x lavaan错误中的值无限或缺失

如何解决x lavaan错误中的值无限或缺失

我确定我缺少明显的东西，但这是我第一次在统计类之外使用SEM。

总的来说，我正在尝试对多元主持的中介分析进行建模，直到今天早晨当我开始遇到此错误时才出现问题：

Error in eigen(VarCov,symmetric = TRUE,only.values = TRUE) : 
  infinite or missing values in 'x'

我想我已经对有关此错误的先前问题进行了尽职调查，并且我推测观察到的协方差矩阵是奇异的。我一直在试图找出导致问题的确切原因，并最终将我的模型分解为一个潜在变量，但仍然出现此错误。因此，我对两个观察到的变量进行了测试，得到了相同的错误，并使用三个不同的观察到的变量进行了尝试：

simple_data = modmed_subset[,c("baq_ff_1RC","baq_ff_2RC")]
simple = '
#measurement model 
feelfat =~ baq_ff_1RC + baq_ff_2RC'

fitted_simple = sem(simple,simple_data,se = "bootstrap",bootstrap = 10)

#>Error in eigen(VarCov,only.values = TRUE) : 
#>  infinite or missing values in 'x'

View(simple_data)
cor(simple_data,use="complete.obs")
#>           baq_ff_1RC baq_ff_2RC
#>baq_ff_1RC  1.0000000  0.6429606
#>baq_ff_2RC  0.6429606  1.0000000
simple_data = modmed_subset[,c("baq_ff_3RC","baq_ff_4RC","baq_ff_5RC")]
simple = '
#measurement model 

feelfat =~ baq_ff_3RC + baq_ff_4RC + baq_ff_5RC

#regression model'

fitted_simple = sem(simple,bootstrap = 10)
#>Error in eigen(VarCov,only.values = TRUE) : 
#>  infinite or missing values in 'x'
cor(simple_data,use="complete.obs")
#>           baq_ff_3RC baq_ff_4RC baq_ff_5RC
#>baq_ff_3RC  1.0000000  0.6016666  0.6527502
#>baq_ff_4RC  0.6016666  1.0000000  0.5865960
#>baq_ff_5RC  0.6527502  0.5865960  1.0000000

我仍然遇到相同的错误。我应该提到的是，我还对仅包含完整数据的案例的数据集进行了测试，并得到了相同的错误。

PS：我知道引导重采样的数量非常少，但是由于我在对模型进行故障排除时已经进行了如此多的重运行，因此我选择了这种方法来保持进展。

以下是一些数据（来自数据框，省略了不完整的情况）

structure(list(baq_ff_3RC = c(4,5,3,4,1,2,3),baq_ff_4RC = c(5,baq_ff_5RC = c(4,4)),row.names = c(1L,2L,3L,4L,5L,6L,7L,8L,9L,10L,11L,12L,13L,14L,15L,16L,17L,18L,19L,20L,21L,22L,23L,24L,25L,26L,27L,28L,29L,30L,31L,32L,33L,35L,36L,37L,38L,39L,40L,43L,44L,45L,46L,47L,48L,49L,50L,51L,52L,53L,54L,55L,56L,57L,58L,59L,60L,61L,62L,63L,64L,65L,66L,67L,68L,69L,71L,72L,73L,74L,76L,77L,78L,79L,80L,81L,82L,84L,85L,86L,87L,88L,89L,90L,91L,92L,93L,94L,95L,96L,97L,98L,99L,100L,101L,102L,103L,104L,105L,106L,107L,108L,109L,110L,111L,112L,113L,114L,115L,116L,117L,118L,119L,120L,121L,122L,123L,124L,125L,126L,127L,128L,129L,130L,132L,133L,134L,135L,136L,137L,138L,139L,141L,142L,143L,144L,145L,146L,147L,148L,149L,151L,152L,153L,154L,155L,156L,157L,158L,159L,160L,162L,163L,164L,165L,166L,167L,168L,169L,170L,171L,172L,174L,175L,177L,179L,180L,181L,182L,184L,186L,187L,188L,189L,190L,191L,192L,194L,195L,196L,197L,198L,199L,200L,201L,202L,203L,204L,205L,206L,207L,208L,209L,210L,211L,212L,214L,215L,216L,217L,218L,219L,220L,221L,222L,223L,224L,225L,227L,228L,229L,230L,231L,232L,233L,234L,235L,236L,237L,238L,239L,240L,241L,242L,243L,244L,245L,246L,247L,248L,249L,250L,251L,252L,253L,254L,256L,257L,258L,259L,260L,261L,262L,263L,264L,265L,266L,267L,268L,269L,271L,272L,273L,274L,275L,276L,277L,278L,280L,281L,282L,283L,284L,285L,286L,287L,288L,289L,290L,291L,292L,293L,294L,295L,296L,297L,298L,299L,300L,301L,302L,303L,304L,305L,308L,309L,310L,311L,312L,313L,314L,315L,316L,317L,318L,319L,320L,321L,322L,325L,326L,327L,328L,329L,330L,331L,332L,333L,334L,335L,336L,337L,338L,339L,340L,341L,342L,343L,344L,345L,346L,347L,348L,350L,351L,352L,353L,354L,355L,357L,358L,360L,361L,363L,364L,366L,367L,368L,369L,370L,371L,372L,373L,374L,375L,376L,377L,378L,379L,380L,381L,382L,383L,385L,386L,387L,388L,390L,391L,392L,393L,394L,395L,396L,397L,398L,399L,400L,401L,402L,403L,404L,405L,406L,407L,408L,409L,410L,411L,412L,414L,415L,416L,417L,418L,419L,420L,421L,422L,423L,424L,425L,427L,428L,429L,430L,431L,432L,433L,434L,435L,436L,437L,438L,439L,440L,441L,442L,443L,444L,445L,446L,447L,448L,449L,450L,451L,453L,455L,456L,457L,458L,460L,461L,462L,463L,464L,465L,466L,467L,468L,469L,470L,471L,472L,473L,474L,475L,476L,477L,478L,479L,480L,482L,483L,484L,485L,486L,487L,488L,489L,490L,491L,492L,493L,495L,496L,497L,498L,499L,500L,501L,502L,503L,504L,505L,506L,507L,508L,509L,510L,511L,512L,513L,514L,515L,516L,517L,518L,519L,520L,521L,522L,523L,524L,525L,526L,527L,528L,529L,530L,531L,532L,533L,534L,535L,536L,537L,538L,539L,540L,541L,542L,543L,544L,546L,547L,548L,549L,550L,551L,552L,553L,554L,555L,556L,557L,558L,559L,560L,561L,562L,563L,564L,565L,566L,567L,568L,569L,570L,571L,572L,573L,574L,575L,576L,577L,578L,579L,580L,581L,582L,583L,584L,585L,586L,587L,588L,589L,590L,591L,592L,593L,594L,595L,596L,597L,598L,599L,600L,601L,602L,603L,604L,605L,606L,607L,608L,609L,610L,611L,612L,613L,614L,615L,616L,617L,618L,619L,620L,621L,622L,623L,624L,625L,626L,627L,628L,629L,630L,631L,632L,633L,634L,635L,636L,641L,642L,643L,645L,646L,647L,648L,649L,650L,651L,652L,653L,654L,655L,656L,657L,658L,659L,660L,661L,662L,663L,664L,665L,666L,667L,668L,669L,670L,671L,672L,673L,674L,675L,676L,677L,678L,679L,680L,681L,682L,683L,684L,685L,686L,687L,688L,690L,691L,692L,693L,694L,695L,696L,698L,699L,700L,701L,703L,704L,705L,706L,707L,708L,709L,710L,711L,712L,713L,714L,715L,717L,718L,719L,720L,721L,722L,723L,724L,725L,726L,727L,728L,729L,730L,731L,732L,733L,734L,735L,736L,737L,738L,739L,740L,741L,742L,743L,744L,745L,746L,747L,748L,749L,750L,751L,752L,753L,755L,756L,757L,758L,759L,760L,761L,762L,763L,764L,765L,766L,767L,768L,769L,770L,771L,772L,773L,774L,775L,776L,777L,778L,779L,780L,781L,782L,783L,784L,785L,786L,787L,788L,789L,790L,791L,792L,793L,794L,795L,796L,797L,798L,799L,800L,801L,802L,803L,804L,806L,808L,809L,810L,811L,812L,813L,814L,815L,816L,817L,818L,819L,820L,821L,822L,823L,824L,825L,826L,827L,828L,829L,830L,831L,832L,833L,834L,835L,836L,837L,838L,839L,841L,842L,843L,844L,845L,846L,847L,848L,849L,850L,851L,852L,853L,854L,855L,856L,857L,858L,859L,860L,861L,862L,863L,864L,865L,866L,867L,869L,870L,871L,873L,874L,875L,876L,877L),class = "data.frame",na.action = structure(c(`34` = 34L,`41` = 41L,`42` = 42L,`70` = 70L,`75` = 75L,`83` = 83L,`131` = 131L,`140` = 140L,`150` = 150L,`161` = 161L,`173` = 173L,`176` = 176L,`178` = 178L,`183` = 183L,`185` = 185L,`193` = 193L,`213` = 213L,`226` = 226L,`255` = 255L,`270` = 270L,`279` = 279L,`306` = 306L,`307` = 307L,`323` = 323L,`324` = 324L,`349` = 349L,`356` = 356L,`359` = 359L,`362` = 362L,`365` = 365L,`384` = 384L,`389` = 389L,`413` = 413L,`426` = 426L,`452` = 452L,`454` = 454L,`459` = 459L,`481` = 481L,`494` = 494L,`545` = 545L,`637` = 637L,`638` = 638L,`639` = 639L,`640` = 640L,`644` = 644L,`689` = 689L,`697` = 697L,`702` = 702L,`716` = 716L,`754` = 754L,`805` = 805L,`807` = 807L,`840` = 840L,`868` = 868L,`872` = 872L),class = "omit"))