我正在为数据分析和统计学习主题做报告。
问题出在聚类阶段。我必须找到最佳的簇数，我已经完成了。但是，当我尝试使用以下函数显示图形时，出现错误:
nb<-NbClust(df, diss=NULL,distance="euclidean",method="average")
fviz_nbclust(nb)
Error in if (class(best_nc) == "numeric") print(best_nc) else if (class(best_nc) == :
the condition has length > 1
df是标准化数据集，只有数值数据。
该错误与 fviz_nbclust 函数有关。
我想知道该怎么办。我已经尝试重新安装软件包、RStudio，但什么都没有。我也在 NbClust() 函数中执行了 min_nc 或 max_nc 之类的操作。
感谢您的帮助。
> dput(df)
structure(c(-1.04227826822844, -0.171114188130418, -0.804688064565345,
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-0.715285206415032, -1.04004516290537, 0.935577905744186, 1.09795788398935,
-0.552905228169863, 1.50390782960228, 0.231931333348454, 1.0438312245743,
0.475501300716207, -0.661158546999976, -1.0671084926129, 0.773197927499017,
-0.769411865830088, -1.28361513027312, -0.634095217292447, -0.0387019637268278
), .Dim = c(193L, 4L), .Dimnames = list(NULL, c("beer_servings",
"spirit_servings", "wine_servings", "total_litres_of_pure_alcohol"
)), "`scaled:center`" = c(beer_servings = 106.284974093264, spirit_servings = 81.1709844559585,
wine_servings = 49.7564766839378, total_litres_of_pure_alcohol = 4.84300518134715
), "`scaled:scale`" = c(beer_servings = 101.014265866079, spirit_servings = 88.1232966003502,
wine_servings = 79.511252951107, total_litres_of_pure_alcohol = 3.69503682956585
))
最佳答案
我遇到了同样的问题，结果发现函数 fviz_nbclust(实际上还有隐藏函数 .viz_NbClust)写错了。具体来说，问题是两个函数中的测试 if (class(best_nc) == "numeric") 和 if (class(best_nc) == "matrix")这是无稽之谈，因为对象 best_nc 是 NbClust 结果 nb$Best.nc 的元素，实际上有两个类:
> class(nb$Best.nc)
[1] "matrix" "array"
因此，测试将失败，因为 if 只能采用一个逻辑值作为条件，但测试会产生两个值:
> class(nb$Best.nc) == "numeric"
[1] FALSE FALSE
> if(class(nb$Best.nc) == "numeric") something()
Error in if (class(nb$Best.nc) == "numeric") something() :
the condition has length > 1
我的解决方案:我必须修复函数 fviz_nbclust 和 .viz_NbClust 以便测试看起来像 if (any( class(best_nc) == "numeric") ) 和 if (any(class(best_nc) == "matrix") )。这是一个工作代码:
library(factoextra)
library(NbClust)
# fix the functions
fviz_nbclust <- function (x, FUNcluster = NULL, method = c("silhouette", "wss",
"gap_stat"), diss = NULL, k.max = 10, nboot = 100, verbose = interactive(),
barfill = "steelblue", barcolor = "steelblue", linecolor = "steelblue",
print.summary = TRUE, ...)
{
set.seed(123)
if (k.max < 2)
stop("k.max must bet > = 2")
method = match.arg(method)
if (!inherits(x, c("data.frame", "matrix")) & !("Best.nc" %in%
names(x)))
stop("x should be an object of class matrix/data.frame or ",
"an object created by the function NbClust() [NbClust package].")
if (inherits(x, "list") & "Best.nc" %in% names(x)) {
best_nc <- x$Best.nc
if (any(class(best_nc) == "numeric") )
print(best_nc)
else if (any(class(best_nc) == "matrix") )
.viz_NbClust(x, print.summary, barfill, barcolor)
}
else if (is.null(FUNcluster))
stop("The argument FUNcluster is required. ", "Possible values are kmeans, pam, hcut, clara, ...")
else if (!is.function(FUNcluster)) {
stop("The argument FUNcluster should be a function. ",
"Check if you're not overriding the specified function name somewhere.")
}
else if (method %in% c("silhouette", "wss")) {
if (is.data.frame(x))
x <- as.matrix(x)
if (is.null(diss))
diss <- stats::dist(x)
v <- rep(0, k.max)
if (method == "silhouette") {
for (i in 2:k.max) {
clust <- FUNcluster(x, i, ...)
v[i] <- .get_ave_sil_width(diss, clust$cluster)
}
}
else if (method == "wss") {
for (i in 1:k.max) {
clust <- FUNcluster(x, i, ...)
v[i] <- .get_withinSS(diss, clust$cluster)
}
}
df <- data.frame(clusters = as.factor(1:k.max), y = v,
stringsAsFactors = TRUE)
ylab <- "Total Within Sum of Square"
if (method == "silhouette")
ylab <- "Average silhouette width"
p <- ggpubr::ggline(df, x = "clusters", y = "y", group = 1,
color = linecolor, ylab = ylab, xlab = "Number of clusters k",
main = "Optimal number of clusters")
if (method == "silhouette")
p <- p + geom_vline(xintercept = which.max(v), linetype = 2,
color = linecolor)
return(p)
}
else if (method == "gap_stat") {
extra_args <- list(...)
gap_stat <- cluster::clusGap(x, FUNcluster, K.max = k.max,
B = nboot, verbose = verbose, ...)
if (!is.null(extra_args$maxSE))
maxSE <- extra_args$maxSE
else maxSE <- list(method = "firstSEmax", SE.factor = 1)
p <- fviz_gap_stat(gap_stat, linecolor = linecolor,
maxSE = maxSE)
return(p)
}
}
.viz_NbClust <- function (x, print.summary = TRUE, barfill = "steelblue",
barcolor = "steelblue")
{
best_nc <- x$Best.nc
if (any(class(best_nc) == "numeric") )
print(best_nc)
else if (any(class(best_nc) == "matrix") ) {
best_nc <- as.data.frame(t(best_nc), stringsAsFactors = TRUE)
best_nc$Number_clusters <- as.factor(best_nc$Number_clusters)
if (print.summary) {
ss <- summary(best_nc$Number_clusters)
cat("Among all indices: \n===================\n")
for (i in 1:length(ss)) {
cat("*", ss[i], "proposed ", names(ss)[i],
"as the best number of clusters\n")
}
cat("\nConclusion\n=========================\n")
cat("* According to the majority rule, the best number of clusters is ",
names(which.max(ss)), ".\n\n")
}
df <- data.frame(Number_clusters = names(ss), freq = ss,
stringsAsFactors = TRUE)
p <- ggpubr::ggbarplot(df, x = "Number_clusters",
y = "freq", fill = barfill, color = barcolor) +
labs(x = "Number of clusters k", y = "Frequency among all indices",
title = paste0("Optimal number of clusters - k = ",
names(which.max(ss))))
return(p)
}
}
# assign them to the factoextra namespace
environment(fviz_nbclust) <- asNamespace("factoextra")
assignInNamespace("fviz_nbclust",fviz_nbclust,"factoextra")
environment(.viz_NbClust) <- asNamespace("factoextra")
assignInNamespace(".viz_NbClust",.viz_NbClust,"factoextra")
# test it
# your data
df <- structure(c(-1.04227826822844, -0.171114188130418, -0.804688064565345,
1.37322213567972, 1.09603356473944, -0.042419494479573, 0.85844336107634,
-0.844286431842528, 1.53161560478845, 1.70980825753577, -0.844286431842528,
0.155572341906342, -0.636395003637318, -1.04227826822844, 0.363463770111553,
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1.28035400282101, -0.887063778497454, -0.523936192098549, 0.588142041248097,
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2.77825529671649, 0.190971243624295, 1.51865648139529, -0.228895028149439,
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-0.42454967606529, -0.575471709797786, -0.61320221823091, 0.455828854040937,
-0.575471709797786, -0.386819167632166, -0.562894873653412, -0.537741201364662,
-0.374242331487792, -0.537741201364662, -0.223320297755295, -0.512587529075913,
-0.575471709797786, -0.0598214278784247, -0.562894873653412,
1.82670399377778, -0.61320221823091, 0.430675181752188, 2.14112489738714,
-0.525164365220288, -0.487433856787164, -0.588048545942161, -0.61320221823091,
-0.61320221823091, -0.575471709797786, -0.575471709797786, -1.04004516290537,
0.0154246956882285, -1.12123515202795, 2.04517442375284, 0.28605799276351,
0.0154246956882285, 0.935577905744186, -0.282271931094581, 1.50390782960228,
1.31446452164958, -0.958855173782785, 0.394311311593623, -0.769411865830088,
-1.04004516290537, 0.394311311593623, 2.5864410179034, 1.53097115930981,
0.529627960131263, -1.01298183319784, -1.20242514115054, -0.282271931094581,
-0.0657652934343561, 0.150741344225869, 0.637881278961376, -1.14829848173548,
1.47684449989475, -0.146955282556941, 0.394311311593623, -0.228145271679525,
-0.228145271679525, -0.715285206415032, 0.258994663055982, 0.908514576036657,
-0.823538525245144, -1.20242514115054, 0.746134597791489, 0.0424880253957566,
-0.174018612264469, -1.28361513027312, -0.850601854952673, 0.28605799276351,
-0.119891952849412, 1.44978117018722, -0.174018612264469, 0.908514576036657,
1.88279444550767, -1.04004516290537, -0.688221876707504, 1.50390782960228,
-1.01298183319784, 0.475501300716207, 0.367247981886095, -0.174018612264469,
-1.25655180056559, -0.715285206415032, 0.258994663055982, -1.17536181144301,
1.26033786223452, -1.12123515202795, -0.769411865830088, 1.39565451077216,
1.88279444550767, 1.09795788398935, -0.661158546999976, 0.150741344225869,
1.74747779697003, -0.823538525245144, 0.935577905744186, 1.9098577752152,
-0.715285206415032, -1.25655180056559, -0.634095217292447, 0.610817949253848,
0.28605799276351, -0.498778568754807, 1.74747779697003, 0.475501300716207,
-0.715285206415032, -1.28361513027312, -0.769411865830088, -1.25655180056559,
1.77454112667756, -0.634095217292447, 0.448437971008679, -0.390525249924694,
0.58375461954632, -1.17536181144301, 0.529627960131263, -0.823538525245144,
-1.04004516290537, -1.04004516290537, -0.661158546999976, 0.367247981886095,
1.53097115930981, -0.796475195537616, -0.552905228169863, -0.471715239047278,
-1.04004516290537, 2.18049107229048, 1.77454112667756, -1.09417182232043,
-0.904728514367729, -1.22948847085807, -1.04004516290537, -1.14829848173548,
0.475501300716207, 1.39565451077216, -0.769411865830088, -0.607031887584919,
0.177804673933397, -0.688221876707504, -1.04004516290537, 0.0154246956882285,
0.0154246956882285, -1.17536181144301, -0.958855173782785, -1.28361513027312,
0.529627960131263, -1.04004516290537, -1.25655180056559, 1.233274532527,
1.20621120281947, -0.363461920217166, -1.28361513027312, 1.15208454340441,
0.58375461954632, 0.502564630423735, -1.12123515202795, -1.22948847085807,
0.556691289838792, 0.637881278961376, -0.904728514367729, 0.664944608668904,
0.340184652178566, -0.0657652934343561, 1.63922447813992, 1.66628780784745,
-1.0671084926129, 1.34152785135711, 0.394311311593623, 1.50390782960228,
1.80160445638509, 0.529627960131263, 0.773197927499017, 1.42271784047969,
0.394311311593623, -0.607031887584919, -0.769411865830088, -0.174018612264469,
-1.28361513027312, -1.22948847085807, 1.28740119194205, -0.201081941971997,
0.502564630423735, -0.904728514367729, 1.77454112667756, 1.55803448901733,
-0.985918503490313, -1.04004516290537, 0.908514576036657, 1.39565451077216,
-0.715285206415032, -0.850601854952673, 0.204868003640925, -0.0387019637268278,
0.637881278961376, 1.44978117018722, -1.04004516290537, -1.22948847085807,
0.421374641301151, -0.255208601387053, -1.28361513027312, -0.958855173782785,
-1.01298183319784, 0.421374641301151, -0.958855173782785, -0.931791844075257,
-0.715285206415032, -1.04004516290537, 0.935577905744186, 1.09795788398935,
-0.552905228169863, 1.50390782960228, 0.231931333348454, 1.0438312245743,
0.475501300716207, -0.661158546999976, -1.0671084926129, 0.773197927499017,
-0.769411865830088, -1.28361513027312, -0.634095217292447, -0.0387019637268278
), .Dim = c(193L, 4L), .Dimnames = list(NULL, c("beer_servings",
"spirit_servings", "wine_servings", "total_litres_of_pure_alcohol"
)), "`scaled:center`" = c(beer_servings = 106.284974093264, spirit_servings = 81.1709844559585,
wine_servings = 49.7564766839378, total_litres_of_pure_alcohol = 4.84300518134715
), "`scaled:scale`" = c(beer_servings = 101.014265866079, spirit_servings = 88.1232966003502,
wine_servings = 79.511252951107, total_litres_of_pure_alcohol = 3.69503682956585
))
# run NbClust
nb<-NbClust(df, diss=NULL,distance="euclidean",method="average")
# run the corrected version of fviz_nbclust
fviz_nbclust(nb)
结果:
关于fviz_nbclust() 函数的 Rstudio 错误，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/72075707/