# Linear VS Quadratic Discriminant Analysis in R

In this post we will look at linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). Discriminant analysis is used when the dependent variable is categorical. Another commonly used option is logistic regression but there are differences between logistic regression and discriminant analysis. Both LDA and QDA are used in situations in which there is a clear separation between the classes you want to predict. If the categories are fuzzier logistic regression is often the better choice.

For our example, we will use the “Mathlevel” dataset found in the “Ecdat” package. Our goal will be to predict the sex of a respondent based on SAT math score, major, foreign language proficiency, as well as the number of math, physic, and chemistry classes a respondent took. Below is some initial code to start our analysis.

library(MASS);library(Ecdat)
data("Mathlevel")

The first thing we need to do is clean up the data set. We have to remove any missing data in order to run our model. We will create a dataset called “math” that has the “Mathlevel” dataset but with the “NA”s removed use the “na.omit” function. After this, we need to set our seed for the purpose of reproducibility using the “set.seed” function. Lastly, we will split the data using the “sample” function using a 70/30 split. The training dataset will be called “math.train” and the testing dataset will be called “math.test”. Below is the code

math<-na.omit(Mathlevel)
set.seed(123)
math.ind<-sample(2,nrow(math),replace=T,prob = c(0.7,0.3))
math.train<-math[math.ind==1,]
math.test<-math[math.ind==2,]

Now we will make our model and it is called “lda.math” and it will include all available variables in the “math.train” dataset. Next we will check the results by calling the modle. Finally, we will examine the plot to see how our model is doing. Below is the code.

lda.math<-lda(sex~.,math.train)
lda.math
## Call:
## lda(sex ~ ., data = math.train)
##
## Prior probabilities of groups:
##      male    female
## 0.5986079 0.4013921
##
## Group means:
##        mathlevel.L mathlevel.Q mathlevel.C mathlevel^4 mathlevel^5
## male   -0.10767593  0.01141838 -0.05854724   0.2070778  0.05032544
## female -0.05571153  0.05360844 -0.08967303   0.2030860 -0.01072169
##        mathlevel^6      sat languageyes  majoreco  majoross   majorns
## male    -0.2214849 632.9457  0.07751938 0.3914729 0.1472868 0.1782946
## female  -0.2226767 613.6416  0.19653179 0.2601156 0.1907514 0.2485549
##          majorhum mathcourse physiccourse chemistcourse
## male   0.05426357   1.441860    0.7441860      1.046512
## female 0.07514451   1.421965    0.6531792      1.040462
##
## Coefficients of linear discriminants:
##                       LD1
## mathlevel.L    1.38456344
## mathlevel.Q    0.24285832
## mathlevel.C   -0.53326543
## mathlevel^4    0.11292817
## mathlevel^5   -1.24162715
## mathlevel^6   -0.06374548
## sat           -0.01043648
## languageyes    1.50558721
## majoreco      -0.54528930
## majoross       0.61129797
## majorns        0.41574298
## majorhum       0.33469586
## mathcourse    -0.07973960
## physiccourse  -0.53174168
## chemistcourse  0.16124610
plot(lda.math,type='both')

Calling “lda.math” gives us the details of our model. It starts be indicating the prior probabilities of someone being male or female. Next is the means for each variable by sex. The last part is the coefficients of the linear discriminants. Each of these values is used to determine the probability that a particular example is male or female. This is similar to a regression equation.

The plot provides us with densities of the discriminant scores for males and then for females. The output indicates a problem. There is a great deal of overlap between male and females in the model. What this indicates is that there is a lot of misclassification going on as the two groups are not clearly separated. Furthermore, this means that logistic regression is probably a better choice for distinguishing between male and females. However, since this is for demonstrating purposes we will not worry about this.

We will now use the “predict” function on the training set data to see how well our model classifies the respondents by gender. We will then compare the prediction of the model with thee actual classification. Below is the code.

math.lda.predict<-predict(lda.math)
math.train$lda<-math.lda.predict$class
table(math.train$lda,math.train$sex)
##
##          male female
##   male    219    100
##   female   39     73
mean(math.train$lda==math.train$sex)
## [1] 0.6774942

As you can see, we have a lot of misclassification happening. A large amount of false negatives which is a lot of males being classified as female. The overall accuracy us only 59% which is not much better than chance.

We will now conduct the same analysis on the test data set. Below is the code.

lda.math.test<-predict(lda.math,math.test)
math.test$lda<-lda.math.test$class
table(math.test$lda,math.test$sex)
##
##          male female
##   male     92     43
##   female   23     20
mean(math.test$lda==math.test$sex)
## [1] 0.6292135

As you can see the results are similar. To put it simply, our model is terrible. The main reason is that there is little distinction between males and females as shown in the plot. However, we can see if perhaps a quadratic discriminant analysis will do better

QDA allows for each class in the dependent variable to have it’s own covariance rather than a shared covariance as in LDA. This allows for quadratic terms in the development of the model. To complete a QDA we need to use the “qda” function from the “MASS” package. Below is the code for the training data set.

math.qda.fit<-qda(sex~.,math.train)
math.qda.predict<-predict(math.qda.fit)
math.train$qda<-math.qda.predict$class
table(math.train$qda,math.train$sex)
##
##          male female
##   male    215     84
##   female   43     89
mean(math.train$qda==math.train$sex)
## [1] 0.7053364

You can see there is almost no difference. Below is the code for the test data.

math.qda.test<-predict(math.qda.fit,math.test)
math.test$qda<-math.qda.test$class
table(math.test$qda,math.test$sex)
##
##          male female
##   male     91     43
##   female   24     20
mean(math.test$qda==math.test$sex)
## [1] 0.6235955

Still disappointing. However, in this post we reviewed linear discriminant analysis as well as learned about the use of quadratic linear discriminant analysis. Both of these statistical tools are used for predicting categorical dependent variables. LDA assumes shared covariance in the dependent variable categories will QDA allows for each category in the dependent variable to have it’s own variance.

# Logistic Regression in R

In this post, we will conduct a logistic regression analysis. Logistic regression is used when you want to predict a categorical dependent variable using continuous or categorical dependent variables. In our example, we want to predict Sex (male or female) when using several continuous variables from the “survey” dataset in the “MASS” package.

library(MASS);library(bestglm);library(reshape2);library(corrplot)
data(survey)
?MASS::survey #explains the variables in the study

The first thing we need to do is remove the independent factor variables from our dataset. The reason for this is that the function that we will use for the cross-validation does not accept factors. We will first use the “str” function to identify factor variables and then remove them from the dataset. We also need to remove in examples that are missing data so we use the “na.omit” function for this. Below is the code

survey$Clap<-NULL survey$W.Hnd<-NULL
survey$Fold<-NULL survey$Exer<-NULL
survey$Smoke<-NULL survey$M.I<-NULL
survey<-na.omit(survey)

We now need to check for collinearity using the “corrplot.mixed” function form the “corrplot” package.

pc<-cor(survey[,2:5])
corrplot.mixed(pc)
corrplot.mixed(pc)

We have extreme correlation between “We.Hnd” and “NW.Hnd” this makes sense because people’s hands are normally the same size. Since this blog post  is a demonstration of logistic regression we will not worry about this too much.

We now need to divide our dataset into a train and a test set. We set the seed for. First we need to make a variable that we call “ind” that is randomly assigns 70% of the number of rows of survey 1 and 30% 2. We then subset the “train” dataset by taking all rows that are 1’s based on the “ind” variable and we create the “test” dataset for all the rows that line up with 2 in the “ind” variable. This means our data split is 70% train and 30% test. Below is the code

set.seed(123)
ind<-sample(2,nrow(survey),replace=T,prob = c(0.7,0.3))
train<-survey[ind==1,]
test<-survey[ind==2,]

We now make our model. We use the “glm” function for logistic regression. We set the family argument to “binomial”. Next, we look at the results as well as the odds ratios.

fit<-glm(Sex~.,family=binomial,train)
summary(fit)
##
## Call:
## glm(formula = Sex ~ ., family = binomial, data = train)
##
## Deviance Residuals:
##     Min       1Q   Median       3Q      Max
## -1.9875  -0.5466  -0.1395   0.3834   3.4443
##
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept) -46.42175    8.74961  -5.306 1.12e-07 ***
## Wr.Hnd       -0.43499    0.66357  -0.656    0.512
## NW.Hnd        1.05633    0.70034   1.508    0.131
## Pulse        -0.02406    0.02356  -1.021    0.307
## Height        0.21062    0.05208   4.044 5.26e-05 ***
## Age           0.00894    0.05368   0.167    0.868
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
##     Null deviance: 169.14  on 122  degrees of freedom
## Residual deviance:  81.15  on 117  degrees of freedom
## AIC: 93.15
##
## Number of Fisher Scoring iterations: 6
exp(coef(fit))
##  (Intercept)       Wr.Hnd       NW.Hnd        Pulse       Height
## 6.907034e-21 6.472741e-01 2.875803e+00 9.762315e-01 1.234447e+00
##          Age
## 1.008980e+00

The results indicate that only height is useful in predicting if someone is a male or female. The second piece of code shares the odds ratios. The odds ratio tell how a one unit increase in the independent variable leads to an increase in the odds of being male in our model. For example, for every one unit increase in height there is a 1.23 increase in the odds of a particular example being male.

We now need to see how well our model does on the train and test dataset. We first capture the probabilities and save them to the train dataset as “probs”. Next we create a “predict” variable and place the string “Female” in the same number of rows as are in the “train” dataset. Then we rewrite the “predict” variable by changing any example that has a probability above 0.5 as “Male”. Then we make a table of our results to see the number correct, false positives/negatives. Lastly, we calculate the accuracy rate. Below is the code.

train$probs<-predict(fit, type = 'response') train$predict<-rep('Female',123)
train$predict[train$probs>0.5]<-"Male"
table(train$predict,train$Sex)
##
##          Female Male
##   Female     61    7
##   Male        7   48
mean(train$predict==train$Sex)
## [1] 0.8861789

Despite the weaknesses of the model with so many insignificant variables it is surprisingly accurate at 88.6%. Let’s see how well we do on the “test” dataset.

test$prob<-predict(fit,newdata = test, type = 'response') test$predict<-rep('Female',46)
test$predict[test$prob>0.5]<-"Male"
table(test$predict,test$Sex)
##
##          Female Male
##   Female     17    3
##   Male        0   26
mean(test$predict==test$Sex)
## [1] 0.9347826

As you can see, we do even better on the test set with an accuracy of 93.4%. Our model is looking pretty good and height is an excellent predictor of sex which makes complete sense. However, in the next post we will use cross-validation and the ROC plot to further assess the quality of it.

# Probability,Odds, and Odds Ratio

In logistic regression, there are three terms that are used frequently but can be confusing if they are not thoroughly explained. These three terms are probability, odds, and odds ratio. In this post, we will look at these three terms and provide an explanation of them.

Probability

Probability is probably (no pun intended) the easiest of these three terms to understand. Probability is simply the likelihood that a certain even will happen.  To calculate the probability in the traditional sense you need to know the number of events and outcomes to find the probability.

Bayesian probability uses prior probabilities to develop a posterior probability based on new evidence. For example, at one point during Super Bowl LI the Atlanta Falcons had a 99.7% chance of winning. This was base don such factors as the number points they were ahead and the time remaining.  As these changed, so did the probability of them winning. yet the Patriots still found a way to win with less then a 1% chance

Bayesian probability was also used for predicting who would win the 2016 US presidential race. It is important to remember that probability is an expression of confidence and not a guarantee as we saw in both examples.

Odds

Odds are the expression of relative probabilities. Odds are calculated using the following equation

probability of the event ⁄ 1 – probability of the event

For example, at one point during Super Bowl LI the odds of the Atlanta Falcons winning were as follows

0.997 ⁄ 1 – 0.997 = 332

This can be interpreted as the odds being 332 to 1! This means that Atlanta was 332 times more likely to win the Super Bowl then loss the Super Bowl.

Odds are commonly used in gambling and this is probably (again no pun intended) where most of us have heard the term before. The odds is just an extension of probabilities and the are most commonly expressed as a fraction such as one in four, etc.

Odds Ratio

A ratio is the comparison of of two numbers and indicates how many times one number is contained or contains another number. For example, a ration of boys to girls is 5 to 1 it means that there are five boys for every one girl.

By  extension odds ratio is the comparison of two different odds. For example, if the odds of Team A making the playoffs is 45% and the odds of Team B making the playoffs is 35% the odds ratio is calculated as follows.

0.45 ⁄ 0.35 = 1.28

Team A is 1.28 more likely to make the playoffs then Team B.

The value of the odds and the odds ratio can sometimes be the same.  Below is the odds ratio of the Atlanta Falcons winning and the New Patriots winning Super Bowl LI

0.997⁄ 0.003 = 332

As such there is little difference between odds and odds ratio except that odds ratio is the ratio of two odds ratio. As you can tell, there is a lot of confusion about this for the average person. However, understanding these terms is critical to the application of logistic regression.

# Best Subset Regression in R

In this post, we will take a look at best subset regression. Best subset regression fits a model for all possible feature or variable combinations and the decision for the most appropriate model is made by the analyst based on judgment or some statistical criteria.

Best subset regression is an alternative to both Forward and Backward stepwise regression. Forward stepwise selection adds one variable at a time based on the lowest residual sum of squares until no more variables continues to lower the residual sum of squares. Backward stepwise regression starts with all variables in the model and removes variables one at a time. The concern with stepwise methods is they can produce biased regression coefficients, conflicting models, and inaccurate confidence intervals.

Best subset regression bypasses these weaknesses of stepwise models by creating all models possible and then allowing you to assess which variables should be including in your final model. The one drawback to best subset is that a large number of variables means a large number of potential models, which can make it difficult to make a decision among several choices.

In this post, we will use the “Fair” dataset from the “Ecdat” package to predict marital satisfaction based on age, Sex, the presence of children, years married, religiosity, education, occupation, and number of affairs in the past year. Below is some initial code.

library(leaps);library(Ecdat);library(car);library(lmtest)
data(Fair)

We begin our analysis by building the initial model with all variables in it. Below is the code

fit<-lm(rate~.,Fair)
summary(fit)
##
## Call:
## lm(formula = rate ~ ., data = Fair)
##
## Residuals:
##     Min      1Q  Median      3Q     Max
## -3.2049 -0.6661  0.2298  0.7705  2.2292
##
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept)  3.522875   0.358793   9.819  < 2e-16 ***
## sexmale     -0.062281   0.099952  -0.623  0.53346
## age         -0.009683   0.007548  -1.283  0.20005
## ym          -0.019978   0.013887  -1.439  0.15079
## childyes    -0.206976   0.116227  -1.781  0.07546 .
## religious    0.042142   0.037705   1.118  0.26416
## education    0.068874   0.021153   3.256  0.00119 **
## occupation  -0.015606   0.029602  -0.527  0.59825
## nbaffairs   -0.078812   0.013286  -5.932 5.09e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.03 on 592 degrees of freedom
## Multiple R-squared:  0.1405, Adjusted R-squared:  0.1289
## F-statistic:  12.1 on 8 and 592 DF,  p-value: 4.487e-16

The initial results are already interesting even though the r-square is low. When couples have children the have less martial satisfaction than couples without children when controlling for the other factors and this is the strongest regression weight. In addition, the more education a person has there is an increase in marital satisfaction. Lastly, as the number of affairs increases there is also a decrease in martial satisfaction. Keep in mind that the “rate” variable goes from 1 to 5 with one meaning a terrible marriage to five being a great one. The mean marital satisfaction was 3.52 when controlling for the other variables.

We will now create our subset models. Below is the code.

sub.fit<-regsubsets(rate~.,Fair)
best.summary<-summary(sub.fit)

In the code above we create the sub models using the “regsubsets” function from the “leaps” package and saved it in the variable called “sub.fit”. We then saved the summary of “sub.fit” in the variable “best.summary”. We will use the “best.summary” “sub.fit variables several times to determine which model to use.

There are many different ways to assess the model. We will use the following statistical methods that come with the results from the “regsubset” function.

• Mallow’ Cp
• Bayesian Information Criteria

We will make two charts for each of the criteria above. The plot to the left will explain how many features to include in the model. The plot to the right will tell you which variables to include. It is important to note that for both of these methods, the lower the score the better the model. Below is the code for Mallow’s Cp.

par(mfrow=c(1,2))
plot(best.summary$cp) plot(sub.fit,scale = "Cp") The plot on the left suggest that a four feature model is the most appropriate. However, this chart does not tell me which four features. The chart on the right is read in reverse order. The high numbers are at the bottom and the low numbers are at the top when looking at the y-axis. Knowing this, we can conclude that the most appropriate variables to include in the model are age, children presence, education, and number of affairs. Below are the results using the Bayesian Information Criterion par(mfrow=c(1,2)) plot(best.summary$bic)
plot(sub.fit,scale = "bic")

These results indicate that a three feature model is appropriate. The variables or features are years married, education, and number of affairs. Presence of children was not considered beneficial. Since our original model and Mallow’s Cp indicated that presence of children was significant we will include it for now.

Below is the code for the model based on the subset regression.

fit2<-lm(rate~age+child+education+nbaffairs,Fair)
summary(fit2)
##
## Call:
## lm(formula = rate ~ age + child + education + nbaffairs, data = Fair)
##
## Residuals:
##     Min      1Q  Median      3Q     Max
## -3.2172 -0.7256  0.1675  0.7856  2.2713
##
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept)  3.861154   0.307280  12.566  < 2e-16 ***
## age         -0.017440   0.005057  -3.449 0.000603 ***
## childyes    -0.261398   0.103155  -2.534 0.011531 *
## education    0.058637   0.017697   3.313 0.000978 ***
## nbaffairs   -0.084973   0.012830  -6.623 7.87e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.029 on 596 degrees of freedom
## Multiple R-squared:  0.1352, Adjusted R-squared:  0.1294
## F-statistic: 23.29 on 4 and 596 DF,  p-value: < 2.2e-16

The results look ok. The older a person is the less satisfied they are with their marriage. If children are presence the marriage is less satisfying. The more educated the more satisfied they are. Lastly, the higher the number of affairs indicate less marital satisfaction. However, before we get excited we need to check for collinearity and homoscedasticity. Below is the code

vif(fit2)
##       age     child education nbaffairs
##  1.249430  1.228733  1.023722  1.014338

No issues with collinearity.For vif values above 5 or 10 indicate a problem. Let’s check for homoscedasticity

par(mfrow=c(2,2))
plot(fit2)

The normal qqplot and residuals vs leverage plot can be used for locating outliers. The residual vs fitted and the scale-location plot do not look good as there appears to be a pattern in the dispersion which indicates homoscedasticity. To confirm this we will use Breusch-Pagan test from the “lmtest” package. Below is the code

bptest(fit2)
##
##  studentized Breusch-Pagan test
##
## data:  fit2
## BP = 16.238, df = 4, p-value = 0.002716

There you have it. Our model violates the assumption of homoscedasticity. However, this model was developed for demonstration purpose to provide an example of subset regression.

# Principal Component Analysis in R

This post will demonstrate the use of principal component analysis (PCA). PCA is useful for several reasons. One it allows you place your examples into groups similar to linear discriminant analysis but you do not need to know beforehand what the groups are. Second, PCA is used for the purpose of dimension reduction. For example, if you have 50 variables PCA can allow you to reduce this while retaining a certain threshold of variance. If you are working with a large dataset this can greatly reduce the computational time and general complexity of your models.

Keep in mind that there really is not a dependent variable as this is unsupervised learning. What you are trying to see is how different examples can be mapped in space based on whatever independent variables are used. For our example, we will use the “Carseats” dataset form the “ISLR”. Our goal is to understanding the relationship among the variables when examining the shelve location of the car seat. Below is the initial code to begin the analysis

library(ggplot2)
library(ISLR)
data("Carseats")

We first need to rearrange the data and remove the variables we are not going to use in the analysis. Below is the code.

Carseats1<-Carseats
Carseats1<-Carseats1[,c(1,2,3,4,5,6,8,9,7,10,11)]
Carseats1$Urban<-NULL Carseats1$US<-NULL

Here is what we did 1. We made a copy of the “Carseats” data called “Careseats1” 2. We rearranged the order of the variables so that the factor variables are at the end. This will make sense later 3.We removed the “Urban” and “US” variables from the table as they will not be a part of our analysis

We will now do the PCA. We need to scale and center our data otherwise the larger numbers will have a much stronger influence on the results than smaller numbers. Fortunately, the “prcomp” function has a “scale” and a “center” argument. We will also use only the first 7 columns for the analysis  as “sheveLoc” is not useful for this analysis. If we hadn’t moved “shelveLoc” to the end of the dataframe it would cause some headache. Below is the code.

Carseats.pca<-prcomp(Carseats1[,1:7],scale. = T,center = T)
summary(Carseats.pca)
## Importance of components:
##                           PC1    PC2    PC3    PC4    PC5     PC6     PC7
## Standard deviation     1.3315 1.1907 1.0743 0.9893 0.9260 0.80506 0.41320
## Proportion of Variance 0.2533 0.2026 0.1649 0.1398 0.1225 0.09259 0.02439
## Cumulative Proportion  0.2533 0.4558 0.6207 0.7605 0.8830 0.97561 1.00000

The summary of “Carseats.pca” Tells us how much of the variance each component explains. Keep in mind that number of components is equal to the number of variables. The “proportion of variance” tells us the contribution each component makes and the “cumulative proportion”.

If your goal is dimension reduction than the number of components to keep depends on the threshold you set. For example, if you need around 90% of the variance you would keep the first 5 components. If you need 95% or more of the variance you would keep the first six. To actually use the components you would take the “Carseats.pca$x” data and move it to your data frame. Keep in mind that the actual components have no conceptual meaning but is a numerical representation of a combination of several variables that were reduce using PCA to fewer variables such as going form 7 variables to 5 variables. This means that PCA is great for reducing variables for prediction purpose but is much harder for explanatory studies unless you can explain what the new components represent. For our purposes, we will keep 5 components. This means that we have reduce our dimensions from 7 to 5 while still keeping almost 90% of the variance. Graphing our results is tricky because we have 5 dimensions but the human mind can only conceptualize 3 at the best and normally 2. As such we will plot the first two components and label them by shelf location using ggplot2. Below is the code scores<-as.data.frame(Carseats.pca$x)
pcaplot<-ggplot(scores,(aes(PC1,PC2,color=Carseats1$ShelveLoc)))+geom_point() pcaplot From the plot you can see there is little separation when using the first two components of the PCA analysis. This makes sense as we can only graph to components so we are missing a lot of the variance. However for demonstration purposes the analysis is complete. # Developing a Data Analysis Plan It is extremely common for beginners and perhaps even experience researchers to lose track of what they are trying to achieve or do when trying to complete a research project. The open nature of research allows for a multitude of equally acceptable ways to complete a project. This leads to an inability to make decision and or stay on course when doing research. One way to reduce and eliminate the roadblock to decision making and focus in research is to develop a plan. In this post we will look at one version of a data analysis plan. Data Analysis Plan A data analysis plan includes many features of a research project in it with a particular emphasis on mapping out how research questions will be answered and what is necessary to answer the question. Below is a sample template of the analysis plan. The majority of this diagram should be familiar to someone who has ever done research. At the top, you state the problem, this is the overall focus of the paper. Next comes the purpose, the purpose is the over-arching goal of a research project. After purpose comes the research questions. The research questions are questions about the problem that are answerable. People struggle with developing clear and answerable research questions. It is critical that research questions are written in a way that they can be answered and that the questions are clearly derived from the problem. Poor questions means poor or even no answers. After the research questions it is important to know what variables are available for the entire study and specifically what variables can be used to answer each research question. Lastly, you must indicate what analysis or visual you will develop in order to answer your research questions about your problem. This requires you to know how you will answer your research questions Example Below is an example of a completed analysis plan for simple undergraduate level research paper In the example above, the student want to understand the perceptions of university students about the cafeteria food quality and their satisfaction with the university. There were four research questions, a demographic descriptive question, a descriptive question about the two main variables, a comparison question, and lastly a relationship question. The variables available for answering the questions are listed of to the left side. Under that, the student indicates the variables needed to answer each question. For example, the demographic variables of sex, class level, and major are needed to answer the question about the demographic profile. The last section is the analysis. For the demographic profile the student found the percentage of the population in each sub group of the demographic variables. Conclusion A data analysis plan provides an excellent way to determine what needs to be done to complete a study. It also helps a researcher to clearly understand what they are trying to do and provides a visuals for those who the research wants to communicate with about the progress of a study. # Generalized Additive Models in R In this post, we will learn how to create a generalized additive model (GAM). GAMs are non-parametric generalized linear models. This means that linear predictor of the model uses smooth functions on the predictor variables. As such, you do not need to specific the functional relationship between the response and continuous variables. This allows you to explore the data for potential relationships that can be more rigorously tested with other statistical models In our example, we will use the “Auto” dataset from the “ISLR” package and use the variables “mpg”,“displacement”,“horsepower”,and “weight” to predict “acceleration”. We will also use the “mgcv” package. Below is some initial code to begin the analysis library(mgcv) library(ISLR) data(Auto) We will now make the model we want to understand the response of “accleration” to the explanatory variables of “mpg”,“displacement”,“horsepower”,and “weight”. After setting the model we will examine the summary. Below is the code model1<-gam(acceleration~s(mpg)+s(displacement)+s(horsepower)+s(weight),data=Auto) summary(model1) ## ## Family: gaussian ## Link function: identity ## ## Formula: ## acceleration ~ s(mpg) + s(displacement) + s(horsepower) + s(weight) ## ## Parametric coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 15.54133 0.07205 215.7 <2e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Approximate significance of smooth terms: ## edf Ref.df F p-value ## s(mpg) 6.382 7.515 3.479 0.00101 ** ## s(displacement) 1.000 1.000 36.055 4.35e-09 *** ## s(horsepower) 4.883 6.006 70.187 < 2e-16 *** ## s(weight) 3.785 4.800 41.135 < 2e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## R-sq.(adj) = 0.733 Deviance explained = 74.4% ## GCV = 2.1276 Scale est. = 2.0351 n = 392 All of the explanatory variables are significant and the adjust r-squared is .73 which is excellent. edf stands for “effective degrees of freedom”. This modified version of the degree of freedoms is due to the smoothing process in the model. GCV stands for generalized cross validation and this number is useful when comparing models. The model with the lowest number is the better model. We can also examine the model visually by using the “plot” function. This will allow us to examine if the curvature fitted by the smoothing process was useful or not for each variable. Below is the code. plot(model1) We can also look at a 3d graph that includes the linear predictor as well as the two strongest predictors. This is done with the “vis.gam” function. Below is the code vis.gam(model1) If multiple models are developed. You can compare the GCV values to determine which model is the best. In addition, another way to compare models is with the “AIC” function. In the code below, we will create an additional model that includes “year” compare the GCV scores and calculate the AIC. Below is the code. model2<-gam(acceleration~s(mpg)+s(displacement)+s(horsepower)+s(weight)+s(year),data=Auto) summary(model2) ## ## Family: gaussian ## Link function: identity ## ## Formula: ## acceleration ~ s(mpg) + s(displacement) + s(horsepower) + s(weight) + ## s(year) ## ## Parametric coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 15.54133 0.07203 215.8 <2e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Approximate significance of smooth terms: ## edf Ref.df F p-value ## s(mpg) 5.578 6.726 2.749 0.0106 * ## s(displacement) 2.251 2.870 13.757 3.5e-08 *** ## s(horsepower) 4.936 6.054 66.476 < 2e-16 *** ## s(weight) 3.444 4.397 34.441 < 2e-16 *** ## s(year) 1.682 2.096 0.543 0.6064 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## R-sq.(adj) = 0.733 Deviance explained = 74.5% ## GCV = 2.1368 Scale est. = 2.0338 n = 392 #model1 GCV model1$gcv.ubre
##   GCV.Cp
## 2.127589
#model2 GCV
model2$gcv.ubre ## GCV.Cp ## 2.136797 As you can see, the second model has a higher GCV score when compared to the first model. This indicates that the first model is a better choice. This makes sense because in the second model the variable “year” is not significant. To confirm this we will calculate the AIC scores using the AIC function. AIC(model1,model2) ## df AIC ## model1 18.04952 1409.640 ## model2 19.89068 1411.156 Again, you can see that model1 s better due to its fewer degrees of freedom and slightly lower AIC score. Conclusion Using GAMs is most common for exploring potential relationships in your data. This is stated because they are difficult to interpret and to try and summarize. Therefore, it is normally better to develop a generalized linear model over a GAM due to the difficulty in understanding what the data is trying to tell you when using GAMs. # Generalized Models in R Generalized linear models are another way to approach linear regression. The advantage of of GLM is that allows the error to follow many different distributions rather than only the normal distribution which is an assumption of traditional linear regression. Often GLM is used for response or dependent variables that are binary or represent count data. THis post will provide a brief explanation of GLM as well as provide an example. Key Information There are three important components to a GLM and they are • Error structure • Linear predictor • Link function The error structure is the type of distribution you will use in generating the model. There are many different distributions in statistical modeling such as binomial, gaussian, poission, etc. Each distribution comes with certain assumptions that govern their use. The linear predictor is the sum of the effects of the independent variables. Lastly, the link function determines the relationship between the linear predictor and the mean of the dependent variable. There are many different link functions and the best link function is the one that reduces the residual deviances the most. In our example, we will try to predict if a house will have air conditioning based on the interactioon between number of bedrooms and bathrooms, number of stories, and the price of the house. To do this, we will use the “Housing” dataset from the “Ecdat” package. Below is some initial code to get started. library(Ecdat) data("Housing") The dependent variable “airco” in the “Housing” dataset is binary. This calls for us to use a GLM. To do this we will use the “glm” function in R. Furthermore, in our example, we want to determine if there is an interaction between number of bedrooms and bathrooms. Interaction means that the two independent variables (bathrooms and bedrooms) influence on the dependent variable (aircon) is not additive, which means that the combined effect of the independnet variables is different than if you just added them together. Below is the code for the model followed by a summary of the results model<-glm(Housing$airco ~ Housing$bedrooms * Housing$bathrms + Housing$stories + Housing$price, family=binomial)
summary(model)
##
## Call:
## glm(formula = Housing$airco ~ Housing$bedrooms * Housing$bathrms + ## Housing$stories + Housing$price, family = binomial) ## ## Deviance Residuals: ## Min 1Q Median 3Q Max ## -2.7069 -0.7540 -0.5321 0.8073 2.4217 ## ## Coefficients: ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) -6.441e+00 1.391e+00 -4.632 3.63e-06 ## Housing$bedrooms                  8.041e-01  4.353e-01   1.847   0.0647
## Housing$bathrms 1.753e+00 1.040e+00 1.685 0.0919 ## Housing$stories                   3.209e-01  1.344e-01   2.388   0.0170
## Housing$price 4.268e-05 5.567e-06 7.667 1.76e-14 ## Housing$bedrooms:Housing$bathrms -6.585e-01 3.031e-01 -2.173 0.0298 ## ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## (Dispersion parameter for binomial family taken to be 1) ## ## Null deviance: 681.92 on 545 degrees of freedom ## Residual deviance: 549.75 on 540 degrees of freedom ## AIC: 561.75 ## ## Number of Fisher Scoring iterations: 4 To check how good are model is we need to check for overdispersion as well as compared this model to other potential models. Overdispersion is a measure to determine if there is too much variablity in the model. It is calcualted by dividing the residual deviance by the degrees of freedom. Below is the solution for this 549.75/540 ## [1] 1.018056 Our answer is 1.01, which is pretty good because the cutoff point is 1, so we are really close. Now we will make several models and we will compare the results of them Model 2 #add recroom and garagepl model2<-glm(Housing$airco ~ Housing$bedrooms * Housing$bathrms + Housing$stories + Housing$price + Housing$recroom + Housing$garagepl, family=binomial)
summary(model2)
##
## Call:
## glm(formula = Housing$airco ~ Housing$bedrooms * Housing$bathrms + ## Housing$stories + Housing$price + Housing$recroom + Housing$garagepl, ## family = binomial) ## ## Deviance Residuals: ## Min 1Q Median 3Q Max ## -2.6733 -0.7522 -0.5287 0.8035 2.4239 ## ## Coefficients: ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) -6.369e+00 1.401e+00 -4.545 5.51e-06 ## Housing$bedrooms                  7.830e-01  4.391e-01   1.783   0.0745
## Housing$bathrms 1.702e+00 1.047e+00 1.626 0.1039 ## Housing$stories                   3.286e-01  1.378e-01   2.384   0.0171
## Housing$price 4.204e-05 6.015e-06 6.989 2.77e-12 ## Housing$recroomyes                1.229e-01  2.683e-01   0.458   0.6470
## Housing$garagepl 2.555e-03 1.308e-01 0.020 0.9844 ## Housing$bedrooms:Housing$bathrms -6.430e-01 3.054e-01 -2.106 0.0352 ## ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## (Dispersion parameter for binomial family taken to be 1) ## ## Null deviance: 681.92 on 545 degrees of freedom ## Residual deviance: 549.54 on 538 degrees of freedom ## AIC: 565.54 ## ## Number of Fisher Scoring iterations: 4 #overdispersion calculation 549.54/538 ## [1] 1.02145 Model 3 model3<-glm(Housing$airco ~ Housing$bedrooms * Housing$bathrms + Housing$stories + Housing$price + Housing$recroom + Housing$fullbase + Housing$garagepl, family=binomial) summary(model3) ## ## Call: ## glm(formula = Housing$airco ~ Housing$bedrooms * Housing$bathrms +
##     Housing$stories + Housing$price + Housing$recroom + Housing$fullbase +
##     Housing$garagepl, family = binomial) ## ## Deviance Residuals: ## Min 1Q Median 3Q Max ## -2.6629 -0.7436 -0.5295 0.8056 2.4477 ## ## Coefficients: ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) -6.424e+00 1.409e+00 -4.559 5.14e-06 ## Housing$bedrooms                  8.131e-01  4.462e-01   1.822   0.0684
## Housing$bathrms 1.764e+00 1.061e+00 1.662 0.0965 ## Housing$stories                   3.083e-01  1.481e-01   2.082   0.0374
## Housing$price 4.241e-05 6.106e-06 6.945 3.78e-12 ## Housing$recroomyes                1.592e-01  2.860e-01   0.557   0.5778
## Housing$fullbaseyes -9.523e-02 2.545e-01 -0.374 0.7083 ## Housing$garagepl                 -1.394e-03  1.313e-01  -0.011   0.9915
## Housing$bedrooms:Housing$bathrms -6.611e-01  3.095e-01  -2.136   0.0327
##
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
##     Null deviance: 681.92  on 545  degrees of freedom
## Residual deviance: 549.40  on 537  degrees of freedom
## AIC: 567.4
##
## Number of Fisher Scoring iterations: 4
#overdispersion calculation
549.4/537
## [1] 1.023091

Now we can assess the models by using the “anova” function with the “test” argument set to “Chi” for the chi-square test.

anova(model, model2, model3, test = "Chi")
## Analysis of Deviance Table
##
## Model 1: Housing$airco ~ Housing$bedrooms * Housing$bathrms + Housing$stories +
##     Housing$price ## Model 2: Housing$airco ~ Housing$bedrooms * Housing$bathrms + Housing$stories + ## Housing$price + Housing$recroom + Housing$garagepl
## Model 3: Housing$airco ~ Housing$bedrooms * Housing$bathrms + Housing$stories +
##     Housing$price + Housing$recroom + Housing$fullbase + Housing$garagepl
##   Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1       540     549.75
## 2       538     549.54  2  0.20917   0.9007
## 3       537     549.40  1  0.14064   0.7076

The results of the anova indicate that the models are all essentially the same as there is no statistical difference. The only criteria on which to select a model is the measure of overdispersion. The first model has the lowest rate of overdispersion and so is the best when using this criteria. Therefore, determining if a hous has air conditioning depends on examining number of bedrooms and bathrooms simultenously as well as the number of stories and the price of the house.

Conclusion

The post explained how to use and interpret GLM in R. GLM can be used primarilyy for fitting data to disrtibutions that are not normal.

# Understanding Decision Trees

Decision trees are yet another method in machine learning that is used for classifying outcomes. Decision trees are very useful for, as you can guess, making decisions based on the characteristics of the data.

In this post we will discuss the following

• Physical traits of decision trees
• How decision trees work
• Pros and cons of decision trees

Physical Traits of a Decision Tree

Decision trees consist of what is called a tree structure. The tree structure consist of a root node, decision nodes, branches and leaf nodes.

A root node is the initial decision made in the tree. This depends on which feature the algorithm selects first.

Following the root node the tree splits into various branches. Each branch leads to an additional decision node where the data is further subdivided. When you reach the bottom of a tree at the terminal node(s) these are also called leaf nodes.

How Decision Trees Work

Decision trees use a heuristic called recursive partitioning. What this does is it splits the overall data set into smaller and smaller subsets until each subset is as close to pure (having the same characteristics) as possible. This process is also know as divide and conquer.

The mathematics for deciding how to split the data is based on an equation called entropy, which measures the purity of a potential decision node. The lower the entropy score the more pure the decision node is. The entropy can range from 0 (most pure) to 1 (most impure).

One of the most popular algorithms for developing decision trees is the C5.0 algorithm. This algorithm in particular uses entropy to assess potential decision nodes.

Pros and Cons

The prose of decision trees include it versatile nature. Decision trees can deal with all types of data as well as missing data. Furthermore, this approach learns automatically and only uses the most important features. Lastly, a deep understanding of mathematics is not necessary to use this method in comparison to more complex models.

Some problems with decision trees is that the can easily overfit the data. This means that the tree does not generalize well to other datasets. In addition, a large complex tree can be hard to interpret, which may be yet another indication of overfitting.

Conclusion

Decision trees provide another vehicle that researchers can use to empower decision making. This model is most useful particular when a decision that was made needs to be explained and defended. For example, when rejecting a person’s loan application. Complex models made provide stronger mathematical reasons but would be difficult to explain to an irate customer.

Therefore, for complex calculation presented in an easy to follow format. Decision trees are one possibility.

# Mixed Methods

Mix Methods research involves the combination of qualitative and quantitative approaches in addressing a research problem. Generally, qualitative and quantitative methods have separate philosophical positions when it comes to how to uncover insights in addressing research questions.

For many, mixed methods have their own philosophical position, which is pragmatism. Pragmatist believe that if it works it’s good. Therefore, if mixed methods leads to a solution it’s an appropriate method to use.

This post will try to explain some of the mixed method designs. Before explaining it is important to understand that there are several common ways to approach mixed methods

• Qualitative and Quantitative are equal (Convergent Parallel Design)
• Quantitative is more important than qualitative (explanatory design)
• Qualitative is more important than quantitative

Convergent Parallel Design

This design involves the simultaneous collecting of qualitative and quantitative data. The results are then compared to provide insights into the problem. The advantage of this design is the quantitative data provides for generalizability while the qualitative data provides information about the context of the study.

However, the challenge is in trying to merge the two types of data. Qualitative and quantitative methods answer slightly different questions about a problem. As such it can be difficult to pain a picture of the results that are comprehensible.

Explanatory Design

This design puts emphasis on the quantitative data with qualitative data playing a secondary role. Normally, the results found in the quantitative data are followed up on in the qualitative part.

For example, if you collect surveys about what students think about college and the results indicate negative opinions, you might conduct interview with students to understand way they are negative towards college. A likert survey will not explain why students are negative. Interviews will help to capture why students have a particular position.

The advantage of this approach is the clear organization of the data. Quantitative data is more important. The drawback is deciding what about the quantitative data to explore when conducting the qualitative data collection.

Exploratory Design

This design is the opposite of explanatory. Now the qualitative data is more important than the quantitative. This design is used when you want to understand a phenomenon in order to measure it.

It is common when developing an instrument to interview people in focus groups to understand the phenomenon. For example, if I want to understand what cellphone addiction is I might ask students to share what they think about this in interviews. From there, I could develop a survey instrument to measure cell phone addiction.

The drawback to this approach is the time consumption. It takes a lot of work to conduct interviews, develop an instrument, and assess the instrument.

Conclusions

Mixed methods are not that new. However, they are still a somewhat unusual approach to research in many fields. Despite this, the approaches of mixed methods can be beneficial depending on the context.

# Intro to Using Machine Learning

Machine learning is about using data to take action. This post will explain common steps that are taking when using machine learning in the analysis of data. In general, there are five steps when applying machine learning.

1. Collecting data
2. Preparing data
3. Exploring data
4. Training a model on the data
5. Assessing the performance of the model
6. Improving the model

We will go through each step briefly

Step One, Collecting Data

Data can come from almost anywhere. Data can come from a database in a structured format like mySQL. Data can also come unstructured such as tweets collected from twitter. However you get the data, you need to develop a way to clean and process it so that it is ready for analysis.

There are some distinct terms used in machine learning that some coming from traditional research my not be familiar.

• example-An example is one set of data. In an excel spreadsheet, an example would be one row. In empirical social science research we would call an example a respondent or participant.
• Unit of observation-This is how an example is measured. The units can be time, money, height, weight, etc.
• feature-A feature is a characteristic of an example. In other forms of research we normally call a feature a variable. For example, ‘salary’ would be a feature in machine learning but a variable in traditional research.

Step Two, Preparing Data

This is actually the most difficult step in machine learning analysis. It can take up to 80% of the time. With data coming from multiple sources and in multiple formats it is a real challenge to get everything where it needs to be for an analysis.

Missing data needs to be addressed, duplicate records, and other issues are a part of this process. Once these challenges are dealt with it is time to explore the data.

Step Three, Explore the Data

Before analyzing the data, it is critical that the data is explored. This is often done visually in the form of plots and graphs but also with summary statistics. You are looking for some insights into the data and the characteristics of different features. You are also looking out for things that might be unusually such as outliers. There are also times when a variable needs to be transformed because there are issues with normality.

Step Four, Training a Model

After exploring the data, you should have an idea of what you want to know if you did not already know. Determining what you want to know helps you to decide which algorithm is most appropriate for developing a model.

To develop a model, we normally split the data into a training and testing set. This allows us to assess the model for its strengths and weaknesses.

Step Five, Assessing the Model

The strength of the model is deteremined. Every model has certain biases that limits its usefulness. How to assess a model depends on what type of model is developed and the purpose of the analysis. Whenever suitable, we want to try and improve the model.

Step Six, Improving the Model

Improve can happen in many ways. You might decide to normalize the variables in a different way. Or you may choose to add or remove features from the model. Perhaps you may switch to a different model.

Conclusion

Success in data analysis involves have a clear path for achieving your goals. The steps presented here provide one way of tackling machine learning.

# Machine Learning: Getting Machines to Learn

One of the most interesting fields in quantitative research today is machine learning. Machine Learning serves the purpose of developing models through the use of algorithms in order to inform action.

Examples of machine learning are all around us. Machine learning is used to make recommendations about what you should purchase at most retail websites. Machine learning is also used to filter spam from your email inbox. Each of these two examples involves making prediction about previous behavior. This is what machines learning does. It learns what will happen based on what has happen. However, the question remains how does a machine actually learn.

The purpose of this post is to explain how machines actually “learn.” The process of learning involves three steps which are…

1. Data input
2. Abstraction
3. Generalization

Data Input

Data input is simply having access to some for of data whether it be numbers, text, pictures or something else. The data is the factual information that is used to develop insights for future action.

The majority of a person’s time in conducting machine learning is involve in organize and cleaning data. This process is actually called data mugging by many. Once the data is ready for analysis, it is time for the abstraction process to begin.

Abstraction

Clean beautiful data still does not provide any useful information yet. Abstraction is the process of deriving meaning from the data. The raw data represent knowledge but as we already are aware the problem is how it is currently represented. Numbers and text mean little to us.

Abstraction takes all of the numbers and or text and develops a model. What a model does is summarize data and provides explanation about the data. For example, if you are doing a study for a a bank who wants to know who will default on loans. You might discover from the data that a person is more likely to default if they are a student with a credit card balance over $2,000. How this information is shared with the researcher can be in one of the following forms • equation • diagram • logic rules This kind of information is hard to find manually and is normally found through the use of an algorithm. Abstraction involves the use of some sort of algorithm. An algorithm is a systemic step-by-step procedure for solving a problem. It is very technical to try and understand algorithms unless you have a graduate degree in statistics. The point to remember is that algorithms are what the computer uses to learn and develop models. Developing a model involves training. Training is achieved when the abstraction process is complete. The completion of this process depends on the criteria of what a “good” model is. This varies depending on the requirements of the model and the preference of the researcher. Generalization A machine has not actually learned anything until the model it developed is assessed for bias. Bias is a result of the educated guesses (heuristics) that an algorithm makes to develop a model that are systematically wrong. For example, let’s say an algorithm learns to identify a man by the length of their hair. If a man has really long hair or if a woman has really short hair the algorithm will misclassify them because each person does not fit the educated guess the algorithm developed. The challenge is that these guesses work most of the time but they struggle with exceptions. The main way of dealing with this is to develop a model on one set of data and test it on another set of data. This will inform the researcher as to what changes are needed for the model. Another problem is noise. Noise is caused by measurement error data reporting issues trying to make your model deal with noise can lead to overfitting which means that your model only works for your data and cannot be applied to other data sets. This can also be addressed by testing your model on other data sets. Conclusion A machine learns through the use of an algorithm to explain relationships in a data set in a specific manner. This process involves the three steps of data input, abstraction and generalization. The results of a machine learning model is a model that can be use to make prediction about the future with a certain degree of accuracy. # Wilcoxon Signed Rank Test in R The Wilcoxon Signed Rank Test is the non-parametric equivalent of the t-test. If you have questions whether or not your data is normally distributed the Wilcoxon Signed Rank Test can still indicate to you if there is a difference between the means of your sample. Th Wilcoxon Test compares the medians of two samples instead of their means. The differences between the median and each individual value for each sample is calculated. Values that come to zero are removed. Any remaining values are ranked from lowest to highest. Lastly, the ranks are summed. If the rank sum is different between the two samples it indicates statistical difference between samples. We will now do an example using r. We want to see if there is a difference in enrollment between private and public universities. Below is the code We will begin by loading the ISLR package. Then we will load the ‘College’ data and take a look at the variables in the “College” dataset by using the ‘str’ function. library(ISLR) data=College str(College) ## 'data.frame': 777 obs. of 18 variables: ##$ Private    : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 2 2 ...
##  $Apps : num 1660 2186 1428 417 193 ... ##$ Accept     : num  1232 1924 1097 349 146 ...
##  $Enroll : num 721 512 336 137 55 158 103 489 227 172 ... ##$ Top10perc  : num  23 16 22 60 16 38 17 37 30 21 ...
##  $Top25perc : num 52 29 50 89 44 62 45 68 63 44 ... ##$ F.Undergrad: num  2885 2683 1036 510 249 ...
##  $P.Undergrad: num 537 1227 99 63 869 ... ##$ Outstate   : num  7440 12280 11250 12960 7560 ...
##  $Room.Board : num 3300 6450 3750 5450 4120 ... ##$ Books      : num  450 750 400 450 800 500 500 450 300 660 ...
##  $Personal : num 2200 1500 1165 875 1500 ... ##$ PhD        : num  70 29 53 92 76 67 90 89 79 40 ...
##  $Terminal : num 78 30 66 97 72 73 93 100 84 41 ... ##$ S.F.Ratio  : num  18.1 12.2 12.9 7.7 11.9 9.4 11.5 13.7 11.3 11.5 ...
##  $perc.alumni: num 12 16 30 37 2 11 26 37 23 15 ... ##$ Expend     : num  7041 10527 8735 19016 10922 ...
##  $Grad.Rate : num 60 56 54 59 15 55 63 73 80 52 ... We will now look at the Enroll variable and see if it is normally distributed hist(College$Enroll)

This variable is highly skewed to the right. This may mean that it is not normally distributed. Therefore, we may not be able to use a regular t-test to compare private and public universities and the Wilcoxon Test is more appropriate. We will now use the Wilcoxon Test. Below are the results

wilcox.test(College$Enroll~College$Private)
##
##  Wilcoxon rank sum test with continuity correction
##
## data:  College$Enroll by College$Private
## W = 104090, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0

The results indicate a difference we will now calculate the medians of the two groups using the ‘aggregate’ function. This function allows us to compare our two groups based on the median. Below is the code with the results.

aggregate(College$Enroll~College$Private, FUN=median)
##   College$Private College$Enroll
## 1              No       1337.5
## 2             Yes        328.0


As you can see, there is a large difference in enrollment in private and public colleges. We can then make the conclusion that there is a difference in the medians of private and public colleges with public colleges have a much higher enrollment.

Conclusion

The Wilcoxon Test is used for a non-parametric analysis of data. This test is useful whenever there are concerns with the normality of the data.

# Big Data & Data Mining

Dealing with large amounts of data has been a problem throughout most of human history. Ancient civilizations had to keep large amounts of clay tablets, papyrus, steles, parchments, scrolls etc. to keep track of all the details of an empire.

However, whenever it seemed as though there would be no way to hold any more information a new technology would be developed to alleviate the problem. When people could not handle keeping records on stone paper scrolls were invented. When scrolls were no longer practical books were developed. When hand-copying books was too much the printing press came along.

By the mid 20th century there were concerns that we would not be able to have libraries large enough to keep all of the books that were being developed. With this problem came the solution of the computer. One computer could hold the information of several dozen if not hundreds of libraries.

Now even a single computer can no longer cope with all of the information that is constantly being developed for just a single subject. This has lead to computers working together in networks to share the task of storing information. With data spread across several computers it makes analyzing data much more challenging. It was now necessary to mine for useful information in a way that people used to mine for gold in the 19th century.

Big data is data that is too large to fit within the memory of a single computer. Analyzing data that is spread across a network of databases takes skills different from traditional statistical analysis. This post will explain some of the characteristics of big data  as well as data mining.

Big Data Traits

The three main traits of big data are volume, velocity, and variety. Volume describes the size of big data, which means data to big to be on only one computer. Velocity is about how fast the data can be processed. Lastly, variety different types of data. common sources of big data includes the following

• Metadata from visual sources such as cameras
• Data from sensors such as in medical equipment

Data Mining

Data mining is the process of discovering a model in a big dataset. Through the development of an algorithm, we can find specific information that helps us to answer our questions. Generally, there are two ways to mine data and these are extraction and summarization.

Extraction is the process of pulling specific information from a big dataset. For example, if we want all the addresses of people who bought a specific book from Amazon the result would be an extraction from a big data set.

Summarization is reducing a dataset to describe it. We might do a cluster analysis in which similar data is combine on a characteristic. For example, if we analyze all the books people ordered through Amazon last year we might notice that one cluster of groups buys mostly religious books while another buys investment books.

Conclusion

Big data will only continue to get bigger. Currently, the response has been to just use more computers and servers. As such, there is now a need for finding information across many computers and servers. This is the purpose of data mining, which is to find pertinent information that answers stakeholders questions.

# Type I and Type II Error

Hypothesis testing in statistics involves deciding whether to reject or not reject a null hypothesis. There are problems that can occur when making decisions about a null hypothesis. A researcher can reject a null when they should not reject it, which is called a type I error. The other mistake is not rejecting a null when they should have, which is a type II error. Both of these mistakes represent can seriously damage the interpretation of data.

An Example

The classic example that explains type I and type II errors is a a court room. In a trial, the defendant is considered innocent until proven guilty. The defendant can be compared to the null hypothesis being true. The prosecutor job is to present evidence that the defendant is guilty. This is the same as provide statistical evidence to reject the null hypothesis which indicates that the null is not true and needs to be rejected.

There are four possible outcomes of our trial and are statistical test…

1. The defendant can be declared guilty when they are really guilty. That’s a correct decision.This is the same as rejecting the null hypothesis.
2. The defendant  could be judged not guilty when they really are innocent. That’s a correct and is the same as not rejecting the null hypothesis.
3. The defendant is convicted when they are actually innocent,which is wrong. This is the same as rejecting the null hypothesis when you should not and is know as a type I error
4. The defendant is  guilty but declared innocent, which is also incorrect. This is the same as not rejecting the null hypothesis when you should have. This is known as a type II error.

Important Notes

The probability of committing a type I error is the same as the alpha or significance level of a statistical test. Common values associated with alpha are o.1, 0.05, and 0.01. This means that the likelihood of committing a type I error depends on the level of the significance that the researcher picks.

The probability of committing a type II error is known as beta. Calculating beta is complicated as you need specific values in your null and alternative hypothesis. It is not always possible to supply this. As such, researcher often do not focus on type II error avoidance as they due with type I.

Another concern is that decrease the risk of committing one type of error increases the risk of committing the other. This means that if you reduce the risk of type I error you increase the risk of committing a type II error.

Conclusion

The risk of error or incorrect judgment of a null hypothesis is a risk in statistical analysis. As such, researchers need to be aware of these problems as they study data.

# Decision Trees in R

Decisions tress are useful for splitting data based into smaller distinct groups based on a a criteria you establish. This post will attempt to explain how to develop decision trees in R.

We are going to use the ‘College’ dataset found in the “ISLR” package. Once you load the package you need to split the data into a training and testing set as shown in the code below. We want to divide the data based on education level, age, and income

library(ISLR); library(ggplot2); library(caret)
data("College")
inTrain<-createDataPartition(y=College$education, p=0.7, list=FALSE) trainingset <- College[inTrain, ] testingset <- College[-inTrain, ] Visualize the Data We will now make a plot of the data based on education as the groups and age and wage as the x and y variable. Below is the code followed by the plot. Please note that education is divided into 5 groups as indicated in the chart. qplot(age, wage, colour=education, data=trainingset) Create the Model We are now going to develop the model for the decision tree. We will use age and wage to predict education as shown in the code below. TreeModel<-train(education~age+income, method='rpart', data=trainingset) Create Visual of the Model We now need to create a visual of the model. This involves installing the package called ‘rattle’. You can install ‘rattle’ separately yourself. After doing this, below is the code for the tree model followed by the diagram. fancyRpartPlot(TreeModel$finalModel)

Here is what the chart means

1. At the top is node 1 which is called ‘HS Grad” the decimals underneath is the percentage of the data that falls within the “HS Grad” category. As the highest node, everything is classified as “HS grad” until we begin to apply our criteria.
2. Underneath nod 1 is a decision about wage. If a person makes less than 112 you go to the left if the make more you go to the right.
3. Nod 2 indicates the percentage of the sample that was classified as HS grade regardless of education. 14% of those with less than a HS diploma were classified as a HS Grade based on wage. 43% of those with a HS diploma were classified as a HS grade based on income. The percentage underneath the decimals indicates the total amount of the sample placed in the HS grad category. Which was 57%.
4. This process is repeated for each node until the data is divided as much as possible.

Predict

You can predict individual values in the data set by using the ‘predict’ function with the test data as shown in the code below.

predict(TreeModel, newdata = testingset)

Conclusion

Prediction Trees are a unique feature in data analysis for determining how well data can be divided into subsets. It also provides a visual of how to move through data sequentially based on characteristics in the data.

# Z-Scores

A z-score indicates how closely related one given score is to mean of the sample. Extremely high or low z-scores indicates that the given data point is unusually above or below the mean of the sample.

In order to understand z-scores you need to be familiar with distribution. In general, data is distributed in a bell shape curve. With the mean being the exact middle of the graph as shown in the picture below.

The Greek letter μ is the mean. In this post, we will go through an example that will try to demonstrate how to use and interpret the z-score. Notice that a z-score + 1 takes of 68% of the potential values a z-score + 2 takes of 95%, a z-score + 3 takes of 99%.

Imagine you know the average test score of students on a quiz. The average is 75%. with a standard deviation of 6.4%. Below is the equation for calculating the z-score.

Let’s say that one student scored 52% on the quiz. We can calculate the likelihood for this data point by using the formula above.

(52 – 75) / 6.4 = -3.59

Our value is negative which indicates that the score is below the mean of the sample. Are score is very exceptionally low from the mean. This makes sense given that the mean is 75% and the standard deviation is 6.4%. To get a 52% on the quiz was really bad performance.

We can convert the z-score to a percentage to indicate the probability of get such a value. To do this you would need to find a z-score conversion table on the internet. A quick glance at the table will show you that the probability of getting a score of 52 on the quiz is less than 1%.

Off course, this is based on the average score of 75% with a standard deviation of 6.4%. A different average and standard deviation would change the probability of getting a 52%.

Standardization

Z-scores are also used to standardize a variable. If you look at our example, the original values were in percentages. By using the z-score formula we converted these numbers into a different value. Specifically, the values of a z-score represent standard deviations from the mean.

In our example, we calculated a z-score of -3.59. In other words, the person who scored 52% on the quiz had  a score 3.59 standard deviations below the mean. When attempting to interpret data the z-score is a foundational piece of information that is used extensively in statistics.

# Multiple Regression Prediction in R

In this post, we will learn how to predict using multiple regression in R. In a previous post, we learn how to predict with simple regression. This post will be a large repeat of this other post with the addition of using more than one predictor variable. We will use the “College” dataset and we will try to predict Graduation rate with the following variables

• Student to faculty ratio
• Percentage of faculty with PhD
• Expenditures per student

Preparing the Data

First we need to load several packages and divide the dataset int training and testing sets. This is not new for this blog. Below is the code for this.

library(ISLR); library(ggplot2); library(caret)
data("College")
inTrain<-createDataPartition(y=College$Grad.Rate, p=0.7, list=FALSE) trainingset <- College[inTrain, ] testingset <- College[-inTrain, ] dim(trainingset); dim(testingset) Visualizing the Data We now need to get a visual idea of the data. Since we are use several variables the code for this is slightly different so we can look at several charts at the same time. Below is the code followed by the plots > featurePlot(x=trainingset[,c("S.F.Ratio","PhD","Expend")],y=trainingset$Grad.Rate, plot="pairs")


To make these plots we did the following

1. We used the ‘featureplot’ function told R to use the ‘trainingset’ data set and subsetted the data to use the three independent variables.
2. Next, we told R what the y= variable was and told R to plot the data in pairs

Developing the Model

We will now develop the model. Below is the code for creating the model. How to interpret this information is in another post.

> TrainingModel <-lm(Grad.Rate ~ S.F.Ratio+PhD+Expend, data=trainingset)
> summary(TrainingModel)

As you look at the summary, you can see that all of our variables are significant and that the current model explains 18% of the variance of graduation rate.

Visualizing the Multiple Regression Model

We cannot use a regular plot because are model involves more than two dimensions.  To get around this problem to see are modeling, we will graph fitted values against the residual values. Fitted values are the predict values while residual values are the acutally values from the data. Below is the code followed by the plot.

> CheckModel<-train(Grad.Rate~S.F.Ratio+PhD+Expend, method="lm", data=trainingset)
> DoubleCheckModel<-CheckModel$finalModel > plot(DoubleCheckModel, 1, pch=19, cex=0.5)   Here is what happen 1. We created the variable ‘CheckModel’. In this variable, we used the ‘train’ function to create a linear model with all of our variables 2. We then created the variable ‘DoubleCheckModel’ which includes the information from ‘CheckModel’ plus the new column of’finalModel’ 3. Lastly, we plot ‘DoubleCheckModel’ The regression line was automatically added for us. As you can see, the model does not predict much but shows some linearity. Predict with Model We will now do one prediction. We want to know the graduation rate when we have the following information • Student-to-faculty ratio = 33 • Phd percent = 76 • Expenditures per Student = 11000 Here is the code with the answer > newdata<-data.frame(S.F.Ratio=33, PhD=76, Expend=11000) > predict(TrainingModel, newdata) 1 57.04367 To put it simply, if the student-to-faculty ratio is 33, the percentage of PhD faculty is 76%, and the expenditures per student is 11,000, we can expect 57% of the students to graduate. Testing We will now test our model with the testing dataset. We will calculate the RMSE. Below is the code for creating the testing model followed by the codes for calculating each RMSE. > TestingModel<-lm(Grad.Rate~S.F.Ratio+PhD+Expend, data=testingset)  > sqrt(sum((TrainingModel$fitted-trainingset$Grad.Rate)^2)) [1] 369.4451 > sqrt(sum((TestingModel$fitted-testingset$Grad.Rate)^2)) [1] 219.4796 Here is what happened 1. We created the ‘TestingModel’ by using the same model as before but using the ‘testingset’ instead of the ‘trainingset’. 2. The next to line of codes should look familiar. 3. From this output the performance of the model improvement on the testing set since the RMSE is lower than compared to the training results. Conclusion This post attempted to explain how to predict and assess models with multiple variables. Although complex for some, prediction is a valuable statistical tool in many situations. # Using Regression for Prediction in R In the last post about R, we looked at plotting information to make predictions. We will now look at an example of making predictions using regression. We will used the same data as last time with the help of the ‘caret’ package as well. The code below sets up the seed and the training and testing sets we need. > library(caret); library(ISLR); library(ggplot2) > data("College");set.seed(1) > PracticeSet<-createDataPartition(y=College$Grad.Rate,
+                                  p=0.5, list=FALSE)
> TrainingSet<-College[PracticeSet, ]; TestingSet<-
+         College[-PracticeSet, ]
> head(TrainingSet)

The code above should look familiar from previous post.

Make the Scatterplot

We will now create the scatterplot showing the relationship between “S.F. Ratio” and “Grad.Rate” with the code below and the scatterplot.

> plot(TrainingSet$S.F.Ratio, TrainingSet$Grad.Rate, pch=5, col="green",
xlab="Student Faculty Ratio", ylab="Graduation Rate")

Here is what we did

1. We used the ‘plot’ function to make this scatterplot. The x variable was ‘S.F.Ratio’ of the ‘TrainingSet’ the y variable was ‘Grad.Rate’.
2. We picked the type of dot to use using the ‘pch’ argument and choosing ’19’
3. Next we chose a color and labeled each axis

Fitting the Model

We will now develop the linear model. This model will help us to predict future models. Furthermore, we will compare the model of the Training Set with the Test Set. Below is the code for developing the model.

> TrainingModel<-lm(Grad.Rate~S.F.Ratio, data=TrainingSet)
> summary(TrainingModel)


How to interpret this information was presented in a previous post. However, to summarize, we can say that when the student to faculty ratio increases one the graduation rate decreases 1.29. In other words, an increase in the student to faculty ratio leads to decrease in the graduation rate.

Adding the Regression Line to the Plot

Below is the code for adding the regression line followed by the scatterplot

> plot(TrainingSet$S.F.Ratio, TrainingSet$Grad.Rate, pch=19, col="green", xlab="Student Faculty Ratio", ylab="Graduation Rate")
> lines(TrainingSet$S.F.Ratio, TrainingModel$fitted, lwd=3)

Predicting New Values

With are model complete we can now predict values. For our example, we will only predict one value. We want to know what the graduation rate would be if we have a student to faculty ratio of 33. Below is the code for this with the answer

> newdata<-data.frame(S.F.Ratio=33)
> predict(TrainingModel, newdata)
1
40.6811

Here is what we did

1. We made a variable called ‘newdata’ and stored a data frame in it with a variable called ‘S.F.Ratio’ with a value of 33. This is x value
2. Next, we used the ‘predict’ function from the ‘caret’ package to determine what the graduation rate would be if the student to faculty ratio is 33. To do this we told caret to use the ‘TrainingModel’ we developed using regression and to run this model with the information in the ‘newdata’ dataframe
3. The answer was 40.68. This means that if the student to faculty ratio is 33 at a university then the graduation rate would be about 41%.

Testing the Model

We will now test the model we made with the training set with the testing set. First, we will make a visual of both models by using the “plot” function. Below is the code follow by the plots.

par(mfrow=c(1,2))
plot(TrainingSet$S.F.Ratio, TrainingSet$Grad.Rate, pch=19, col=’green’,  xlab=”Student Faculty Ratio”, ylab=’Graduation Rate’)
lines(TrainingSet$S.F.Ratio, predict(TrainingModel), lwd=3) plot(TestingSet$S.F.Ratio,  TestingSet$Grad.Rate, pch=19, col=’purple’, xlab=”Student Faculty Ratio”, ylab=’Graduation Rate’) lines(TestingSet$S.F.Ratio,  predict(TrainingModel, newdata = TestingSet),lwd=3)

In the code, all that is new is the “par” function which allows us to see to plots at the same time. We also used the ‘predict’ function to set the plots. As you can see, the two plots are somewhat differ based on a visual inspection. To determine how much so, we need to calculate the error. This is done through computing the root mean square error as shown below.

> sqrt(sum((TrainingModel$fitted-TrainingSet$Grad.Rate)^2))
[1] 328.9992
> sqrt(sum((predict(TrainingModel, newdata=TestingSet)-TestingSet$Grad.Rate)^2)) [1] 315.0409 The main take away from this complicated calculation is the number 328.9992 and 315.0409. These numbers tell you the amount of error in the training model and testing model. The lower the number the better the model. Since the error number in the testing set is lower than the training set we know that our model actually improves when using the testing set. This means that our model is beneficial in assessing graduation rates. If there were problems we may consider using other variables in the model. Conclusion This post shared ways to develop a regression model for the purpose of prediction and for model testing. # Using Plots for Prediction in R It is common in machine learning to look at the training set of your data visually. This helps you to decide what to do as you begin to build your model. In this post, we will make several different visual representations of data using datasets available in several R packages. We are going to explore data in the “College” dataset in the “ISLR” package. If you have not done so already, you need to download the “ISLR” package along with “ggplot2” and the “caret” package. Once these packages are installed in R you want to look at a summary of the variables use the summary function as shown below. summary(College) You should get a printout of information about 18 different variables. Based on this printout, we want to explore the relationship between graduation rate “Grad.Rate” and student to faculty ratio “S.F.Ratio”. This is the objective of this post. Next we need to create a training and testing data set below is the code to do this. > library(ISLR);library(ggplot2);library(caret) > data("College") > PracticeSet<-createDataPartition(y=College$Enroll, p=0.7,
+                                  list=FALSE)
> trainingSet<-College[PracticeSet,]
> testSet<-College[-PracticeSet,]
> dim(trainingSet); dim(testSet)
[1] 545  18
[1] 232  18

The explanation behind this code was covered in predicting with caret so we will not explain it again. You just need to know that the dataset you will use for the rest of this post is called “trainingSet”.

Developing a Plot

We now want to explore the relationship between graduation rates and student to faculty ratio. We will be used the ‘ggpolt2’  package to do this. Below is the code for this followed by the plot.

As you can see, there appears to be a negative relationship between student faculty ratio and grad rate. In other words, as the ration of student to faculty increases there is a decrease in the graduation rate.

Next, we will color the plots on the graph based on whether they are a public or private university to get a better understanding of the data. Below is the code for this followed by the plot.

> qplot(S.F.Ratio, Grad.Rate, colour = Private, data=trainingSet)

It appears that private colleges usually have lower student to faculty ratios and also higher graduation rates than public colleges

We will now plot the same data but will add a regression line. This will provide us with a visual of the slope. Below is the code followed by the plot.

> collegeplot<-qplot(S.F.Ratio, Grad.Rate, colour = Private, data=trainingSet) > collegeplot+geom_smooth(method = ‘lm’,formula=y~x)

Most of this code should be familiar to you. We saved the plot as the variable ‘collegeplot’. In the second line of code we add specific coding for ‘ggplot2’ to add the regression line. ‘lm’ means linear model and formula is for creating the regression.

Cutting the Data

We will now divide the data based on the student-faculty ratio into three equal size groups to look for additional trends. To do this you need the “Hmisc” packaged. Below is the code followed by the table

> library(Hmisc)

> divide_College<-cut2(trainingSet$S.F.Ratio, g=3) > table(divide_College) divide_College [ 2.9,12.3) [12.3,15.2) [15.2,39.8] 185 179 181 Our data is now divided into three equal sizes. Box Plots Lastly, we will make a box plot with our three equal size groups based on student-faculty ratio. Below is the code followed by the box plot CollegeBP<-qplot(divide_College, Grad.Rate, data=trainingSet, fill=divide_College, geom=c(“boxplot”)) > CollegeBP As you can see, the negative relationship continues even when student-faculty is divided into three equally size groups. However, our information about private and public college is missing. To fix this we need to make a table as shown in the code below. > CollegeTable<-table(divide_College, trainingSet$Private)
> CollegeTable

divide_College  No Yes
[ 2.9,12.3)  14 171
[12.3,15.2)  27 152
[15.2,39.8] 106  75

This table tells you how many public and private colleges there based on the division of the student faculty ratio into three groups. We can also get proportions by using the following

> prop.table(CollegeTable, 1)

divide_College         No        Yes
[ 2.9,12.3) 0.07567568 0.92432432
[12.3,15.2) 0.15083799 0.84916201
[15.2,39.8] 0.58563536 0.41436464

In this post, we found that there is a negative relationship between student-faculty ratio and graduation rate. We also found that private colleges have lower student-faculty ratio and a higher graduation rate than public colleges. In other words, the status of a university as public or private moderates the relationship between student-faculty ratio and graduation rate.

You can probably tell by now that R can be a lot of fun with some basic knowledge of coding.