Lambda Issue, or corss validation : I am doing double cross validation with LASSO, however when I plot the results I am getting lambda of 0 - 150000 which is unrealistic in my case, not sure what is wrong I am doing, can someone point me in the right direction. Thanks in advance!

    calcium = read.csv("calciumgood.csv", header=TRUE)
    dim(calcium)
    n = dim(calcium)[1]
    calcium = na.omit(calcium)
    names(calcium)

    library(glmnet)  # use LASSO model from package glmnet 
    lambdalist = exp((-1200:1200)/100)  # defines models to consider


    fulldata.in = calcium
    x.in = model.matrix(CAMMOL~. - CAMLEVEL - AGE,data=fulldata.in)
    y.in = fulldata.in[,2]
    k.in = 10 
    n.in = dim(fulldata.in)[1]
    groups.in = c(rep(1:k.in,floor(n.in/k.in)),1:(n.in%%k.in))  
    set.seed(8)
    cvgroups.in = sample(groups.in,n.in)  #orders randomly, with seed (8) 
    #LASSO cross-validation
    cvLASSOglm.in = cv.glmnet(x.in, y.in, lambda=lambdalist, alpha = 1, nfolds=k.in, foldid=cvgroups.in)
    plot(cvLASSOglm.in$lambda,cvLASSOglm.in$cvm,type="l",lwd=2,col="red",xlab="lambda",ylab="CV(10)")
    whichlowestcvLASSO.in = order(cvLASSOglm.in$cvm)[1];     min(cvLASSOglm.in$cvm)
    bestlambdaLASSO = (cvLASSOglm.in$lambda)[whichlowestcvLASSO.in];     bestlambdaLASSO
    abline(v=bestlambdaLASSO)
    bestlambdaLASSO  # this is the lambda for the best LASSO model
    LASSOfit.in = glmnet(x.in, y.in, alpha = 1,lambda=lambdalist)  # fit the model across possible lambda
    LASSObestcoef = coef(LASSOfit.in, s = bestlambdaLASSO); LASSObestcoef # coefficients for the best model fit