rSCA.inference: Inference for Stepwise Cluster Analysis

In rSCA: An R Package for Stepwise Cluster Analysis

Description

This function is used for statistical inference or prediction based on an existing stepwise cluster analysis (SCA) model. The results are saved into a text file (file name: rsl_modelname.txt) under the model's output folder. In some cases, the training process of SCA with large samples will be very slow, but users may want to use the trained model to do multiple predictions. If this is the case, users can construct a SCA model based on previously-generated tree and map files (that means you only need to run rSCA.modeling function once). Such model can be assigned to the last parameter (i.e., "model") of this function to perform multiple predictions. Please refer to the following example codes for more details.

Usage

	rSCA.inference(xfile, x.row.names = FALSE, x.col.names = FALSE, 
        x.missing.flag = "NA", x.type = ".txt", model)

Arguments

xfile	a string to specify the full filename of the independent (x) data file, only supports files in *.txt or *.csv.
x.row.names	a logical value to specify if the independent (x) data file contains row names or not. Default value is FALSE.
x.col.names	a logical value to specify if the independent (x) data file contains column names or not. Default value is FALSE.
x.missing.flag	a string to specify the missing flag used in the independent (x) data file. Default value is "NA".
x.type	a string to specify the type of independent (x) data file. Default value is ".txt".
model	a SCA model object to be used for statistical inference or prediction.

Author(s)

Xiuquan Wang <[e-mail]>

References

Wang, X., G. Huang, S. Zhao, and G. Guo (2015), An open-source software package for multivariate modeling and clustering: applications to air quality management. Environmental Science and Pollution Research, 22(18), 14220-14233.

Wang, X., G. Huang, Q. Lin, X. Nie, G. Cheng, Y. Fan, Z. Li, Y. Yao, and M. Suo (2013), A stepwise cluster analysis approach for downscaled climate projection - A Canadian case study. Environmental Modelling & Software, 49, 141-151.

Examples

## Load rSCA package
library(rSCA)

## X data file
xdata <- c("A B C D\r", "0.095 0.044 39.9 27\r", 
           "0.810 0.058 9.1 8\r", "0.101 0.077 11.4 14\r",
           "0.006 0.141 20.5 29\r", "0.070 0.281 27.3 26\r",
           "0.481 0.514 30.2 48\r", "0.120 0.286 36.4 39\r",
           "0.480 0.199 40.9 27\r", "0.112 0.101 29.9 18\r",
           "0.026 0.203 48.1 28\r", "0.128 1.235 48.2 61\r",
           "2.681 0.439 51.1 98\r", "1.601 0.333 56.1 99\r",
           "1.398 0.455 19.3 103\r", "1.256 0.314 14.9 17\r",
           "2.618 0.609 9.1 19\r", "1.217 0.880 17.2 73\r",
           "1.411 2.115 19.6 203\r", "0.245 6.839 49.2 296\r",
           "0.724 3.060 17.1 192\r", "0.019 2.252 29.1 123\r",
           "1.321 5.730 41.1 288\r", "0.903 3.078 39.0 97\r",
           "0.714 1.013 16.7 5\r", "0.581 1.398 11.7 57\r",
           "0.080 1.734 10.2 52\r", "0.120 1.848 6.6 132\r",
           "0.089 1.357 10.3 148\r", "0.112 0.585 19.3 79\r",
           "0.192 0.675 6.9 39\r", "0.301 1.937 11.9 6\r")
xdatafile <- tempfile()
writeLines(xdata, xdatafile)

## Y data file
ydata <- c("Y1 Y2 Y3\r", "0.020 0.034 10.01\r",
           "0.011 0.011 6.92\r", "0.016 0.018 9.53\r",
           "0.022 0.018 5.04\r", "0.031 0.029 8.90\r",
           "0.057 0.036 9.98\r", "0.040 0.048 12.96\r",
           "0.061 0.050 9.84\r", "0.023 0.031 8.84\r",
           "0.025 0.020 4.66\r", "0.041 0.042 9.02\r",
           "0.070 0.029 11.37\r", "0.077 0.022 11.88\r",
           "0.105 0.038 11.06\r", "0.038 0.027 11.64\r",
           "0.058 0.019 8.25\r", "0.051 0.050 10.01\r",
           "0.073 0.038 9.20\r", "0.123 0.080 9.91\r",
           "0.089 0.046 9.37\r", "0.073 0.039 7.99\r",
           "0.139 0.069 13.28\r", "0.095 0.048 9.80\r",
           "0.034 0.040 8.50\r", "0.055 0.034 9.21\r",
           "0.020 0.050 8.67\r", "0.070 0.036 8.03\r",
           "0.058 0.039 8.01\r", "0.057 0.031 6.30\r",
           "0.050 0.014 7.92\r", "0.039 0.040 8.08\r")
ydatafile <- tempfile()
writeLines(ydata, ydatafile)

## New X data
xnewdata <- c("A B C D\r", "0.085 0.054 35.9 29\r", 
           "0.820 0.068 9.2 7\r", "0.121 0.067 12.4 13\r",
           "0.016 0.151 21.5 24\r", "0.075 0.283 25.3 16\r",
           "0.581 0.524 31.2 38\r", "0.130 0.486 33.4 36\r")
xnewdatafile <- tempfile()
writeLines(xnewdata, xnewdatafile)

## Modeling the relationship between Y and X with SCA
myModel = rSCA.modeling(xfile = xdatafile, yfile = ydatafile,
              x.col.names = TRUE, y.col.names = TRUE)
			  
## Predict Y with the SCA model
rSCA.inference(xfile = xnewdatafile, x.col.names = TRUE, model = myModel)

## Perform multiple predictions based on a previously-trained model
## Step 1. Construct a model based on previous tree and map files: 
## >> preModel = list(treefile = path_to_tree_file, mapfile = path_to_map_file, type = "mean")
## Note that the value for the parameter of "type" should be the same as what you 
## used in the previous traning
## Step 3. Perform multiple predictions with different xfiles
## >> rSCA.inference(xfile = xnewdatafile_1, x.col.names = TRUE, model = preModel)
## >> rSCA.inference(xfile = xnewdatafile_2, x.col.names = TRUE, model = preModel)

rSCA documentation built on March 13, 2020, 2:15 a.m.