Here is the full code. Look to the last part, denoted #(f) for the question
being asked in this post:
#(a) Split datapoints into training (70 points) and test (30 points) sets.
#Read in ass4-data.txt and ass3-phodata.txt
ass4data =
read.delim('http://www.moseslab.csb.utoronto.ca/alan/ass4-data.txt', header
= FALSE, sep = "\t")
#Separate all positive and negative hits
ass4q1.neg = ass4data[which(ass4data[,1] == 0),]
ass4q1.pos = ass4data[which(ass4data[,1] == 1),]
#Reset row names
rownames(ass4q1.neg) = NULL
rownames(ass4q1.pos) = NULL
#Sample 70% (35 out of 50 in each positive/negative set) for training set,
rest for testing set
ass4q1.negRid = sample(1:nrow(ass4q1.neg),floor(0.7*nrow(ass4q1.neg)))
ass4q1.posRid = sample(1:nrow(ass4q1.pos),floor(0.7*nrow(ass4q1.pos)))
#Combine negative and positive values from each data set to create training
and testing arrays
ass4q1.trainSet = as.matrix(rbind(ass4q1.neg[ass4q1.negRid,],
ass4q1.pos[ass4q1.posRid,]))
ass4q1.testSet =
rbind(ass4q1.neg[-(ass4q1.negRid),],ass4q1.pos[-(ass4q1.posRid),])
#Reset row names
rownames(ass4q1.trainSet) = NULL
rownames(ass4q1.testSet) = NULL
ass4q1.trainSetDF = as.data.frame(ass4q1.trainSet)
ass4q1.trainSetDF$V1 = factor(ass4q1.trainSetDF$V1)
ass4q1.testSetDF = as.data.frame(ass4q1.testSet)
ass4q1.testSetDF$V1 = factor(ass4q1.testSetDF$V1)
##############
#(b)Load MASS, e1071 and glmnet
library(MASS)
library(e1071)
library(glmnet)
#############
#(c)How many features does the data contain?
#The data contains 32 features (columns of data)
#############
#(d)How does the number of parameters required for Naïve Bayes, LDA, and
Logistic
#Regression (unregularized) scale as a function of the number of features?
#If Y is binary with<X1 ... Xp> features, then the number of parameters is
P(Y).
#NaiveBayes
#P(Y) = p • (mew(Y=1), mew(Y=0), sigma(Y=1), sigma(Y=0))
# = 1 + 4p
#Linear Discriminant Analysis
#Have to estimate one covariance matrix and p mean values for each class.
#To compute the covariance matrix is p x p, but since the upper or lower
halfsymetrical, we disregard half, but include the
#middle diagonal by multiplying p x (p + 1) and dividing by 2.
#Calculating p mean values for each class is 2p (2 classes of binary Y).
#Thus:
P(Y) = (p(p + 1) / 2) + 2p
#Logistic Regression
#P(Y) = 1 + p
#To plot the relationship:
ass4q1.dVS= matrixmatrix(,ncol(ass4q1.trainSet)-1,3)
for (p in 1:ncol(ass4q1.trainSet)-1){
ass4q1.dVS[p,1] = (1 + (4*p))
ass4q1.dVS[p,2] = ((p *(p + 1) / 2) + 2*p)
ass4q1.dVS[p,3] = (1 + p)
}
png('ass4q1.dVS.png')
plot(ass4q1.dVS[,2], type="o", col="blue",ylim=c(0,max(ass4q1.dVS)),
ann=FALSE)
lines(ass4q1.dVS[,1], type="o", pch=22, lty=2, col="red")
lines(ass4q1.dVS[,3], type="o", pch=23, lty=3, col="green")
title(main = "Number of parameters as a function of features",
col.main="red", font.main=4)
title(xlab= "Features", col.lab="red")
title(ylab= "Parameters", col.lab="red")
legend(1, max(ass4q1.dVS), c("LDA", "Naive Bayes", "Logistic Regression"),
cex=0.8, col=c("blue","red","green"), pch=21:23, lty=1:3)
dev.off()
#############
#(e)Train Naïve Bayes, LDA and Logistic Regression to classify the training
data
#using the first two, four, eight, 16 or 32 features, starting from the left
of the file. Plot
#the classification error (FP + FN)/(TP+FP+TN+FN) on the training data as a
function
#of the number of parameters for each method.
#Contingency table organized as:
#TN FN
#FP TP
#Organize tables to store data:
ass4q1.dNBtable = matrix(,5,2)
ass4q1.dLDAtable = matrix(,5,2)
ass4q1.dGLMtable = matrix(,5,2)
i = 1
for(p in c(2,4,8,16,32)){
ass4q1.dNBtable[i,1] = (1 + (4*p))
ass4q1.dLDAtable[i,1] = ((p *(p + 1) / 2) + 2*p)
ass4q1.dGLMtable[i,1] = (1+p)
i = i+1
}
#Copying blank tables for part (f)
ass4q1.dNBtable.testData = ass4q1.dNBtable
ass4q1.dLDAtable.testData = ass4q1.dLDAtable
ass4q1.dGLMtable.testData = ass4q1.dGLMtable
#############
#(e)Train Naïve Bayes, LDA and Logistic Regression to classify the training
data
#using the first two, four, eight, 16 or 32 features, starting from the left
of the file. Plot
#the classification error (FP + FN)/(TP+FP+TN+FN) on the training data as a
function
#of the number of parameters for each method.
#Contingency table organized as:
#TN FN
#FP TP
#Organize tables to store data:
ass4q1.dNBtable = matrix(,5,2)
ass4q1.dLDAtable = matrix(,5,2)
ass4q1.dGLMtable = matrix(,5,2)
i = 1
for(p in c(2,4,8,16,32)){
ass4q1.dNBtable[i,1] = (1 + (4*p))
ass4q1.dLDAtable[i,1] = ((p *(p + 1) / 2) + 2*p)
ass4q1.dGLMtable[i,1] = (1+p)
i = i+1
}
#Copying blank tables for part (f)
ass4q1.dNBtable.testData = ass4q1.dNBtable
ass4q1.dLDAtable.testData = ass4q1.dLDAtable
ass4q1.dGLMtable.testData = ass4q1.dGLMtable
#############
#2 Features
#2 features for NaiveBayes
ass4q1.dNB = naiveBayes(ass4q1.trainSetDF[,2:3], ass4q1.trainSetDF[,1])
ass4q1.dNB.cTable = table(predict(ass4q1.dNB, ass4q1.trainSetDF[,2:3]),
ass4q1.trainSetDF[,1])
ass4q1.dNBtable[1,2] = (ass4q1.dNB.cTable[2,1] + ass4q1.dNB.cTable
[1,2])/(sum(ass4q1.dNB.cTable))
#2 features for LDA
ass4q1.dLDA = lda(ass4q1.trainSet[,1]~ass4q1.trainSet[,2:3])
ass4q1.dLDA.cTable = table(predict(ass4q1.dLDA,
ass4q1.trainSetDF[,2:3])$class, ass4q1.trainSetDF[,1])
ass4q1.dLDAtable[1,2] = (ass4q1.dLDA.cTable[2,1] + ass4q1.dLDA.cTable
[1,2])/(sum(ass4q1.dLDA.cTable))
#2 features for GLM
ass4q1.dGLM = glm(ass4q1.trainSet[,1]~ass4q1.trainSet[,2:3], family =
"binomial")
ass4q1.dGLM.cTable = table(predict(ass4q1.dGLM,
ass4q1.trainSetDF[,2:3]),ass4q1.trainSetDF[,1])
ass4q1.dGLMtable[1,2] = ((35-sum(ass4q1.dGLM.cTable[1:35,1]) +
(35-sum(ass4q1.dGLM.cTable[36:70,2]))) / 70)
#############
#4 Features
#4 features for NaiveBayes
ass4q1.dNB = naiveBayes(ass4q1.trainSetDF[,2:5], ass4q1.trainSetDF[,1])
ass4q1.dNB.cTable = table(predict(ass4q1.dNB, ass4q1.trainSetDF[,2:5]),
ass4q1.trainSetDF[,1])
ass4q1.dNBtable[2,2] = (ass4q1.dNB.cTable[2,1] + ass4q1.dNB.cTable
[1,2])/(sum(ass4q1.dNB.cTable))
#4 features for LDA
ass4q1.dLDA = lda(ass4q1.trainSet[,1]~ass4q1.trainSet[,2:5])
ass4q1.dLDA.cTable = table(predict(ass4q1.dLDA,
ass4q1.trainSetDF[,2:5])$class, ass4q1.trainSetDF[,1])
ass4q1.dLDAtable[2,2] = (ass4q1.dLDA.cTable[2,1] + ass4q1.dLDA.cTable
[1,2])/(sum(ass4q1.dLDA.cTable))
#4 features for GLM
ass4q1.dGLM = glm(ass4q1.trainSet[,1]~ass4q1.trainSet[,2:5], family =
"binomial")
ass4q1.dGLM.cTable = table(predict(ass4q1.dGLM,
ass4q1.trainSetDF[,2:5]),ass4q1.trainSetDF[,1])
ass4q1.dGLMtable[2,2] = ((35-sum(ass4q1.dGLM.cTable[1:35,1]) +
(35-sum(ass4q1.dGLM.cTable[36:70,2]))) / 70)
#############
#8 Features
#8 features for NaiveBayes
ass4q1.dNB = naiveBayes(ass4q1.trainSetDF[,2:7], ass4q1.trainSetDF[,1])
ass4q1.dNB.cTable = table(predict(ass4q1.dNB, ass4q1.trainSetDF[,2:7]),
ass4q1.trainSetDF[,1])
ass4q1.dNBtable[3,2] = (ass4q1.dNB.cTable[2,1] + ass4q1.dNB.cTable
[1,2])/(sum(ass4q1.dNB.cTable))
#8 features for LDA
ass4q1.dLDA = lda(ass4q1.trainSet[,1]~ass4q1.trainSet[,2:7])
ass4q1.dLDA.cTable = table(predict(ass4q1.dLDA,
ass4q1.trainSetDF[,2:7])$class, ass4q1.trainSetDF[,1])
ass4q1.dLDAtable[3,2] = (ass4q1.dLDA.cTable[2,1] + ass4q1.dLDA.cTable
[1,2])/(sum(ass4q1.dLDA.cTable))
#8 features for GLM
ass4q1.dGLM = glm(ass4q1.trainSet[,1]~ass4q1.trainSet[,2:7], family =
"binomial")
ass4q1.dGLM.cTable = table(predict(ass4q1.dGLM,
ass4q1.trainSetDF[,2:7]),ass4q1.trainSetDF[,1])
ass4q1.dGLMtable[3,2] = ((35-sum(ass4q1.dGLM.cTable[1:35,1]) +
(35-sum(ass4q1.dGLM.cTable[36:70,2]))) / 70)
#############
#16 Features
#16 features for NaiveBayes
ass4q1.dNB = naiveBayes(ass4q1.trainSetDF[,2:17], ass4q1.trainSetDF[,1])
ass4q1.dNB.cTable = table(predict(ass4q1.dNB, ass4q1.trainSetDF[,2:17]),
ass4q1.trainSetDF[,1])
ass4q1.dNBtable[4,2] = (ass4q1.dNB.cTable[2,1] + ass4q1.dNB.cTable
[1,2])/(sum(ass4q1.dNB.cTable))
#16 features for LDA
ass4q1.dLDA = lda(ass4q1.trainSet[,1]~ass4q1.trainSet[,2:17])
ass4q1.dLDA.cTable = table(predict(ass4q1.dLDA,
ass4q1.trainSetDF[,2:17])$class, ass4q1.trainSetDF[,1])
ass4q1.dLDAtable[4,2] = (ass4q1.dLDA.cTable[2,1] + ass4q1.dLDA.cTable
[1,2])/(sum(ass4q1.dLDA.cTable))
#16 features for GLM
ass4q1.dGLM = glm(ass4q1.trainSet[,1]~ass4q1.trainSet[,2:17], family =
"binomial")
ass4q1.dGLM.cTable = table(predict(ass4q1.dGLM,
ass4q1.trainSetDF[,2:17]),ass4q1.trainSetDF[,1])
ass4q1.dGLMtable[4,2] = ((35-sum(ass4q1.dGLM.cTable[1:35,1]) +
(35-sum(ass4q1.dGLM.cTable[36:70,2]))) / 70)
#############
#32 Features
#32 features for NaiveBayes
ass4q1.dNB = naiveBayes(ass4q1.trainSetDF[,2:33], ass4q1.trainSetDF[,1])
ass4q1.dNB.cTable = table(predict(ass4q1.dNB, ass4q1.trainSetDF[,2:33]),
ass4q1.trainSetDF[,1])
ass4q1.dNBtable[5,2] = (ass4q1.dNB.cTable[2,1] + ass4q1.dNB.cTable
[1,2])/(sum(ass4q1.dNB.cTable))
#32 features for LDA
ass4q1.dLDA = lda(ass4q1.trainSet[,1]~ass4q1.trainSet[,2:33])
ass4q1.dLDA.cTable = table(predict(ass4q1.dLDA,
ass4q1.trainSetDF[,2:33])$class, ass4q1.trainSetDF[,1])
ass4q1.dLDAtable[5,2] = (ass4q1.dLDA.cTable[2,1] + ass4q1.dLDA.cTable
[1,2])/(sum(ass4q1.dLDA.cTable))
#16 features for GLM
ass4q1.dGLM = glm(ass4q1.trainSet[,1]~ass4q1.trainSet[,2:33], family =
"binomial")
ass4q1.dGLM.cTable = table(predict(ass4q1.dGLM,
ass4q1.trainSetDF[,2:33]),ass4q1.trainSetDF[,1])
ass4q1.dLDAtable[5,2] = ((35-sum(ass4q1.dGLM.cTable[1:35,1]) +
(35-sum(ass4q1.dGLM.cTable[36:70,2]))) / 70)
png('ass4q1.dTables.png')
plot(ass4q1.dLDAtable[,1],ass4q1.dLDAtable[,2], type="o",
col="blue",ylim=c(0,.4), ann=FALSE)
lines(ass4q1.dNBtable[,1], ass4q1.dNBtable[,2], type="o", pch=22, lty=2,
col="red")
lines(ass4q1.dGLMtable[,1], ass4q1.dGLMtable[,2], type="o", pch=23, lty=3,
col="green")
title(main = "Classification error as a function of number of parameters",
col.main="red", font.main=4)
title(xlab= "Parameters", col.lab="red")
title(ylab= "(FP + FN)/(TP+FP+TN+FN)", col.lab="red")
legend(300, .4, c("LDA", "Naive Bayes", "Logistic Regression"), cex=0.8,
col=c("blue","red","green"), pch=21:23, lty=1:3)
dev.off()
#############
#(f)Plot the classification error as a function of the number of parameters
on the test
#data for each method. Does this differ from your answer in part (e)?
Explain why.
#############
#2 Features
#2 features for NaiveBayes
ass4q1.dNB.cTable = table(predict(ass4q1.dNB, ass4q1.testSetDF[,2:3]),
ass4q1.testSetDF[,1])
ass4q1.dNBtable.testData[1,2] = (ass4q1.dNB.cTable[2,1] + ass4q1.dNB.cTable
[1,2])/(sum(ass4q1.dNB.cTable))
#WORKS!
#2 features for LDA
ass4q1.dLDA.cTable = table(predict(ass4q1.dLDA,
ass4q1.testSetDF[,2:3])$class, ass4q1.testSetDF[,1])
#DOESN'T WORK!
ass4q1.dLDAtable.tesetData[1,2] = (ass4q1.dLDA.cTable[2,1] +
ass4q1.dLDA.cTable [1,2])/(sum(ass4q1.dLDA.cTable))
#2 features for GLM
ass4q1.dGLM.cTable = table(predict(ass4q1.dGLM, ass4q1.testSetDF[,2:3], s =
),ass4q1.testSetDF[,1])
#DOESN'T WORK!
ass4q1.dGLMtable[1,2] = ((35-sum(ass4q1.dGLM.cTable[1:35,1]) +
(35-sum(ass4q1.dGLM.cTable[36:70,2]))) / 70)
Uwe Ligges-3 wrote
On 22.03.2012 03:24, palanski wrote:
Hi!
I'm using GLM, LDA and NaiveBayes for binomial classification. My
training
set is 70 rows long with 32 features, and my test set is 30 rows long
with
32 features.
Using Naive Bayes, I can train a model, and then predict the test set
with
it like so:
ass4q1.dLDA = lda(ass4q1.trainSet[,1]~ass4q1.trainSet[,2:3])
table(predict(ass4q1.dNB, ass4q1.testSetDF[,2:3]), ass4q1.testSetDF[,1])
However, when the same is done for LDA or GLM, suddenly it tells me that
the
number of rows doesn't match and doesn't predict my test data. The error
for
GLM, as an example, is:
Error in table(predict(ass4q1.dGLM, ass4q1.testSetDF[, 2:3]),
ass4q1.testSetDF[, :
all arguments must have the same length
In addition: Warning message:
'newdata' had 30 rows but variable(s) found have 70 rows
>
What am I missing?
A correct formula describing the model with separate variables with the
data.frame passed to the data argument of the lda() function.
A reproducible example is missing, hence this is just a guess.
Uwe Ligges
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and provide commented, minimal, self-contained, reproducible code.
______________________________________________
R-help@ mailing list
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PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Uwe Ligges-3 wrote
On 22.03.2012 03:24, palanski wrote:
Hi!
I'm using GLM, LDA and NaiveBayes for binomial classification. My
training
set is 70 rows long with 32 features, and my test set is 30 rows long
with
32 features.
Using Naive Bayes, I can train a model, and then predict the test set
with
it like so:
ass4q1.dLDA = lda(ass4q1.trainSet[,1]~ass4q1.trainSet[,2:3])
table(predict(ass4q1.dNB, ass4q1.testSetDF[,2:3]), ass4q1.testSetDF[,1])
However, when the same is done for LDA or GLM, suddenly it tells me that
the
number of rows doesn't match and doesn't predict my test data. The error
for
GLM, as an example, is:
Error in table(predict(ass4q1.dGLM, ass4q1.testSetDF[, 2:3]),
ass4q1.testSetDF[, :
all arguments must have the same length
In addition: Warning message:
'newdata' had 30 rows but variable(s) found have 70 rows
>
What am I missing?
A correct formula describing the model with separate variables with the
data.frame passed to the data argument of the lda() function.
A reproducible example is missing, hence this is just a guess.
Uwe Ligges
--
View this message in context:
http://r.789695.n4.nabble.com/predict-for-LDA-and-GLM-tp4494381p4494381.html
Sent from the R help mailing list archive at Nabble.com.
______________________________________________
R-help@ mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
______________________________________________
R-help@ mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
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______________________________________________
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