I'm new in R, so help me please to understand what is wrong.
I'm trying to predict some data, but object that predict function returns (it is strange class (factor)) contains low data. Test set size is 5886 obs. of 160 variables, when predict object lenght is 110... I expected vector of predicted classes or data frame back. What do I understand wrong?
library(MASS)
library(e1071)
set.seed(333)
data <- read.csv(file="D:\\MaŃhLearningAssign\\pml-training.csv", head=TRUE, sep=",")
index <- 1:nrow(data)
testindex <- sample(index, trunc(length(index)*30/100))
train <- data[-testindex, ]
test <- data[testindex, ]
model <- svm(classe~., data = train, kernel="radial", gamma=0.001, cost=10)
prediction <- predict(model, test)
summary(prediction)
Output:
A B C D E
28 24 25 12 22
Dataset here
svm doesn't handle missing observations and your data set is full of NAs:
> dim(data[complete.cases(data), ])
[1] 406 160
You can try to remove columns with NAs and then train svm
> data <- data[, which(colSums(apply(data, 2, is.na)) == 0)]
> dim(data)
[1] 19622 93
Now you can try to split your data and fit svm. I would be careful though. It still pretty big data set and svm is rather resource hungry.
Hint: I looked at your data and if it is what I think it is please be sure read carefully data set description. You have two, completely different types of rows. It should explain not only abundance of NAs, but also give the idea which will be useful for prediction given your test set.
Related
I'm trying to perform KNN in R on a dataframe, following 3-way classification for vehicle types (car, boat, plane), using columns such as mpg, cost as features.
To start, when I run:
knn.pred=knn(train.X,test.X,train.VehicleType,k=3)
then
knn.pred
returns
factor(0) Levels: car boat plane
And
table(knn.pred,VehicleType.All)
returns
Error in table(knn.pred, VehicleType.All) :
all arguments must have the same length
I think my problem is that I can successfully load train.X with cbind() but when I try the same for test.X it remains an empty matrix. My code looks like this:
train=(DATA$Values<=200) # to train for all 200 entries including cars, boats and planes
train.X = cbind(DATA$mpg,DATA$cost)[train,]
summary(train.X)
Here, summary(train.X) returns correctly, but when I try the same for test.X:
test.X = cbind(DATA$mpg,DATA$cost)[!train,]
When I try and print test.X it returns an empty matrix like so:
[,1] [,2]
Apologies for such a long question and I'm probably not including all relevant info. If anyone has any idea what's going wrong here or why my test.X isn't loading through any data I'd appreciate it!
Without any info on your data, it is hard to guess where the problem is. You should post a minimal reproducible example
or at least dput your data or part of it. However here I show 2 methods for training a knn model, using 2 different package (class, and caret) with the mtcars built-in dataset.
with class
library(class)
data("mtcars")
str(mtcars)
mtcars$gear <- as.factor(mtcars$gear)
ind <- sample(1:nrow(mtcars),20)
train.X <- mtcars[ind,]
test.X <- mtcars[-ind,]
train.VehicleType <- train.X[,"gear"]
VehicleType.All <- test.X[,"gear"]
knn.pred=knn(train.X,test.X,train.VehicleType,k=3)
table(knn.pred,VehicleType.All)
with caret
library(caret)
ind <- createDataPartition(mtcars$gear,p=0.60,list=F)
train.X <- mtcars[ind,]
test.X <- mtcars[-ind,]
control <-trainControl(method = "cv",number = 10)
grid <- expand.grid(k=2:10)
knn.pred <- train(gear~.,data=train.X,method="knn",tuneGrid=grid)
pred <- predict(knn.pred,test.X[,-10])
cm <- confusionMatrix(pred,test.X$gear)
the caret package allows performing cross-validation for parameters tuning during model fitting, in a straightforward way. By default train perform a 25 rep bootstrap cross-validation to find the best value of k among the values I've supplied in the grid object.
From your example, it seems that your test object is empty so the result of knn is a 0-length vector. Probably your problem is in the data reading. However, a better way to subset your DATA can be this:
#insetad of
train.X = cbind(DATA$mpg,DATA$cost)[train,]
#you should do:
train.X <- DATA[train,c("mpg","cost")]
test.X <- DATA[-train,c("mpg","cost")]
However, I do not understand what variable is DATA$Values, Firstly I was thinking it was the outcome, but, this line confused me a lot:
train=(DATA$Values<=200)
You can work on these examples to catch your error on your own. If you can't post an example that reproduces your situation.
I'm modeling burrito prices in San Diego to determine whether some burritos are over/under priced (according to the model). I'm attempting to use regsubsets() to determine the best linear model, using the BIC, on a data frame of 76 observations of 14 variables. However, I keep getting an error saying that variable lengths differ, and thus a linear model doesn't work.
I've tried rounding all the observations in the data frame to one decimal place, I've used the length() function on each variable in the data frame to make sure they're all the same length, and before I made the model I used na.omit() on the data frame to make sure no NAs were present. By the way, the original dataset can be found here: https://www.kaggle.com/srcole/burritos-in-san-diego. I cleaned it up a bit in Excel first, removing all the categorical variables that appeared after the "overall" column.
burritos <- read.csv("/Users/Jack/Desktop/R/STOR 565 R Projects/Burritos.csv")
burritos <- burritos[ ,-c(1,2,5)]
burritos <- na.exclude(burritos)
burritos <- round(burritos, 1)
library(leaps)
library(MASS)
yelp <- burritos$Yelp
google <- burritos$Google
cost <- burritos$Cost
hunger <- burritos$Hunger
tortilla <- burritos$Tortilla
temp <- burritos$Temp
meat <- burritos$Meat
filling <- burritos$Meat.filling
uniformity <- burritos$Uniformity
salsa <- burritos$Salsa
synergy <- burritos$Synergy
wrap <- burritos$Wrap
overall <- burritos$overall
variable <- sample(1:nrow(burritos), 50)
train <- burritos[variable, ]
test <- burritos[-variable, ]
null <- lm(cost ~ 1, data = train)
full <- regsubsets(cost ~ ., data = train) #This is where error occurs
I am trying to predict new observations after multiple imputation. Both the newdata and the model to use are list objects. The correctness of the approach is not the issue but how to use the predict function after multiple imputation we I have a new data that is a list. Below are my code.
library(betareg)
library(mice)
library(mgcv)
data(GasolineYield)
dat1 <- GasolineYield
dat1 <- GasolineYield
dat1$yield <- with(dat1,
ifelse(yield > 0.40 | yield < 0.17,NA,yield)) # created missing values
datim <- mice(dat1,m=30) #imputing missing values
mod1 <- with(datim,gam(yield ~ batch + emp,family=betar(link="logit"))) #fit models using gam
creating data set to be used for prediction
datnew <- complete(datim,"long")
datsplit <- split(datnew,datnew$.imp)
the code below just testing out the predict without newdata. The problem I observed was that tp is saved as 1 by 32 matrix instead of 30 by 32 matrix. But the print option prints out a 30 by 32 but then I couldn't save it as such.
tot <- 0
for(i in 1:30){
tot <- mod1$analyses[[i]]
tp <- predict.gam(tot,type = "response")
print(tp)
}
the code below is me trying to predict new observation using newdata. Here I am just lost I am not sure how to go about it.
datnew <- complete(datim,"long")
datsplit <- split(datnew,datnew$.imp)
tot <- 0
for(i in 1:30){
tot <- mod1$analyses[[i]]
tp <- predict.gam(tot,newdata=datsplit[[i]], type = "response")
print(tp)
}
Can someone help me out on how best to go about it?
I finally find solved the problem. Here is the solution:
datnew <- complete(datim,"long")# stack all the imputation data
though I have to point out that this should be your new dataset
I am assuming that this is not used in building the model. My aim of opening this #thread was to address the question of how to predict observations using new data after multiple imputation/using model built with multiple imputation dataset.
datsplit <- split(datnew,datnew$.imp)
tot <- list()
tot_ <- list()
for(i in 1:30){
for(j in 1:30){
tot[[j]] <- predict.gam(mod1$analyses[[i]],newdata=datsplit[[j]])
}
tot_[[i]] <- tot
}
# flatten the lists within lists
totfl <- tot_ %>% flatten()
#nrow is the number of observations to be predicted as contained in the
#newdata set (datsplit)
totn <- matrix(unlist(totfl),nrow=32)
apply(totn,1,mean) #takes the means of prediction across the 30 data set
I hope this helps those with similar questions. I once came across a question on how to predict newdata after multiple imputation, I guess this will answer some of the questions contained in that thread.
In QDA (Quadratic Discriminant Analysis), do i need to keep length of training and test data exactly same? If not, how do you find a Confusion Matrix in such cases?
Here's psuedo data.
Because if I keep training-data and test data sets of different lengths, it gives an error (Using R Studio):
"Error in table(pred, true) : all arguments must have the same length".
Tried to remove NAs using na.omit() on both data sets as well as pred and true; and using na.action = na.exclude for qda(), but it didn't work.
After dividing the data set in exactly half; half of it as training and half as test; it worked perfectly after na.omit() on pred and true.
Following is the code used for either of approaches. In approach 2, with data split into equal halves, it worked perfectly fine.
#Approach 1: divide data age-wise
train <- vif_data$Age < 30
# there are around 400 values passing (TRUE) above condition and around 50 failing (FALSE)
train_vif <- vif_data[train,]
test_vif <- vif_data[!train,]
#taking QDA
zone_qda <- qda(train_vif$Awareness~train_vif$Zone, na.action = na.exclude)
#compare QDA against test data
zone_pred <- predict(zone_qda, test_vif)
#omitting nulls
pred <- na.omit(zone_pred$class)
true <- na.omit(test_vif$Awareness)
length(pred) # result: 399
length(true) # result: 47
#that's where it throws error: "Error in table(zone_pred$class, train_vif) : all arguments must have the same length"
zone_aware <- table(zone_pred$class, train_vif)
# OR
zone_aware <- table(pred, true)
accur <- mean(zone_pred$class==test_vif$Awareness)
###############################
#Approach 2: divide data into random halves
train <- splitSample(dataset = vif_data, div = 2, path = "./", type = "csv")
train_data <- read.csv("splitSample_s1.csv")
test_data <- read.csv("splitSample_s2.csv")
#taking QDA
zone_qda <- qda(train_vif$Awareness~train_vif$Zone, na.action = na.exclude)
#compare QDA against test data
zone_pred <- predict(zone_qda, test_vif)
#omitting nulls
pred <- na.omit(zone_pred$class)
true <- na.omit(test_vif$Awareness)
length(train_vif)
# this works fine
zone_aware <- table(zone_pred$class, train_vif)
# OR
zone_aware <- table(pred, true)
accur <- mean(zone_pred$class==test_vif$Awareness)
Want to know if there is any method by which we can have a confusion matrix with data set unequally divided into training and test data set.
Thanks!
Are you plugging in your training inputs instead of your test set input data to predict? Notice how this yields the same error message:
table(c(1,2),c(1,2,3))
If pred isn't the right length, then you're probably predicting incorrectly. At the moment, you haven't shared any of your code, so I cannot say anything more. But there is no reason that you shouldn't be able to get a confusion matrix using test data of different size than your training data.
In the past few days I have developed multiple PLS models in R for spectral data (wavebands as explanatory variables) and various vegetation parameters (as individual response variables). In total, the dataset comprises of 56. The first 28 (training set) have been used for model calibration, now all I want to do is to predict the response values for the remaining 28 observations in the tesset. For some reason, however, R keeps on the returning the fitted values of the calibration set for a given number of components rather than predictions for the independent test set. Here is what the model looks like in short.
# first simulate some data
set.seed(123)
bands=101
data <- data.frame(matrix(runif(56*bands),ncol=bands))
colnames(data) <- paste0(1:bands)
data$height <- rpois(56,10)
data$fbm <- rpois(56,10)
data$nitrogen <- rpois(56,10)
data$carbon <- rpois(56,10)
data$chl <- rpois(56,10)
data$ID <- 1:56
data <- as.data.frame(data)
caldata <- data[1:28,] # define model training set
valdata <- data[29:56,] # define model testing set
# define explanatory variables (x)
spectra <- caldata[,1:101]
# build PLS model using training data only
library(pls)
refl.pls <- plsr(height ~ spectra, data = caldata, ncomp = 10, validation =
"LOO", jackknife = TRUE)
It was then identified that a model comprising of 3 components yielded the best performance without over-fitting. Hence, the following command was used to predict the values of the 28 observations in the testing set using the above calibrated PLS model with 3 components:
predict(refl.pls, ncomp = 3, newdata = valdata)
Sensible as the output may seem, I soon discovered that all this piece of code generates are the fitted values of the PLS model for the calibration/training data, rather than predictions. I discovered this because the below code, in which newdata = is omitted, yields identical results.
predict(refl.pls, ncomp = 3)
Surely something must be going wrong, although I cannot seem to find out what specifically is. Is there someone out there who can, and is willing to help me move in the right direction?
I think the problem is with the nature of the input data. Looking at ?plsr and str(yarn) that goes with the example, plsr requires a very specific data frame that I find tricky to work with. The input data frame should have a matrix as one of its elements (in your case, the spectral data). I think the following works correctly (note I changed the size of the training set so that it wasn't half the original data, for troubleshooting):
library("pls")
set.seed(123)
bands=101
spectra = matrix(runif(56*bands),ncol=bands)
DF <- data.frame(spectra = I(spectra),
height = rpois(56,10),
fbm = rpois(56,10),
nitrogen = rpois(56,10),
carbon = rpois(56,10),
chl = rpois(56,10),
ID = 1:56)
class(DF$spectra) <- "matrix" # just to be certain, it was "AsIs"
str(DF)
DF$train <- rep(FALSE, 56)
DF$train[1:20] <- TRUE
refl.pls <- plsr(height ~ spectra, data = DF, ncomp = 10, validation =
"LOO", jackknife = TRUE, subset = train)
res <- predict(refl.pls, ncomp = 3, newdata = DF[!DF$train,])
Note that I got the spectral data into the data frame as a matrix by protecting it with I which equates to AsIs. There might be a more standard way to do this, but it works. As I said, to me a matrix inside of a data frame is not completely intuitive or easy to grok.
As to why your version didn't work quite right, I think the best explanation is that everything needs to be in the one data frame you pass to plsr for the data sources to be completely unambiguous.