
# Remove time 0h from the stats
data_stats <- data_subset %>%
filter(Time != 0)
# Mixed-effects model with random effects
model <- lmer(log(NewCFU) ~ bacteria * Time
+ (1|Line) + (1|Line:Date),
data = data_stats)
#Anova
anova <- anova(model)
anova_df <- as.data.frame(anova) %>%
mutate(sign = case_when(
`Pr(>F)` < cutoff_pvalue ~ "*",
TRUE ~ "")) %>%
mutate_if(is.numeric, ~ format(., digits = 2, scientific = TRUE))
# Calculate Individual contrasts
emmeans_model <- emmeans(model, ~ bacteria * Time)
emmeans_time <- pairs(emmeans_model, simple = "Time", adjust = "none")
# Extract random effects and convert to dataframe (if not singular)
random_effects_df <- as.data.frame(VarCorr(model)) %>%
mutate(proportion = round(100 * (vcov / sum(vcov)), 2))
# Adds predictions based on fixed effects, averaged over random effects. It gives a population estimate
data_stats <- cbind(data_stats, predval = predict(model,re.form = NA, se.fit = TRUE))
data_summary_df <- data_stats %>%
group_by(Combined, bacteria_label) %>%
summarize(mean.real = mean(NewCFU),
mean.predval = mean(predval.fit),
mean.predval.se = mean(predval.se.fit)) %>%
mutate(exp.mean.predval = exp(mean.predval),
max = exp(mean.predval + 2*mean.predval.se),
min = exp(mean.predval - 2*mean.predval.se))
# Extract time contrasts from emmeans_model, convert to dataframe and adjust pvalues
contrasts_time_df <- as.data.frame(summary(emmeans_time)) %>%
mutate(Temp = each_temp) %>%
mutate(p.adj.holm = p.adjust(p.value, method = "holm")) %>%
mutate(sign = case_when(
p.adj.holm < cutoff_pvalue ~ "*",
TRUE ~ ""))
# Edits to the contrast dataframe to include pvalue brackets in plot
contrasts_time_df <- contrasts_time_df %>%
separate(contrast, into = c("Time1", "Time2"), sep = " - ") %>%
mutate(Time1 = sub("Time", "", Time1),
Time2 = sub("Time", "", Time2)) %>%
mutate(condition1 = paste(bacteria, Time1, sep = "."),
condition2 = paste(bacteria, Time2, sep = "."))
contrasts_time_df$group1 <- Bac_order$bacteria_label[match(contrasts_time_df$condition1, Bac_order$Combined)]
contrasts_time_df$group2 <- Bac_order$bacteria_label[match(contrasts_time_df$condition2, Bac_order$Combined)]
# Calculate fold-change values for each contrast
contrasts_time_df <- contrasts_time_df %>%
ungroup() %>%
left_join(select(data_summary_df, Combined, exp.mean.predval), by = join_by(condition1 == Combined)) %>%
left_join(select(data_summary_df, Combined, exp.mean.predval), by = join_by(condition2 == Combined), suffix = c(".1", ".2")) %>%
mutate(FC = exp.mean.predval.1 / exp.mean.predval.2,
FC = if_else(FC < 1, -1 / FC, FC),
highlighted = case_when(
FC <= -cutoff_FC ~ "+",
FC >= cutoff_FC ~ "-",
TRUE ~ ""))
# Select p values to plot and define their location
contrast_sign <- contrasts_time_df %>%
filter(sign != "" & highlighted != "") %>%
mutate(p.adj.holm = format(p.adj.holm, digits = 2, scientific = TRUE))
location <- log10(max(data_subset$NewCFU, na.rm = TRUE)) * 1.1
# Standard Boxplot
plot_1 <- ggplot() +
geom_boxplot(data = data_subset, aes(x = bacteria_label, y = NewCFU, fill = bacteria_label)) +
geom_jitter(data = data_subset, aes(x = bacteria_label, y = NewCFU, shape = Line),
fill = "grey50", color = "grey30", size = 2, width = 0.05, stroke = 0.75) +
scale_fill_manual(values = c("#AA35E3","#AA35E3","#AA35E3",
"#2e67f2","#2e67f2","#2e67f2",
"#927ed1","#927ed1","#927ed1")) +
scale_shape_manual(values = c(21, 22, 24)) +
scale_y_log10(breaks = trans_breaks("log10", function(x) 10^x),
labels = trans_format("log10", math_format(10^.x))) +
labs(x = "Hours post-inoculation",
y = paste0("CFUs/HNO at ", each_temp, " °C")) +
theme_bw() +
theme(panel.grid = element_blank(),
legend.position = "none",
text = element_text(size = 20),
axis.text.x = element_markdown(),
axis.text.y = element_text(color = "black"))
# Plot with predicted means and standard errors of the estimates
plot_2 <- ggplot() +
geom_point(data = data_subset,
aes(x = bacteria_label, y = NewCFU, fill = bacteria_label, color = bacteria_label, shape = Line, group = Line),
position = position_jitterdodge(dodge.width = 0.7, jitter.width = 0.2),
size = 3, alpha = 0.75, stroke = 0.75) +
geom_point(data = data_summary_df, aes(x = bacteria_label, y = exp.mean.predval), shape = 3, size = 3) +
geom_errorbar(data = data_summary_df, aes(x = bacteria_label,
y = exp.mean.predval,
ymax = max,
ymin = min),
width = 0.5) +
scale_fill_manual(values = c("#AA35E3","#AA35E3","#AA35E3","#2e67f2","#2e67f2","#2e67f2","#927ed1","#927ed1","#927ed1")) +
scale_color_manual(values = c("#AA35E3","#AA35E3","#AA35E3","#2e67f2","#2e67f2","#2e67f2","#927ed1","#927ed1","#927ed1")) +
scale_shape_manual(values = c(21, 22, 24)) +
scale_y_log10(breaks = trans_breaks("log10", function(x) 10^x),
labels = trans_format("log10", math_format(10^.x)),
expand = c(0.1,0)) +
labs(x = "Hours post-inoculation",
y = paste0("CFUs/HNO at ", each_temp, " °C")) +
theme_bw() +
theme(panel.grid = element_blank(),
legend.position = "none",
text = element_text(size = 20),
axis.text.x = element_markdown(),
axis.text.y = element_text(color = "black"))
# Conditionally add p-value annotations layer
if (nrow(contrast_sign) > 0) {
plot_2 <- plot_2 +
stat_pvalue_manual(contrast_sign, label = "p.adj.holm", y.position = location,
tip.length = 0.02, bracket.shorten = 0.2, vjust = -0.2, bracket.size = 0.3, size = 4.5)
} else {
plot_2 <- plot_2
}
# Arrange plot and tables for summary pdf
table <- contrasts_time_df %>%
select(condition1, condition2, p.adj.holm, sign, exp.mean.predval.1, exp.mean.predval.2, FC, highlighted) %>%
mutate(p.adj.holm = format(p.adj.holm, digits = 2, scientific = TRUE))
Tmin <- ttheme_minimal()
panel <- ggarrange(plot_1 + theme(plot.margin = unit(c(0.25,0.25,0.25,0.25), "in")),
plot_2 + theme(plot.margin = unit(c(0.25,0.25,0.25,0.25), "in")),
tableGrob(anova_df, theme = Tmin),
tableGrob(random_effects_df, theme = Tmin, rows = NULL),
tableGrob(table, theme = Tmin, rows = NULL),
ncol = 1, heights = c(0.7, 0.7, 0.2, 0.2, 0.2),
labels = c(" Standard Boxplot ", "Predicted Mean ± 2*SE", " Anova ", "Random Effects", " Contrasts "))
panel <- annotate_figure(panel, top = text_grob(
paste0("Analysis for ", each_temp, "C. P-value: ", cutoff_pvalue, " and FC: ", cutoff_FC),
face = "bold", size = 14, color = "red"))
# Save files
ggsave(panel, filename = paste0(figures_folder, "/summaryCFU_", each_temp, ".pdf"), width = 10, height = 15)
ggsave(plot_2, filename = paste0(figures_folder, "/plotCFU_", each_temp, ".png"), width = 7, height = 6)
saveRDS(plot_2, file.path(figures_folder, paste0("plotCFU_", each_temp, ".rds")))
write_csv(anova_df, file.path(stats_folder, paste0("anova_", each_temp, ".csv")))
write_csv(random_effects_df, file.path(stats_folder, paste0("stats_random_effects_", each_temp, ".csv")))
write_csv(contrasts_time_df, file.path(stats_folder, paste0("stats_contrasts_", each_temp, ".csv")))
write_csv(data_summary_df, file.path(stats_folder, paste0("stats_summary_", each_temp, ".csv")))
return(list(
anova = anova_df,
random_effects = random_effects_df,
contrasts_time = contrasts_time_df,
data_summary = data_summary_df,
data_stats = data_stats,
plot_1 = plot_1,
plot_2 = plot_2,
panel = panel
))
}Apply to each temp
Main Data: 34C
analysis_function(CFU_data, each_temp = "34", cutoff_pvalue = 0.05, cutoff_FC = 1)$anova
Sum Sq Mean Sq NumDF DenDF F value Pr(>F) sign
bacteria 2.2e+02 1.1e+02 2 8.4e+01 1.0e+01 1.3e-04 *
Time 1.2e+00 1.2e+00 1 7.4e+01 1.1e-01 7.4e-01
bacteria:Time 4.6e+02 2.3e+02 2 7.4e+01 2.1e+01 4.9e-08 *
$random_effects
grp var1 var2 vcov sdcor proportion
1 Line:Date (Intercept) <NA> 6.414043 2.532596 37.19
2 Line (Intercept) <NA> 0.000000 0.000000 0.00
3 Residual <NA> <NA> 10.831722 3.291158 62.81
$contrasts_time
Time1 Time2 bacteria estimate SE df t.ratio p.value Temp
1 24 48 Dpi 2.807217 1.097053 73.38104 2.558872 1.256251e-02 34
2 24 48 Sau -5.797778 1.128858 73.38104 -5.135968 2.230214e-06 34
3 24 48 Spn 3.651203 1.163600 73.38104 3.137850 2.450515e-03 34
p.adj.holm sign condition1 condition2
1 1.256251e-02 * Dpi.24 Dpi.48
2 6.690641e-06 * Sau.24 Sau.48
3 4.901030e-03 * Spn.24 Spn.48
group1
1 <b><span style='color:#0443DC;'>24</span></b>
2 <b><span style='color:#6D05A0;'>24</span></b>
3 <b><span style='color:#927ED1;'>24</span></b>
group2 exp.mean.predval.1
1 <b><span style='color:#0443DC;'>48</span></b> 54171.03
2 <b><span style='color:#6D05A0;'>48</span></b> 28289.45
3 <b><span style='color:#927ED1;'>48</span></b> 1216148.62
exp.mean.predval.2 FC highlighted
1 3270.455 16.56376 -
2 9323251.362 -329.56637 +
3 31571.076 38.52097 -
$data_summary
# A tibble: 6 × 8
# Groups: Combined [6]
Combined bacteria_label mean.real mean.predval mean.predval.se
<fct> <fct> <dbl> <dbl> <dbl>
1 Dpi.24 <b><span style='color:#0443DC… 3.81e5 10.9 0.951
2 Sau.24 <b><span style='color:#6D05A0… 4.42e6 10.3 0.973
3 Spn.24 <b><span style='color:#927ED1… 1.04e7 14.0 0.998
4 Dpi.48 <b><span style='color:#0443DC… 8.63e4 8.09 0.951
5 Sau.48 <b><span style='color:#6D05A0… 1.36e8 16.0 0.973
6 Spn.48 <b><span style='color:#927ED1… 1.34e7 10.4 0.998
# ℹ 3 more variables: exp.mean.predval <dbl>, max <dbl>, min <dbl>
$data_stats
Combined Date Line Temp Time bacteria Strain CFUs LOD
1 Dpi.24 2/14/23 HNO919 34 24 Dpi 3065 1.31250e+06 FALSE
2 Dpi.24 2/7/23 HNO204 34 24 Dpi 3065 1.98750e+03 FALSE
3 Dpi.24 9/7/22 HNO918 34 24 Dpi 3065 6.26250e+05 FALSE
4 Dpi.24 11/2/22 HNO919 34 24 Dpi 3065 1.01250e+03 FALSE
5 Dpi.24 8/16/22 HNO919 34 24 Dpi 3065 9.11250e+04 FALSE
6 Dpi.24 1/17/23 HNO204 34 24 Dpi 3065 1.05000e+03 FALSE
7 Dpi.24 8/9/22 HNO918 34 24 Dpi 3065 8.25000e+05 FALSE
8 Dpi.24 8/10/22 HNO204 34 24 Dpi 3065 4.83750e+05 FALSE
9 Dpi.24 9/14/22 HNO919 34 24 Dpi 3065 5.13750e+04 FALSE
10 Dpi.24 8/23/22 HNO204 34 24 Dpi 3065 1.91250e+06 FALSE
11 Dpi.24 11/1/22 HNO919 34 24 Dpi 3065 4.12500e+05 FALSE
12 Dpi.24 8/31/22 HNO204 34 24 Dpi 3065 1.46250e+04 FALSE
13 Dpi.24 1/24/23 HNO919 34 24 Dpi 3065 9.00000e+05 FALSE
14 Dpi.24 2/28/23 HNO204 34 24 Dpi 3065 8.25000e+03 FALSE
15 Dpi.24 8/30/22 HNO918 34 24 Dpi 3065 2.36250e+03 FALSE
16 Dpi.24 8/17/22 HNO918 34 24 Dpi 3065 3.30000e+04 FALSE
17 Dpi.24 9/13/22 HNO204 34 24 Dpi 3065 9.60000e+01 FALSE
18 Dpi.24 9/6/22 HNO919 34 24 Dpi 3065 1.83750e+05 FALSE
19 Dpi.48 2/14/23 HNO919 34 48 Dpi 3065 2.77500e+04 FALSE
20 Dpi.48 9/7/22 HNO918 34 48 Dpi 3065 3.78000e+02 FALSE
21 Dpi.48 8/16/22 HNO919 34 48 Dpi 3065 7.80000e+03 FALSE
22 Dpi.48 2/7/23 HNO204 34 48 Dpi 3065 1.95000e+02 FALSE
23 Dpi.48 1/17/23 HNO204 34 48 Dpi 3065 0.00000e+00 TRUE
24 Dpi.48 8/23/22 HNO204 34 48 Dpi 3065 6.33750e+05 FALSE
25 Dpi.48 9/14/22 HNO919 34 48 Dpi 3065 6.45000e+04 FALSE
26 Dpi.48 11/1/22 HNO919 34 48 Dpi 3065 3.67500e+04 FALSE
27 Dpi.48 11/2/22 HNO919 34 48 Dpi 3065 2.55000e+02 FALSE
28 Dpi.48 2/28/23 HNO204 34 48 Dpi 3065 9.00000e+01 FALSE
29 Dpi.48 9/13/22 HNO204 34 48 Dpi 3065 0.00000e+00 TRUE
30 Dpi.48 8/10/22 HNO204 34 48 Dpi 3065 6.18750e+05 FALSE
31 Dpi.48 8/9/22 HNO918 34 48 Dpi 3065 2.47500e+04 FALSE
32 Dpi.48 9/6/22 HNO919 34 48 Dpi 3065 2.02500e+02 FALSE
33 Dpi.48 8/31/22 HNO204 34 48 Dpi 3065 1.20000e+05 FALSE
34 Dpi.48 8/30/22 HNO918 34 48 Dpi 3065 1.31250e+03 FALSE
35 Dpi.48 1/24/23 HNO919 34 48 Dpi 3065 1.61250e+04 FALSE
36 Dpi.48 8/17/22 HNO918 34 48 Dpi 3065 7.12500e+02 FALSE
37 Sau.24 8/17/22 HNO918 34 24 Sau 2115 1.08750e+03 FALSE
38 Sau.24 2/7/23 HNO204 34 24 Sau 2115 0.00000e+00 TRUE
39 Sau.24 10/11/22 HNO204 34 24 Sau 2115 2.85000e+06 FALSE
40 Sau.24 9/27/22 HNO919 34 24 Sau 2115 2.55000e+03 FALSE
41 Sau.24 8/30/22 HNO918 34 24 Sau 2115 1.95000e+05 FALSE
42 Sau.24 10/4/22 HNO918 34 24 Sau 2115 1.68750e+04 FALSE
43 Sau.24 8/31/22 HNO204 34 24 Sau 2115 9.75000e+04 FALSE
44 Sau.24 9/6/22 HNO919 34 24 Sau 2115 7.12500e+03 FALSE
45 Sau.24 9/14/22 HNO919 34 24 Sau 2115 8.62500e+03 FALSE
46 Sau.24 8/16/22 HNO919 34 24 Sau 2115 1.53750e+03 FALSE
47 Sau.24 2/28/23 HNO204 34 24 Sau 2115 4.57500e+05 FALSE
48 Sau.24 10/25/22 HNO204 34 24 Sau 2115 4.68750e+04 FALSE
49 Sau.24 9/21/22 HNO204 34 24 Sau 2115 2.32500e+03 FALSE
50 Sau.24 9/20/22 HNO918 34 24 Sau 2115 1.68750e+03 FALSE
51 Sau.24 2/14/23 HNO919 34 24 Sau 2115 2.70000e+04 FALSE
52 Sau.24 9/7/22 HNO918 34 24 Sau 2115 7.12500e+07 FALSE
53 Sau.24 1/17/23 HNO204 34 24 Sau 2115 1.61250e+05 FALSE
54 Sau.48 8/17/22 HNO918 34 48 Sau 2115 1.50000e+08 FALSE
55 Sau.48 9/6/22 HNO919 34 48 Sau 2115 1.46250e+04 FALSE
56 Sau.48 8/16/22 HNO919 34 48 Sau 2115 2.47500e+05 FALSE
57 Sau.48 2/7/23 HNO204 34 48 Sau 2115 1.01250e+08 FALSE
58 Sau.48 8/30/22 HNO918 34 48 Sau 2115 5.77500e+06 FALSE
59 Sau.48 9/14/22 HNO919 34 48 Sau 2115 3.86250e+05 FALSE
60 Sau.48 10/4/22 HNO918 34 48 Sau 2115 1.31250e+08 FALSE
61 Sau.48 1/17/23 HNO204 34 48 Sau 2115 1.65000e+06 FALSE
62 Sau.48 10/11/22 HNO204 34 48 Sau 2115 3.90000e+08 FALSE
63 Sau.48 8/31/22 HNO204 34 48 Sau 2115 1.27500e+08 FALSE
64 Sau.48 2/14/23 HNO919 34 48 Sau 2115 1.05000e+06 FALSE
65 Sau.48 10/25/22 HNO204 34 48 Sau 2115 6.26250e+06 FALSE
66 Sau.48 9/20/22 HNO918 34 48 Sau 2115 1.05000e+09 FALSE
67 Sau.48 9/21/22 HNO204 34 48 Sau 2115 2.66250e+04 FALSE
68 Sau.48 9/27/22 HNO919 34 48 Sau 2115 1.35000e+08 FALSE
69 Sau.48 2/28/23 HNO204 34 48 Sau 2115 2.10000e+04 FALSE
70 Sau.48 9/7/22 HNO918 34 48 Sau 2115 2.17500e+08 FALSE
71 Spn.24 8/10/22 HNO204 34 24 Spn 1906 1.22625e+07 FALSE
72 Spn.24 1/24/23 HNO919 34 24 Spn 1906 5.58750e+06 FALSE
73 Spn.24 9/13/22 HNO204 34 24 Spn 1906 2.40000e+04 FALSE
74 Spn.24 8/9/22 HNO918 34 24 Spn 1906 2.52750e+07 FALSE
75 Spn.24 8/23/22 HNO204 34 24 Spn 1906 1.65750e+07 FALSE
76 Spn.24 2/7/23 HNO204 34 24 Spn 1906 9.75000e+06 FALSE
77 Spn.24 8/31/22 HNO204 34 24 Spn 1906 1.80000e+04 FALSE
78 Spn.24 1/17/23 HNO204 34 24 Spn 1906 3.30000e+06 FALSE
79 Spn.24 2/28/23 HNO204 34 24 Spn 1906 6.71250e+04 FALSE
80 Spn.24 9/14/22 HNO919 34 24 Spn 1906 5.73750e+05 FALSE
81 Spn.24 8/30/22 HNO918 34 24 Spn 1906 2.36250e+05 FALSE
82 Spn.24 2/14/23 HNO919 34 24 Spn 1906 3.30000e+07 FALSE
83 Spn.24 9/27/22 HNO919 34 24 Spn 1906 2.92500e+07 FALSE
84 Spn.24 9/6/22 HNO919 34 24 Spn 1906 9.03750e+04 FALSE
85 Spn.24 10/4/22 HNO918 34 24 Spn 1906 3.03750e+07 FALSE
86 Spn.24 10/11/22 HNO204 34 24 Spn 1906 1.23750e+04 FALSE
87 Spn.48 8/10/22 HNO204 34 48 Spn 1906 1.53750e+07 FALSE
88 Spn.48 1/24/23 HNO919 34 48 Spn 1906 1.35000e+06 FALSE
89 Spn.48 9/13/22 HNO204 34 48 Spn 1906 0.00000e+00 TRUE
90 Spn.48 8/23/22 HNO204 34 48 Spn 1906 3.78750e+07 FALSE
91 Spn.48 1/17/23 HNO204 34 48 Spn 1906 0.00000e+00 TRUE
92 Spn.48 8/9/22 HNO918 34 48 Spn 1906 6.15000e+07 FALSE
93 Spn.48 8/31/22 HNO204 34 48 Spn 1906 6.78750e+04 FALSE
94 Spn.48 9/6/22 HNO919 34 48 Spn 1906 5.55000e+01 FALSE
95 Spn.48 2/7/23 HNO204 34 48 Spn 1906 0.00000e+00 TRUE
96 Spn.48 2/28/23 HNO204 34 48 Spn 1906 1.03875e+04 FALSE
97 Spn.48 10/4/22 HNO918 34 48 Spn 1906 6.41250e+07 FALSE
98 Spn.48 9/14/22 HNO919 34 48 Spn 1906 6.75000e+02 FALSE
99 Spn.48 2/14/23 HNO919 34 48 Spn 1906 5.73750e+06 FALSE
100 Spn.48 9/27/22 HNO919 34 48 Spn 1906 2.55000e+07 FALSE
101 Spn.48 10/11/22 HNO204 34 48 Spn 1906 2.13750e+06 FALSE
102 Spn.48 8/30/22 HNO918 34 48 Spn 1906 0.00000e+00 TRUE
NewCFU bacteria_label predval.fit
1 1.31250e+06 <b><span style='color:#0443DC;'>24</span></b> 10.899902
2 1.98750e+03 <b><span style='color:#0443DC;'>24</span></b> 10.899902
3 6.26250e+05 <b><span style='color:#0443DC;'>24</span></b> 10.899902
4 1.01250e+03 <b><span style='color:#0443DC;'>24</span></b> 10.899902
5 9.11250e+04 <b><span style='color:#0443DC;'>24</span></b> 10.899902
6 1.05000e+03 <b><span style='color:#0443DC;'>24</span></b> 10.899902
7 8.25000e+05 <b><span style='color:#0443DC;'>24</span></b> 10.899902
8 4.83750e+05 <b><span style='color:#0443DC;'>24</span></b> 10.899902
9 5.13750e+04 <b><span style='color:#0443DC;'>24</span></b> 10.899902
10 1.91250e+06 <b><span style='color:#0443DC;'>24</span></b> 10.899902
11 4.12500e+05 <b><span style='color:#0443DC;'>24</span></b> 10.899902
12 1.46250e+04 <b><span style='color:#0443DC;'>24</span></b> 10.899902
13 9.00000e+05 <b><span style='color:#0443DC;'>24</span></b> 10.899902
14 8.25000e+03 <b><span style='color:#0443DC;'>24</span></b> 10.899902
15 2.36250e+03 <b><span style='color:#0443DC;'>24</span></b> 10.899902
16 3.30000e+04 <b><span style='color:#0443DC;'>24</span></b> 10.899902
17 9.60000e+01 <b><span style='color:#0443DC;'>24</span></b> 10.899902
18 1.83750e+05 <b><span style='color:#0443DC;'>24</span></b> 10.899902
19 2.77500e+04 <b><span style='color:#0443DC;'>48</span></b> 8.092684
20 3.78000e+02 <b><span style='color:#0443DC;'>48</span></b> 8.092684
21 7.80000e+03 <b><span style='color:#0443DC;'>48</span></b> 8.092684
22 1.95000e+02 <b><span style='color:#0443DC;'>48</span></b> 8.092684
23 3.75000e+00 <b><span style='color:#0443DC;'>48</span></b> 8.092684
24 6.33750e+05 <b><span style='color:#0443DC;'>48</span></b> 8.092684
25 6.45000e+04 <b><span style='color:#0443DC;'>48</span></b> 8.092684
26 3.67500e+04 <b><span style='color:#0443DC;'>48</span></b> 8.092684
27 2.55000e+02 <b><span style='color:#0443DC;'>48</span></b> 8.092684
28 9.00000e+01 <b><span style='color:#0443DC;'>48</span></b> 8.092684
29 3.75000e+00 <b><span style='color:#0443DC;'>48</span></b> 8.092684
30 6.18750e+05 <b><span style='color:#0443DC;'>48</span></b> 8.092684
31 2.47500e+04 <b><span style='color:#0443DC;'>48</span></b> 8.092684
32 2.02500e+02 <b><span style='color:#0443DC;'>48</span></b> 8.092684
33 1.20000e+05 <b><span style='color:#0443DC;'>48</span></b> 8.092684
34 1.31250e+03 <b><span style='color:#0443DC;'>48</span></b> 8.092684
35 1.61250e+04 <b><span style='color:#0443DC;'>48</span></b> 8.092684
36 7.12500e+02 <b><span style='color:#0443DC;'>48</span></b> 8.092684
37 1.08750e+03 <b><span style='color:#6D05A0;'>24</span></b> 10.250244
38 3.75000e+00 <b><span style='color:#6D05A0;'>24</span></b> 10.250244
39 2.85000e+06 <b><span style='color:#6D05A0;'>24</span></b> 10.250244
40 2.55000e+03 <b><span style='color:#6D05A0;'>24</span></b> 10.250244
41 1.95000e+05 <b><span style='color:#6D05A0;'>24</span></b> 10.250244
42 1.68750e+04 <b><span style='color:#6D05A0;'>24</span></b> 10.250244
43 9.75000e+04 <b><span style='color:#6D05A0;'>24</span></b> 10.250244
44 7.12500e+03 <b><span style='color:#6D05A0;'>24</span></b> 10.250244
45 8.62500e+03 <b><span style='color:#6D05A0;'>24</span></b> 10.250244
46 1.53750e+03 <b><span style='color:#6D05A0;'>24</span></b> 10.250244
47 4.57500e+05 <b><span style='color:#6D05A0;'>24</span></b> 10.250244
48 4.68750e+04 <b><span style='color:#6D05A0;'>24</span></b> 10.250244
49 2.32500e+03 <b><span style='color:#6D05A0;'>24</span></b> 10.250244
50 1.68750e+03 <b><span style='color:#6D05A0;'>24</span></b> 10.250244
51 2.70000e+04 <b><span style='color:#6D05A0;'>24</span></b> 10.250244
52 7.12500e+07 <b><span style='color:#6D05A0;'>24</span></b> 10.250244
53 1.61250e+05 <b><span style='color:#6D05A0;'>24</span></b> 10.250244
54 1.50000e+08 <b><span style='color:#6D05A0;'>48</span></b> 16.048022
55 1.46250e+04 <b><span style='color:#6D05A0;'>48</span></b> 16.048022
56 2.47500e+05 <b><span style='color:#6D05A0;'>48</span></b> 16.048022
57 1.01250e+08 <b><span style='color:#6D05A0;'>48</span></b> 16.048022
58 5.77500e+06 <b><span style='color:#6D05A0;'>48</span></b> 16.048022
59 3.86250e+05 <b><span style='color:#6D05A0;'>48</span></b> 16.048022
60 1.31250e+08 <b><span style='color:#6D05A0;'>48</span></b> 16.048022
61 1.65000e+06 <b><span style='color:#6D05A0;'>48</span></b> 16.048022
62 3.90000e+08 <b><span style='color:#6D05A0;'>48</span></b> 16.048022
63 1.27500e+08 <b><span style='color:#6D05A0;'>48</span></b> 16.048022
64 1.05000e+06 <b><span style='color:#6D05A0;'>48</span></b> 16.048022
65 6.26250e+06 <b><span style='color:#6D05A0;'>48</span></b> 16.048022
66 1.05000e+09 <b><span style='color:#6D05A0;'>48</span></b> 16.048022
67 2.66250e+04 <b><span style='color:#6D05A0;'>48</span></b> 16.048022
68 1.35000e+08 <b><span style='color:#6D05A0;'>48</span></b> 16.048022
69 2.10000e+04 <b><span style='color:#6D05A0;'>48</span></b> 16.048022
70 2.17500e+08 <b><span style='color:#6D05A0;'>48</span></b> 16.048022
71 1.22625e+07 <b><span style='color:#927ED1;'>24</span></b> 14.011200
72 5.58750e+06 <b><span style='color:#927ED1;'>24</span></b> 14.011200
73 2.40000e+04 <b><span style='color:#927ED1;'>24</span></b> 14.011200
74 2.52750e+07 <b><span style='color:#927ED1;'>24</span></b> 14.011200
75 1.65750e+07 <b><span style='color:#927ED1;'>24</span></b> 14.011200
76 9.75000e+06 <b><span style='color:#927ED1;'>24</span></b> 14.011200
77 1.80000e+04 <b><span style='color:#927ED1;'>24</span></b> 14.011200
78 3.30000e+06 <b><span style='color:#927ED1;'>24</span></b> 14.011200
79 6.71250e+04 <b><span style='color:#927ED1;'>24</span></b> 14.011200
80 5.73750e+05 <b><span style='color:#927ED1;'>24</span></b> 14.011200
81 2.36250e+05 <b><span style='color:#927ED1;'>24</span></b> 14.011200
82 3.30000e+07 <b><span style='color:#927ED1;'>24</span></b> 14.011200
83 2.92500e+07 <b><span style='color:#927ED1;'>24</span></b> 14.011200
84 9.03750e+04 <b><span style='color:#927ED1;'>24</span></b> 14.011200
85 3.03750e+07 <b><span style='color:#927ED1;'>24</span></b> 14.011200
86 1.23750e+04 <b><span style='color:#927ED1;'>24</span></b> 14.011200
87 1.53750e+07 <b><span style='color:#927ED1;'>48</span></b> 10.359997
88 1.35000e+06 <b><span style='color:#927ED1;'>48</span></b> 10.359997
89 3.75000e+00 <b><span style='color:#927ED1;'>48</span></b> 10.359997
90 3.78750e+07 <b><span style='color:#927ED1;'>48</span></b> 10.359997
91 3.75000e+00 <b><span style='color:#927ED1;'>48</span></b> 10.359997
92 6.15000e+07 <b><span style='color:#927ED1;'>48</span></b> 10.359997
93 6.78750e+04 <b><span style='color:#927ED1;'>48</span></b> 10.359997
94 5.55000e+01 <b><span style='color:#927ED1;'>48</span></b> 10.359997
95 3.75000e+00 <b><span style='color:#927ED1;'>48</span></b> 10.359997
96 1.03875e+04 <b><span style='color:#927ED1;'>48</span></b> 10.359997
97 6.41250e+07 <b><span style='color:#927ED1;'>48</span></b> 10.359997
98 6.75000e+02 <b><span style='color:#927ED1;'>48</span></b> 10.359997
99 5.73750e+06 <b><span style='color:#927ED1;'>48</span></b> 10.359997
100 2.55000e+07 <b><span style='color:#927ED1;'>48</span></b> 10.359997
101 2.13750e+06 <b><span style='color:#927ED1;'>48</span></b> 10.359997
102 3.75000e+00 <b><span style='color:#927ED1;'>48</span></b> 10.359997
predval.se.fit
1 0.9512642
2 0.9512642
3 0.9512642
4 0.9512642
5 0.9512642
6 0.9512642
7 0.9512642
8 0.9512642
9 0.9512642
10 0.9512642
11 0.9512642
12 0.9512642
13 0.9512642
14 0.9512642
15 0.9512642
16 0.9512642
17 0.9512642
18 0.9512642
19 0.9512642
20 0.9512642
21 0.9512642
22 0.9512642
23 0.9512642
24 0.9512642
25 0.9512642
26 0.9512642
27 0.9512642
28 0.9512642
29 0.9512642
30 0.9512642
31 0.9512642
32 0.9512642
33 0.9512642
34 0.9512642
35 0.9512642
36 0.9512642
37 0.9725831
38 0.9725831
39 0.9725831
40 0.9725831
41 0.9725831
42 0.9725831
43 0.9725831
44 0.9725831
45 0.9725831
46 0.9725831
47 0.9725831
48 0.9725831
49 0.9725831
50 0.9725831
51 0.9725831
52 0.9725831
53 0.9725831
54 0.9725831
55 0.9725831
56 0.9725831
57 0.9725831
58 0.9725831
59 0.9725831
60 0.9725831
61 0.9725831
62 0.9725831
63 0.9725831
64 0.9725831
65 0.9725831
66 0.9725831
67 0.9725831
68 0.9725831
69 0.9725831
70 0.9725831
71 0.9982937
72 0.9982937
73 0.9982937
74 0.9982937
75 0.9982937
76 0.9982937
77 0.9982937
78 0.9982937
79 0.9982937
80 0.9982937
81 0.9982937
82 0.9982937
83 0.9982937
84 0.9982937
85 0.9982937
86 0.9982937
87 0.9982937
88 0.9982937
89 0.9982937
90 0.9982937
91 0.9982937
92 0.9982937
93 0.9982937
94 0.9982937
95 0.9982937
96 0.9982937
97 0.9982937
98 0.9982937
99 0.9982937
100 0.9982937
101 0.9982937
102 0.9982937
$plot_1
$plot_2
$panel
Supplemental Data: 37C
analysis_function(CFU_data, each_temp = "37", cutoff_pvalue = 0.05, cutoff_FC = 1)$anova
Sum Sq Mean Sq NumDF DenDF F value Pr(>F) sign
bacteria 3.4e+02 1.7e+02 2 5.8e+01 1.2e+01 4.5e-05 *
Time 3.0e-02 3.0e-02 1 4.4e+01 2.1e-03 9.6e-01
bacteria:Time 2.4e+02 1.2e+02 2 4.4e+01 8.4e+00 7.8e-04 *
$random_effects
grp var1 var2 vcov sdcor proportion
1 Line:Date (Intercept) <NA> 7.890447 2.808994 35.99
2 Line (Intercept) <NA> 0.000000 0.000000 0.00
3 Residual <NA> <NA> 14.034296 3.746238 64.01
$contrasts_time
Time1 Time2 bacteria estimate SE df t.ratio p.value Temp
1 24 48 Dpi 5.003889 1.529395 44.47372 3.271809 0.002069846 37
2 24 48 Sau -3.851043 1.597401 44.47372 -2.410818 0.020113866 37
3 24 48 Spn -1.027038 1.597401 44.47372 -0.642943 0.523562322 37
p.adj.holm sign condition1 condition2
1 0.006209537 * Dpi.24 Dpi.48
2 0.040227732 * Sau.24 Sau.48
3 0.523562322 Spn.24 Spn.48
group1
1 <b><span style='color:#0443DC;'>24</span></b>
2 <b><span style='color:#6D05A0;'>24</span></b>
3 <b><span style='color:#927ED1;'>24</span></b>
group2 exp.mean.predval.1
1 <b><span style='color:#0443DC;'>48</span></b> 47592.1
2 <b><span style='color:#6D05A0;'>48</span></b> 294997.5
3 <b><span style='color:#927ED1;'>48</span></b> 109063.3
exp.mean.predval.2 FC highlighted
1 3.194285e+02 148.991409 -
2 1.387730e+07 -47.042095 +
3 3.045900e+05 -2.792781 +
$data_summary
# A tibble: 6 × 8
# Groups: Combined [6]
Combined bacteria_label mean.real mean.predval mean.predval.se
<fct> <fct> <dbl> <dbl> <dbl>
1 Dpi.24 <b><span style='color:#0443DC… 2.36e5 10.8 1.31
2 Sau.24 <b><span style='color:#6D05A0… 1.91e7 12.6 1.36
3 Spn.24 <b><span style='color:#927ED1… 1.79e7 11.6 1.35
4 Dpi.48 <b><span style='color:#0443DC… 1.23e4 5.77 1.31
5 Sau.48 <b><span style='color:#6D05A0… 1.36e8 16.4 1.36
6 Spn.48 <b><span style='color:#927ED1… 2.90e7 12.6 1.35
# ℹ 3 more variables: exp.mean.predval <dbl>, max <dbl>, min <dbl>
$data_stats
Combined Date Line Temp Time bacteria Strain CFUs LOD
1 Dpi.24 9/13/22 HNO204 37 24 Dpi 3065 3.487500e+03 FALSE
2 Dpi.24 11/1/22 HNO919 37 24 Dpi 3065 7.875000e+04 FALSE
3 Dpi.24 8/23/22 HNO204 37 24 Dpi 3065 1.050000e+06 FALSE
4 Dpi.24 9/14/22 HNO919 37 24 Dpi 3065 4.875000e+05 FALSE
5 Dpi.24 9/7/22 HNO918 37 24 Dpi 3065 4.012500e+05 FALSE
6 Dpi.24 8/31/22 HNO204 37 24 Dpi 3065 7.125000e+04 FALSE
7 Dpi.24 8/10/22 HNO204 37 24 Dpi 3065 4.650000e+05 FALSE
8 Dpi.24 8/9/22 HNO918 37 24 Dpi 3065 2.100000e+05 FALSE
9 Dpi.24 8/30/22 HNO918 37 24 Dpi 3065 3.375000e+02 FALSE
10 Dpi.24 11/2/22 HNO919 37 24 Dpi 3065 0.000000e+00 TRUE
11 Dpi.24 8/17/22 HNO918 37 24 Dpi 3065 3.450000e+04 FALSE
12 Dpi.24 9/6/22 HNO919 37 24 Dpi 3065 3.525000e+04 FALSE
13 Dpi.48 9/14/22 HNO919 37 48 Dpi 3065 1.237500e+03 FALSE
14 Dpi.48 11/1/22 HNO919 37 48 Dpi 3065 1.875000e+01 FALSE
15 Dpi.48 8/10/22 HNO204 37 48 Dpi 3065 1.080000e+05 FALSE
16 Dpi.48 9/13/22 HNO204 37 48 Dpi 3065 0.000000e+00 TRUE
17 Dpi.48 9/7/22 HNO918 37 48 Dpi 3065 1.500000e+01 FALSE
18 Dpi.48 8/23/22 HNO204 37 48 Dpi 3065 1.012500e+03 FALSE
19 Dpi.48 8/9/22 HNO918 37 48 Dpi 3065 3.412500e+04 FALSE
20 Dpi.48 8/30/22 HNO918 37 48 Dpi 3065 6.000000e+01 FALSE
21 Dpi.48 8/31/22 HNO204 37 48 Dpi 3065 2.925000e+03 FALSE
22 Dpi.48 11/2/22 HNO919 37 48 Dpi 3065 0.000000e+00 TRUE
23 Dpi.48 8/17/22 HNO918 37 48 Dpi 3065 7.500000e+01 FALSE
24 Dpi.48 9/6/22 HNO919 37 48 Dpi 3065 2.100000e+01 FALSE
25 Sau.24 9/20/22 HNO918 37 24 Sau 2115 1.125000e+07 FALSE
26 Sau.24 2/28/23 HNO204 37 24 Sau 2115 3.562500e+05 FALSE
27 Sau.24 10/4/22 HNO918 37 24 Sau 2115 1.012500e+06 FALSE
28 Sau.24 8/17/22 HNO918 37 24 Sau 2115 1.050000e+06 FALSE
29 Sau.24 10/11/22 HNO204 37 24 Sau 2115 1.575000e+07 FALSE
30 Sau.24 9/6/22 HNO919 37 24 Sau 2115 4.275000e+03 FALSE
31 Sau.24 9/27/22 HNO919 37 24 Sau 2115 3.937500e+04 FALSE
32 Sau.24 10/25/22 HNO204 37 24 Sau 2115 1.800000e+08 FALSE
33 Sau.24 9/21/22 HNO204 37 24 Sau 2115 2.512500e+02 FALSE
34 Sau.24 8/30/22 HNO918 37 24 Sau 2115 6.187500e+05 FALSE
35 Sau.24 9/14/22 HNO919 37 24 Sau 2115 8.250000e+03 FALSE
36 Sau.48 10/11/22 HNO204 37 48 Sau 2115 2.700000e+07 FALSE
37 Sau.48 9/27/22 HNO919 37 48 Sau 2115 1.162500e+08 FALSE
38 Sau.48 2/28/23 HNO204 37 48 Sau 2115 1.200000e+05 FALSE
39 Sau.48 8/17/22 HNO918 37 48 Sau 2115 1.762500e+07 FALSE
40 Sau.48 10/4/22 HNO918 37 48 Sau 2115 4.875000e+08 FALSE
41 Sau.48 8/30/22 HNO918 37 48 Sau 2115 6.112500e+07 FALSE
42 Sau.48 9/20/22 HNO918 37 48 Sau 2115 1.987500e+06 FALSE
43 Sau.48 10/25/22 HNO204 37 48 Sau 2115 4.575000e+08 FALSE
44 Sau.48 9/6/22 HNO919 37 48 Sau 2115 4.875000e+03 FALSE
45 Sau.48 9/14/22 HNO919 37 48 Sau 2115 3.000000e+08 FALSE
46 Sau.48 9/21/22 HNO204 37 48 Sau 2115 2.475000e+07 FALSE
47 Spn.24 8/23/22 HNO204 37 24 Spn 1906 4.387500e+07 FALSE
48 Spn.24 8/10/22 HNO204 37 24 Spn 1906 1.027500e+07 FALSE
49 Spn.24 9/13/22 HNO204 37 24 Spn 1906 1.537500e+03 FALSE
50 Spn.24 8/31/22 HNO204 37 24 Spn 1906 0.000000e+00 TRUE
51 Spn.24 10/11/22 HNO204 37 24 Spn 1906 2.812500e+07 FALSE
52 Spn.24 8/9/22 HNO918 37 24 Spn 1906 1.950000e+07 FALSE
53 Spn.24 8/17/22 HNO918 37 24 Spn 1906 4.500000e+01 FALSE
54 Spn.24 9/6/22 HNO919 37 24 Spn 1906 2.887500e+03 FALSE
55 Spn.24 10/4/22 HNO918 37 24 Spn 1906 4.312500e+07 FALSE
56 Spn.24 9/27/22 HNO919 37 24 Spn 1906 5.250000e+07 FALSE
57 Spn.24 8/30/22 HNO918 37 24 Spn 1906 2.887500e+03 FALSE
58 Spn.48 10/11/22 HNO204 37 48 Spn 1906 3.065625e+02 FALSE
59 Spn.48 8/17/22 HNO918 37 48 Spn 1906 2.025000e+05 FALSE
60 Spn.48 8/10/22 HNO204 37 48 Spn 1906 4.800000e+06 FALSE
61 Spn.48 8/31/22 HNO204 37 48 Spn 1906 7.800000e+07 FALSE
62 Spn.48 10/4/22 HNO918 37 48 Spn 1906 7.200000e+07 FALSE
63 Spn.48 8/30/22 HNO918 37 48 Spn 1906 1.875000e+06 FALSE
64 Spn.48 9/13/22 HNO204 37 48 Spn 1906 0.000000e+00 TRUE
65 Spn.48 9/6/22 HNO919 37 48 Spn 1906 6.900000e+01 FALSE
66 Spn.48 8/23/22 HNO204 37 48 Spn 1906 4.687500e+07 FALSE
67 Spn.48 9/27/22 HNO919 37 48 Spn 1906 3.000000e+07 FALSE
68 Spn.48 8/9/22 HNO918 37 48 Spn 1906 8.550000e+07 FALSE
NewCFU bacteria_label predval.fit
1 3.487500e+03 <b><span style='color:#0443DC;'>24</span></b> 10.770422
2 7.875000e+04 <b><span style='color:#0443DC;'>24</span></b> 10.770422
3 1.050000e+06 <b><span style='color:#0443DC;'>24</span></b> 10.770422
4 4.875000e+05 <b><span style='color:#0443DC;'>24</span></b> 10.770422
5 4.012500e+05 <b><span style='color:#0443DC;'>24</span></b> 10.770422
6 7.125000e+04 <b><span style='color:#0443DC;'>24</span></b> 10.770422
7 4.650000e+05 <b><span style='color:#0443DC;'>24</span></b> 10.770422
8 2.100000e+05 <b><span style='color:#0443DC;'>24</span></b> 10.770422
9 3.375000e+02 <b><span style='color:#0443DC;'>24</span></b> 10.770422
10 3.750000e+00 <b><span style='color:#0443DC;'>24</span></b> 10.770422
11 3.450000e+04 <b><span style='color:#0443DC;'>24</span></b> 10.770422
12 3.525000e+04 <b><span style='color:#0443DC;'>24</span></b> 10.770422
13 1.237500e+03 <b><span style='color:#0443DC;'>48</span></b> 5.766533
14 1.875000e+01 <b><span style='color:#0443DC;'>48</span></b> 5.766533
15 1.080000e+05 <b><span style='color:#0443DC;'>48</span></b> 5.766533
16 3.750000e+00 <b><span style='color:#0443DC;'>48</span></b> 5.766533
17 1.500000e+01 <b><span style='color:#0443DC;'>48</span></b> 5.766533
18 1.012500e+03 <b><span style='color:#0443DC;'>48</span></b> 5.766533
19 3.412500e+04 <b><span style='color:#0443DC;'>48</span></b> 5.766533
20 6.000000e+01 <b><span style='color:#0443DC;'>48</span></b> 5.766533
21 2.925000e+03 <b><span style='color:#0443DC;'>48</span></b> 5.766533
22 3.750000e+00 <b><span style='color:#0443DC;'>48</span></b> 5.766533
23 7.500000e+01 <b><span style='color:#0443DC;'>48</span></b> 5.766533
24 2.100000e+01 <b><span style='color:#0443DC;'>48</span></b> 5.766533
25 1.125000e+07 <b><span style='color:#6D05A0;'>24</span></b> 12.594722
26 3.562500e+05 <b><span style='color:#6D05A0;'>24</span></b> 12.594722
27 1.012500e+06 <b><span style='color:#6D05A0;'>24</span></b> 12.594722
28 1.050000e+06 <b><span style='color:#6D05A0;'>24</span></b> 12.594722
29 1.575000e+07 <b><span style='color:#6D05A0;'>24</span></b> 12.594722
30 4.275000e+03 <b><span style='color:#6D05A0;'>24</span></b> 12.594722
31 3.937500e+04 <b><span style='color:#6D05A0;'>24</span></b> 12.594722
32 1.800000e+08 <b><span style='color:#6D05A0;'>24</span></b> 12.594722
33 2.512500e+02 <b><span style='color:#6D05A0;'>24</span></b> 12.594722
34 6.187500e+05 <b><span style='color:#6D05A0;'>24</span></b> 12.594722
35 8.250000e+03 <b><span style='color:#6D05A0;'>24</span></b> 12.594722
36 2.700000e+07 <b><span style='color:#6D05A0;'>48</span></b> 16.445765
37 1.162500e+08 <b><span style='color:#6D05A0;'>48</span></b> 16.445765
38 1.200000e+05 <b><span style='color:#6D05A0;'>48</span></b> 16.445765
39 1.762500e+07 <b><span style='color:#6D05A0;'>48</span></b> 16.445765
40 4.875000e+08 <b><span style='color:#6D05A0;'>48</span></b> 16.445765
41 6.112500e+07 <b><span style='color:#6D05A0;'>48</span></b> 16.445765
42 1.987500e+06 <b><span style='color:#6D05A0;'>48</span></b> 16.445765
43 4.575000e+08 <b><span style='color:#6D05A0;'>48</span></b> 16.445765
44 4.875000e+03 <b><span style='color:#6D05A0;'>48</span></b> 16.445765
45 3.000000e+08 <b><span style='color:#6D05A0;'>48</span></b> 16.445765
46 2.475000e+07 <b><span style='color:#6D05A0;'>48</span></b> 16.445765
47 4.387500e+07 <b><span style='color:#927ED1;'>24</span></b> 11.599684
48 1.027500e+07 <b><span style='color:#927ED1;'>24</span></b> 11.599684
49 1.537500e+03 <b><span style='color:#927ED1;'>24</span></b> 11.599684
50 3.750000e+00 <b><span style='color:#927ED1;'>24</span></b> 11.599684
51 2.812500e+07 <b><span style='color:#927ED1;'>24</span></b> 11.599684
52 1.950000e+07 <b><span style='color:#927ED1;'>24</span></b> 11.599684
53 4.500000e+01 <b><span style='color:#927ED1;'>24</span></b> 11.599684
54 2.887500e+03 <b><span style='color:#927ED1;'>24</span></b> 11.599684
55 4.312500e+07 <b><span style='color:#927ED1;'>24</span></b> 11.599684
56 5.250000e+07 <b><span style='color:#927ED1;'>24</span></b> 11.599684
57 2.887500e+03 <b><span style='color:#927ED1;'>24</span></b> 11.599684
58 3.065625e+02 <b><span style='color:#927ED1;'>48</span></b> 12.626722
59 2.025000e+05 <b><span style='color:#927ED1;'>48</span></b> 12.626722
60 4.800000e+06 <b><span style='color:#927ED1;'>48</span></b> 12.626722
61 7.800000e+07 <b><span style='color:#927ED1;'>48</span></b> 12.626722
62 7.200000e+07 <b><span style='color:#927ED1;'>48</span></b> 12.626722
63 1.875000e+06 <b><span style='color:#927ED1;'>48</span></b> 12.626722
64 3.750000e+00 <b><span style='color:#927ED1;'>48</span></b> 12.626722
65 6.900000e+01 <b><span style='color:#927ED1;'>48</span></b> 12.626722
66 4.687500e+07 <b><span style='color:#927ED1;'>48</span></b> 12.626722
67 3.000000e+07 <b><span style='color:#927ED1;'>48</span></b> 12.626722
68 8.550000e+07 <b><span style='color:#927ED1;'>48</span></b> 12.626722
predval.se.fit
1 1.306561
2 1.306561
3 1.306561
4 1.306561
5 1.306561
6 1.306561
7 1.306561
8 1.306561
9 1.306561
10 1.306561
11 1.306561
12 1.306561
13 1.306561
14 1.306561
15 1.306561
16 1.306561
17 1.306561
18 1.306561
19 1.306561
20 1.306561
21 1.306561
22 1.306561
23 1.306561
24 1.306561
25 1.355826
26 1.355826
27 1.355826
28 1.355826
29 1.355826
30 1.355826
31 1.355826
32 1.355826
33 1.355826
34 1.355826
35 1.355826
36 1.355826
37 1.355826
38 1.355826
39 1.355826
40 1.355826
41 1.355826
42 1.355826
43 1.355826
44 1.355826
45 1.355826
46 1.355826
47 1.351123
48 1.351123
49 1.351123
50 1.351123
51 1.351123
52 1.351123
53 1.351123
54 1.351123
55 1.351123
56 1.351123
57 1.351123
58 1.351123
59 1.351123
60 1.351123
61 1.351123
62 1.351123
63 1.351123
64 1.351123
65 1.351123
66 1.351123
67 1.351123
68 1.351123
$plot_1
$plot_2
$panel
Merged Files
pdf_output <- file.path(figures_folder, "HNOBac_SummaryCFUs.pdf")
# Check if the file exists, and delete if it does
if (file.exists(pdf_output)) {
file.remove(pdf_output)
}[1] TRUE# Now combine the PDFs into a new file
pdf_files <- list.files(figures_folder, pattern = "\\.pdf$", full.names = TRUE)
qpdf::pdf_combine(input = pdf_files, output = pdf_output)[1] "data/outputs/CFUs/figures/HNOBac_SummaryCFUs.pdf"