CODEWITHSUNDEEP
rmachine-learninglasso-regression
Shafa Haider
Shafa Haider

Reputation: 431

Format of newx in Lasso regression gives error in R

I am trying to implement lasso linear regression. I train my model but when I try to make prediction on unknown data it gives me the following error:

 Error in cbind2(1, newx) %*% nbeta : 
     invalid class 'NA' to dup_mMatrix_as_dgeMatrix

Summary of my data is:

enter image description here

I want to predict the unknown percent_gc. I initially train my model using data for which percent_gc is known

 set.seed(1)

 ###training data
 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))

  ###data for prediction
  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_gc

cv.out <- cv.glmnet (x, y, alpha = 1,family  = "gaussian")
best.lambda= cv.out$lambda.min

fit <- glmnet(x,y,alpha=1)

I then want to make predictions for which percent_gc in not known.

newX = matrix(data = data.prediction %>% select(-percent_gc)) 
data.prediction$percent_gc <- 
 predict(object = fit ,type="response", s=best.lambda, newx=newX)

And this generates the error I mentioned above.

I don't understand which format newX should be in order to get rid of this help. Insights would be appreciated.

Upvotes: 0

Views: 592

Answers (1)

Julian_Hn
Julian_Hn

Reputation: 2141

I could not really figure out how to construct a appropiate matrix, but package glmnetUtils provides functionality to directly fit a formula on a dataframe and predict. With this I got it to predict values:

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.min

predict(object = fit,data.prediction,s=best.lambda)

Upvotes: 1

Related Questions