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cabaJr wants to merge 1 commit into from
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issue fix with plotting #1

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4 changes: 2 additions & 2 deletions R/fct_Annotate.R
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Original file line number Diff line number Diff line change
Expand Up @@ -184,7 +184,7 @@ Annotate <-

plt_title <- stringr::str_c(data$sampleName)#, data$img_number, sep = ", Image ")
plt_sub <- data$vesselName
yaxis <- stringr::str_wrap(data$metric, width = 26)
yaxis <- stringr::str_wrap(data$metric, width = 40)
# get data
df <- data.frame(timevar = timevar, ch1 = ch1)
group_label <- if (!is.null(data$sampleGroup) && nzchar(data$sampleGroup) && data$sampleGroup != "Exp") {
Expand Down Expand Up @@ -245,7 +245,7 @@ Annotate <-
plot.title = ggplot2::element_text(hjust = 0.5, size = 14),
plot.subtitle = ggplot2::element_text(hjust = 0.5, size = 12),
axis.text = ggplot2::element_text(size = 11),
axis.title.y = ggplot2::element_text(size = 13, margin = ggplot2::margin(r = 12)),
axis.title.y = ggplot2::element_text(size = 13, margin = ggplot2::margin(r = 30)),
axis.title.x = ggplot2::element_text(size = 13),
panel.grid.major.x = ggplot2::element_line(color = "black",
linewidth = 0.5,
Expand Down
57 changes: 39 additions & 18 deletions R/fct_Custom_tables.R
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Expand Up @@ -443,15 +443,36 @@ Custom_tables <- R6::R6Class("Custom_tables",
#' @return a table of resultys containing Period length, phase, amplitude, offset and error

fft_nlls_period = function(data) {
# Perform FFT
n <- length(data$value)
dt <- mean(diff(data$t))
fft_result <- stats::fft(data$value)
# Drop NAs before FFT — NAs propagate through fft() and make
# which.max() return integer(0), causing nls.lm length mismatch.
valid_idx <- !is.na(data$value)
fft_vals <- data$value[valid_idx]
fft_t <- data$t[valid_idx]
n_valid <- length(fft_vals)

if (n_valid < 10) {
return(list(period = NA_real_, amplitude = NA_real_,
phase_rad = NA_real_, phase_circ = NA_real_,
phase_abs = NA_real_, offset = NA_real_,
error = NA_real_, GOF = NA_real_, RAE = NA_real_))
}

# Perform FFT on complete cases
n <- n_valid
dt <- mean(diff(fft_t))
fft_result <- stats::fft(fft_vals)
frequencies <- seq(0, 1/dt, length.out = n)

# Identify the dominant frequency (excluding the zero frequency)
dominant_frequency <- frequencies[which.max(base::Mod(fft_result)[2:(n/2)]) + 1]
initial_period <- 1 / dominant_frequency
dominant_idx <- which.max(base::Mod(fft_result)[2:floor(n/2)])
if (length(dominant_idx) == 0) {
initial_period <- 24 # fallback to circadian period
} else {
dominant_frequency <- frequencies[dominant_idx + 1]
initial_period <- if (is.finite(dominant_frequency) && dominant_frequency > 0) {
1 / dominant_frequency
} else 24
}

# Define the sinusoidal model function
sinusoidal_model = function(params, t) {
Expand All @@ -467,18 +488,18 @@ Custom_tables <- R6::R6Class("Custom_tables",
return(y - sinusoidal_model(params, t))
}

# Initial parameter estimates
initial_amplitude <- (max(data$value) - min(data$value)) / 2
# Initial parameter estimates (using NA-free vectors)
initial_amplitude <- (max(fft_vals) - min(fft_vals)) / 2
initial_phase <- 0
initial_offset <- mean(data$value)
initial_offset <- mean(fft_vals)
initial_params <- c(initial_amplitude, initial_period, initial_phase, initial_offset)

# Perform nonlinear least squares fitting
# Perform nonlinear least squares fitting on complete cases
fit <- minpack.lm::nls.lm(
par = initial_params,
fn = residuals,
t = data$t,
y = data$value,
t = fft_t,
y = fft_vals,
lower = c(-Inf, 12, -Inf, -Inf),
upper = c(Inf, 32, Inf, Inf)
)
Expand Down Expand Up @@ -512,17 +533,17 @@ Custom_tables <- R6::R6Class("Custom_tables",
phase_circadian <- circular::conversion.circular(fitted_phase, units = "hours")
phase_absolute <- phase_circadian*(fitted_period/24)

# Compute residual error
fitted_values <- sinusoidal_model(fitted_params, data$t)
residual_error <- sqrt(mean((data$value - fitted_values)^2))
# Compute residual error (using NA-free vectors)
fitted_values <- sinusoidal_model(fitted_params, fft_t)
residual_error <- sqrt(mean((fft_vals - fitted_values)^2))

# Calculate R-squared (GOF)
ss_total <- sum((data$value - mean(data$value))^2)
ss_res <- sum((data$value - fitted_values)^2)
ss_total <- sum((fft_vals - mean(fft_vals))^2)
ss_res <- sum((fft_vals - fitted_values)^2)
r_squared <- 1 - (ss_res / ss_total)

# Compute standard deviation of residuals
residual_std <- sqrt(sum((data$value - fitted_values)^2) / (length(data$value) - length(fitted_params)))
residual_std <- sqrt(sum((fft_vals - fitted_values)^2) / (length(fft_vals) - length(fitted_params)))

# Compute RAE
RAE <- residual_std / fitted_amplitude
Expand Down
2 changes: 1 addition & 1 deletion R/fct_R6_clean_data.R
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Expand Up @@ -288,7 +288,7 @@ Clean_sample_data <-
detrend = function(grade){
outliers_rm = FALSE
# runs a check if the dataset with removed outliers is available
if(!is.null(self$intensity_clean)){
if(length(self$intensity_clean) > 0){
outliers_rm = TRUE
# import data from cleaned values
intensity <- self$intensity_clean
Expand Down
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