discovered the issue with population cosinor through a default return…

… class from sapply in the chain, which should now be resolved, however need to test coefficients in additional dataframes #1

R/cosinor-constructor.R

@@ -172,7 +172,7 @@ cosinor_bridge <- function(processed, tau, population, data, ...) {
 
   } else if (length(population) == length(predictors)) {
 
-    # Modified function, usings cosinor_impl internally
+    # Modified function, using `cosinor_impl()` internally
     fit <- cosinor_pop_impl(predictors, outcomes, tau, population)
     type <- "Population"
 

R/cosinor-fit.R

@@ -33,20 +33,31 @@ cosinor_impl <- function(predictors, outcomes, tau) {
   # y(t) = M + sum_j[beta_j * x_j + gamma_j * z_j] + error(t)
 
   # Number of parameters will be the number of taus
-    # e.g. single component = 3 components, where 3 = 2p + 1 (p = 1 component)
+  # 	e.g. single component = 3 components, where 3 = 2p + 1 (p = 1 component)
   p <- length(tau)
 
+  # Create null variables
+  mesor <- NULL
+  for (i in 1:p) {
+    assign(paste0("x", i), NULL)
+    assign(paste0("z", i), NULL)
+    assign(paste("amp", i), NULL)
+    assign(paste("phi", i), NULL)
+    assign(paste("beta", i), NULL)
+    assign(paste("gamma", i), NULL)
+  }
+
   # Single parameters
   y <- outcomes
   t <- predictors
   n  <- length(t)
 
   # Normal equation for 3 components
-    # Normal equations (where M, beta, gamma are the coefficients to solve for)
-    # sum(y) = M*n + beta*sum(x) + gamma*sum(z)
-    # sum(y*x) = M*sum(x) + beta*sum(x^2) + gamma*sum(x*z)
-    # sum(y*z) = M*sum(z) + beta*sum(x*z) + gamma*sum(z^2)
-    # d = Su (for single component, 3 equations with 3 unknowns)
+  # 	Normal equations (where M, beta, gamma are the coefficients to solve for)
+  # 	sum(y) = M*n + beta*sum(x) + gamma*sum(z)
+  # 	sum(y*x) = M*sum(x) + beta*sum(x^2) + gamma*sum(x*z)
+  # 	sum(y*z) = M*sum(z) + beta*sum(x*z) + gamma*sum(z^2)
+  # 	d = Su (for single component, 3 equations with 3 unknowns)
 
   # For multiple components, the matrix must be expanded
 
@@ -160,6 +171,17 @@ cosinor_pop_impl <- function(predictors, outcomes, tau, population) {
   # Period
   p <- length(tau) # Number of parameters ... single cosinor ~ 2p + 1 = 3
 
+  # Create null variables based on number of parameters
+  mesor <- NULL
+  for (i in 1:p) {
+    assign(paste0("x", i), NULL)
+    assign(paste0("z", i), NULL)
+    assign(paste("amp", i), NULL)
+    assign(paste("phi", i), NULL)
+    assign(paste("beta", i), NULL)
+    assign(paste("gamma", i), NULL)
+  }
+
   # Create data frame for split/apply approach
   df <- na.omit(data.frame(predictors, outcomes, population))
 
@@ -193,15 +215,19 @@ cosinor_pop_impl <- function(predictors, outcomes, tau, population) {
   # Create matrix that we can apply cosinor to subgroups
   kCosinors <- with(
     df,
-    by(df, population, function(x) {
-      cosinor_impl(x$predictors, x$outcomes, tau)
+    by(df, population, function(.x) {
+      cosinor_impl(.x$predictors, .x$outcomes, tau)
     })
   )
 
   ### Coefficients
 
   # Fits of individual cosinors
-  kfits <- sapply(kCosinors, stats::fitted)
+  # Must be a data frame to have column names
+  kfits <- sapply(kCosinors, stats::fitted, USE.NAMES = TRUE)
+  if (inherits(kfits, "matrix")) {
+  	kfits <- as.data.frame(kfits)
+  }
   fits <- data.frame(
     population = rep(names(kfits), sapply(kfits, length)),
     yhat = unlist(kfits)
@@ -223,7 +249,10 @@ cosinor_pop_impl <- function(predictors, outcomes, tau, population) {
     assign(paste0("gamma", i), unname(coefs[paste0("gamma", i)]))
 
     # Amplitude
-    assign(paste0("amp", i), sqrt(get(paste0("beta", i))^2 + get(paste0("gamma", i))^2))
+    # Currently using the population mean method
+    # Eventually will require implementation of trigonometric method
+    #assign(paste0("amp", i), sqrt(get(paste0("beta", i))^2 + get(paste0("gamma", i))^2))
+    assign(paste0("amp", i), unname(coefs[paste0("amp", i)]))
 
     # Phi / acrophase
     sb <- sign(get(paste0("beta", i)))
@@ -381,7 +410,7 @@ confint.cosinor <- function(object, parm, level = 0.95, ...) {
 				covMat <- cov(cbind(beta_i, gamma_i))
 
 				# Eigen decomposition of covariance matrix
-				eig <- stats::eigen(covMat)
+				eig <- eigen(covMat)
 				V <- eig$vectors
 				D <- diag(eig$values)
 
@@ -413,7 +442,7 @@ confint.cosinor <- function(object, parm, level = 0.95, ...) {
 				seGamma <- sqrt(var(gamma_i) / k)
 
 				# Compute t-value for beta and gamma
-				tValueBetaGamma <- qt(1 - alpha / 2, df)
+				tValueBetaGamma <- qt(1 - a / 2, df)
 
 				# Confidence intervals for beta and gamma
 				betaLower <- betaBar - tValueBetaGamma * seBeta
@@ -448,15 +477,15 @@ confint.cosinor <- function(object, parm, level = 0.95, ...) {
 
 				# Add acrophase to outputs
 				ciMatrix <- rbind(ciMatrix, c(phiLower, phiUpper))
-				rownames(ciMatrix)[nrow(ciMatrix)] <- paste0("acrophase", i)
+				rownames(ciMatrix)[nrow(ciMatrix)] <- paste0("phi", i)
 
 				# Standard error for acrophase
 				acrophase_i <- atan2(-gamma_i, beta_i)
 				acrophase_i <- (acrophase_i + pi) %% (2 * pi) - pi
 				# Compute angular standard deviation
 				seAcrophase <- sqrt(-2 * log(abs(mean(exp(1i * acrophase_i)))))
 				seVector <- c(seVector, seAcrophase)
-				names(seVector)[length(seVector)] <- paste0("acrophase", i)
+				names(seVector)[length(seVector)] <- paste0("phi", i)
 			}
 
 			# Name the columns of ciMatrix according to confidence levels

R/globals.R

@@ -37,12 +37,6 @@ utils::globalVariables(c(
 	"models",
 	"mesor",
 	"x",
-	paste0("x", 1:3),
-	paste0("z", 1:3),
-	paste0("beta", 1:3),
-	paste0("gamma", 1:3),
-	paste0("amp", 1:3),
-	paste0("phi", 1:3),
 	paste0("acro", 1:3),
 	"glabs",
 	"term",

tests/testthat/test-cosinor-fit.R

@@ -25,7 +25,38 @@ test_that("population cosinors can be fit", {
 	f <- sDYX ~ hour
 	data <- twins
 	population = "patid"
-	m <- cosinor(formula = f, data = data, tau = 24, population = population)
+	m1 <- cosinor(formula = f, data = data, tau = 24, population = population)
+	m2 <- cosinor(formula = f, data = data, tau = c(24, 12,8), population = population)
 
+})
+
+
+test_that("kfits dataframe is appropriate for population mean", {
+
+	df <- as.data.frame(matrix(NA,3120,3)) # data frame for data
+	names(df) <- c("time","subject", "HR") # variable names
+	t <- c(1:520) # time
+	df[,1] <- rep(1:520,6) # six subjects
+	df[,2] <- rep(1:6,520)[order(rep(1:6,520))] # time for each subject
+	set.seed(1) # seed for rnd
+
+	# generates six different signals with some noise
+	for (i in 1:6){
+		M <- rnorm(1, mean=70, sd=5)
+		A <- rnorm(1, mean=3, sd=0.1)
+		phi <- rnorm(1, mean=60, sd=10)
+		e <- rnorm(c(1:520), mean=0, sd=5)
+		hr.curve <- M + A*cos((2*pi/260)*t+phi)+e
+		df[520*(i-1)+(1:520),3] <- hr.curve
+		#print(plot(t,hr.curve))
+	}
+
+	formula <- HR ~ time
+	data <- df
+	tau <- 260
+	population <- "subject"
+	# Was erroring in the past because of a sapply leading to a matrix
+	m <- cosinor(formula = formula, data = data, tau = tau, population = population)
+	expect_s3_class(m, "cosinor")
 
 })