R Glmnet:"(List) Object Cannot Be Coerced to Type 'Double' "

R glmnet : (list) object cannot be coerced to type 'double'

cv.glmnet expects a matrix of predictors, not a data frame. Generally you can obtain this via

X <- model.matrix(<formula>, data=<data>)

but in your case, you can probably get there more easily with

X <- as.matrix(t2[,-c(1,2,7,12)])

since you don't appear to have any factor variables or other issues that might complicate matters.

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Since this answer is getting plenty of hits: the glmnetUtils package provides a formula-based interface to glmnet, like that used for most R modelling functions. It includes methods for glmnet and cv.glmnet, as well as a new cva.glmnet function to do crossvalidation for both alpha and lambda.

The above would become

cv.glmnet(X2 ~ ., data=t2[-1], family="multinomial")

NA's are handled automatically, so you don't have to exclude columns with missing values.

Error: (list) object cannot be coerced to type 'double' with multiple variables

We can do:

res<-sapply(iris[,-5], as.numeric)
attr(res,"dimnames") <- NULL

Or as @markus suggests simply:

unname(as.matrix(iris[,1:4]))

Result:

       [,1] [,2] [,3] [,4]
  [1,]  5.1  3.5  1.4  0.2
  [2,]  4.9  3.0  1.4  0.2
  [3,]  4.7  3.2  1.3  0.2
  [4,]  4.6  3.1  1.5  0.2

In the R, meet problem 'list' object cannot be coerced to type 'double' when using 'by' function

by works on the data.frame that is split by mtcars$am, so your function needs to work on a data.frame instead of a vector, so example below I use sapply to go through each column and calculate mean and sd:

by(mtcars[vars],mtcars$am, FUN = function(u)sapply(u,dstats)) 
mtcars$am: 0
           mpg       hp        wt
mean 17.147368 160.2632 3.7688947
sd    3.833966  53.9082 0.7774001
------------------------------------------------------------ 
mtcars$am: 1
           mpg        hp        wt
mean 24.392308 126.84615 2.4110000
sd    6.166504  84.06232 0.6169816

It's not so easy to get back the same format as your output for aggregate which is a nested data.frame:

res = aggregate(mtcars[vars],by=list(am=mtcars$am), FUN = dstats)
class(res$hp)
[1] "matrix"
# to get mean hp for different groups, you have to do:
res$hp[,"mean"]
[1] 160.2632 126.8462

To get something similar with by, it's a bit more elaborate:

res = do.call(rbind,
by(mtcars[vars],mtcars$am,function(i){
unlist(lapply(i,dstats))
})
)
res = data.frame(am=levels(factor(mtcars$am)),res)

  am mpg.mean   mpg.sd  hp.mean    hp.sd  wt.mean     wt.sd
0  0 17.14737 3.833966 160.2632 53.90820 3.768895 0.7774001
1  1 24.39231 6.166504 126.8462 84.06232 2.411000 0.6169816

How to solve error: Error in storage.mode(x) - double : 'list' object cannot be coerced to type 'double' and get results

You should coerce the object to a matrix or a data frame.
From the kmeans() documentation:

x = numeric matrix of data, or an object that can be coerced to
such a matrix (such as a numeric vector or a data frame with
all numeric columns)

somClusters2 <- kmeans(data.frame(som_model2$codes), centers = 6)
somClusters2 

K-means clustering with 6 clusters of sizes 14, 42, 11, 3, 3, 27

Cluster means:
       MONEY      VISIT      CROSS        API
1 -0.4217639 -0.5810061 -0.8014610  1.1764434
2 -0.3080303 -0.3977217 -0.4555428 -0.1649521
3  1.1239411  1.6704112  1.6129638 -0.7312019
4 -0.4606414 -0.6480549 -0.9548480  3.5595079
5  5.1169992  3.9174431  2.7584212 -0.8306366
6  0.1443774  0.2317915  0.5778101 -0.5416495

Error in logistic regression in R: 'list' object cannot be coerced to type 'double'

You can't compare model1 with 0.5

This is model1 structure:

model1

Call:  glm(formula = id ~ speed + dist, family = "binomial", data = cars)

Coefficients:
(Intercept)        speed         dist  
  -1158.863       73.588        1.366  

Degrees of Freedom: 49 Total (i.e. Null);  47 Residual
Null Deviance:      69.31 
Residual Deviance: 2.932e-08    AIC: 6 

You have to pass new data to the model and than compare the prediction (using function predict) value with 0.5

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