report significance of interaction detection results (#96)

See #95 for more details, now also report significance of interaction detection results in `gozh` and `gozh_detector`.

Author: SpatLyu

NEWS.md

@@ -1,6 +1,8 @@
 # gdverse 1.6
 
-- Improve robustness of non-centrality parameter estimation for q-statistic confidence intervals (#93).
+* Report significance of interaction detection results in geographical detector (#95).
+
+* Improve robustness of non-centrality parameter estimation for q-statistic confidence intervals (#93).
 
 # gdverse 1.5.1
 

R/gozh.R

@@ -164,8 +164,8 @@ gozh_detector = \(formula, data, cores = 1,
   }
   calcul_pd = \(xvar,...){
     discdf = gdverse::rpart_disc(paste0(yname,'~',paste(xvar,collapse = '+')),data = dti,...)
-    qv = gdverse::factor_detector(dti[,yname,drop = TRUE],discdf)[[1]]
-    return(qv)
+    qv = gdverse::factor_detector(dti[,yname,drop = TRUE],discdf)
+    return(unlist(qv))
   }
   interact_type = \(qv1,qv2,qv12){
     if (qv12 < min(qv1, qv2)) {
@@ -198,21 +198,26 @@ gozh_detector = \(formula, data, cores = 1,
             variable1 = purrr::map_chr(seq_along(xinteract), \(x) xinteract[[x]][1])
             variable2 = purrr::map_chr(seq_along(xinteract), \(x) xinteract[[x]][2])
             if (doclust) {
-              qv12 = as.numeric(parallel::parLapply(cl,xinteract,calcul_pd))
+              qv12 = tibble::as_tibble(do.call(rbind,parallel::parLapply(cl,xinteract,calcul_pd)))
             } else {
-              qv12 = purrr::map_dbl(xinteract,calcul_pd)
+              qv12 = purrr::map_dfr(xinteract,calcul_pd)
             }
             res = gdverse::gd(paste0(yname,' ~ .'),data = newdata,type = "factor")[[1]]
             qv = res[,2,drop = TRUE]
-            names(qv) = res[,1,drop = TRUE]
+            pv = res[,3,drop = TRUE]
+            names(qv) = names(pv) = res[,1,drop = TRUE]
             qv1 = qv[variable1]
             qv2 = qv[variable2]
+            pv1 = pv[variable1]
+            pv2 = pv[variable2]
             res = tibble::tibble(
-              "Variable1 Q-statistics" = qv1,"Variable2 Q-statistics" = qv2,
-              "Variable1 and Variable2 interact Q-statistics" = qv12,
+              "Variable1 Q-statistics" = qv1, "Variable2 Q-statistics" = qv2,
+              "Variable1 and Variable2 interact Q-statistics" = qv12[,1,drop = TRUE],
               "variable1" = variable1, "variable2" = variable2,
-              "Interaction" = purrr::pmap_chr(list(qv1 = qv1,qv2 = qv2,qv12 = qv12),
-                                              interact_type)) %>%
+              "Interaction" = purrr::pmap_chr(list(qv1 = qv1,qv2 = qv2,qv12 = qv12[,1,drop = TRUE]),
+                                              interact_type),
+              "Variable1 P-value" = pv1, "Variable2 P-value" = pv2,
+              "Variable1 and Variable2 interact P-value" = qv12[,2,drop = TRUE]) %>%
                                dplyr::select(variable1,variable2,Interaction,
                                              dplyr::everything())
             res = list("interaction" = res)