# Function: newDataFrap## Description: Create a new instance of the "frapclass" class.## Parameters:# - name: Name of the FRAP object.# - bleach: Percentage of sample bleaching.## Returns: An instance of the "frapclass" class.## Usage:# frap_obj <- newDataFrap(name, bleach)
A <-newDataFrap("Neurites", 80)B <-newDataFrap("Secondary.Neurites", 80)C <-newDataFrap("Dendrites", 80)
plotRecover
# Function: plotRecover## Description: Plot the fluorescence recovery curves.## Parameters:# - ...: Additional parameters for the plot function.# - index: Index or indices of the FRAP objects to plot.# - type: Type of recovery curve to plot (default: "RCB").# - area: Whether to include area normalization in the plot (default: TRUE).# - stand: Whether to standardize the recovery curve (default: TRUE).# - AB: Whether to include only the recovery phase of the curve (default: FALSE).# - new.plot: Whether to create a new plot (default: TRUE).# - plot.lines: Whether to plot lines (default: FALSE).# - plot.points: Whether to plot points (default: FALSE).# - plot.shadow: Whether to plot shadowed areas (default: FALSE).# - plot.mean: Whether to plot the mean curve (default: FALSE).# - col: Color of the curves (default: NULL).# - getGroup: Whether to return the group information (default: FALSE).## Usage:# plotRecover(..., index = NA, type = "RCB", area = TRUE, stand = TRUE, AB = FALSE,# new.plot = TRUE, plot.lines = FALSE, plot.points = FALSE, plot.shadow = FALSE,# plot.mean = FALSE, col = NULL, getGroup = FALSE)
# Example 1: Plotting fluorescence recovery curves for FRAP objects A, B, and C.plotRecover(A, B, C, plot.lines =TRUE, col =c("blue", "red", "green"))# Example 2: Plotting specific indices of recovery curves.plotRecover(A, B, C, index =c(1, 3), plot.points =TRUE, col ="purple")# Example 3: Customizing the plot with shadowed areas and mean curve.plotRecover(A, B, C, plot.lines =TRUE, plot.shadow =TRUE, plot.mean =TRUE)# Example 4: Plotting standardized recovery curves without area normalization.plotRecover(A, B, C, area =FALSE, stand =TRUE, plot.lines =TRUE, col ="orange")# Example 5: Plotting recovery curves from different FRAP objects in the same plot.plotRecover(A, Exp, plot.lines =TRUE, col ="blue")plotRecover(B, Gam, new.plot =FALSE, plot.lines =TRUE, col ="red")plotRecover(C, Wei, new.plot =FALSE, plot.lines =TRUE, col ="green")
newFit
# Function: newFit## Description: Create a new instance of the "fitclass" class for FRAP data fitting.## Parameters:# - name: Name of the fitting model.# - fun: Fitting function.# - param: Names of the fitting parameters.# - interval: Interval for each fitting parameter.## Usage:# newFit(name, fun, param, interval)
# Example 1: Creating a new fitting model using the Exponential function.Exp <-newFit("Exponential", pexp, "rate", list(c(0, 1)))# Example 2: Creating a new fitting model using the Gamma function.Gam <-newFit("Gamma", pgamma, c("shape", "rate"), list(c(0, 5), c(0, 5)))# Example 3: Creating a new fitting model using the Weibull function.Wei <-newFit("Weibull", pweibull, c("shape", "scale"), list(c(0, 5), c(0, 2000)))# Example 4: Creating a new fitting model with custom parameters and intervals.fit_custom <-newFit("Custom", myfitfunction, c("param1", "param2", "param3"),list(c(0, 10), c(-1, 1), c(0, 100)))
compareFit
# Function: compareFit## Description: Compare the goodness of fit between data and multiple fits.## Parameters:# - data: Data to compare (numeric vector or data frame).# - fit: List of fits to compare (list of numeric vectors or data frames).# - col.lines: Color palette for the fit lines (default: NULL).# - lty.lines: Line type for the fit lines (default: 1).# - lwd.lines: Line width for the fit lines (default: 1).# - lwd.mean: Line width for the mean fit line (default: 1).# - digits: Number of digits to round the fit statistics (default: 4).# - ...: Additional parameters for the plot function.## Usage:# compareFit(data, fit, col.lines = NULL, lty.lines = 1, lwd.lines = 1, lwd.mean = 1,# digits = 4, ...)
# Example 1: Comparing the goodness of fit for different models using pre-loaded datacompareFit(B, list(Exp, Wei), col.lines =c("red", "yellow"), lty.lines =c(2, 1),lwd.lines =2, lwd.mean =2)# Example 2: Comparing the goodness of fit for different models with custom colors and line typescompareFit(B, list(Exp, Gam, Wei), col.lines =c("blue", "green", "purple"),lty.lines =c(1, 3, 2), lwd.lines =2, lwd.mean =2)
plotFit
# Function: plotFit## Description: Plot the fit of fluorescence recovery curves.## Parameters:# - ...: Additional parameters for the plot function.# - fit: Fit objects obtained from the newFit function.# - index: Index or indices of the fit objects to plot.# - type: Type of recovery curve to plot (default: "RCB").# - area: Whether to include area normalization in the plot (default: TRUE).# - stand: Whether to standardize the recovery curve (default: TRUE).# - simulated: Whether to include simulated recovery curves (default: FALSE).# - Nsim: Number of simulated curves to generate (default: 50).# - seed: Seed value for reproducibility of simulated curves (default: NA).# - npoints: Number of points to generate in simulated curves (default: 100).# - displacement: Whether to add random displacement to simulated curves (default: FALSE).# - new.plot: Whether to create a new plot (default: TRUE).# - plot.lines: Whether to plot lines (default: FALSE).# - plot.points: Whether to plot points (default: FALSE).# - plot.shadow: Whether to plot shadowed areas (default: FALSE).# - plot.mean: Whether to plot the mean curve (default: FALSE).# - col: Color of the curves (default: NULL).## Usage:# plotFit(..., fit, index = NA, type = "RCB", area = TRUE, stand = TRUE, simulated = FALSE,# Nsim = 50, seed = NA, npoints = 100, displacement = FALSE,# new.plot = TRUE, plot.lines = FALSE, plot.points = FALSE, plot.shadow = FALSE,# plot.mean = FALSE, col = NULL)
# Example 1: Plotting the fit of fluorescence recovery curves for objects B and C with Exp fitplotFit(B, C, fit = Exp, plot.lines =TRUE, col =c("blue", "red", "green"),ylim =c(0.2, 1), xdigits =0)# Example 2: Plotting the fit for object B with Gam fitplotFit(B, fit = Gam, plot.lines =TRUE, col =c("blue", "red", "green"),ylim =c(0.2, 1), xdigits =0)# Example 3: Plotting fits for objects B and C with multiple fits and pointsplotFit(B, C, fit =c(Exp, Gam), plot.lines =TRUE, plot.points =TRUE,col =c("blue", "red", "green"), ylim =c(0.2, 1), xdigits =0)# Example 4: Plotting fit for object C with Wei fit and shadowed areasplotFit(C, fit = Wei, plot.lines =TRUE, plot.shadow =TRUE,col =c("blue", "red", "green"), ylim =c(0.2, 1), xdigits =0)
compareParam
# Function: compareParam## Description: Compare parameter estimates between two datasets based on the fit results.## Parameters:# - data1: First dataset of fluorescence recovery curves.# - data2: Second dataset of fluorescence recovery curves.# - fit: Fit object containing the results of fitting the data.# - param: Parameter to compare between the datasets.# - type: Type of recovery curve to consider (default: "RCB").# - area: Whether to include area normalization in the comparison (default: TRUE).# - stand: Whether to standardize the recovery curves (default: TRUE).# - simulated: Whether to use simulated data for the comparison (default: FALSE).# - Nsim: Number of simulated datasets to generate (default: 50).# - seed: Seed value for reproducible simulations (default: NA).# - saveable: Whether to enable saving of simulation results (default: TRUE if seed is specified).# - conf.level: Confidence level for the comparison (default: 0.95).# - alternative: Type of alternative hypothesis for the comparison (default: "two.sided").# - return: Whether to return the comparison results (default: FALSE).# - new.plot: Whether to create a new plot (default: TRUE).# - plot.lines: Whether to plot lines in the comparison plot (default: TRUE).# - plot.points: Whether to plot points in the comparison plot (default: FALSE).# - col: Color of the curves in the comparison plot (default: NULL).# - ...: Additional parameters for the comparison plot.## Usage:# compareParam(data1, data2, fit, param, type = "RCB", area = TRUE, stand = TRUE,# simulated = FALSE, Nsim = 50, seed = NA, conf.level = 0.95,# alternative = "two.sided", return = FALSE, new.plot = TRUE,# plot.lines = TRUE, plot.points = FALSE, col = NULL, ...)
# Example 1: Comparing the parameter "MF" between datasets B and C using the Wei fitcompareParam(B, C, fit = Wei, param ="MF", lwd.lines =2, col.lines ="#9084c9",simulated =TRUE, seed =516)# Example 2: Comparing the parameter "UF" between datasets B and C using the Wei fit# with a different confidence level and alternative hypothesiscompareParam(B, C, fit = Wei, param ="UF", lwd.lines =2, col.lines ="#d65b22",simulated =TRUE, seed =6544, conf.level =0.99, alternative ="greater")# Example 3: Comparing the parameter "alpha" between datasets B and B using the Exp and Wei fits# and returning the comparison resultsresults <-compareParam(B, B, fit =c(Exp, Wei), param ="alpha", lwd.lines =2,col.lines ="#2e337e", simulated =TRUE, seed =44648)# Example 4: Comparing the parameter "MF" between datasets B and C using the Gamma fit# and customizing the comparison plotcompareParam(B, C, fit = Gam, param ="MF", simulated =TRUE, seed =5959, return =TRUE)
##
## Welch Two Sample t-test
##
## data: MF_GammaSim_Secondary.Neurites and MF_GammaSim_Dendrites
## t = 1.773, df = 93.713, p-value = 0.07948
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.005227574 0.092406709
## sample estimates:
## mean of x mean of y
## 0.8700160 0.8264265
compareMean
# Function: compareMean## Description: Compare the mean values between two datasets using FRAP fits.## Parameters:# - data1: Dataset 1 for comparison.# - data2: Dataset 2 for comparison.# - fit: FRAP fit object(s) to use for the comparison.# - type: Type of recovery curve to use for the comparison (default: "RCB").# - area: Whether to include area normalization in the comparison (default: TRUE).# - stand: Whether to standardize the recovery curves for comparison (default: TRUE).# - simulated: Whether to perform simulated comparisons (default: FALSE).# - Nsim: Number of simulated comparisons to perform (default: 50).# - seed: Seed value for reproducible simulations (default: NA).# - saveable: Whether to enable saving of simulation results (default: TRUE if seed is specified).# - conf.level: Confidence level for the comparison (default: 0.95).# - alternative: Type of alternative hypothesis for the comparison (default: "two.sided").# - return: Whether to return the comparison results (default: FALSE).# - p.value: Whether to include p-values in the comparison results (default: TRUE).# - npoints: Number of points to use for plotting the comparison curve (default: 100).# - new.plot: Whether to create a new plot (default: TRUE).# - plot.lines: Whether to plot lines for the comparison curve (default: TRUE).# - plot.points: Whether to plot points for the comparison curve (default: FALSE).# - col: Color of the comparison curve (default: NULL).# - ...: Additional parameters for the plot function.## Usage:# compareMean(data1, data2, fit, type = "RCB", area = TRUE, stand = TRUE,# simulated = FALSE, Nsim = 50, seed = NA, saveable = !is.na(seed),# conf.level = 0.95, alternative = "two.sided", return = FALSE,# p.value = TRUE, npoints = 100, new.plot = TRUE, plot.lines = TRUE,# plot.points = FALSE, col = NULL, ...)
# Example 1: Comparing the mean values of FRAP fits between datasets B and C using multiple fitscompareMean(B, C, fit =c(Exp, Gam), lwd.lines =2,col.lines =c("red", "green", "purple"))# Example 2: Comparing the mean values using the Gam fit and customizing the plotcompareMean(B, C, fit = Gam, lwd.lines =2, col.lines ="orange",plot.lines =TRUE, plot.points =TRUE)# Example 3: Comparing the mean values using the Wei fitcompareMean(B, C, fit = Wei, lwd.lines =2, lty.lines =c(2, 1),ylim =c(0, 0.2), col.lines =c("red", "#C65153"),xdigits =0, ydigits =4)compareMean(B, C, fit = Wei, lwd.lines =2, col.lines ="#79189F",simulated =TRUE, seed =486, new.plot =FALSE)# Example 4: Comparing the mean values using the Exp fitcompareMean(B, C, fit = Exp, lwd.lines =2, col.lines ="blue", return = T)[[2]]
simFit
# Function: simFit## Description: Simulate fluorescence recovery curves based on a given FRAP fit.## Parameters:# - fit: FRAP fit object to use for simulation.# - Nsim: Number of simulations to perform (default: 50).# - seed: Seed for random number generation (default: NA).# - saveable: Whether the simulation results can be saved (default: !is.na(seed)).## Usage:# simFit(fit, Nsim = 50, seed = NA, saveable = !is.na(seed))
# Example 1: Simulate fluorescence recovery curves based on the Wei fit with 100 simulationssimFit(Wei(B), Nsim =100)# Example 2: Simulate fluorescence recovery curves based on the Exp fit with a specific seedsimFit(Exp(C), seed =12345)# Example 3: Simulate fluorescence recovery curves based on the Gam fit and save the resultssimFit(Gam(B), saveable =TRUE)
tableFit
# Function: tableFit## Description: Create a table summarizing the fit results for multiple FRAP objects.## Parameters:# - ...: Multiple FRAP objects or a list of FRAP objects.# - fit: FRAP fit objects to include in the table.# - digits: Number of digits to round the table values (default: 4).## Usage:# tableFit(..., fit, digits = 4)
# Example 1: Create a table summarizing the fit results for objects A, B, and C using the Wei fittableFit(A, B, C, fit = Wei)# Example 2: Create a table summarizing the fit results for objects B and C using multiple fitstableFit(B, C, fit =c(Exp, Gam, Wei))# Example 3: Create a table summarizing the fit results for objects B, C, and D using custom digitstableFit(B, C, D, fit = Exp, digits =2)