using ksvm of kernlab package for predicting has an error - r
I use ksvm function to train the data, but in predicting I have an error,here is the code:
svmmodel4 <- ksvm(svm_train[,1]~., data=svm_train,kernel = "rbfdot",C=2.4,
kpar=list(sigma=.12),cross=5)
Warning message:
In .local(x, ...) : Variable(s) `' constant. Cannot scale data.
pred <- predict(svmmodel4, svm_test[,-1])
Error in eval(expr, envir, enclos) : object 'res_var' not found.
If I add the response variable, it works:
pred <- predict(svmmodel4, svm_test)
But if you add the response variable,how can it be "predict"? what is wrong with my code? Thanks for your help!
The complete code:
library(kernlab)
svmData <- read.csv("svmData.csv",header=T,stringsAsFactors = F)
svmData$res_var <- as.factor(svmData$res_var)
svm_train <- svmData1[1:2110,]
svm_test <- svmData1[2111:2814,]
svmmodel4 <- ksvm(svm_train[,1]~.,data = svm_train,kernel = "rbfdot",C=2.4,
kpar=list(sigma=.12),cross=5)
pred1 <- predict(svmmodel4,svm_test[,-1])
You can not remove your response column from your test dataset. You simply divide your data horizontally, meaning the response column must be in your training and testing datasets, or even validation dataset if you have one.
your function
pred <- predict(svmmodel4, svm_test)
is working just fine, the predict function will take your data, knowing your factored column, and test the rest against the model. Your training and testing datasets must have the same number of columns, but the number of rows could be different.
Related
How can I include both my categorical and numeric predictors in my elastic net model? r
As a note beforehand, I think I should mention that I am working with highly sensitive medical data that is protected by HIPAA. I cannot share real data with dput- it would be illegal to do so. That is why I made a fake dataset and explained my processes to help reproduce the error.
I have been trying to estimate an elastic net model in r using glmnet. However, I keep getting an error. I am not sure what is causing it. The error happens when I go to train the data. It sounds like it has something to do with the data type and matrix.
I have provided a sample dataset. Then I set the outcomes and certain predictors to be factors. After setting certain variables to be factors, I label them. Next, I create an object with the column names of the predictors I want to use. That object is pred.names.min. Then I partition the data into the training and test data frames. 65% in the training, 35% in the test. With the train control function, I specify a few things I want to have happen with the model- random paraments for lambda and alpha, as well as the leave one out method. I also specify that it is a classification model (categorical outcome). In the last step, I specify the training model. I write my code to tell it to use all of the predictor variables in the pred.names.min object for the trainingset data frame.
library(dplyr)
library(tidyverse)
library(glmnet),0,1,0
library(caret)
#creating sample dataset
df<-data.frame("BMIfactor"=c(1,2,3,2,3,1,2,1,3,2,1,3,1,1,3,2,3,2,1,2,1,3),
"age"=c(0,4,8,1,2,7,4,9,9,2,2,1,8,6,1,2,9,2,2,9,2,1),
"L_TartaricacidArea"=c(0,1,1,0,1,1,1,0,0,1,0,1,1,0,1,0,0,1,1,0,1,1),
"Hydroxymethyl_5_furancarboxylicacidArea_2"=
c(1,1,0,1,0,0,1,0,1,1,0,1,1,0,1,1,0,1,0,1,0,1),
"Anhydro_1.5_D_glucitolArea"=
c(8,5,8,6,2,9,2,8,9,4,2,0,4,8,1,2,7,4,9,9,2,2),
"LevoglucosanArea"=
c(6,2,9,2,8,6,1,8,2,1,2,8,5,8,6,2,9,2,8,9,4,2),
"HexadecanolArea_1"=
c(4,9,2,1,2,9,2,1,6,1,2,6,2,9,2,8,6,1,8,2,1,2),
"EthanolamineArea"=
c(6,4,9,2,1,2,4,6,1,8,2,4,9,2,1,2,9,2,1,6,1,2),
"OxoglutaricacidArea_2"=
c(4,7,8,2,5,2,7,6,9,2,4,6,4,9,2,1,2,4,6,1,8,2),
"AminopentanedioicacidArea_3"=
c(2,5,5,5,2,9,7,5,9,4,4,4,7,8,2,5,2,7,6,9,2,4),
"XylitolArea"=
c(6,8,3,5,1,9,9,6,6,3,7,2,5,5,5,2,9,7,5,9,4,4),
"DL_XyloseArea"=
c(6,9,5,7,2,7,0,1,6,6,3,6,8,3,5,1,9,9,6,6,3,7),
"ErythritolArea"=
c(6,7,4,7,9,2,5,5,8,9,1,6,9,5,7,2,7,0,1,6,6,3),
"hpresponse1"=
c(1,0,1,1,0,1,1,0,0,1,0,0,1,0,1,1,1,0,1,0,0,1),
"hpresponse2"=
c(1,0,1,0,0,1,1,1,0,1,0,1,0,1,1,0,1,0,1,0,0,1))
#setting variables as factors
df$hpresponse1<-as.factor(df$hpresponse1)
df$hpresponse2<-as.factor(df$hpresponse2)
df$BMIfactor<-as.factor(df$BMIfactor)
df$L_TartaricacidArea<- as.factor(df$L_TartaricacidArea)
df$Hydroxymethyl_5_furancarboxylicacidArea_2<-
as.factor(df$Hydroxymethyl_5_furancarboxylicacidArea_2)
#labeling factor levels
df$hpresponse1 <- factor(df$hpresponse1, labels = c("group1.2", "group3.4"))
df$hpresponse2 <- factor(df$hpresponse2, labels = c("group1.2.3", "group4"))
df$L_TartaricacidArea <- factor(df$L_TartaricacidArea, labels =c ("No",
"Yes"))
df$Hydroxymethyl_5_furancarboxylicacidArea_2 <-
factor(df$Hydroxymethyl_5_furancarboxylicacidArea_2, labels =c ("No",
"Yes"))
df$BMIfactor <- factor(df$BMIfactor, labels = c("<40", ">=40and<50",
">=50"))
#creating list of predictor names
pred.start.min <- which(colnames(df) == "BMIfactor"); pred.start.min
pred.stop.min <- which(colnames(df) == "ErythritolArea"); pred.stop.min
pred.names.min <- colnames(df)[pred.start.min:pred.stop.min]
#partition data into training and test (65%/35%)
set.seed(2)
n=floor(nrow(df)*0.65)
train_ind=sample(seq_len(nrow(df)), size = n)
trainingset=df[train_ind,]
testingset=df[-train_ind,]
#specifying that I want to use the leave one out cross-
#validation method and
use "random" as search for elasticnet
tcontrol <- trainControl(method = "LOOCV",
search="random",
classProbs = TRUE)
#training model
elastic_model1 <- train(as.matrix(trainingset[,
pred.names.min]),
trainingset$hpresponse1,
data = trainingset,
method = "glmnet",
trControl = tcontrol)
After I run the last chunk of code, I end up with this error:
Error in { :
task 1 failed - "error in evaluating the argument 'x' in selecting a
method for function 'as.matrix': object of invalid type "character" in
'matrix_as_dense()'"
In addition: There were 50 or more warnings (use warnings() to see the first
50)
I tried removing the "as.matrix" arguemtent:
elastic_model1 <- train((trainingset[, pred.names.min]),
trainingset$hpresponse1,
data = trainingset,
method = "glmnet",
trControl = tcontrol)
It still produces a similar error.
Error in { :
task 1 failed - "error in evaluating the argument 'x' in selecting a method
for function 'as.matrix': object of invalid type "character" in
'matrix_as_dense()'"
In addition: There were 50 or more warnings (use warnings() to see the first
50)
When I tried to make none of the predictors factors (but keep outcome as factor), this is the error I get:
Error: At least one of the class levels is not a valid R variable name; This
will cause errors when class probabilities are generated because the
variables names will be converted to X0, X1 . Please use factor levels that
can be used as valid R variable names (see ?make.names for help).
How can I fix this? How can I use my predictors (both the numeric and categorical ones) without producing an error?
glmnet does not handle factors well. The recommendation currently is to dummy code and re-code to numeric where possible: Using LASSO in R with categorical variables
Fail to predict woe in R
I used this formula to get woe with
library("woe")
woe.object <- woe(data, Dependent="target", FALSE,
Independent="shop_id", C_Bin=20, Bad=0, Good=1)
Then I want to predict woe for the test data
test.woe <- predict(woe.object, newdata = test, replace = TRUE)
And it gives me an error
Error in UseMethod("predict") :
no applicable method for 'predict' applied to an object of class "data.frame"
Any suggestions please?
For prediction, you cannot do it with the package woe. You need to use the package. Take note of the masking of the function woe, see below: #let's say we woe and then klaR was loaded library(klaR) data = data.frame(target=sample(0:1,100,replace=TRUE), shop_id = sample(1:3,100,replace=TRUE), another_var = sample(letters[1:3],100,replace=TRUE)) #make sure both dependent and independent are factors data$target=factor(data$target) data$shop_id = factor(data$shop_id) data$another_var = factor(data$another_var) You need two or more dependent variables: woemodel <- klaR::woe(target~ shop_id+another_var, data = data) If you only provide one, you have an error: woemodel <- klaR::woe(target~ shop_id, data = data) Error in woe.default(x, grouping, weights = weights, ...) : All factors with unique levels. No woes calculated! In addition: Warning message: In woe.default(x, grouping, weights = weights, ...) : Only one single input variable. Variable name in resulting object$woe is only conserved in formula call. If you want to predict the dependent variable with only one independent, something like logistic regression will work: mdl = glm(target ~ shop_id,data=data,family="binomial") prob = predict(mdl,data,type="response") predicted_label = ifelse(prob>0.5,levels(data$target)[1],levels(data$target)[0])
R svyglm na.exclude predict and na padding
I'm to use predict with the svlgm function and I'm having trouble getting predict to pad out the resulting vector with NAs as I would expect (and indeed can achieve with a non-survey glm using na.exclude). Running svyglm with na.exclude gives the following error Warning message: In model.matrix(glm.object) * resid(glm.object, "working") : longer object length is not a multiple of shorter object length Am I asking svyglm/predict to do something I shouldn't, or is this an error with the svyglm package? Is there a way of getting predict to produce a vector padded with NAs from a svyglm object? Any advice/help greatly appreciated library(survey) y=c(0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,1,1,1,1,1) x1=c(1,0,1,1,1,NA,2,2,2,2,1,0,NA,1,0,0,0,2,2,2) x2=c(10,21,33,55,40,30,26,84,NA,87,20,21,23,25,NA,60,76,84,71,87) x3=runif(20) foo=data.frame(y,x1,x2,x3) m1=glm(y~x1+x2, family=binomial(logit),na.action=na.exclude) predict(m1) svy1 <- svydesign( ids=~0, data = foo, weights = ~x3 ) m2 <- svyglm(y ~ x1+x2, svy1,na.action=na.exclude) predict(m2)
glmnet training throws error on x,y dataframe arguments: am I using it wrong?
I'm trying to learn a penalized logistic regression method with glmnet. I'm trying to predict if a car from the mtcars example data will have an automatic transmission or manual. I think my code is pretty straightforward, but I seem to be getting an error:
This first block simply splits mtcars into an 80% train set and a 20% test set
library(glmnet)
attach(mtcars)
smp_size <- floor(0.8 * nrow(mtcars))
set.seed(123)
train_ind <- sample(seq_len(nrow(mtcars)), size=smp_size)
train <- mtcars[train_ind,]
test <- mtcars[-train_ind,]
I know the x data is supposed to be in a matrix form without the response, so I separate the two training sets into a non-response matrix (train_x) and a response vector (train_y)
train_x <- train[,!(names(train) %in% c("am"))]
train_y <- train$am
But when trying to train the model,
p1 <- glmnet(train_x, train_y)
I get the error:
Error in elnet(x, is.sparse, ix, jx, y, weights, offset, type.gaussian,
:(list) object cannot be coerced to type 'double'
Am I missing something?
Coercing the first argument as a matrix solve for me : p1 <- glmnet(as.matrix(train_x), train_y) In fact , form glmnet? looks that the first argument should be a matrix/sparse matrix: x: input matrix, of dimension nobs x nvars; each row is an observation vector. Can be in sparse matrix format (inherit from class "sparseMatrix" as in package Matrix; not yet available for family="cox")
Extracting predictions from a GAM model with splines and lagged predictors
I have some data and am trying to teach myself about utilize lagged predictors within regression models. I'm currently trying to generate predictions from a generalized additive model that uses splines to smooth the data and contains lags. Let's say I have the following data and have split the data into training and test samples. head(mtcars) Train <- sample(1:nrow(mtcars), ceiling(nrow(mtcars)*3/4), replace=FALSE) Great, let's train the gam model on the training set. f_gam <- gam(hp ~ s(qsec, bs="cr") + s(lag(disp, 1), bs="cr"), data=mtcars[Train,]) summary(f_gam) When I go to predict on the holdout sample, I get an error message. f_gam.pred <- predict(f_gam, mtcars[-Train,]); f_gam.pred Error in ExtractData(object, data, NULL) : 'names' attribute [1] must be the same length as the vector [0] Calls: predict ... predict.gam -> PredictMat -> Predict.matrix3 -> ExtractData Can anyone help diagnose the issue and help with a solution. I get that lag(__,1) leaves a data point as NA and that is likely the reason for the lengths being different. However, I don't have a solution to the problem.
I'm going to assume you're using gam() from the mgcv library. It appears that gam() doesn't like functions that are not defined in "base" in the s() terms. You can get around this by adding a column which include the transformed variable and then modeling using that variable. For example tmtcars <- transform(mtcars, ldisp=lag(disp,1)) Train <- sample(1:nrow(mtcars), ceiling(nrow(mtcars)*3/4), replace=FALSE) f_gam <- gam(hp ~ s(qsec, bs="cr") + s(ldisp, bs="cr"), data= tmtcars[Train,]) summary(f_gam) predict(f_gam, tmtcars[-Train,]) works without error. The problem appears to be coming from the mgcv:::get.var function. It tires to decode the terms with something like eval(parse(text = txt), data, enclos = NULL) and because they explicitly set the enclosure to NULL, variable and function names outside of base cannot be resolved. So because mean() is in the base package, this works eval(parse(text="mean(x)"), data.frame(x=1:4), enclos=NULL) # [1] 2.5 but because var() is defined in stats, this does not eval(parse(text="var(x)"), data.frame(x=1:4), enclos=NULL) # Error in eval(expr, envir, enclos) : could not find function "var" and lag(), like var() is defined in the stats package.