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| r_github_packages: | ||
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| print.CV_Result <- function(x,...){ | ||
| cat("\nContaining the cross validation result. \n"); | ||
| if (!is.null(x$s_optimal)) | ||
| print(paste0("Optimal r parameter is: ", x$r_optimal)); | ||
| cat(paste0("Optimal r parameter is: ", x$r_optimal, "\n")); | ||
| if (!is.null(x$s_optimal)) | ||
| print(paste0("Optimal s parameter is: ", x$s_optimal)); | ||
| cat(paste0("Optimal s parameter is: ", x$s_optimal, "\n")); | ||
| } |
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| summary.CV_Result <- function(object,...){ | ||
| cat("\nContaining the cross validation result. \n"); | ||
| if (!is.null(object$r_optimal)) | ||
| print(paste0("Optimal r parameter is: ",object$r_optimal)); | ||
| cat(paste0("Optimal r parameter is: ",object$r_optimal, "\n")); | ||
| if (!is.null(object$s_optimal)) | ||
| print(paste0("Optimal s parameter is: ",object$s_optimal)); | ||
| cat(paste0("Optimal s parameter is: ",object$s_optimal, "\n")); | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,103 @@ | ||
| \name{plot.PAFit_result} | ||
| \alias{plot.PAFit_result} | ||
| %- Also NEED an '\alias' for EACH other topic documented here. | ||
| \title{ | ||
| Plotting the estimated attachment function and node fitness of a \code{PAFit_result} object | ||
| } | ||
| \description{ | ||
| This function plots the estimated attachment function \eqn{A_k} and node fitness \eqn{eta_i}, together with additional information such as their confidence intervals or the estimated attachment exponent (\eqn{\alpha} when assuming \eqn{A_k = k^\alpha}) of a \code{PAFit_result} object. This object is stored in the field \code{$estimate_result} of a \code{Full_PAFit_result} object, which in turn is the returning value of \code{\link{only_A_estimate}}, \code{\link{only_F_estimate}} or \code{\link{joint_estimate}}. | ||
| } | ||
| \usage{ | ||
| \method{plot}{PAFit_result}(x, | ||
| net_stat , | ||
| true_f = NULL , plot = "A" , plot_bin = TRUE , | ||
| line = FALSE , confidence = TRUE , high_deg_A = 1 , | ||
| high_deg_f = 5 , | ||
| shade_point = 0.5 , col_point = "grey25" , pch = 16 , | ||
| shade_interval = 0.5 , col_interval = "lightsteelblue" , label_x = NULL , | ||
| label_y = NULL , | ||
| max_A = NULL , min_A = NULL , f_min = NULL , | ||
| f_max = NULL , plot_true_degree = FALSE , | ||
| ...) | ||
| } | ||
| %- maybe also 'usage' for other objects documented here. | ||
| \arguments{ | ||
| \item{x}{ | ||
| An object of class \code{PAFit_result}. | ||
| } | ||
| \item{net_stat}{ | ||
| An object of class \code{PAFit_data}, containing the summerized statistics. | ||
| } | ||
| \item{true_f}{ | ||
| Vector. Optional parameter for the true value of node fitnesses (only available in simulated datasets). If this parameter is specified and \code{plot == "true_f"}, a plot of estimated \eqn{\eta} versus true \eqn{\eta} is produced (after a suitable rescaling of the estimated \eqn{f}). | ||
| } | ||
| \item{plot}{ | ||
| String. Indicates which plot is produced. | ||
| \itemize{ | ||
| \item If \code{"A"} then PA function is plotted. | ||
| \item If \code{"f"} then the histogram of estimated fitness is plotted. | ||
| \item If \code{"true_f"} then estimated fitness and true fitness are plotted together (require supplement of true fitness). | ||
| } | ||
| Default value is \code{"A"}. | ||
| } | ||
| \item{plot_bin}{Logical. If \code{TRUE} then only the center of each bin is plotted. Default is \code{TRUE}.} | ||
| \item{line}{ | ||
| Logical. Indicates whether to plot the line fitted from the log-linear model or not. Default value is \eqn{TRUE}. | ||
| } | ||
| \item{confidence}{ | ||
| Logical. Indicates whether to plot the confidence intervals of \eqn{A_k} and \eqn{eta_i} or not. If confidence == TRUE, a 2-sigma confidence interval will be plotted at each \eqn{A_k} and \eqn{eta_i}. | ||
| } | ||
| \item{high_deg_A}{Integer. The estimated PA function is plotted starting from \code{high_deg_A}. Default value is \code{1}.} | ||
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| \item{high_deg_f}{Integer. If \code{plot == "true_f"}, only nodes whose number of edges acquired is not less than \code{high_deg_f} are plotted. Default value is \code{5}.} | ||
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| \item{col_point}{String. The name of the color of the points. Default value is \code{"black"}.} | ||
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| \item{shade_point}{ | ||
| Numeric. Value between 0 and 1. This is the transparency level of the points. Default value is \code{0.5}. | ||
| } | ||
| \item{pch}{Numeric. The plot symbol. Default value is \code{16}.} | ||
| \item{shade_interval}{ | ||
| Numeric. Value between 0 and 1. This is the transparency level of the confidence intervals. Default value is \code{0.5}. | ||
| } | ||
| \item{max_A}{Numeric. Specify the maximum of the axis of PA.} | ||
| \item{min_A}{Numeric. Specify the minimum of the axis of PA.} | ||
| \item{f_min}{Numeric. Specify the minimum of the axis of fitness.} | ||
| \item{f_max}{Numeric. Specify the maximum of the axis of fitness.} | ||
| \item{plot_true_degree}{Logical. The degree of each node is plotted or not.} | ||
| \item{label_x}{String. The label of x-axis.} | ||
| \item{label_y}{String. The label of y-axis.} | ||
| \item{col_interval}{String. The name of the color of the confidence intervals. Default value is \code{"lightsteelblue"}.} | ||
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| \item{\dots}{ | ||
| %% ~~Describe \code{\dots} here~~ | ||
| } | ||
| } | ||
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| \value{ | ||
| Outputs the desired plot. | ||
| } | ||
| \author{ | ||
| Thong Pham \email{thongpham@thongpham.net} | ||
| } | ||
| \examples{ | ||
| ## Since the runtime is long, we do not let this example run on CRAN | ||
| \dontrun{ | ||
| library("PAFit") | ||
| set.seed(1) | ||
| # a network from Bianconi-Barabasi model | ||
| net <- generate_BB(N = 1000 , m = 50 , | ||
| num_seed = 100 , multiple_node = 100, | ||
| s = 10) | ||
| net_stats <- get_statistics(net) | ||
| result <- joint_estimate(net, net_stats) | ||
| #plot A | ||
| plot(result$estimate_result , net_stats , plot = "A") | ||
| true_A <- c(1,result$estimate_result$center_k[-1]) | ||
| lines(result$estimate_result$center_k + 1 , true_A , col = "red") # true line | ||
| legend("topleft" , legend = "True function" , col = "red" , lty = 1 , bty = "n") | ||
| #plot true_f | ||
| plot(result, net_stats , net$fitness, plot = "true_f") | ||
| } | ||
| } |
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| @@ -0,0 +1,44 @@ | ||
| \name{print.CV_Result} | ||
| \alias{print.CV_Result} | ||
| %- Also NEED an '\alias' for EACH other topic documented here. | ||
| \title{ | ||
| Printing simple information of the cross-validation result | ||
| } | ||
| \description{ | ||
| This function prints simple information of the cross-validation result stored in a \code{CV_Result} object. This object is the field \code{$cv_result} of a \code{Full_PAFit_result} object, which in turn is the returning value of \code{\link{only_A_estimate}}, \code{\link{only_F_estimate}} or \code{\link{joint_estimate}}. | ||
| } | ||
| \usage{ | ||
| \method{print}{CV_Result}(x,...) | ||
| } | ||
| %- maybe also 'usage' for other objects documented here. | ||
| \arguments{ | ||
| \item{x}{ | ||
| An object of class \code{CV_Result}. | ||
| } | ||
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| \item{\dots}{ | ||
| %% ~~Describe \code{\dots} here~~ | ||
| } | ||
| } | ||
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| \value{ | ||
| Prints simple information of the cross-validation result. | ||
| } | ||
| \author{ | ||
| Thong Pham \email{thongpham@thongpham.net} | ||
| } | ||
| \examples{ | ||
| ## Since the runtime is long, we do not let this example run on CRAN | ||
| \dontrun{ | ||
| library("PAFit") | ||
| set.seed(1) | ||
| # a network from Bianconi-Barabasi model | ||
| net <- generate_BB(N = 1000 , m = 50 , | ||
| num_seed = 100 , multiple_node = 100, | ||
| s = 10) | ||
| net_stats <- get_statistics(net) | ||
| result <- joint_estimate(net, net_stats) | ||
| print(result$cv_result) | ||
| } | ||
| } |
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| \name{print.CV_Data} | ||
| \alias{print.CV_Data} | ||
| %- Also NEED an '\alias' for EACH other topic documented here. | ||
| \title{ | ||
| Printing simple information of the cross-validation data | ||
| } | ||
| \description{ | ||
| This function prints simple information of the cross-validation data stored in a \code{CV_Data} object. This object is the field \code{$cv_data} of a \code{Full_PAFit_result} object, which in turn is the returning value of \code{\link{only_A_estimate}}, \code{\link{only_F_estimate}} or \code{\link{joint_estimate}}. | ||
| } | ||
| \usage{ | ||
| \method{print}{CV_Data}(x,...) | ||
| } | ||
| %- maybe also 'usage' for other objects documented here. | ||
| \arguments{ | ||
| \item{x}{ | ||
| An object of class \code{CV_Data}. | ||
| } | ||
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| \item{\dots}{ | ||
| %% ~~Describe \code{\dots} here~~ | ||
| } | ||
| } | ||
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| \value{ | ||
| Prints simple information of the cross-validation data. | ||
| } | ||
| \author{ | ||
| Thong Pham \email{thongpham@thongpham.net} | ||
| } | ||
| \examples{ | ||
| ## Since the runtime is long, we do not let this example run on CRAN | ||
| \dontrun{ | ||
| library("PAFit") | ||
| set.seed(1) | ||
| # a network from Bianconi-Barabasi model | ||
| net <- generate_BB(N = 1000 , m = 50 , | ||
| num_seed = 100 , multiple_node = 100, | ||
| s = 10) | ||
| net_stats <- get_statistics(net) | ||
| result <- joint_estimate(net, net_stats) | ||
| print(result$cv_data) | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,43 @@ | ||
| \name{print.PAFit_data} | ||
| \alias{print.PAFit_data} | ||
| %- Also NEED an '\alias' for EACH other topic documented here. | ||
| \title{ | ||
| Printing simple information on the statistics of the network stored in a \code{PAFit_data} object | ||
| } | ||
| \description{ | ||
| This function prints simple information of the statistics stored in a \code{PAFit_data} object. This object is the returning value of \code{\link{get_statistics}}. | ||
| } | ||
| \usage{ | ||
| \method{print}{PAFit_data}(x,...) | ||
| } | ||
| %- maybe also 'usage' for other objects documented here. | ||
| \arguments{ | ||
| \item{x}{ | ||
| An object of class \code{PAFit_data}. | ||
| } | ||
|
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| \item{\dots}{ | ||
| %% ~~Describe \code{\dots} here~~ | ||
| } | ||
| } | ||
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| \value{ | ||
| Prints simple information of the network statistics. | ||
| } | ||
| \author{ | ||
| Thong Pham \email{thongpham@thongpham.net} | ||
| } | ||
| \examples{ | ||
| ## Since the runtime is long, we do not let this example run on CRAN | ||
| \dontrun{ | ||
| library("PAFit") | ||
| set.seed(1) | ||
| # a network from Bianconi-Barabasi model | ||
| net <- generate_BB(N = 1000 , m = 50 , | ||
| num_seed = 100 , multiple_node = 100, | ||
| s = 10) | ||
| net_stats <- get_statistics(net) | ||
| print(net_stats) | ||
| } | ||
| } |
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