| Type: | Package |
| Title: | Analysis of Single-Cell Viral Growth Curves |
| Version: | 0.3.0 |
| Description: | Aims to quantify time intensity data by using sigmoidal and double sigmoidal curves. It fits straight lines, sigmoidal, and double sigmoidal curves on to time vs intensity data. Then all the fits are used to make decision on which model best describes the data. This method was first developed in the context of single-cell viral growth analysis (for details, see Caglar et al. (2018) <doi:10.7717/peerj.4251>), and the package name stands for "SIngle CEll Growth Analysis in R". |
| URL: | https://github.com/wilkelab/sicegar/, https://hardin47.github.io/sicegar/ |
| Imports: | minpack.lm, fBasics, ggplot2, stats, dplyr |
| License: | GPL-2 | GPL-3 |
| Suggests: | covr, cowplot, testthat, knitr, rmarkdown |
| VignetteBuilder: | knitr |
| BugReports: | https://github.com/hardin47/sicegar/issues |
| Collate: | 'globals.R' 'categorize.R' 'categorize_h0.R' 'mainFunctions.R' 'multipleFitFunction.R' 'multipleFitFunction_h0.R' 'sigmoidalFitFunctions.R' 'sigmoidalFitFunctions_h0.R' 'doublesigmoidalFitFunctions.R' 'doublesigmoidalFitFunctions_h0.R' 'normalizationFunction.R' 'sicegar.R' 'dataInputCheck.R' 'parameterCalculation.R' 'parameterCalculation_h0.R' 'figureGeneration.R' |
| RoxygenNote: | 7.3.2 |
| Encoding: | UTF-8 |
| NeedsCompilation: | no |
| Packaged: | 2025-11-15 16:41:22 UTC; jsh04747 |
| Author: | Johanna Hardin 
[aut, cre],
Samuel Butler [aut],
Phineus Choi [aut],
Thomas Matheis [aut],
Mira Terdiman [aut],
M. Umut Caglar [aut],
Claus O. Wilke
[aut] |
| Maintainer: | Johanna Hardin <jo.hardin@pomona.edu> |
| Repository: | CRAN |
| Date/Publication: | 2025-11-15 22:40:02 UTC |
Categorize input data by comparing the AIC values of the three fitted models.
Description
Categorizes the input data using the results of two model fits and chosen thresholds.
Usage
categorize(
parameterVectorSigmoidal,
parameterVectorDoubleSigmoidal,
threshold_intensity_range = 0.1,
threshold_minimum_for_intensity_maximum = 0.3,
threshold_bonus_sigmoidal_AIC = 0,
threshold_sm_tmax_IntensityRatio = 0.85,
threshold_dsm_tmax_IntensityRatio = 0.75,
threshold_AIC = -10,
threshold_t0_max_int = 1e+10,
showDetails = FALSE
)
Arguments
parameterVectorSigmoidal |
Output of the sigmoidalFitFunction. |
parameterVectorDoubleSigmoidal |
Output of the doublesigmoidalFitFunction. |
threshold_intensity_range |
Minimum for intensity range, i.e. it is the lower limit for the allowed difference between the maximum and minimum of the intensities (Default is 0.1, and the values are based on actual, not the rescaled data.). |
threshold_minimum_for_intensity_maximum |
Minimum allowed value for intensity maximum. (Default is 0.3, and the values are based on actual, not the normalized data.). |
threshold_bonus_sigmoidal_AIC |
Bonus AIC points for sigmoidal fit. Negative values help the sigmoidal model to win. Only helps in competition between sigmoidal and double sigmoidal fit at decision step "9", i.e. if none of the models fail in any of the tests and stay as a candidate until the last step (Default is 0). |
threshold_sm_tmax_IntensityRatio |
The threshold for the minimum intensity ratio between the intensity at the last observed time points and the theoretical maximum intensity of the sigmoidal curve. If the value is below the threshold, then the data can not be represented with the sigmoidal model. (Default is 0.85) |
threshold_dsm_tmax_IntensityRatio |
The threshold for the minimum intensity ratio between the intensity at the last observed time points and the maximum intensity of the double sigmoidal curve. If the value is above the threshold, then the data can not be represented with the double sigmoidal model. (Default is 0.75) |
threshold_AIC |
Maximum AIC values in order to have a meaningful fit (Default is -10). |
threshold_t0_max_int |
Maximum allowed intensity of the fitted curve at time is equal to zero (t=0). (Default is 1E10, and the values are based on actual, not the normalized data.). |
showDetails |
Logical to choose if we want to see details or not. Default is "FALSE" |
Value
The returned object contains extensive information about the decision process, but the key component is the decision variable. The decision variable can be one of the following four; "no_signal", "sigmoidal","double sigmoidal" or "ambiguous".
Examples
# runif() is used here for consistency with previous versions of the sicegar package. However,
# rnorm() will generate symmetric errors, producing less biased numerical parameter estimates.
# We recommend errors generated with rnorm() for any simulation studies on sicegar.
# Example 1 with double sigmoidal data
time=seq(3, 24, 0.1)
#simulate intensity data and add noise
noise_parameter <- 0.2
intensity_noise <- runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity <- sicegar::doublesigmoidalFitFormula(time,
finalAsymptoteIntensityRatio = .3,
maximum = 4,
slope1Param = 1,
midPoint1Param = 7,
slope2Param = 1,
midPointDistanceParam = 8)
intensity <- intensity + intensity_noise
dataInput <- data.frame(intensity = intensity, time = time)
normalizedInput <- sicegar::normalizeData(dataInput,
dataInputName = "sample001")
# Fit sigmoidal model
sigmoidalModel <- sicegar::multipleFitFunction(dataInput = normalizedInput,
model = "sigmoidal",
n_runs_min = 20,
n_runs_max = 500,
showDetails = FALSE)
# Fit double sigmoidal model
doubleSigmoidalModel <- sicegar::multipleFitFunction(dataInput = normalizedInput,
model = "doublesigmoidal",
n_runs_min = 20,
n_runs_max = 500,
showDetails = FALSE)
# Calculate additional parameters
sigmoidalModel <- sicegar::parameterCalculation(sigmoidalModel)
doubleSigmoidalModel <- sicegar::parameterCalculation(doubleSigmoidalModel)
outputCluster <- sicegar::categorize(parameterVectorSigmoidal = sigmoidalModel,
parameterVectorDoubleSigmoidal = doubleSigmoidalModel)
utils::str(outputCluster)
Categorize input data by comparing the AIC values of the three fitted models.
Description
Categorizes the input data using the results of two model fits and chosen thresholds.
Usage
categorize_h0(
parameterVectorSigmoidal,
parameterVectorDoubleSigmoidal,
threshold_intensity_range = 0.1,
threshold_minimum_for_intensity_maximum = 0.3,
threshold_bonus_sigmoidal_AIC = 0,
threshold_sm_tmax_IntensityRatio = 0.85,
threshold_dsm_tmax_IntensityRatio = 0.75,
threshold_AIC = -10,
threshold_t0_max_int = 1e+10,
showDetails = FALSE
)
Arguments
parameterVectorSigmoidal |
Output of the sigmoidalFitFunction. |
parameterVectorDoubleSigmoidal |
Output of the doublesigmoidalFitFunction. |
threshold_intensity_range |
Minimum for intensity range, i.e. it is the lower limit for the allowed difference between the maximum and minimum of the intensities (Default is 0.1, and the values are based on actual, not the rescaled data.). |
threshold_minimum_for_intensity_maximum |
Minimum allowed value for intensity maximum. (Default is 0.3, and the values are based on actual, not the normalized data.). |
threshold_bonus_sigmoidal_AIC |
Bonus AIC points for sigmoidal fit. Negative values help the sigmoidal model to win. Only helps in competition between sigmoidal and double sigmoidal fit at decision step "9", i.e. if none of the models fail in any of the tests and stay as a candidate until the last step (Default is 0). |
threshold_sm_tmax_IntensityRatio |
The threshold for the minimum intensity ratio between the intensity at the last observed time points and the theoretical maximum intensity of the sigmoidal curve. If the value is below the threshold, then the data can not be represented with the sigmoidal model. (Default is 0.85) |
threshold_dsm_tmax_IntensityRatio |
The threshold for the minimum intensity ratio between the intensity at the last observed time points and the maximum intensity of the double sigmoidal curve. If the value is above the threshold, then the data can not be represented with the double sigmoidal model. (Default is 0.75) |
threshold_AIC |
Maximum AIC values in order to have a meaningful fit (Default is -10). |
threshold_t0_max_int |
Maximum allowed intensity of the fitted curve at time is equal to zero (t=0). (Default is 1E10, and the values are based on actual, not the normalized data.). |
showDetails |
Logical to choose if we want to see details or not. Default is "FALSE" |
Value
The returned object contains extensive information about the decision process, but the key component is the decision variable. The decision variable can be one of the following four; "no_signal", "sigmoidal","double sigmoidal" or "ambiguous".
Examples
# runif() is used here for consistency with previous versions of the sicegar package. However,
# rnorm() will generate symmetric errors, producing less biased numerical parameter estimates.
# We recommend errors generated with rnorm() for any simulation studies on sicegar.
# Example 1 with double sigmoidal data
time=seq(3, 24, 0.1)
#simulate intensity data and add noise
noise_parameter <- 0.2
intensity_noise <- runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity <- sicegar::doublesigmoidalFitFormula_h0(time,
finalAsymptoteIntensityRatio = .3,
maximum = 4,
slope1Param = 1,
midPoint1Param = 7,
slope2Param = 1,
midPointDistanceParam = 8,
h0 = 1)
intensity <- intensity + intensity_noise
dataInput <- data.frame(intensity = intensity, time = time)
normalizedInput <- sicegar::normalizeData(dataInput,
dataInputName = "sample001")
# Fit sigmoidal model
sigmoidalModel <- sicegar::multipleFitFunction_h0(dataInput = normalizedInput,
model = "sigmoidal",
n_runs_min = 20,
n_runs_max = 500,
showDetails = FALSE)
# Fit double sigmoidal model
doubleSigmoidalModel <- sicegar::multipleFitFunction_h0(dataInput = normalizedInput,
model = "doublesigmoidal",
n_runs_min = 20,
n_runs_max = 500,
showDetails = FALSE)
# Calculate additional parameters
sigmoidalModel <- sicegar::parameterCalculation_h0(sigmoidalModel)
doubleSigmoidalModel <- sicegar::parameterCalculation_h0(doubleSigmoidalModel)
outputCluster <- sicegar::categorize_h0(parameterVectorSigmoidal = sigmoidalModel,
parameterVectorDoubleSigmoidal = doubleSigmoidalModel)
utils::str(outputCluster)
Checks if data is in correct format.
Description
Checks if the input data is appropriate and if it is not, the function converts it into a suitable form. The input data frame should contain two columns named time and intensity related to time variable and intensity variable respectively. If the data frame is in a list its name in the list should be $timeIntensityData.
Usage
dataCheck(data, showDetails = TRUE)
Arguments
data |
the input data. It can be either a list that contains a data frame in .$timeIntensityData or can be a data frame by itself. |
showDetails |
logical, if TRUE the function will provide an output "check done" if everything is OK. Default is FALSE |
Examples
# runif() is used here for consistency with previous versions of the sicegar package. However,
# rnorm() will generate symmetric errors, producing less biased numerical parameter estimates.
# We recommend errors generated with rnorm() for any simulation studies on sicegar.
# Example 1
# generate data frame
time <- seq(3, 48, 0.5)
intensity <- runif(length(time), 3.0, 7.5)
dataInput <- data.frame(time, intensity)
# Apply dataCheck function
dataOutputVariable <- dataCheck(dataInput)
# Example 2
# generate data frame
time <- seq(3, 48, 0.5)
intensity <- runif(length(time), 3.0, 7.5)
dataInput <- data.frame(time, intensity)
# Normalize Data
dataOutput <- normalizeData(dataInput)
dataInput2 <- dataOutput
# Apply dataCheck function
dataOutputVariable2 <- dataCheck(dataInput2)
Double Sigmoidal Formula
Description
Calculates intensities using the double sigmoidal model fit and the parameters (maximum, final asymptote intensity, slope1Param, midpoint1Param, slope2Param, and midpoint distance).
Usage
doublesigmoidalFitFormula(
x,
finalAsymptoteIntensityRatio,
maximum,
slope1Param,
midPoint1Param,
slope2Param,
midPointDistanceParam
)
Arguments
x |
the "time" column of the dataframe |
finalAsymptoteIntensityRatio |
this is the ratio between the final asymptote intensity and maximum intensity of the fitted curve. |
maximum |
the maximum intensity that the double sigmoidal function reaches. |
slope1Param |
the slope parameter of the sigmoidal function at the steepest point in the exponential phase of the viral production. |
midPoint1Param |
the x axis value of the steepest point in the function. |
slope2Param |
the slope parameter of the sigmoidal function at the steepest point in the lysis phase. i.e when the intensity is decreasing. |
midPointDistanceParam |
the distance between the time of steepest increase and steepest decrease in the intensity data. In other words the distance between the x axis values of arguments of slope1Param and slope2Param. |
Value
Returns the predicted intensities for the given time points with the double sigmoidal fitted parameters for the double sigmoidal fit.
Examples
# runif() is used here for consistency with previous versions of the sicegar package. However,
# rnorm() will generate symmetric errors, producing less biased numerical parameter estimates.
# We recommend errors generated with rnorm() for any simulation studies on sicegar.
time <- seq(3, 24, 0.1)
#simulate intensity data and add noise
noise_parameter <- 0.2
intensity_noise <- stats::runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity <- doublesigmoidalFitFormula(time,
finalAsymptoteIntensityRatio = .3,
maximum = 4,
slope1Param = 1,
midPoint1Param = 7,
slope2Param = 1,
midPointDistanceParam = 8)
intensity <- intensity + intensity_noise
dataInput <- data.frame(intensity = intensity, time = time)
normalizedInput <- normalizeData(dataInput)
parameterVector <- doublesigmoidalFitFunction(normalizedInput, tryCounter = 2)
#Check the results
if(parameterVector$isThisaFit){
intensityTheoretical <-
doublesigmoidalFitFormula(
time,
finalAsymptoteIntensityRatio = parameterVector$finalAsymptoteIntensityRatio_Estimate,
maximum = parameterVector$maximum_Estimate,
slope1Param = parameterVector$slope1Param_Estimate,
midPoint1Param = parameterVector$midPoint1Param_Estimate,
slope2Param = parameterVector$slope2Param_Estimate,
midPointDistanceParam = parameterVector$midPointDistanceParam_Estimate)
comparisonData <- cbind(dataInput, intensityTheoretical)
require(ggplot2)
ggplot(comparisonData) +
geom_point(aes(x = time, y = intensity)) +
geom_line(aes(x = time, y = intensityTheoretical)) +
expand_limits(x = 0, y = 0)
}
if(!parameterVector$isThisaFit){
print(parameterVector)
}
Double Sigmoidal Formula with h0
Description
Calculates intensities using the double sigmoidal model fit and the parameters (maximum, final asymptote intensity, slope1Param, midpoint1Param, slope2Param, and midpoint distance).
Usage
doublesigmoidalFitFormula_h0(
x,
finalAsymptoteIntensityRatio,
maximum,
slope1Param,
midPoint1Param,
slope2Param,
midPointDistanceParam,
h0
)
Arguments
x |
the "time" column of the dataframe |
finalAsymptoteIntensityRatio |
this is the ratio between the final asymptote intensity and maximum intensity of the fitted curve. |
maximum |
the maximum intensity that the double sigmoidal function reaches. |
slope1Param |
the slope parameter of the sigmoidal function at the steepest point in the exponential phase of the viral production. |
midPoint1Param |
the x axis value of the steepest point in the function. |
slope2Param |
the slope parameter of the sigmoidal function at the steepest point in the lysis phase. i.e when the intensity is decreasing. |
midPointDistanceParam |
the distance between the time of steepest increase and steepest decrease in the intensity data. In other words the distance between the x axis values of arguments of slope1Param and slope2Param. |
h0 |
the lower asymptote (baseline) intensity |
Value
Returns the predicted intensities for the given time points with the double sigmoidal fitted parameters for the double sigmoidal fit.
Examples
# runif() is used here for consistency with previous versions of the sicegar package. However,
# rnorm() will generate symmetric errors, producing less biased numerical parameter estimates.
# We recommend errors generated with rnorm() for any simulation studies on sicegar.
time <- seq(3, 24, 0.1)
#simulate intensity data and add noise
noise_parameter <- 0.2
intensity_noise <- stats::runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity <- doublesigmoidalFitFormula_h0(time,
finalAsymptoteIntensityRatio = .3,
maximum = 4,
slope1Param = 1,
midPoint1Param = 7,
slope2Param = 1,
midPointDistanceParam = 8,
h0 = 1)
intensity <- intensity + intensity_noise
dataInput <- data.frame(intensity = intensity, time = time)
normalizedInput <- normalizeData(dataInput)
parameterVector <- doublesigmoidalFitFunction_h0(normalizedInput, tryCounter = 1)
#Check the results
# doublesigmoidalFitFunction_h0() is run on the startList param values (because 'tryCounter = 1')
# use multipleFitFunction() for multiple random starts in order to optimize
if(parameterVector$isThisaFit){
intensityTheoretical <-
doublesigmoidalFitFormula_h0(
time,
finalAsymptoteIntensityRatio = parameterVector$finalAsymptoteIntensityRatio_Estimate,
maximum = parameterVector$maximum_Estimate,
slope1Param = parameterVector$slope1Param_Estimate,
midPoint1Param = parameterVector$midPoint1Param_Estimate,
slope2Param = parameterVector$slope2Param_Estimate,
midPointDistanceParam = parameterVector$midPointDistanceParam_Estimate,
h0 = parameterVector$h0_Estimate)
comparisonData <- cbind(dataInput, intensityTheoretical)
require(ggplot2)
ggplot(comparisonData) +
geom_point(aes(x = time, y = intensity)) +
geom_line(aes(x = time, y = intensityTheoretical)) +
expand_limits(x = 0, y = 0)
}
if(!parameterVector$isThisaFit){
print(parameterVector)
}
Double sigmoidal fit function with h0.
Description
The function fits a double sigmoidal curve to given data by using likelihood maximization (LM) algorithm and provides the parameters (maximum, final asymptote intensity, slope1Param, midpoint1Param, slope2Param, and midpoint distance) describing the double sigmoidal fit as output. It also contains information about the goodness of fits such as AIC, BIC, residual sum of squares, and log likelihood.
Usage
doublesigmoidalFitFunction(
dataInput,
tryCounter,
startList = list(finalAsymptoteIntensityRatio = 0, maximum = 1, slope1Param = 1,
midPoint1Param = 0.33, slope2Param = 1, midPointDistanceParam = 0.29),
lowerBounds = c(finalAsymptoteIntensityRatio = 0, maximum = 0.3, slope1Param = 0.01,
midPoint1Param = -0.52, slope2Param = 0.01, midPointDistanceParam = 0.04),
upperBounds = c(finalAsymptoteIntensityRatio = 1, maximum = 1.5, slope1Param = 180,
midPoint1Param = 1.15, slope2Param = 180, midPointDistanceParam = 0.63),
min_Factor = 1/2^20,
n_iterations = 1000
)
Arguments
dataInput |
A dataframe or a list containing the dataframe. The dataframe should be composed of at least two columns. One represents time, and the other represents intensity. The data should be normalized with the normalize data function sicegar::normalizeData() before being imported into this function. |
tryCounter |
A counter that shows the number of times the data was fit via maximum likelihood function. |
startList |
The vector containing the initial set of parameters that algorithm tries for the first fit attempt for the relevant parameters. The vector composes of seven elements; 'finalAsymptoteIntensityRatio', 'maximum', 'slope1Param', 'midPoint1Param' , 'slope2Param', 'midPointDistanceParam', and 'h0'. Detailed explanations of those parameters can be found in vignettes. Defaults are finalAsymptoteIntensityRatio = 0, maximum = 1, slope1Param = 1, midPoint1Param = 0.33, slope2Param = 1, midPointDistanceParam=0.29, and h0 = 0. The numbers are in normalized time intensity scale. |
lowerBounds |
The lower bounds for the randomly generated start parameters. The vector is composed of seven elements; 'finalAsymptoteIntensityRatio', 'maximum', 'slope1Param', 'midPoint1Param' , 'slope2Param', 'midPointDistanceParam', and 'h0'. Detailed explanations of those parameters can be found in vignettes. Defaults are finalAsymptoteIntensityRatio = 0, maximum = 0.3, slope1Param = .01, midPoint1Param = -0.52, slope2Param = .01, midPointDistanceParam = 0.04, and h0 = -0.1. The numbers are in normalized time intensity scale. |
upperBounds |
The upper bounds for the randomly generated start parameters. The vector is composed of seven elements; 'finalAsymptoteIntensityRatio', 'maximum', 'slope1Param', 'midPoint1Param' , 'slope2Param', 'midPointDistanceParam', and 'h0'. Detailed explanations of those parameters can be found in vignettes. Defaults are finalAsymptoteIntensityRatio = 1, maximum = 1.5, slope1Param = 180, midPoint1Param = 1.15, slope2Param = 180, midPointDistanceParam = 0.63, and h0 = 0.3. The numbers are in normalized time intensity scale. |
min_Factor |
Defines the minimum step size used by the fitting algorithm. Default is 1/2^20. |
n_iterations |
Defines maximum number of iterations used by the fitting algorithm. Default is 1000. |
Value
Returns the fitted parameters and goodness of fit metrics.
Examples
# runif() is used here for consistency with previous versions of the sicegar package. However,
# rnorm() will generate symmetric errors, producing less biased numerical parameter estimates.
# We recommend errors generated with rnorm() for any simulation studies on sicegar.
time=seq(3, 24, 0.1)
#simulate intensity data and add noise
noise_parameter <- 0.2
intensity_noise <- stats::runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity <- doublesigmoidalFitFormula(time,
finalAsymptoteIntensityRatio = .3,
maximum = 4,
slope1Param = 1,
midPoint1Param = 7,
slope2Param = 1,
midPointDistanceParam = 8)
intensity <- intensity + intensity_noise
dataInput <- data.frame(intensity = intensity, time = time)
normalizedInput <- normalizeData(dataInput)
parameterVector <- doublesigmoidalFitFunction(normalizedInput, tryCounter = 2)
#Check the results
if(parameterVector$isThisaFit){
intensityTheoretical <-
doublesigmoidalFitFormula(
time,
finalAsymptoteIntensityRatio = parameterVector$finalAsymptoteIntensityRatio_Estimate,
maximum = parameterVector$maximum_Estimate,
slope1Param = parameterVector$slope1Param_Estimate,
midPoint1Param = parameterVector$midPoint1Param_Estimate,
slope2Param = parameterVector$slope2Param_Estimate,
midPointDistanceParam = parameterVector$midPointDistanceParam_Estimate)
comparisonData <- cbind(dataInput, intensityTheoretical)
require(ggplot2)
ggplot(comparisonData) +
geom_point(aes(x = time, y = intensity)) +
geom_line(aes(x = time, y = intensityTheoretical)) +
expand_limits(x = 0, y = 0)}
if(!parameterVector$isThisaFit) {print(parameterVector)}
Double sigmoidal fit function with h0.
Description
The function fits a double sigmoidal curve to given data by using likelihood maximization (LM) algorithm and provides the parameters (maximum, final asymptote intensity, slope1Param, midpoint1Param, slope2Param, and midpoint distance) describing the double sigmoidal fit as output. It also contains information about the goodness of fits such as AIC, BIC, residual sum of squares, and log likelihood.
Usage
doublesigmoidalFitFunction_h0(
dataInput,
tryCounter,
startList = list(finalAsymptoteIntensityRatio = 0, maximum = 1, slope1Param = 1,
midPoint1Param = 0.33, slope2Param = 1, midPointDistanceParam = 0.29, h0 = 0.1),
lowerBounds = c(finalAsymptoteIntensityRatio = 0, maximum = 0.3, slope1Param = 0.01,
midPoint1Param = -0.52, slope2Param = 0.01, midPointDistanceParam = 0.04, h0 = 0),
upperBounds = c(finalAsymptoteIntensityRatio = 1, maximum = 1.5, slope1Param = 180,
midPoint1Param = 1.15, slope2Param = 180, midPointDistanceParam = 0.63, h0 = 0.3),
min_Factor = 1/2^20,
n_iterations = 1000
)
Arguments
dataInput |
A dataframe or a list containing the dataframe. The dataframe should be composed of at least two columns. One represents time, and the other represents intensity. The data should be normalized with the normalize data function sicegar::normalizeData() before being imported into this function. |
tryCounter |
A counter that shows the number of times the data was fit via maximum likelihood function. |
startList |
The vector containing the initial set of parameters that algorithm tries for the first fit attempt for the relevant parameters. The vector composes of seven elements; 'finalAsymptoteIntensityRatio', 'maximum', 'slope1Param', 'midPoint1Param' , 'slope2Param', 'midPointDistanceParam', and 'h0'. Detailed explanations of those parameters can be found in vignettes. Defaults are finalAsymptoteIntensityRatio = 0, maximum = 1, slope1Param = 1, midPoint1Param = 0.33, slope2Param = 1, midPointDistanceParam=0.29, and h0 = 0. The numbers are in normalized time intensity scale. |
lowerBounds |
The lower bounds for the randomly generated start parameters. The vector is composed of seven elements; 'finalAsymptoteIntensityRatio', 'maximum', 'slope1Param', 'midPoint1Param' , 'slope2Param', 'midPointDistanceParam', and 'h0'. Detailed explanations of those parameters can be found in vignettes. Defaults are finalAsymptoteIntensityRatio = 0, maximum = 0.3, slope1Param = .01, midPoint1Param = -0.52, slope2Param = .01, midPointDistanceParam = 0.04, and h0 = -0.1. The numbers are in normalized time intensity scale. |
upperBounds |
The upper bounds for the randomly generated start parameters. The vector is composed of seven elements; 'finalAsymptoteIntensityRatio', 'maximum', 'slope1Param', 'midPoint1Param' , 'slope2Param', 'midPointDistanceParam', and 'h0'. Detailed explanations of those parameters can be found in vignettes. Defaults are finalAsymptoteIntensityRatio = 1, maximum = 1.5, slope1Param = 180, midPoint1Param = 1.15, slope2Param = 180, midPointDistanceParam = 0.63, and h0 = 0.3. The numbers are in normalized time intensity scale. |
min_Factor |
Defines the minimum step size used by the fitting algorithm. Default is 1/2^20. |
n_iterations |
Defines maximum number of iterations used by the fitting algorithm. Default is 1000. |
Value
Returns the fitted parameters and goodness of fit metrics.
Examples
# runif() is used here for consistency with previous versions of the sicegar package. However,
# rnorm() will generate symmetric errors, producing less biased numerical parameter estimates.
# We recommend errors generated with rnorm() for any simulation studies on sicegar.
time=seq(3, 24, 0.1)
#simulate intensity data and add noise
noise_parameter <- 0.2
intensity_noise <- stats::runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity <- doublesigmoidalFitFormula_h0(time,
finalAsymptoteIntensityRatio = .3,
maximum = 4,
slope1Param = 1,
midPoint1Param = 7,
slope2Param = 1,
midPointDistanceParam = 8,
h0 = 1)
intensity <- intensity + intensity_noise
dataInput <- data.frame(intensity = intensity, time = time)
normalizedInput <- normalizeData(dataInput)
parameterVector <- doublesigmoidalFitFunction_h0(normalizedInput, tryCounter = 1)
#Check the results
# doublesigmoidalFitFunction_h0() is run on the startList param values (because 'tryCounter = 1')
# use multipleFitFunction() for multiple random starts in order to optimize
if(parameterVector$isThisaFit){
intensityTheoretical <-
doublesigmoidalFitFormula_h0(
time,
finalAsymptoteIntensityRatio = parameterVector$finalAsymptoteIntensityRatio_Estimate,
maximum = parameterVector$maximum_Estimate,
slope1Param = parameterVector$slope1Param_Estimate,
midPoint1Param = parameterVector$midPoint1Param_Estimate,
slope2Param = parameterVector$slope2Param_Estimate,
midPointDistanceParam = parameterVector$midPointDistanceParam_Estimate,
h0 = parameterVector$h0_Estimate)
comparisonData <- cbind(dataInput, intensityTheoretical)
require(ggplot2)
ggplot(comparisonData) +
geom_point(aes(x = time, y = intensity)) +
geom_line(aes(x = time, y = intensityTheoretical)) +
expand_limits(x = 0, y = 0)}
if(!parameterVector$isThisaFit) {print(parameterVector)}
Generate model associated figures.
Description
Generates figures using ggplot that shows the input data and the fitted curves.
Usage
figureModelCurves(
dataInput,
sigmoidalFitVector = NULL,
doubleSigmoidalFitVector = NULL,
showParameterRelatedLines = FALSE,
xlabelText = "time",
ylabelText = "intensity",
fittedXmin = 0,
fittedXmax = NA,
use_h0 = FALSE
)
Arguments
dataInput |
A dataframe or a list containing the dataframe. The data frame should be composed of at least two columns. One represents time, and the other represents intensity. The data should be normalized with the normalize data function sicegar::normalizeData() before imported into this function. |
sigmoidalFitVector |
the output of the sicegar::sigmoidalFitFunction(), or the augmented version of the output generated by sicegar::parameterCalculation(), which contains parameters from the sigmoidal model. Default is NULL. |
doubleSigmoidalFitVector |
the output of the sicegar::doubleSigmoidalFitFunction(), or the augmented version of the output generated by sicegar::parameterCalculation(), which contains parameters from the double sigmoidal model. Default is NULL. |
showParameterRelatedLines |
if equal to TRUE, figure will show parameter related lines on the curves. Default is FALSE. |
xlabelText |
the x-axis name; with default "time" |
ylabelText |
the y-axis name; with default "intensity" |
fittedXmin |
the minimum of the fitted data that will be plotted (Default 0) |
fittedXmax |
the maximum of the fitted data that will be plotted (Default timeRange) |
use_h0 |
Boolean which decides whether to fix the lower asymptote of h0 at 0 (FALSE, default) or to freely estimate h0 (TRUE) |
Value
Returns inflection curve figures.
Examples
# runif() is used here for consistency with previous versions of the sicegar package. However,
# rnorm() will generate symmetric errors, producing less biased numerical parameter estimates.
# We recommend errors generated with rnorm() for any simulation studies on sicegar.
time <- seq(3, 24, 0.1)
#simulate intensity data and add noise
noise_parameter <- 0.2
intensity_noise <- runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity <- sicegar::doublesigmoidalFitFormula(time,
finalAsymptoteIntensityRatio = .3,
maximum = 4,
slope1Param = 1,
midPoint1Param = 7,
slope2Param = 1,
midPointDistanceParam = 8)
intensity <- intensity + intensity_noise
dataInput <- data.frame(intensity = intensity, time = time)
normalizedInput <- sicegar::normalizeData(dataInput, dataInputName = "sample001")
# Do the double sigmoidal fit
doubleSigmoidalModel <- sicegar::multipleFitFunction(dataInput = normalizedInput,
model = "doublesigmoidal",
n_runs_min = 20,
n_runs_max = 500,
showDetails = FALSE)
doubleSigmoidalModel <- sicegar::parameterCalculation(doubleSigmoidalModel)
fig01 <- sicegar::figureModelCurves(dataInput = normalizedInput,
doubleSigmoidalFitVector = doubleSigmoidalModel,
showParameterRelatedLines = TRUE)
print(fig01)
Fit and categorize.
Description
Fits the sigmoidal and double-sigmoidal models to the data and then categorizes the data according to which model fits best.
Usage
fitAndCategorize(
dataInput,
dataInputName = NA,
n_runs_min_sm = 20,
n_runs_max_sm = 500,
n_runs_min_dsm = 20,
n_runs_max_dsm = 500,
showDetails = FALSE,
startList_sm = list(maximum = 1, slopeParam = 1, midPoint = 0.33),
lowerBounds_sm = c(maximum = 0.3, slopeParam = 0.01, midPoint = -0.52),
upperBounds_sm = c(maximum = 1.5, slopeParam = 180, midPoint = 1.15),
min_Factor_sm = 1/2^20,
n_iterations_sm = 1000,
startList_dsm = list(finalAsymptoteIntensityRatio = 0, maximum = 1, slope1Param = 1,
midPoint1Param = 0.33, slope2Param = 1, midPointDistanceParam = 0.29),
lowerBounds_dsm = c(finalAsymptoteIntensityRatio = 0, maximum = 0.3, slope1Param =
0.01, midPoint1Param = -0.52, slope2Param = 0.01, midPointDistanceParam = 0.04),
upperBounds_dsm = c(finalAsymptoteIntensityRatio = 1, maximum = 1.5, slope1Param = 180,
midPoint1Param = 1.15, slope2Param = 180, midPointDistanceParam = 0.63),
min_Factor_dsm = 1/2^20,
n_iterations_dsm = 1000,
threshold_intensity_range = 0.1,
threshold_minimum_for_intensity_maximum = 0.3,
threshold_bonus_sigmoidal_AIC = 0,
threshold_sm_tmax_IntensityRatio = 0.85,
threshold_dsm_tmax_IntensityRatio = 0.75,
threshold_AIC = -10,
threshold_t0_max_int = 1e+10,
stepSize = 1e-05,
startList_sm_h0 = list(maximum = 1, slopeParam = 1, midPoint = 0.33, h0 = 0),
lowerBounds_sm_h0 = c(maximum = 0.3, slopeParam = 0.01, midPoint = -0.52, h0 = -0.1),
upperBounds_sm_h0 = c(maximum = 1.5, slopeParam = 180, midPoint = 1.15, h0 = 0.3),
startList_dsm_h0 = list(finalAsymptoteIntensityRatio = 0, maximum = 1, slope1Param = 1,
midPoint1Param = 0.33, slope2Param = 1, midPointDistanceParam = 0.29, h0 = 0),
lowerBounds_dsm_h0 = c(finalAsymptoteIntensityRatio = 0, maximum = 0.3, slope1Param =
0.01, midPoint1Param = -0.52, slope2Param = 0.01, midPointDistanceParam = 0.04, h0 =
-0.1),
upperBounds_dsm_h0 = c(finalAsymptoteIntensityRatio = 1, maximum = 1.5, slope1Param =
180, midPoint1Param = 1.15, slope2Param = 180, midPointDistanceParam = 0.63, h0 =
0.3),
use_h0 = FALSE,
...
)
Arguments
dataInput |
Unnormalized input data that will be fitted then transferred into relevant functions |
dataInputName |
Name of data set (Default is 'NA'). |
n_runs_min_sm |
This number indicates the lower limit of the successful fitting attempts for sigmoidal model. It should be smaller than the upper limit of the fitting attempts (n_runs_max_sm). Default is 20 |
n_runs_max_sm |
This number indicates the upper limit of the fitting attempts for sigmoidal model. Default is 500 |
n_runs_min_dsm |
This number indicates the lower limit of the successful fitting attempts for double sigmoidal model. It should be smaller than the upper limit of the fitting attempts (n_runs_max_dsm). Default is 20 |
n_runs_max_dsm |
This number indicates the upper limit of the fitting attempts for sigmoidal model for double sigmoidal model. Default is 500 |
showDetails |
Logical if TRUE prints details of intermediate steps of individual fits (Default is FALSE). |
startList_sm |
The vector containing the initial set of parameters that sigmoidal fit algorithm tries for the first fit attempt. The vector is composed of three elements; 'maximum', 'slopeParam' and, 'midPoint'. Detailed explanations of those parameters can be found in vignettes. Defaults are maximum = 1, slopeParam = 1 and, midPoint = 0.33. The numbers are in normalized time intensity scale. |
lowerBounds_sm |
The lower bounds for the randomly generated start parameters for the sigmoidal fit. The vector is composed of three elements; 'maximum', 'slopeParam' and, 'midPoint'. Detailed explanations of those parameters can be found in vignettes. Defaults are maximum = 0.3, slopeParam = 0.01, and midPoint = -0.52. The numbers are in normalized time intensity scale. |
upperBounds_sm |
The upper bounds for the randomly generated start parameters for the sigmoidal fit. The vector is composed of three elements; 'maximum', 'slopeParam' and, 'midPoint'. Detailed explanations of those parameters can be found in vignettes. Defaults are maximum = 1.5, slopeParam = 180, midPoint = 1.15. The numbers are in normalized time intensity scale. |
min_Factor_sm |
Defines the minimum step size used by the sigmoidal fit algorithm. Default is 1/2^20. |
n_iterations_sm |
Defines maximum number of iterations used by the sigmoidal fit algorithm. Default is 1000 |
startList_dsm |
The vector containing the initial set of parameters that double sigmoidal fit algorithm tries for the first fit attempt. The vector is composed of six elements; 'finalAsymptoteIntensityRatio', 'maximum', 'slope1Param', 'midPoint1Param' , 'slope2Param', and 'midPointDistanceParam'. Detailed explanations of those parameters can be found in vignettes. Defaults are finalAsymptoteIntensityRatio = 0, maximum = 1, slope1Param = 1, midPoint1Param = 0.33, slope2Param = 1, and midPointDistanceParam=0.29. The numbers are in normalized time intensity scale. |
lowerBounds_dsm |
The lower bounds for the randomly generated start parameters for double sigmoidal fit. The vector is composed of six elements; 'finalAsymptoteIntensityRatio', 'maximum', 'slope1Param', 'midPoint1Param' , 'slope2Param', and 'midPointDistanceParam'. Detailed explanations of those parameters can be found in vignettes. Defaults are finalAsymptoteIntensityRatio = 0, maximum = 0.3, slope1Param = .01, midPoint1Param = -0.52, slope2Param = .01, and midPointDistanceParam = 0.04. The numbers are in normalized time intensity scale. |
upperBounds_dsm |
The upper bounds for the randomly generated start parameters for double sigmoidal fit. The vector is composed of six elements; 'finalAsymptoteIntensityRatio', 'maximum', 'slope1Param', 'midPoint1Param' , 'slope2Param', and 'midPointDistanceParam'. Detailed explanations of those parameters can be found in vignettes. Defaults are finalAsymptoteIntensityRatio = 1, maximum = 1.5, slope1Param = 180, midPoint1Param = 1.15, slope2Param = 180, and midPointDistanceParam = 0.63. The numbers are in normalized time intensity scale. |
min_Factor_dsm |
Defines the minimum step size used by the double sigmoidal fit algorithm. Default is 1/2^20. |
n_iterations_dsm |
Define maximum number of iterations used by the double sigmoidal fit algorithm. Default is 1000 |
threshold_intensity_range |
Minimum for intensity range, i.e. it is the lower limit for the allowed difference between the maximum and minimum of the intensities (Default is 0.1, and the values are based on actual, not the normalized data.). |
threshold_minimum_for_intensity_maximum |
Minimum allowed value for intensity maximum. (Default is 0.3, and the values are based on actual, not the normalized data.). |
threshold_bonus_sigmoidal_AIC |
Bonus AIC points for sigmoidal fit. Negative values help the sigmoidal model to win. Only helps in competition between sigmoidal and double sigmoidal fit at decision step "9", i.e. if none of the models fail in any of the tests and stay as a candidate until the last step (Default is 0). |
threshold_sm_tmax_IntensityRatio |
The threshold for the minimum intensity ratio between the intensity of the last observed time points and theoretical maximum intensity of the sigmoidal curve. If the value is below the threshold, then the data can not be represented with the sigmoidal model. (Default is 0.85) |
threshold_dsm_tmax_IntensityRatio |
The threshold for the minimum intensity ratio between the intenstiy of the last observed time points and maximum intensity of the double sigmoidal curve. If the value is above the threshold, then the data can not be represented with the double sigmoidal model. (Default is 0.75) |
threshold_AIC |
Maximum AIC value in order to have a meaningful fit (Default is -10). |
threshold_t0_max_int |
Maximum allowed intensity of the fitted curve at time equal to zero (t=0). (Default is 1E10, and the values are based on actual, not the normalized data.). |
stepSize |
Step size used by the fitting algorithm. Smaller numbers give more accurate results than larger numbers, and larger numbers give the results faster than small numbers. The default value is 0.00001. |
startList_sm_h0 |
The vector containing the initial set of parameters that sigmoidal fit algorithm tries for the first fit attempt. The vector is composed of four elements; 'maximum', 'slopeParam', 'midPoint', and 'h0'. Detailed explanations of those parameters can be found in vignettes. Defaults are maximum = 1, slopeParam = 1 and, midPoint = 0.33, h0 = 0. The numbers are in normalized time intensity scale. |
lowerBounds_sm_h0 |
The lower bounds for the randomly generated start parameters for the sigmoidal fit. The vector is composed of four elements; 'maximum', 'slopeParam', 'midPoint', and 'h0'. Detailed explanations of those parameters can be found in vignettes. Defaults are maximum = 0.3, slopeParam = 0.01, midPoint = -0.52, and h0 = -0.1. The numbers are in normalized time intensity scale. |
upperBounds_sm_h0 |
The upper bounds for the randomly generated start parameters for the sigmoidal fit. The vector is composed of four elements; 'maximum', 'slopeParam', 'midPoint', and 'h0'. Detailed explanations of those parameters can be found in vignettes. Defaults are maximum = 1.5, slopeParam = 180, midPoint = 1.15, and h0 = 0.3. The numbers are in normalized time intensity scale. |
startList_dsm_h0 |
The vector containing the initial set of parameters that double sigmoidal fit algorithm tries for the first fit attempt. The vector is composed of seven elements; 'finalAsymptoteIntensityRatio', 'maximum', 'slope1Param', 'midPoint1Param' , 'slope2Param', 'midPointDistanceParam', and 'h0'. Detailed explanations of those parameters can be found in vignettes. Defaults are finalAsymptoteIntensityRatio = 0, maximum = 1, slope1Param = 1, midPoint1Param = 0.33, slope2Param = 1, midPointDistanceParam=0.29, h0 = 0. The numbers are in normalized time intensity scale. |
lowerBounds_dsm_h0 |
The lower bounds for the randomly generated start parameters for double sigmoidal fit. The vector is composed of seven elements; 'finalAsymptoteIntensityRatio', 'maximum', 'slope1Param', 'midPoint1Param' , 'slope2Param', 'midPointDistanceParam', and 'h0'. Detailed explanations of those parameters can be found in vignettes. Defaults are finalAsymptoteIntensityRatio = 0, maximum = 0.3, slope1Param = .01, midPoint1Param = -0.52, slope2Param = .01, midPointDistanceParam = 0.04, and h0 = -0.1. The numbers are in normalized time intensity scale. |
upperBounds_dsm_h0 |
The upper bounds for the randomly generated start parameters for double sigmoidal fit. The vector is composed of seven elements; 'finalAsymptoteIntensityRatio', 'maximum', 'slope1Param', 'midPoint1Param' , 'slope2Param', 'midPointDistanceParam', and 'h0'. Detailed explanations of those parameters can be found in vignettes. Defaults are finalAsymptoteIntensityRatio = 1, maximum = 1.5, slope1Param = 180, midPoint1Param = 1.15, slope2Param = 180, midPointDistanceParam = 0.63, and h0 = 0.3. The numbers are in normalized time intensity scale. |
use_h0 |
Boolean which decides whether to fix the lower asymptote of h0 at 0 (FALSE, default) or to freely estimate h0 (TRUE) |
... |
All other arguments that model functions ("sigmoidalFitFunction" and, "doublesigmoidalFitFunction") may need. |
Value
Returns the parameters related with the curve fitted to the input data.
Examples
# runif() is used here for consistency with previous versions of the sicegar package. However,
# rnorm() will generate symmetric errors, producing less biased numerical parameter estimates.
# We recommend errors generated with rnorm() for any simulation studies on sicegar.
# Example 1
time <- seq(3, 24, 0.5)
#simulate intensity data and add noise
noise_parameter <- 0.2
intensity_noise <- stats::runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity <- sicegar::doublesigmoidalFitFormula(time,
finalAsymptoteIntensityRatio = .3,
maximum = 4,
slope1Param = 1,
midPoint1Param = 7,
slope2Param = 1,
midPointDistanceParam = 8)
intensity <- intensity + intensity_noise
dataInput <- data.frame(intensity = intensity, time = time)
fitObj <- sicegar::fitAndCategorize(dataInput = dataInput)
multiple fit function.
Description
Calls the fitting algorithms to fit the data multiple times with starting from different randomly generated initial parameters in each run. Multiple attempts at fitting the data are necessary to avoid local minima.
Usage
multipleFitFunction(
dataInput,
dataInputName = NA,
model,
n_runs_min = 20,
n_runs_max = 500,
showDetails = FALSE,
...
)
Arguments
dataInput |
A dataframe or a list containing the dataframe. The dataframe should be composed of at least two columns. One represents time, and the other represents intensity. The data should be normalized with the normalize data function sicegar::normalizeData() before being imported into this function. |
dataInputName |
Name of data set (Default is 'NA'). |
model |
Type of fit model that will be used. Can be "sigmoidal", or "double_sigmoidal". |
n_runs_min |
This number indicates the lower limit of the successful fitting attempts. It should be smaller than the upper limit of the fitting attempts (n_runs_max). Default is 20. |
n_runs_max |
This number indicates the upper limit of the fitting attempts. Default is 500. |
showDetails |
Logical if TRUE prints details of intermediate steps of individual fits (Default is FALSE). |
... |
All other arguments that model functions ("sigmoidalFitFunction" and, "doublesigmoidalFitFunction") may need. |
Value
Returns the parameters related with the model fitted for the input data.
Examples
# runif() is used here for consistency with previous versions of the sicegar package. However,
# rnorm() will generate symmetric errors, producing less biased numerical parameter estimates.
# We recommend errors generated with rnorm() for any simulation studies on sicegar.
# Example 1 (sigmoidal function with normalization)
time <- seq(3, 24, 0.5)
#simulate intensity data and add noise
noise_parameter <- 2.5
intensity_noise <- stats::runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity <- sigmoidalFitFormula(time, maximum = 4, slopeParam = 1, midPoint = 8)
intensity <- intensity + intensity_noise
dataInput <- data.frame(intensity = intensity, time = time)
normalizedInput <- normalizeData(dataInput, dataInputName = "sample001")
parameterVector <- multipleFitFunction(dataInput = normalizedInput,
model = "sigmoidal",
n_runs_min = 20,
n_runs_max = 500)
#Check the results
if(parameterVector$isThisaFit){
intensityTheoretical <- sigmoidalFitFormula(time,
maximum = parameterVector$maximum_Estimate,
slopeParam = parameterVector$slopeParam_Estimate,
midPoint = parameterVector$midPoint_Estimate)
comparisonData <- cbind(dataInput, intensityTheoretical)
print(parameterVector$residual_Sum_of_Squares)
require(ggplot2)
ggplot(comparisonData)+
geom_point(aes(x = time, y = intensity)) +
geom_line(aes(x = time, y = intensityTheoretical), color = "orange") +
expand_limits(x = 0, y = 0)
}
if(!parameterVector$isThisaFit){
print(parameterVector)
}
# Example 2 (doublesigmoidal function with normalization)
time <- seq(3, 24, 0.1)
#simulate intensity data with noise
noise_parameter <- 0.2
intensity_noise <- stats::runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity <- doublesigmoidalFitFormula(time,
finalAsymptoteIntensityRatio = .3,
maximum = 4,
slope1Param = 1,
midPoint1Param = 7,
slope2Param = 1,
midPointDistanceParam = 8)
intensity <- intensity + intensity_noise
dataInput <- data.frame(intensity = intensity, time = time)
normalizedInput <- normalizeData(dataInput)
parameterVector <- multipleFitFunction(dataInput = normalizedInput,
dataInputName="sample001",
model = "doublesigmoidal",
n_runs_min = 20,
n_runs_max = 500,
showDetails = FALSE)
#Check the results
if(parameterVector$isThisaFit){
intensityTheoretical <-
doublesigmoidalFitFormula(
time,
finalAsymptoteIntensityRatio = parameterVector$finalAsymptoteIntensityRatio_Estimate,
maximum = parameterVector$maximum_Estimate,
slope1Param = parameterVector$slope1Param_Estimate,
midPoint1Param = parameterVector$midPoint1Param_Estimate,
slope2Param = parameterVector$slope2Param_Estimate,
midPointDistanceParam = parameterVector$midPointDistanceParam_Estimate)
comparisonData <- cbind(dataInput, intensityTheoretical)
require(ggplot2)
ggplot(comparisonData) +
geom_point(aes(x = time, y = intensity)) +
geom_line(aes(x = time, y = intensityTheoretical), color = "orange") +
expand_limits(x = 0, y = 0)
}
if(!parameterVector$isThisaFit){
print(parameterVector)
}
multiple fit function h0.
Description
Calls the fitting algorithms to fit the data multiple times with starting from different randomly generated initial parameters in each run. Multiple attempts at fitting the data are necessary to avoid local minima.
Usage
multipleFitFunction_h0(
dataInput,
dataInputName = NA,
model,
n_runs_min = 20,
n_runs_max = 500,
showDetails = FALSE,
...
)
Arguments
dataInput |
A dataframe or a list containing the dataframe. The dataframe should be composed of at least two columns. One represents time, and the other represents intensity. The data should be normalized with the normalize data function sicegar::normalizeData() before being imported into this function. |
dataInputName |
Name of data set (Default is 'NA'). |
model |
Type of fit model that will be used. Can be "sigmoidal", or "double_sigmoidal". |
n_runs_min |
This number indicates the lower limit of the successful fitting attempts. It should be smaller than the upper limit of the fitting attempts (n_runs_max). Default is 20. |
n_runs_max |
This number indicates the upper limit of the fitting attempts. Default is 500. |
showDetails |
Logical if TRUE prints details of intermediate steps of individual fits (Default is FALSE). |
... |
All other arguments that model functions ("sigmoidalFitFunction" and, "doublesigmoidalFitFunction") may need. |
Value
Returns the parameters related with the model fitted for the input data.
Examples
# runif() is used here for consistency with previous versions of the sicegar package. However,
# rnorm() will generate symmetric errors, producing less biased numerical parameter estimates.
# We recommend errors generated with rnorm() for any simulation studies on sicegar.
# Example 1 (sigmoidal function with normalization)
time <- seq(3, 24, 0.5)
#simulate intensity data and add noise
noise_parameter <- 2.5
intensity_noise <- stats::runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity <- sigmoidalFitFormula_h0(time, maximum = 4, slopeParam = 1, midPoint = 8, h0 = 1)
intensity <- intensity + intensity_noise
dataInput <- data.frame(intensity = intensity, time = time)
normalizedInput <- normalizeData(dataInput, dataInputName = "sample001")
parameterVector <- multipleFitFunction_h0(dataInput = normalizedInput,
model = "sigmoidal",
n_runs_min = 20,
n_runs_max = 500)
#Check the results
if(parameterVector$isThisaFit){
intensityTheoretical <- sigmoidalFitFormula_h0(time,
maximum = parameterVector$maximum_Estimate,
slopeParam = parameterVector$slopeParam_Estimate,
midPoint = parameterVector$midPoint_Estimate,
h0 = parameterVector$h0_Estimate)
comparisonData <- cbind(dataInput, intensityTheoretical)
print(parameterVector$residual_Sum_of_Squares)
require(ggplot2)
ggplot(comparisonData)+
geom_point(aes(x = time, y = intensity)) +
geom_line(aes(x = time, y = intensityTheoretical), color = "orange") +
expand_limits(x = 0, y = 0)
}
if(!parameterVector$isThisaFit){
print(parameterVector)
}
# Example 2 (doublesigmoidal function with normalization)
time <- seq(3, 24, 0.1)
#simulate intensity data with noise
noise_parameter <- 0.2
intensity_noise <- stats::runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity <- doublesigmoidalFitFormula_h0(time,
finalAsymptoteIntensityRatio = .3,
maximum = 4,
slope1Param = 1,
midPoint1Param = 7,
slope2Param = 1,
midPointDistanceParam = 8,
h0 = 1)
intensity <- intensity + intensity_noise
dataInput <- data.frame(intensity = intensity, time = time)
normalizedInput <- normalizeData(dataInput)
parameterVector <- multipleFitFunction_h0(dataInput = normalizedInput,
dataInputName="sample001",
model = "doublesigmoidal",
n_runs_min = 20,
n_runs_max = 500,
showDetails = FALSE)
#Check the results
if(parameterVector$isThisaFit){
intensityTheoretical <-
doublesigmoidalFitFormula_h0(
time,
finalAsymptoteIntensityRatio = parameterVector$finalAsymptoteIntensityRatio_Estimate,
maximum = parameterVector$maximum_Estimate,
slope1Param = parameterVector$slope1Param_Estimate,
midPoint1Param = parameterVector$midPoint1Param_Estimate,
slope2Param = parameterVector$slope2Param_Estimate,
midPointDistanceParam = parameterVector$midPointDistanceParam_Estimate,
h0 = parameterVector$h0_Estimate)
comparisonData <- cbind(dataInput, intensityTheoretical)
require(ggplot2)
ggplot(comparisonData) +
geom_point(aes(x = time, y = intensity)) +
geom_line(aes(x = time, y = intensityTheoretical), color = "orange") +
expand_limits(x = 0, y = 0)
}
if(!parameterVector$isThisaFit){
print(parameterVector)
}
Normalization of given data
Description
Maps the given time-intensity data into a rescaled dataframe where time is scaled to between 0 and 1, and intensity is scaled to be between 0 and 1.
Usage
normalizeData(dataInput, dataInputName = NA)
Arguments
dataInput |
A dataframe or a list containing the dataframe. The data frame should be composed of at least two columns. One represents time, and the other represents intensity. |
dataInputName |
experiment name (Default is 'NA'). |
Value
Function returns a new dataframe, scaling factors and scaling constants that connects the initial data frame to the new one. The new data frame includes 2 columns: normalized time and normalized intensity. The time and intensity constants and scaling factors are the parameters to transform data from the unnormalized dataframe to normalized data frame.
Examples
# runif() is used here for consistency with previous versions of the sicegar package. However,
# rnorm() will generate symmetric errors, producing less biased numerical parameter estimates.
# We recommend errors generated with rnorm() for any simulation studies on sicegar.
# generateRandomData
time <- seq(3, 48, 0.5)
intensity <- runif(length(time), 3.0, 7.5)
dataInput <- data.frame(time, intensity)
# Normalize Data
dataOutput <- normalizeData(dataInput, dataInputName="sample001")
additional parameter calculation with help of fits
Description
Generates useful values for external use, using parameters existing in the parameterVector.
Usage
parameterCalculation(parameterVector, stepSize = 1e-05)
Arguments
parameterVector |
Output of multiple fit function sicegar::multipleFitFunction() that gives the variables generated by the sigmoidal or double sigmoidal fit. |
stepSize |
Step size used by the fitting algorithm. Smaller numbers gave more accurate results than larger numbers, and larger numbers gave the results faster than small numbers. The default value is 0.00001. |
Value
Returns the expanded parameter vector. This vector includes useful derived values such as time and intensity of the start point, in addition to the standard values that the fit algorithms produce that are necessary to define the curves.
Examples
# runif() is used here for consistency with previous versions of the sicegar package. However,
# rnorm() will generate symmetric errors, producing less biased numerical parameter estimates.
# We recommend errors generated with rnorm() for any simulation studies on sicegar.
time <- seq(3, 24, 0.1)
#simulate intensity data with noise
noise_parameter <- 0.2
intensity_noise <- stats::runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity <- sicegar::doublesigmoidalFitFormula(time,
finalAsymptoteIntensityRatio = .3,
maximum = 4,
slope1Param = 1,
midPoint1Param = 7,
slope2Param = 1,
midPointDistanceParam = 8)
intensity <- intensity+intensity_noise
dataInput <- data.frame(intensity = intensity, time = time)
normalizedInput <- sicegar::normalizeData(dataInput)
parameterVector <- sicegar::multipleFitFunction(dataInput = normalizedInput,
dataInputName = "sample01",
model = "doublesigmoidal",
n_runs_min = 20,
n_runs_max = 500,
showDetails = FALSE)
if(parameterVector$isThisaFit){
parameterVector <- sicegar::parameterCalculation(parameterVector)
}
print(t(parameterVector))
additional parameter calculation with help of fits
Description
Generates useful values for external use, using parameters existing in the parameterVector.
Usage
parameterCalculation_h0(parameterVector, stepSize = 1e-05)
Arguments
parameterVector |
Output of multiple fit function sicegar::multipleFitFunction() that gives the variables generated by the sigmoidal or double sigmoidal fit. |
stepSize |
Step size used by the fitting algorithm. Smaller numbers gave more accurate results than larger numbers, and larger numbers gave the results faster than small numbers. The default value is 0.00001. |
Value
Returns the expanded parameter vector. This vector includes useful derived values such as time and intensity of the start point, in addition to the standard values that the fit algorithms produce that are necessary to define the curves.
Examples
# runif() is used here for consistency with previous versions of the sicegar package. However,
# rnorm() will generate symmetric errors, producing less biased numerical parameter estimates.
# We recommend errors generated with rnorm() for any simulation studies on sicegar.
time <- seq(3, 24, 0.1)
#simulate intensity data with noise
noise_parameter <- 0.2
intensity_noise <- stats::runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity <- sicegar::doublesigmoidalFitFormula_h0(time,
finalAsymptoteIntensityRatio = .3,
maximum = 4,
slope1Param = 1,
midPoint1Param = 7,
slope2Param = 1,
midPointDistanceParam = 8,
h0 = 1)
intensity <- intensity+intensity_noise
dataInput <- data.frame(intensity = intensity, time = time)
normalizedInput <- sicegar::normalizeData(dataInput)
parameterVector <- sicegar::multipleFitFunction_h0(dataInput = normalizedInput,
dataInputName = "sample01",
model = "doublesigmoidal",
n_runs_min = 20,
n_runs_max = 500,
showDetails = FALSE)
if(parameterVector$isThisaFit){
parameterVector <- sicegar::parameterCalculation_h0(parameterVector)
}
print(t(parameterVector))
Checks for signal in the data.
Description
Checks if the signal is present in the data. Often a high percentage of high through-put data does not contain a signal. Checking if data does not contain signal before doing a sigmoidal or double sigmoidal fit can make the analysis of data from high-throughput experiments much faster.
Usage
preCategorize(
normalizedInput,
threshold_intensity_range = 0.1,
threshold_minimum_for_intensity_maximum = 0.3
)
Arguments
normalizedInput |
is the output of the sicegar::normalizeData() function. |
threshold_intensity_range |
minimum for intensity range, i.e. it is the lower limit for the allowed difference between the maximum and minimum of the intensities (Default is 0.1, and the values are based on actual, not the rescaled data.). |
threshold_minimum_for_intensity_maximum |
minimum allowed value for intensity maximum. (Default is 0.3, and the values are based on actual, not the rescaled data.). |
Value
Function returns a brief decision list that includes information about the decision process. Post important part of this information is decisionList$decisionwhich might be either "no_signal" or "not_no_signal".
Examples
# runif() is used here for consistency with previous versions of the sicegar package. However,
# rnorm() will generate symmetric errors, producing less biased numerical parameter estimates.
# We recommend errors generated with rnorm() for any simulation studies on sicegar.
# Example 1 with double sigmoidal data
time=seq(3, 24, 0.1)
#simulate intensity data and add noise
noise_parameter = 0.2
intensity_noise = runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity = sicegar::doublesigmoidalFitFormula(time,
finalAsymptoteIntensityRatio = .3,
maximum = 4,
slope1Param = 1,
midPoint1Param = 7,
slope2Param = 1,
midPointDistanceParam = 8)
intensity <- intensity + intensity_noise
dataInput <- data.frame(intensity = intensity, time = time)
normalizedInput <- sicegar::normalizeData(dataInput, dataInputName = "sample001")
isThis_nosignal <- sicegar::preCategorize(normalizedInput = normalizedInput)
# Example 2 with no_signal data
time <- seq(3, 24, 0.1)
#simulate intensity data and add noise
noise_parameter <- 0.05
intensity_noise <- runif(n = length(time), min = 0, max = 1) * noise_parameter * 2e-04
intensity <- sicegar::doublesigmoidalFitFormula(time,
finalAsymptoteIntensityRatio = .3,
maximum = 2e-04,
slope1Param = 1,
midPoint1Param = 7,
slope2Param = 1,
midPointDistanceParam = 8)
intensity <- intensity + intensity_noise
dataInput <- data.frame(intensity=intensity, time=time)
normalizedInput <- sicegar::normalizeData(dataInput,dataInputName = "sample001")
isThis_nosignal <- sicegar::preCategorize(normalizedInput = normalizedInput)
Check that data came from the same source.
Description
Checks if the provided data and models came from same source by looking to ".dataInputName" columns of the inputs.
Usage
sameSourceDataCheck(dataInput, sigmoidalFitVector, doubleSigmoidalFitVector)
Arguments
dataInput |
a dataframe composed of two columns. One is for time and the other is for intensity. Should be normalized data generated by sicegar::normalizeData(). |
sigmoidalFitVector |
is the output of sigmoidalFitFunction. Default is NULL. |
doubleSigmoidalFitVector |
is the output of double sigmoidal fit function. Default is NULL. |
Value
Returns TRUE if models came from same source, FALSE otherwise.
sigmoidalFitFormula
Description
Calculates intensities for given time points (x) by using sigmoidal fit model and parameters (maximum, slopeParam, and midpoint).
Usage
sigmoidalFitFormula(x, maximum, slopeParam, midPoint)
Arguments
x |
the "time" column of the dataframe. |
maximum |
the maximum intensity that the sigmoidal function can reach as time approaches infinity. |
slopeParam |
the slope parameter of the sigmoidal function at the steepest point. |
midPoint |
the x axis value of the steepest point in the function. |
Value
Returns the predicted intensities for given time points with the given sigmoidal fit parameters.
Examples
# runif() is used here for consistency with previous versions of the sicegar package. However,
# rnorm() will generate symmetric errors, producing less biased numerical parameter estimates.
# We recommend errors generated with rnorm() for any simulation studies on sicegar.
time <- seq(3, 24, 0.5)
#simulate intensity data and add noise
noise_parameter <- 0.1
intensity_noise <- stats::runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity <- sigmoidalFitFormula(time, maximum = 4, slopeParam = 1, midPoint = 8)
intensity <- intensity + intensity_noise
dataInput <- data.frame(intensity = intensity, time = time)
normalizedInput <- normalizeData(dataInput)
parameterVector <- sigmoidalFitFunction(normalizedInput, tryCounter = 2)
#Check the results
if(parameterVector$isThisaFit){
intensityTheoretical <- sigmoidalFitFormula(time,
maximum = parameterVector$maximum_Estimate,
slopeParam = parameterVector$slopeParam_Estimate,
midPoint = parameterVector$midPoint_Estimate)
comparisonData <- cbind(dataInput, intensityTheoretical)
require(ggplot2)
ggplot(comparisonData) +
geom_point(aes(x = time, y = intensity)) +
geom_line(aes(x = time, y = intensityTheoretical)) +
expand_limits(x = 0, y = 0)
}
if(!parameterVector$isThisaFit){
print(parameterVector)
}
sigmoidalFitFormula_h0
Description
Calculates intensities for given time points (x) by using sigmoidal fit model and parameters (maximum, slopeParam, midpoint, and h0).
Usage
sigmoidalFitFormula_h0(x, maximum, slopeParam, midPoint, h0)
Arguments
x |
the "time" column of the dataframe. |
maximum |
the maximum intensity that the sigmoidal function can reach while time approaches infinity. |
slopeParam |
the slope parameter of the sigmoidal function at the steepest point. |
midPoint |
the x axis value of the steepest point in the function. |
h0 |
the lower asymptote (baseline) intensity |
Value
Returns the predicted intensities for given time points with the given sigmoidal fit parameters.
Examples
# runif() is used here for consistency with previous versions of the sicegar package. However,
# rnorm() will generate symmetric errors, producing less biased numerical parameter estimates.
# We recommend errors generated with rnorm() for any simulation studies on sicegar.
time <- seq(3, 24, 0.5)
#simulate intensity data and add noise
noise_parameter <- 0.1
intensity_noise <- stats::runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity <- sigmoidalFitFormula_h0(time, maximum = 4, slopeParam = 1, midPoint = 8, h0 = 1)
intensity <- intensity + intensity_noise
dataInput <- data.frame(intensity = intensity, time = time)
normalizedInput <- normalizeData(dataInput)
parameterVector <- sigmoidalFitFunction_h0(normalizedInput, tryCounter = 1)
#Check the results
# sigmoidalFitFunction_h0() is run on the startList param values (because 'tryCounter = 1')
# use multipleFitFunction() for multiple random starts in order to optimize
if(parameterVector$isThisaFit){
intensityTheoretical <- sigmoidalFitFormula_h0(time,
maximum = parameterVector$maximum_Estimate,
slopeParam = parameterVector$slopeParam_Estimate,
midPoint = parameterVector$midPoint_Estimate,
h0 = parameterVector$h0_Estimate)
comparisonData <- cbind(dataInput, intensityTheoretical)
require(ggplot2)
ggplot(comparisonData) +
geom_point(aes(x = time, y = intensity)) +
geom_line(aes(x = time, y = intensityTheoretical)) +
expand_limits(x = 0, y = 0)
}
if(!parameterVector$isThisaFit){
print(parameterVector)
}
Sigmoidal fit function
Description
The function fits a sigmoidal curve to given data by using likelihood maximization (LM) algorithm and provides the parameters (maximum, slopeParam, midPoint, and h0) describing the sigmoidal fit as output. It also contains information about the goodness of fits such as AIC, BIC, residual sum of squares, and log likelihood.
Usage
sigmoidalFitFunction(
dataInput,
tryCounter,
startList = list(maximum = 1, slopeParam = 1, midPoint = 0.33),
lowerBounds = c(maximum = 0.3, slopeParam = 0.01, midPoint = -0.52),
upperBounds = c(maximum = 1.5, slopeParam = 180, midPoint = 1.15),
min_Factor = 1/2^20,
n_iterations = 1000
)
Arguments
dataInput |
A dataframe or a list containing the dataframe. The dataframe should be composed of at least two columns. One represents time, and the other represents intensity. The data should be normalized with the normalize data function sicegar::normalizeData() before being imported into this function. |
tryCounter |
A counter that shows the number of times the data was fit via maximum likelihood function. |
startList |
A vector containing the initial set of parameters that the algorithm tries for the first fit attempt for the relevant parameters. The vector is composed of four elements; 'maximum', 'slopeParam', 'midPoint', and 'h0'. Detailed explanations of those parameters can be found in vignettes. Defaults are maximum = 1, slopeParam = 1, midPoint = 0.33, and h0 = 0. The numbers are in normalized time intensity scale. |
lowerBounds |
The lower bounds for the randomly generated start parameters. The vector is composed of four elements; 'maximum', 'slopeParam', 'midPoint', and 'h0'. Detailed explanations of those parameters can be found in vignettes. Defaults are maximum = 0.3, slopeParam = 0.01, midPoint = -0.52, and h0 = -0.1. The numbers are in normalized time intensity scale. |
upperBounds |
The upper bounds for the randomly generated start parameters. The vector is composed of four elements; 'maximum', 'slopeParam','midPoint', and 'h0'. Detailed explanations of those parameters can be found in vignettes. Defaults are maximum = 1.5, slopeParam = 180, midPoint = 1.15, and h0 = 0.3. The numbers are in normalized time intensity scale. |
min_Factor |
Defines the minimum step size used by the fitting algorithm. Default is 1/2^20. |
n_iterations |
Defines maximum number of iterations used by the fitting algorithm. Default is 1000 |
Value
Returns fitted parameters for the sigmoidal model.
Examples
# runif() is used here for consistency with previous versions of the sicegar package. However,
# rnorm() will generate symmetric errors, producing less biased numerical parameter estimates.
# We recommend errors generated with rnorm() for any simulation studies on sicegar.
time <- seq(3, 24, 0.5)
#simulate intensity data and add noise
noise_parameter <- 0.1
intensity_noise <- stats::runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity <- sigmoidalFitFormula(time, maximum = 4, slopeParam = 1, midPoint = 8)
intensity <- intensity + intensity_noise
dataInput <- data.frame(intensity = intensity, time = time)
normalizedInput <- normalizeData(dataInput)
parameterVector <- sigmoidalFitFunction(normalizedInput, tryCounter = 2)
#Check the results
if(parameterVector$isThisaFit){
intensityTheoretical <- sigmoidalFitFormula(time,
maximum = parameterVector$maximum_Estimate,
slopeParam = parameterVector$slopeParam_Estimate,
midPoint = parameterVector$midPoint_Estimate)
comparisonData <- cbind(dataInput, intensityTheoretical)
require(ggplot2)
ggplot(comparisonData) +
geom_point(aes(x = time, y = intensity)) +
geom_line(aes(x = time, y = intensityTheoretical)) +
expand_limits(x = 0, y = 0)
}
if(!parameterVector$isThisaFit){
print(parameterVector)
}
Sigmoidal fit function with h0 estimation
Description
The function fits a sigmoidal curve to given data by using likelihood maximization (LM) algorithm and provides the parameters (maximum, slopeParam, midPoint, and h0) describing the sigmoidal fit as output. It also contains information about the goodness of fits such as AIC, BIC, residual sum of squares, and log likelihood.
Usage
sigmoidalFitFunction_h0(
dataInput,
tryCounter,
startList = list(maximum = 1, slopeParam = 1, midPoint = 0.33, h0 = 0),
lowerBounds = c(maximum = 0.3, slopeParam = 0.01, midPoint = -0.52, h0 = -0.1),
upperBounds = c(maximum = 1.5, slopeParam = 180, midPoint = 1.15, h0 = 0.3),
min_Factor = 1/2^20,
n_iterations = 1000
)
Arguments
dataInput |
A dataframe or a list containing the dataframe. The dataframe should be composed of at least two columns. One represents time, and the other represents intensity. The data should be normalized with the normalize data function sicegar::normalizeData() before being imported into this function. |
tryCounter |
A counter that shows the number of times the data was fit via maximum likelihood function. |
startList |
A vector containing the initial set of parameters that the algorithm tries for the first fit attempt for the relevant parameters. The vector is composed of four elements; 'maximum', 'slopeParam', 'midPoint', and 'h0'. Detailed explanations of those parameters can be found in vignettes. Defaults are maximum = 1, slopeParam = 1, midPoint = 0.33, and h0 = 0. The numbers are in normalized time intensity scale. |
lowerBounds |
The lower bounds for the randomly generated start parameters. The vector is composed of four elements; 'maximum', 'slopeParam', 'midPoint', and 'h0'. Detailed explanations of those parameters can be found in vignettes. Defaults are maximum = 0.3, slopeParam = 0.01, midPoint = -0.52, and h0 = -0.1. The numbers are in normalized time intensity scale. |
upperBounds |
The upper bounds for the randomly generated start parameters. The vector is composed of four elements; 'maximum', 'slopeParam','midPoint', and 'h0'. Detailed explanations of those parameters can be found in vignettes. Defaults are maximum = 1.5, slopeParam = 180, midPoint = 1.15, and h0 = 0.3. The numbers are in normalized time intensity scale. |
min_Factor |
Defines the minimum step size used by the fitting algorithm. Default is 1/2^20. |
n_iterations |
Defines maximum number of iterations used by the fitting algorithm. Default is 1000 |
Value
Returns fitted parameters for the sigmoidal model.
Examples
# runif() is used here for consistency with previous versions of the sicegar package. However,
# rnorm() will generate symmetric errors, producing less biased numerical parameter estimates.
# We recommend errors generated with rnorm() for any simulation studies on sicegar.
time <- seq(3, 24, 0.5)
#simulate intensity data and add noise
noise_parameter <- 0.1
intensity_noise <- stats::runif(n = length(time), min = 0, max = 1) * noise_parameter
intensity <- sigmoidalFitFormula_h0(time, maximum = 4, slopeParam = 1, midPoint = 8, h0 = 1)
intensity <- intensity + intensity_noise
dataInput <- data.frame(intensity = intensity, time = time)
normalizedInput <- normalizeData(dataInput)
parameterVector <- sigmoidalFitFunction_h0(normalizedInput, tryCounter = 1)
#Check the results
# sigmoidalFitFunction_h0() is run on the startList param values (because 'tryCounter = 1')
# use multipleFitFunction() for multiple random starts in order to optimize
if(parameterVector$isThisaFit){
intensityTheoretical <- sigmoidalFitFormula_h0(time,
maximum = parameterVector$maximum_Estimate,
slopeParam = parameterVector$slopeParam_Estimate,
midPoint = parameterVector$midPoint_Estimate,
h0 = parameterVector$h0_Estimate)
comparisonData <- cbind(dataInput, intensityTheoretical)
require(ggplot2)
ggplot(comparisonData) +
geom_point(aes(x = time, y = intensity)) +
geom_line(aes(x = time, y = intensityTheoretical)) +
expand_limits(x = 0, y = 0)
}
if(!parameterVector$isThisaFit){
print(parameterVector)
}
Unnormalization of given data
Description
Maps the given time-intensity data into a rescaled frame where time is between zero and one intensity is also between zero and one.
Usage
unnormalizeData(dataInput)
Arguments
dataInput |
a list file composed of two parts First part is the data that will be unnormalized, which is a dataframe composed of two columns. One is for time and the other is for intensity Second part is the scaling parameters of the data which is a vector that has three components. The first is related with time and second two are related with intensity. The second value represents the min value of the intensity set. First and third values represent the difference between max and min value in the relevant column. |
Value
Returns a dataframe, scaling factors, and scaling constants for time and intensity. The other data frame includes 2 columns: normalized time and normalized intensity. The time and intensity constants and scaling factors are the parameters to transform data from given set to scaled set.
Examples
# runif() is used here for consistency with previous versions of the sicegar package. However,
# rnorm() will generate symmetric errors, producing less biased numerical parameter estimates.
# We recommend errors generated with rnorm() for any simulation studies on sicegar.
# generateRandomData
time <- seq(3, 48, 0.5)
intensity <- runif(length(time), 3.0, 7.5)
dataInput <- data.frame(time, intensity)
# Normalize Data
dataOutput <- normalizeData(dataInput)
dataInput2 <- dataOutput
# Un Normalize it
dataOutput2 <- unnormalizeData(dataInput2)