mistake in manual/computed fixed

BrainEditor · May 27, 2019 · dbd3b75 · dbd3b75
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commit dbd3b75
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Showing 3 changed files with 45 additions and 45 deletions.
diff --git a/r/.Rhistory b/r/.Rhistory
@@ -1,45 +1,3 @@
-corrplot(res2$r, type="upper", order="hclust",
-p.mat = res2$P, sig.level = 0.01, insig = "blank",
-tl.col = "black", tl.cex = 0.75, method="number", number.cex = 0.65)
-title('correlation matrix', adj = 0.05, line = -2.75)
-title('correlation matrix', adj = 3, line = -7)
-corrplot(res2$r, type="upper", order="hclust",
-p.mat = res2$P, sig.level = 0.01, insig = "blank",
-tl.col = "black", tl.cex = 0.75, method="number", number.cex = 0.65)
-title('correlation matrix', adj = 3, line = -7)
-title('correlation matrix', adj = 0.3, line = -7)
-title('correlation matrix', adj = 0.1, line = -7)
-corrplot(res2$r, type="upper", order="hclust",
-p.mat = res2$P, sig.level = 0.01, insig = "blank",
-tl.col = "black", tl.cex = 0.75, method="number", number.cex = 0.65)
-title('correlation matrix', adj = 0.1, line = -7)
-title('correlation matrix', adj = 0.5, line = -7)
-corrplot(res2$r, type="upper", order="hclust",
-p.mat = res2$P, sig.level = 0.01, insig = "blank",
-tl.col = "black", tl.cex = 0.75, method="number", number.cex = 0.65)
-title('correlation matrix', adj = 0.5, line = -7)
-title('correlation matrix', adj = 0.3, line = -7.3)
-corrplot(res2$r, type="upper", order="hclust",
-p.mat = res2$P, sig.level = 0.01, insig = "blank",
-tl.col = "black", tl.cex = 0.75, method="number", number.cex = 0.65)
-title('correlation matrix', adj = 0.3, line = -8)
-title('correlation matrix', adj = 0.3, line = -12)
-title('correlation matrix', adj = 0.35, line = -12)
-# Insignificant correlation are left blank
-res2 <- rcorr(as.matrix(coh_all_measures[3:19]))
-corrplot(res2$r, type="upper", order="hclust",
-p.mat = res2$P, sig.level = 0.01, insig = "blank",
-tl.col = "black", tl.cex = 0.75, method="number", number.cex = 0.65)
-title('correlation matrix', adj = 0.35, line = -12)
-corrplot(res2$r, type="upper", order="hclust",
-p.mat = res2$P, sig.level = 0.01, insig = "blank",
-tl.col = "black", tl.cex = 0.75, method="number", number.cex = 0.65)
-title('correlation matrix', adj = 0.325, line = -12)
-corrplot(res2$r, type="upper", order="hclust",
-p.mat = res2$P, sig.level = 0.01, insig = "blank",
-tl.col = "black", tl.cex = 0.75, method="number", number.cex = 0.65)
-title('correlation matrix', adj = 0.325, line = -12)
-title('correlation matrix')
 library(tidyverse)
 library(cluster)
 library(languageR)
@@ -510,3 +468,45 @@ coh_ca <- CA(no_nutt_nodiagnosis, graph = FALSE)
 coh_ca <- CA(no_nutt_no_diagnosis, graph = FALSE)
 fviz_ca_col(coh_ca, col.col = manual, repel=TRUE,
 title = 'Principal Correspondence Analysis using factoextra & FactoMineR, biplot of manual and computer measures of coherence')+theme(plot.title = element_text(size = 12))
+manual <- as.factor(c('manual measure','manual measure','manual measure','manual measure', 'manual measure','manual measure',
+'manual measure','manual measure','manual measure','manual measure','manual measure',
+'computed measure','computed measure','computed measure',
+'computed measure','computed measure'))
+library(tidyverse)
+library(cluster)
+library(factoextra)
+library(languageR)
+library(FactoMineR)
+library(ggfortify)
+coh_all_measures <- read_csv('all_measures.csv')
+no_nutt_no_diagnosis <- coh_all_measures[4:18]
+row.names(no_nutt_no_diagnosis) <- coh_all_measures[[1]]
+row.names(no_nutt_no_diagnosis) <- coh_all_measures[[1]]
+no_nutt_no_diagnosis <- coh_all_measures[4:18]
+library(tidyverse)
+library(cluster)
+library(languageR)
+library(factoextra)
+library(FactoMineR)
+library(ggfortify)
+coh_all_measures <- read_csv('all_measures.csv')
+no_nutt_no_diagnosis <- coh_all_measures[4:18]
+no_nutt_no_diagnosis <-
+coh_all_measures[4:18]
+no_nutt_no_diagnosis <-
+coh_all_measures[4:18]
+library(tidyverse)
+library(cluster)
+library(languageR)
+library(factoextra)
+library(FactoMineR)
+library(ggfortify)
+coh_all_measures <- read_csv('all_measures.csv')
+no_nutt_no_diagnosis <-
+coh_all_measures[4:18]
+no_nutt_no_diagnosis <- coh_all_measures[4:18]
+row.names(no_nutt_no_diagnosis) <- coh_all_measures[[1]]
+manual <- as.factor(c('manual measure','manual measure','manual measure','manual measure', 'manual measure','manual measure',
+'manual measure','manual measure','manual measure','manual measure','manual measure',
+'computed measure','computed measure','computed measure',
+'computed measure','computed measure'))
diff --git a/r/all_measures.csv b/r/all_measures.csv
@@ -17,4 +17,4 @@ HP-v15,control,38,0.971428571,0.743421053,0.598684211,0.539473684,0.736842105,0.
 HP-v21,control,20,1,1,0.75,0.65,0.95,0.85,0.85,0.15,0,0.2,0.865153429,0.978299308,0.865577951,0.782154305,0.965179138
 HP-v22,control,43,0.714285714,0.790697675,0.63372093,0.534883721,0.697674419,0.674418605,0.720930233,0.302325581,0.23255814,0.23255814,0.87528855,0.985079133,0.898549389,0.841654517,0.933273928
 HP-v25,control,36,0.857142857,0.875,0.694444445,0.611111111,0.833333333,0.777777778,0.777777778,0.305555556,0.055555556,0.166666667,0.88688507,0.986033342,0.872935609,0.80116834,0.952595994
-HP-v27,control,47,1,0.946808511,0.776595745,0.691489362,0.914893617,0.893617021,0.936170213,0,0.063829787,0.042553191,0.871625781,0.985671157,0.857770191,0.773966613,0.947780833
+HP-v27,control,47,1,0.946808511,0.776595745,0.691489362,0.914893617,0.893617021,0.936170213,0,0.063829787,0.042553191,0.871625781,0.985671157,0.857770191,0.773966613,0.947780833
diff --git a/r/clustering.R b/r/clustering.R
@@ -10,10 +10,10 @@ coh_all_measures <- read_csv('all_measures.csv')
 no_nutt_no_diagnosis <- coh_all_measures[4:18]
 row.names(no_nutt_no_diagnosis) <- coh_all_measures[[1]]
 
-manual <- as.factor(c('manual measure','manual measure','manual measure','manual measure','manual measure',
+manual <- as.factor(c('manual measure','manual measure','manual measure','manual measure', 'manual measure','manual measure',
                      'manual measure','manual measure','manual measure','manual measure','manual measure',
                      'computed measure','computed measure','computed measure',
-                     'computed measure','computed measure','computed measure'))
+                     'computed measure','computed measure'))
 
 color_diagnosis <- ifelse(coh_all_measures$diagnosis == 'shizo', 'blue','red')