Using the cleveland data from MCI data respository, I want to generate missing values on the data to apply some imputation techniques.
heart.ds <- read.csv(file.choose())
head(heart.ds)
attach(heart.ds)
sum(is.na(heart.ds))
str(heart.ds)
#Changing Appropriate Variables to Factors
heart.ds$sex<-as.factor(heart.ds$sex)
heart.ds$cp<-as.factor(heart.ds$cp)
heart.ds$fbs<-as.factor(heart.ds$fbs)
heart.ds$exang<-as.factor(heart.ds$exang)
heart.ds$restecg<-as.factor(heart.ds$restecg)
heart.ds$slope<-as.factor(heart.ds$slope)
heart.ds$thal<-as.factor(heart.ds$thal)
heart.ds$target<-as.factor(heart.ds$target)
str(heart.ds)
Now i want to generate missing values using the MCAR mechanism. Below is the loop code;
p = c(0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.09,0.1)
hd_mcar = rep(0, length(heart.ds)) #to generate empty bins of 10 different percentages of missingness using the MCAR package
for(i in 1:length(p)){
hd_mcar[i] <- delete_MCAR(heart.ds, p[i]) #to generate 10 different percentages of missingness using the MCAR package
}
The problem here is that, after the above code, i dont get the data been generated in it original values like in a data frame where i will have n variables and n rows.
Below is a picture of the output i had through the above code;
enter image description here
But when i use only one missingness percentage i get an accurate results; below is the coe for only one missing percentage
#Missing Completely at Random(MCAR)
hd_mcar <- delete_MCAR(heart.ds, 0.05)
sum(is.na(hd_mcar))
Below is the output of the results;
enter image description here
Please I need help to to solve the looping problem. Thank you.
Now I want to apply the MICE and other imputations methods like HMISC, Amelia, mi, and missForest inside the loop but it is giving me an error saying "Error: Data should be a matrix or data frame"
The code below is for only MICE;
#1. Method(MICE)
mice_mcar[[i]] <- mice(hd_mcar, m=ip, method = c("pmm","logreg","polyreg","pmm","pmm","logreg",
"polyreg","pmm","logreg","pmm","polyreg","pmm",
"polyreg","logreg"), maxit = 20)
#Diagnostic check
summary(heart.ds$age)
mice_mcar$imp$age
#Finding the means of the impuatations
app1 <- apply(mice_mcar$imp$age, MARGIN = 2, FUN = mean)
min1 <- abs(app1-mean(heart.ds$age))
#Selecting the minimum index
sm1 <- which(min1==min(min1))
#Selecting final imputation
final_clean_hd_mcar =mice::complete(mice_mcar,sm1)
mice.mcar = final_clean_hd_mcar
How do i go about to make it fit into the loop and works perfectly?
Your problem was this line:
hd_mcar = rep(0, length(heart.ds)) #to generate empty bins of 10 different percentages of missingness using the MCAR package
You are creating a vector here rather than a list. You can't assign a data frame to an element of a vector without coercing it into something that is not a data frame. You want to do this:
p <- c(0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.09,0.1)
hd_mcar <- vector(mode = "list", length = length(p))
for(i in 1:length(p)){
hd_mcar[[i]] <- delete_MCAR(heart.ds, p[i]) #to generate 10 different percentages of missingness using the MCAR package
}
Note that because it's a list now, hd_mcar[[i]] uses the [[ rather than [ subscript.
Related
I have two data matrices both having the same dimensions. I want to extract the same series of columns vectors. Then take both series as vectors, then calculate different errors for example mean absolute error (mae), mean percentage error (mape) and root means square error
(rmse). My data matrix is quite large dimensional so I try to explain with an example and calculate these errors manually as:
mat1<- matrix(6:75,ncol=10,byrow=T)
mat2<- matrix(30:99,ncol=10,byrow=T)
mat1_seri1 <- as.vector(mat1[,c(1+(0:4)*2)])
mat1_seri2<- as.vector(mat1[,c(2+(0:4)*2)])
mat2_seri1 <- as.vector(mat1[,c(1+(0:4)*2)])
mat2_seri2<- as.vector(mat1[,c(2+(0:4)*2)])
mae1<-mean(abs(mat1_seri1-mat2_seri1))
mae2<-mean(abs(mat1_seri2-mat2_seri2))
For mape
mape1<- mean(abs(mat1_seri1-mat2_seri1)/mat1_seri1)*100
mape2<- mean(abs(mat1_seri2-mat2_seri2)/mat1_seri2)*100
similarly, I calculate rmse from their formula, as I have large data matrices so manually it is quite time-consuming. Is it's possible to do this using looping which gives an output of the errors (mae,mape,rmse) term for each series separately.
I'm not sure if this is what you are looking for, but here is a function that could automate the process, maybe there is also a better way:
fn <- function(m1, m2) {
stopifnot(dim(m1) == dim(m2))
mat1_seri1 <- as.vector(m1[, (1:ncol(m1))[(1:ncol(m1))%%2 != 0]])
mat1_seri2 <- as.vector(m1[, (1:ncol(m1))[!(1:ncol(m1))%%2]])
mat2_seri1 <- as.vector(m2[, (1:ncol(m2))[(1:ncol(m2))%%2 != 0]])
mat2_seri2 <- as.vector(m2[, (1:ncol(m2))[!(1:ncol(m2))%%2]])
mae1 <- mean(abs(mat1_seri1-mat2_seri1))
mae2 <- mean(abs(mat1_seri2-mat2_seri2))
mape1 <- mean(abs(mat1_seri1-mat2_seri1)/mat1_seri1)*100
mape2 <- mean(abs(mat1_seri2-mat2_seri2)/mat1_seri2)*100
setNames(as.data.frame(matrix(c(mae1, mae2, mape1, mape2), ncol = 4)),
c("mae1", "mae2", "mape1", "mape2"))
}
fn(mat1, mat2)
mae1 mae2 mape1 mape2
1 24 24 92.62581 86.89572
I am performing a PLS-DA analysis in R using the mixOmics package. I have one binary Y variable (presence or absence of wetland) and 21 continuous predictor variables (X) with values ranging from 1 to 100.
I have made the model with the data_training dataset and want to predict new outcomes with the data_validation dataset. These datasets have exactly the same structure.
My code looks like:
library(mixOmics)
model.plsda<-plsda(X,Y, ncomp = 10)
myPredictions <- predict(model.plsda, newdata = data_validation[,-1], dist = "max.dist")
I want to predict the outcome based on 10, 9, 8, ... to 2 principal components. By using the get.confusion_matrix function, I want to estimate the error rate for every number of principal components.
prediction <- myPredictions$class$max.dist[,10] #prediction based on 10 components
confusion.mat = get.confusion_matrix(truth = data_validatie[,1], predicted = prediction)
get.BER(confusion.mat)
I can do this seperately for 10 times, but I want do that a little faster. Therefore I was thinking of making a list with the results of prediction for every number of components...
library(BBmisc)
prediction_test <- myPredictions$class$max.dist
predictions_components <- convertColsToList(prediction_test, name.list = T, name.vector = T, factors.as.char = T)
...and then using lapply with the get.confusion_matrix and get.BER function. But then I don't know how to do that. I have searched on the internet, but I can't find a solution that works. How can I do this?
Many thanks for your help!
Without reproducible there is no way to test this but you need to convert the code you want to run each time into a function. Something like this:
confmat <- function(x) {
prediction <- myPredictions$class$max.dist[,x] #prediction based on 10 components
confusion.mat = get.confusion_matrix(truth = data_validatie[,1], predicted = prediction)
get.BER(confusion.mat)
}
Now lapply:
results <- lapply(10:2, confmat)
That will return a list with the get.BER results for each number of PCs so results[[1]] will be the results for 10 PCs. You will not get values for prediction or confusionmat unless they are included in the results returned by get.BER. If you want all of that, you need to replace the last line to the function with return(list(prediction, confusionmat, get.BER(confusion.mat)). This will produce a list of the lists so that results[[1]][[1]] will be the results of prediction for 10 PCs and results[[1]][[2]] and results[[1]][[3]] will be confusionmat and get.BER(confusion.mat) respectively.
For a paper I'm writing I have subsetted a larger dataset into 3 groups, because I thought the strength of correlations between 2 variables in those groups would differ (they did). I want to see if subsetting my data into random groupings would also significantly affect the strength of correlations (i.e., whether what I'm seeing is just an effect of subsetting, or if those groupings are actually significant).
To this end, I am trying to generate n new data frames by randomly sampling 150 rows from an existing dataset, and then want to calculate correlation coefficients for two variables in those n new data frames, saving the correlation coefficient and significance in a new file.
But, HOW?
I can do it manually, e.g., with dplyr, something like
newdata <- sample_n(Random_sample_data, 150)
output <- cor.test(newdata$x, newdata$y, method="kendall")
I'd obviously like to not type this out 1000 or 100000 times, and have been trying things with loops and lapply (see below) but they've not worked (undoubtedly due to something really obvious that I'm missing!).
Here I have tried to assign each row to a different group, with 10 groups in total, and then to do correlations between x and y by those groups:
Random_sample_data<-select(Range_corrected, x, y)
cat <- sample(1:10, 1229, replace=TRUE)
Random_sample_cats<-cbind(Random_sample_data,cat)
correlation <- function(c) {
c <- cor.test(x,y, method="kendall")
return(c)
}
b<- daply(Random_sample_cats, .(cat), correlation)
Error message:
Error in cor.test(x, y, method = "kendall") :
object 'x' not found
Once you have the code for what you want to do once, you can put it in replicate to do it n times. Here's a reproducible example on built-in data
result = replicate(n = 10, expr = {
newdata <- sample_n(mtcars, 10)
output <- cor.test(newdata$wt, newdata$qsec, method="kendall")
})
replicate will save the result of the last line of what you did (output <- ...) for each replication. It will attempt to simplify the result, in this case cor.test returns a list of length 8, so replicate will simplify the results to a matrix with 8 rows and 10 columns (1 column per replication).
You may want to clean up the results a little bit so that, e.g., you only save the p-value. Here, we store only the p-value, so the result is a vector with one p-value per replication, not a matrix:
result = replicate(n = 10, expr = {
newdata <- sample_n(mtcars, 10)
cor.test(newdata$wt, newdata$qsec, method="kendall")$p.value
})
I have a testing data-set of 88 observations. I have built a model and predicting on this new data-set.
Here is the twist. I am adding a new column to it and trying to store the predictions into a dataframe.
My training and test data are all matching.
Now when I execute this loop, I am not getting the desired output.
#creating an EMI vector
em = c(10000,20000,30000,40000,50000,60000,70000,80000,90000,100000)
#my dataframe where i want to store predictions
v <- c()
v <- data.frame(v)
for(i in em){
newdata$EMI.Amount=i
prediction=predict(rf,newdata,type="response")
kl <- table(prediction)
v <- rbind(v,kl)
}
I am getting predictions of only the last EMI value from the vector em.
i.e for 1,00,000.
Here is the output
I want the output for each em vector i.e the predictions of the binary class to be in the dataframe like this.
You don't need to run a loop.
Instead I would simply run the code
newdata$EMI.Amount <- em
v = predict(rf, newdata, type="response")
I hope that is the answer to your problem.
I am not so sure how your code works as it is not reproducible. Maybe you would like something like this?
#creating an EMI vector
em=c(10000,20000,30000,40000,50000,60000,70000,80000,90000,100000)
#my dataframe where i want to store predictions
v<-c()
v <- data.frame(v)
newdata$EMI.Amount<-em
prediction=predict(rf,newdata,type="response")
kl <- table(prediction)
v <- rbind(v,kl)
I want to check all the permutations and combinations of columns while selecting models in R. I have 8 columns in my data set and the below piece of code lets me check some of the models, but not all. Models like column 1+6, 1+2+5 will not be covered by this loop. Is there any better way to accomplish this?
best_model <- rep(0,3) #store the best model in this array
for(i in 1:8){
for(j in 1:8){
for(x in k){
diabetes_prediction <- knn(train = diabetes_training[, i:j], test = diabetes_test[, i:j], cl = diabetes_train_labels, k = x)
accuracy[x] <- 100 * sum(diabetes_test_labels == diabetes_prediction)/183
if( best_model[1] < accuracy[x] ){
best_model[1] = accuracy[x]
best_model[2] = i
best_model[3] = j
}
}
}
}
Well, this answer isn't complete, but maybe it'll get you started. You want to be able to subset by all possible subsets of columns. So instead of having i:j for some i and j, you want to be able to subset by c(1,6) or c(1,2,5), etc.
Using the sets package, you can for the power set (set of all subsets) of a set. That's the easy part. I'm new to R, so the hard part for me is understanding the difference between sets, lists, vectors, etc. I'm used to Mathematica, in which they're all the same.
library(sets)
my.set <- 1:8 # you want column indices from 1 to 8
my.power.set <- set_power(my.set) # this creates the set of all subsets of those indices
my.names <- c("a") #I don't know how to index into sets, so I created names (that are numbers, but of type characters)
for(i in 1:length(my.power.set)) {my.names[i] <- as.character(i)}
names(my.power.set) <- my.names
my.indices <- vector("list",length(my.power.set)-1)
for(i in 2:length(my.power.set)) {my.indices[i-1] <- as.vector(my.power.set[[my.names[i]]])} #this is the line I couldn't get to work
I wanted to create a list of lists called my.indices, so that my.indices[i] was a subset of {1,2,3,4,5,6,7,8} that could be used in place of where you have i:j. Then, your for loop would have to run from 1:length(my.indices).
But alas, I have been spoiled by Mathematica, and thus cannot decipher the incredibly complicated world of R data types.
Solved it, below is the code with explanatory comments:
# find out the best model for this data
number_of_columns_to_model <- ncol(diabetes_training)-1
best_model <- c()
best_model_accuracy = 0
for(i in 2:2^number_of_columns_to_model-1){
# ignoring the first case i.e. i=1, as it doesn't represent any model
# convert the value of i to binary, e.g. i=5 will give combination = 0 0 0 0 0 1 0 1
combination = as.binary(i, n=number_of_columns_to_model) # from the binaryLogic package
model <- c()
for(i in 1:length(combination)){
# choose which columns to consider depending on the combination
if(combination[i])
model <- c(model, i)
}
for(x in k){
# for the columns decides by model, find out the accuracies of model for k=1:27
diabetes_prediction <- knn(train = diabetes_training[, model, with = FALSE], test = diabetes_test[, model, with = FALSE], cl = diabetes_train_labels, k = x)
accuracy[x] <- 100 * sum(diabetes_test_labels == diabetes_prediction)/length(diabetes_test_labels)
if( best_model_accuracy < accuracy[x] ){
best_model_accuracy = accuracy[x]
best_model = model
print(model)
}
}
}
I trained with Pima.tr and tested with Pima.te. KNN Accuracy for pre-processed predictors was 78% and 80% without pre-processing (and this because of the large influence of some variables).
The 80% performance is at par with a Logistic Regression model. You don't need to preprocess variables in Logistic Regression.
RandomForest, and Logistic Regression provide a hint on which variables to drop, so you don't need to go and perform all possible combinations.
Another way is to look at a matrix Scatter plot
You get a sense that there is difference between type 0 and type 1 when it comes to npreg, glu, bmi, age
You also notice the highly skewed ped and age, and you notice that there may be an anomaly data point between skin and and and other variables (you may need to remove that observation before going further)
Skin Vs Type box plot shows that for type Yes, an extreme outlier exist (try removing it)
You also notice that most of the boxes for Yes type are higher than No type=> the variables may add prediction to the model (you can confirm this through a Wilcoxon Rank Sum Test)
The high correlation between Skin and bmi means that you can use one or the other or an interact of both.
Another approach to reducing the number of predictors is to use PCA