我正在尝试实现Lasso线性回归。我训练了我的模型，但在尝试对未知数据进行预测时，出现了以下错误：
Error in cbind2(1, newx) %*% nbeta : invalid class 'NA' to dup_mMatrix_as_dgeMatrix我的数据摘要如下：
我想预测未知的percent_gc。我最初使用已知percent_gc的数据来训练模型
set.seed(1) ###训练数据 data.all <- tibble(description = c('Xylanimonas cellulosilytica XIL07, DSM 15894','Teredinibacter turnerae T7901', 'Desulfotignum phosphitoxidans FiPS-3, DSM 13687','Brucella melitensis bv. 1 16M'), phylum = c('Actinobacteria','Proteobacteria','Proteobacteria','Bacteroidetes'), genus = c('Acaryochloris','Acetohalobium','Acidimicrobium','Acidithiobacillus'), Latitude = c('63.93','69.372','3.493.11','44.393.704'), Longitude = c('-22.1','88.235','134.082.527','-0.130781'), genome_size = c(8361599,2469596,2158157,3207552), percent_gc = c(34,24,55,44), percent_psuedo = c(0.0032987747,0.0291222313,0.0353728489,0.0590663703), percent_signalpeptide = c(0.02987198,0.040607055,0.048757170,0.061606859)) ###用于预测的数据 data.prediction <- tibble(description = c('Liberibacter crescens BT-1','Saprospira grandis Lewin', 'Sinorhizobium meliloti AK83','Bifidobacterium asteroides ATCC 25910'), phylum = c('Actinobacteria','Proteobacteria','Proteobacteria','Bacteroidetes'), genus = c('Acaryochloris','Acetohalobium','Acidimicrobium','Acidithiobacillus'), Latitude = c('39.53','69.372','5.493.12','44.393.704'), Longitude = c('20.1','-88.235','134.082.527','-0.130781'), genome_size = c(474832,2469837,2158157,3207552), percent_gc = c(NA,NA,NA,NA), percent_psuedo = c(0.0074639239,0.0291222313,0.0353728489,0.0590663703), percent_signalpeptide = c(0.02987198,0.040607055,0.048757170,0.061606859))x=model.matrix(percent_gc~.,data.all)y=data.all$percent_gccv.out <- cv.glmnet (x, y, alpha = 1,family = "gaussian")best.lambda= cv.out$lambda.minfit <- glmnet(x,y,alpha=1)然后我想对未知percent_gc的数据进行预测。
newX = matrix(data = data.prediction %>% select(-percent_gc)) data.prediction$percent_gc <- predict(object = fit ,type="response", s=best.lambda, newx=newX)这就产生了我上面提到的错误。
我不明白newX应该采用什么格式才能解决这个问题。欢迎提供见解。
回答：
我无法真正弄清楚如何构建一个合适的矩阵，但glmnetUtils包提供了直接在数据框上拟合公式并进行预测的功能。通过这种方式，我成功预测了数值：
library(glmnetUtils)fit <- glmnet(percent_gc~.,data.all,alpha=1)cv.out <- cv.glmnet (percent_gc~.,data.all, alpha = 1,family = "gaussian")best.lambda= cv.out$lambda.minpredict(object = fit,data.prediction,s=best.lambda)