Is it possible to get the p value for nodes in the analysis of a categorical tree using R?

Question

Is it possible to get the p-value for nodes in the analysis of a categorical tree with R? I am using rpart and cannot find the p value for each node. Perhaps this is only possible with regression, and not with categories.

structure(list(subj = c(702L, 702L, 702L, 702L, 702L, 702L, 702L, 702L, 702L, 702L, 702L, 702L, 702L, 702L, 702L, 702L, 702L, 702L, 702L, 702L, 702L, 702L, 702L, 702L), visit = c(4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), run = structure(c(1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("A", "B", "C", "D", "E", "xdur", "xend60", "xpre" ), class = "factor"), ho = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L ), hph = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), longexer = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("10min", "60min"), class = "factor"), esq_sick = c(NA, NA, 0L, NA, NA, NA, NA, NA, NA, NA, 0L, NA, NA, NA, NA, NA, NA, NA, 0L, NA, NA, NA, NA, NA), esq_sick2 = c(NA, NA, 0L, NA, NA, NA, NA, NA, NA, NA, 0L, NA, NA, NA, NA, NA, NA, NA, 0L, NA, NA, NA, NA, NA), ll_sick = c(NA, NA, 0L, NA, NA, NA, NA, NA, NA, NA, 0L, NA, NA, NA, NA, NA, NA, NA, 0L, NA, NA, NA, NA, NA), ll_sick2 = c(NA, NA, 0L, NA, NA, NA, NA, NA, NA, NA, 0L, NA, NA, NA, NA, NA, NA, NA, 0L, NA, NA, NA, NA, NA), esq_01 = c(NA, NA, 2L, NA, NA, NA, NA, NA, NA, NA, 2L, NA, NA, NA, NA, NA, NA, NA, 1L, NA, NA, NA, NA, NA), esq_02 = c(NA, NA, 1L, NA, NA, NA, NA, NA, NA, NA, 2L, NA, NA, NA, NA, NA, NA, NA, 1L, NA, NA, NA, NA, NA), esq_03 = c(NA, NA, 0L, NA, NA, NA, NA, NA, NA, NA, 1L, NA, NA, NA, NA, NA, NA, NA, 0L, NA, NA, NA, NA, NA), esq_04 = c(NA, NA, 0L, NA, NA, NA, NA, NA, NA, NA, 0L, NA, NA, NA, NA, NA, NA, NA, 0L, NA, NA, NA, NA, NA), esq_05 = c(NA, NA, 0L, NA, NA, NA, NA, NA, NA, NA, 0L, NA, NA, NA, NA, NA, NA, NA, 0L, NA, NA, NA, NA, NA), esq_06 = c(NA, NA, 1L, NA, NA, NA, NA, NA, NA, NA, 1L, NA, NA, NA, NA, NA, NA, NA, 1L, NA, NA, NA, NA, NA), esq_07 = c(NA, NA, 0L, NA, NA, NA, NA, NA, NA, NA, 0L, NA, NA, NA, NA, NA, NA, NA, 1L, NA, NA, NA, NA, NA), esq_08 = c(NA, NA, 0L, NA, NA, NA, NA, NA, NA, NA, 0L, NA, NA, NA, NA, NA, NA, NA, 0L, NA, NA, NA, NA, NA), esq_09 = c(NA, NA, 0L, NA, NA, NA, NA, NA, NA, NA, 0L, NA, NA, NA, NA, NA, NA, NA, 0L, NA, NA, NA, NA, NA), esq_10 = c(NA, NA, 0L, NA, NA, NA, NA, NA, NA, NA, 0L, NA, NA, NA, NA, NA, NA, NA, 0L, NA, NA, NA, NA, NA)), .Names = c("subj", "visit", "run", "ho", "hph", "longexer", "esq_sick", "esq_sick2", "ll_sick", "ll_sick2", "esq_01", "esq_02", "esq_03", "esq_04", "esq_05", "esq_06", "esq_07", "esq_08", "esq_09", "esq_10"), row.names = 7:30, class = "data.frame") alldata = read.table('symptomology CSV2.csv',header=TRUE,sep=",") library(rpart) fit <- rpart(esq_sick2~esq_01_bin + esq_02_bin + esq_03_bin + esq_04_bin + esq_05_bin + esq_06_bin + esq_07_bin + esq_08_bin + esq_09_bin + esq_10_bin + esq_11_bin + esq_12_bin + esq_13_bin + esq_14_bin + esq_15_bin + esq_16_bin + esq_17_bin + esq_18_bin + esq_19_bin + esq_20_bin, method="class", data=alldata) plot(fit, uniform = FALSE, branch = 1, compress = FALSE, nspace, margin = 0.1, minbranch = 0.3) text(fit, use.n=TRUE, all=TRUE, cex=.8) 

Answer 1

Here is an example that might help you. I use the built-in data airquality to provide airquality and the example provided in the help for ctree :

 library(partykit) # For the sctest function to extract p-values (see help for ctree and sctest) library(strucchange) # Data we'll use airq <- subset(airquality, !is.na(Ozone)) # Build the tree airct <- ctree(Ozone ~ ., data = airq) 

Look at the tree:

 airct Model formula: Ozone ~ Solar.R + Wind + Temp + Month + Day Fitted party: [1] root | [2] Temp <= 82 | | [3] Wind <= 6.9: 55.600 (n = 10, err = 21946.4) | | [4] Wind > 6.9 | | | [5] Temp <= 77: 18.479 (n = 48, err = 3956.0) | | | [6] Temp > 77: 31.143 (n = 21, err = 4620.6) | [7] Temp > 82 | | [8] Wind <= 10.3: 81.633 (n = 30, err = 15119.0) | | [9] Wind > 10.3: 48.714 (n = 7, err = 1183.4) 

Extract the p values:

 sctest(airct) $'1' Solar.R Wind Temp Month Day statistic 13.34761286 4.161370e+01 5.608632e+01 3.1126596 0.02011554 p.value 0.00129309 5.560572e-10 3.468337e-13 0.3325881 0.99998175 $'2' Solar.R Wind Temp Month Day statistic 5.4095322 12.968549828 11.298951405 0.2148961 2.970294 p.value 0.0962041 0.001582833 0.003871534 0.9941976 0.357956 $'3' NULL $'4' Solar.R Wind Temp Month Day statistic 9.547191843 2.307676 11.598966936 0.06604893 0.2513143 p.value 0.009972755 0.497949 0.003295072 0.99965679 0.9916670 $'5' Solar.R Wind Temp Month Day statistic 6.14094026 1.3865355 1.9986304 0.8268341 1.3580462 p.value 0.06432172 0.7447599 0.5753799 0.8952749 0.7528481 $'6' Solar.R Wind Temp Month Day statistic 5.1824354 0.02060939 0.9270013 0.165171 4.6220522 p.value 0.1089932 0.99998062 0.8705785 0.996871 0.1481643 $'7' Solar.R Wind Temp Month Day statistic 0.8083249 11.711564549 6.77148538 0.1307643 0.03992875 p.value 0.8996614 0.003101788 0.04546281 0.9982052 0.99990034 $'8' Solar.R Wind Temp Month Day statistic 0.9056479 3.1585094 2.9285252 0.008106707 0.008686293 p.value 0.8759687 0.3247585 0.3657072 0.999998099 0.999997742 $'9' NULL