R中的波尔卡未执行。我收到一条奇怪的错误消息

我对R很陌生，我遇到了一个错误，我不知道我为什么会遇到这个错误。我正在Windows 10上运行R Studio 1.3.959版，并尝试使用poLCA软件包

这是我的密码：

library(scatterplot3d)
library(MASS)
library(poLCA)

data <- read.csv(file.choose(), header = TRUE)

data[-3] <- lapply(data[-3],gsub,pattern = "1", replacement = "2", fixed = TRUE)
data[-3] <- lapply(data[-3],gsub,pattern = "0", replacement = "1", fixed = TRUE)
cols.num<- c("A", "B", "C", "D", "E")
data[cols.num] <- sapply(data[cols.num],as.numeric)

AMER_all <- data[which(data$SALES_LEVEL_1 == "AMERICAS"),]
AMER_ALL_LSA <- cbind("A", "B", "C", "D", "E")
AMER_less_1000 <- AMER_all[which(AMER_all$WALLET_BINS == "<$2k"),]

AMER_less_1000_LCA <- poLCA(AMER_ALL_LSA, data = AMER_less_1000, nclass = 3, graphs = TRUE)

当我看到这个警告时，我得到了这样的结论：

In FUN(newX[, i], ...) : no non-missing arguments to max; returning -Inf

知道我为什么会犯这个错误吗？我想知道这是不是我下载的R版本。我最近需要重新整理我的笔记本电脑，再次从fresh下载R和RStudio

样本数据

structure(list(ï..COMPANY_ID = c("GUC_123", 
"GUC_111", "GUC_112", "GUC_113", "GUC_114", 
"GUC_115", "GUC_116", "GUC_117", 
"GUC_118", "GUC_119", "GUC_120", "GUC_121", 
"GUC_122", "GUC_123", "GUC_124", 
"GUC_125", "GUC_126", "GUC_127", 
"GUC_128", "GUC_129"), SALES_LEVEL_1 = c("AMERICAS", 
"APJC__", "AMERICAS", "AMERICAS", "APJC__", "AMERICAS", "EMEAR-REGION", 
"AMERICAS", "AMERICAS", "EMEAR-REGION", "AMERICAS", "AMERICAS", 
"AMERICAS", "AMERICAS", "EMEAR-REGION", "AMERICAS", "AMERICAS", 
"AMERICAS", "APJC__", "EMEAR-REGION"), WALLET_BIN = c("$1k-$15k", 
"$15k-$50", "$1k-$15k", "$100k-$200k", "$1k-$15k", "$15k-$50", 
"$1k-$15k", "$1k-$15k", "$15k-$50", "$15k-$50", "$1k-$15k", "$1k-$15k", 
"$15k-$50", "$1k-$15k", "$1k-$15k", "$50k-$100k", "$50k-$100k", 
"$15k-$50", "$50k-$100k", "$1k-$15k"), A = c(1, 
1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1), B = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), C = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), D = c(2, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1), E = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), F = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), G = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), H = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1), I = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1), J = c(2, 
1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1), K = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), L = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), M = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), N = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), O = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1), P = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1), row.names = c(NA, 
20L), class = "data.frame")

---

看起来非常愚蠢的问题是定义没有引号的公式，如中所述：

库（poLCA）
谢谢。我已经用数据样本更新了我注意到在你的第一个列名中有一个
I
，这是有意的吗？您提供的数据没有
AMER\u all$WALLET\u bin==“嗨，伊恩，我不知道为什么这个角色会出现在第一栏。我试图创建一个新的列名，但它仍然显示出来。知道它为什么会出现吗？我已将代码更新为以下内容：``AMER\u ALL\u LSA No，它非常敏感。SorrySo我通过在CSV文件的右侧添加一个虚拟列来删除该字符，然后使用
数据删除该列。这是可行的，但我仍然需要将数据限制在钱包中。。当我添加那段额外的代码时，我得到了相同的错误
AMER_less_1000您发布在pastebin上的数据没有bin
“天哪，这只是一个愚蠢的错误！谢谢。我看这个太久了！哈。谢谢您的帮助！
structure(list(ï..COMPANY_ID = c("GUC_123", 
"GUC_111", "GUC_112", "GUC_113", "GUC_114", 
"GUC_115", "GUC_116", "GUC_117", 
"GUC_118", "GUC_119", "GUC_120", "GUC_121", 
"GUC_122", "GUC_123", "GUC_124", 
"GUC_125", "GUC_126", "GUC_127", 
"GUC_128", "GUC_129"), SALES_LEVEL_1 = c("AMERICAS", 
"APJC__", "AMERICAS", "AMERICAS", "APJC__", "AMERICAS", "EMEAR-REGION", 
"AMERICAS", "AMERICAS", "EMEAR-REGION", "AMERICAS", "AMERICAS", 
"AMERICAS", "AMERICAS", "EMEAR-REGION", "AMERICAS", "AMERICAS", 
"AMERICAS", "APJC__", "EMEAR-REGION"), WALLET_BIN = c("$1k-$15k", 
"$15k-$50", "$1k-$15k", "$100k-$200k", "$1k-$15k", "$15k-$50", 
"$1k-$15k", "$1k-$15k", "$15k-$50", "$15k-$50", "$1k-$15k", "$1k-$15k", 
"$15k-$50", "$1k-$15k", "$1k-$15k", "$50k-$100k", "$50k-$100k", 
"$15k-$50", "$50k-$100k", "$1k-$15k"), A = c(1, 
1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1), B = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), C = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), D = c(2, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1), E = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), F = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), G = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), H = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1), I = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1), J = c(2, 
1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1), K = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), L = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), M = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), N = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), O = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1), P = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1), row.names = c(NA, 
20L), class = "data.frame")

library(poLCA)
data <- read.delim("https://pastebin.com/raw/TNJNCxkH")
data[,5:ncol(data)] <- lapply(data[,5:ncol(data)],gsub,pattern = "1", replacement = "2", fixed = TRUE)
data[,5:ncol(data)] <- lapply(data[,5:ncol(data)],gsub,pattern = "0", replacement = "1", fixed = TRUE)
data[,5:ncol(data)] <- sapply(data[,5:ncol(data)],as.numeric)
AMER_all <- data[which(data$SALES_LEVEL_1 == "AMERICAS"),]
AMER_ALL_LSA <- cbind(PB_1,PB_2,PB_3,PB_4,PB_5)~1
AMER_less_1000_LCA <- poLCA(AMER_ALL_LSA, data = AMER_all, nclass = 3, graphs = TRUE)
#Conditional item response (column) probabilities,
# by outcome variable, for each class (row) 
#...
#========================================================= 
#Fit for 3 latent classes: 
#========================================================= 
#number of observations: 1016 
#number of estimated parameters: 17 
#residual degrees of freedom: 14 
#maximum log-likelihood: -1068.675  
#AIC(3): 2171.351
#BIC(3): 2255.052
#G^2(3): 1.362823 (Likelihood ratio/deviance statistic) 
#X^2(3): 0.7509686 (Chi-square goodness of fit) 
# 
#ALERT: iterations finished, MAXIMUM LIKELIHOOD NOT FOUND