1 peter fox data analytics – itws-4963/itws-6965 week 5b, february 21, 2014 interpreting...

1

Peter Fox

Data Analytics – ITWS-4963/ITWS-6965

Week 5b, February 21, 2014

Interpreting regression, kNN and K-means results,

evaluating models

Plot tools/ tipshttp://statmethods.net/advgraphs/layout.html

plot

points

2

Linear and least-squares> multivariate <- read.csv(”EPI_data.csv")

> attach(EPI_data);

> boxplot(ENVHEALTH,DALY,AIR_H,WATER_H)

> lmENVH<-lm(ENVHEALTH~DALY+AIR_H+WATER_H)

> lmENVH

Let’s recall what this taught you!

> summary(lmENVH)

> cENVH<-coef(lmENVH) 3

Linear and least-squares> lmENVH<-lm(ENVHEALTH~DALY+AIR_H+WATER_H)

> lmENVH

Call:

lm(formula = ENVHEALTH ~ DALY + AIR_H + WATER_H)

Coefficients:

(Intercept) DALY AIR_H WATER_H

-2.673e-05 5.000e-01 2.500e-01 2.500e-01

> summary(lmENVH)

…

> cENVH<-coef(lmENVH)

4

Linear and least-squares> summary(lmENVH)

Call:

lm(formula = ENVHEALTH ~ DALY + AIR_H + WATER_H)

Residuals:

Min 1Q Median 3Q Max

-0.0072734 -0.0027299 0.0001145 0.0021423 0.0055205

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) -2.673e-05 6.377e-04 -0.042 0.967

DALY 5.000e-01 1.922e-05 26020.669 <2e-16 ***

AIR_H 2.500e-01 1.273e-05 19645.297 <2e-16 ***

WATER_H 2.500e-01 1.751e-05 14279.903 <2e-16 ***

---

5

p < 0.01 : very strong presumption against null hypothesis vs. this fit 0.01 < p < 0.05 : strong presumption against null hypothesis 0.05 < p < 0.1 : low presumption against null hypothesis p > 0.1 : no presumption against the null hypothesis

Linear and least-squaresContinued:

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.003097 on 178 degrees of freedom

(49 observations deleted due to missingness)

Multiple R-squared: 1, Adjusted R-squared: 1

F-statistic: 3.983e+09 on 3 and 178 DF, p-value: < 2.2e-16

> names(lmENVH)

[1] "coefficients" "residuals" "effects" "rank" "fitted.values" "assign"

[7] "qr" "df.residual" "na.action" "xlevels" "call" "terms"

[13] "model" 6

Object of class lm:An object of class "lm" is a list containing at least the following components:

coefficients a named vector of coefficients

residuals the residuals, that is response minus fitted values.

fitted.values the fitted mean values.

rank the numeric rank of the fitted linear model.

weights (only for weighted fits) the specified weights.

df.residual the residual degrees of freedom.

call the matched call.

terms the terms object used.

contrasts (only where relevant) the contrasts used.

xlevels (only where relevant) a record of the levels of the factors used in fitting.

offset the offset used (missing if none were used).

y if requested, the response used.

x if requested, the model matrix used.

model if requested (the default), the model frame used. 7

> plot(ENVHEALTH,col="red")

> points(lmENVH$fitted.values,col="blue")

> Huh?

8

Plot original versus fitted

Try again!

9

> plot(ENVHEALTH[!is.na(ENVHEALTH)], col="red")

> points(lmENVH$fitted.values,col="blue")

Predict> cENVH<-coef(lmENVH)

> DALYNEW<-c(seq(5,95,5)) #2

> AIR_HNEW<-c(seq(5,95,5)) #3

> WATER_HNEW<-c(seq(5,95,5)) #4

10

Predict> NEW<-data.frame(DALYNEW,AIR_HNEW,WATER_HNEW)

> pENV<- predict(lmENVH,NEW,interval=“prediction”)

> cENV<- predict(lmENVH,NEW,interval=“confidence”) # look up what this does

11

Predict object returnspredict.lm produces a vector of predictions or a matrix of predictions and bounds with column names fit, lwr, and upr if interval is set. Access via [,1] etc.

If se.fit is TRUE, a list with the following components is returned:

fit vector or matrix as above

se.fit standard error of predicted means

residual.scale residual standard deviations

df degrees of freedom for residual

12

Output from predict> head(pENV)

fit lwr upr

1 NA NA NA

2 11.55213 11.54591 11.55834

3 18.29168 18.28546 18.29791

4 NA NA NA

5 69.92533 69.91915 69.93151

6 90.20589 90.19974 90.21204

…13

> tail(pENV)

fit lwr upr

226 NA NA NA

227 NA NA NA

228 34.95256 34.94641 34.95871

229 59.00213 58.99593 59.00834

230 24.20951 24.20334 24.21569

231 38.03701 38.03084 38.04319

14

Did you repeat this for: ?AIR_E

CLIMATE

15

K Nearest Neighbors (classification)Scripts – Lab4b_0_2014.R

> nyt1<-read.csv(“nyt1.csv")

… from week 4b slides or script

> classif<-knn(train,test,cg,k=5)

#

> head(true.labels)

[1] 1 0 0 1 1 0

> head(classif)

[1] 1 1 1 1 0 0

Levels: 0 1

> ncorrect<-true.labels==classif

> table(ncorrect)["TRUE"] # or > length(which(ncorrect))

> What do you conclude?16

Contingency tables> table(nyt1$Impressions,nyt1$Gender) #

0 1

1 69 85

2 389 395

3 975 937

4 1496 1572

5 1897 2012

6 1822 1927

7 1525 1696

8 1142 1203

9 722 711

10 366 400

11 214 200

12 86 101

13 41 43

14 10 9

15 5 7

16 0 4

17 0 1

17

Contingency table - displays the (multivariate) frequency distribution of the variable.

Tests for significance (not now)

> table(nyt1$Clicks,nyt1$Gender) 0 1 1 10335 10846 2 415 440 3 9 17

Regression> plot(log(bronx$GROSS.SQUARE.FEET), log(bronx$SALE.PRICE) )

> m1<-lm(log(bronx$SALE.PRICE)~log(bronx$GROSS.SQUARE.FEET),data=bronx)

You were reminded that log(0) is … not fun

THINK through what you are doing…

Filtering is somewhat inevitable:

> bronx<-bronx[which(bronx$GROSS.SQUARE.FEET>0 & bronx$LAND.SQUARE.FEET>0 & bronx$SALE.PRICE>0),]

> m1<-lm(log(bronx$SALE.PRICE)~log(bronx$GROSS.SQUARE.FEET),data=bronx)

18

Interpreting this!Call:

lm(formula = log(SALE.PRICE) ~ log(GROSS.SQUARE.FEET), data = bronx)

Residuals:

Min 1Q Median 3Q Max

-14.4529 0.0377 0.4160 0.6572 3.8159

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 7.0271 0.3088 22.75 <2e-16 ***

log(GROSS.SQUARE.FEET) 0.7013 0.0379 18.50 <2e-16 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.95 on 2435 degrees of freedom

Multiple R-squared: 0.1233, Adjusted R-squared: 0.1229

F-statistic: 342.4 on 1 and 2435 DF, p-value: < 2.2e-16 19

Plots – tell me what they tell you!

20

Solution model 2> m2<-lm(log(bronx$SALE.PRICE)~log(bronx$GROSS.SQUARE.FEET)+log(bronx$LAND.SQUARE.FEET)+factor(bronx$NEIGHBORHOOD),data=bronx)

> summary(m2)

> plot(resid(m2))

#

> m2a<-lm(log(bronx$SALE.PRICE)~0+log(bronx$GROSS.SQUARE.FEET)+log(bronx$LAND.SQUARE.FEET)+factor(bronx$NEIGHBORHOOD),data=bronx)

> summary(m2a)

> plot(resid(m2a))21

22

How do you interpret this residual plot?

Solution model 3 and 4> m3<-lm(log(bronx$SALE.PRICE)~0+log(bronx$GROSS.SQUARE.FEET)+log(bronx$LAND.SQUARE.FEET)+factor(bronx$NEIGHBORHOOD)+factor(bronx$BUILDING.CLASS.CATEGORY),data=bronx)

> summary(m3)

> plot(resid(m3))

#

> m4<-lm(log(bronx$SALE.PRICE)~0+log(bronx$GROSS.SQUARE.FEET)+log(bronx$LAND.SQUARE.FEET)+factor(bronx$NEIGHBORHOOD)*factor(bronx$BUILDING.CLASS.CATEGORY),data=bronx)

> summary(m4)

> plot(resid(m4))

23

24

And this one?

Did you get to create the sales map?

table(mapcoord$NEIGHBORHOOD) # contingency table

mapcoord$NEIGHBORHOOD <- as.factor(mapcoord$NEIGHBORHOOD) # and this?

geoPlot(mapcoord,zoom=12,color=mapcoord$NEIGHBORHOOD) # this one is easier

25

26

Did you forget the KNN?#almost there.

mapcoord$class<as.numeric(mapcoord$NEIGHBORHOOD)

nclass<-dim(mapcoord)[1]

split<-0.8

trainid<-sample.int(nclass,floor(split*nclass))

testid<-(1:nclass)[-trainid]

##mappred<-mapcoord[testid,]

##mappred$class<as.numeric(mappred$NEIGHBORHOOD) 27

KNN!Did you loop over k?

knnpred<-knn(mapcoord[trainid,3:4],mapcoord[testid,3:4],cl=mapcoord[trainid,2],k=5)

knntesterr<-sum(knnpred!=mapcoord [testid,2] )/length(testid)

28

K-Means!> mapmeans<-data.frame(adduse$ZIP.CODE, as.numeric(mapcoord$NEIGHBORHOOD), adduse$TOTAL.UNITS, adduse$"LAND.SQUARE.FEET", adduse$GROSS.SQUARE.FEET, adduse$SALE.PRICE, adduse$'querylist$latitude', adduse$'querylist$longitude')

> mapobj<-kmeans(mapmeans,5, iter.max=10, nstart=5, algorithm = c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen"))

> fitted(mapobj,method=c("centers","classes")) 29

Return objectcluster A vector of integers (from 1:k) indicating the cluster to which each point is allocated.

centers A matrix of cluster centres.

totss The total sum of squares.

withinss Vector of within-cluster sum of squares, one component per cluster.

tot.withinss Total within-cluster sum of squares, i.e., sum(withinss).

betweenss The between-cluster sum of squares, i.e. totss-tot.withinss.

size The number of points in each cluster. 30

31

Huh?What is this?

plot(mapmeans,mapobj$cluster)

Plotting clusters (preview)library(cluster)

clusplot(mapmeans, mapobj$cluster, color=TRUE, shade=TRUE, labels=2, lines=0)

# Centroid Plot against 1st 2 discriminant functions

library(fpc)

plotcluster(mapmeans, mapobj$cluster)

32

Comparing cluster fits (e.g. different k)

library(fpc)

cluster.stats(d, fit1$cluster, fit2$cluster)

Use help.

> help(plotcluster)

> help(cluster.stats)

33

Assignment 3?• Preliminary and Statistical Analysis. Due next

Friday. 15% (written)– Distribution analysis and comparison, visual

‘analysis’, statistical model fitting and testing of some of the nyt1…31 datasets.

• How is it going?

34

Assignments to come

• Assignment 4: Pattern, trend, relations: model development and evaluation. Due ~ early March. 15% (10% written and 5% oral; individual);

• Assignment 5: Term project proposal. Due ~ week 7. 5% (0% written and 5% oral; individual);

• Assignment 6: Predictive and Prescriptive Analytics. Due ~ week 9. 15% (15% written; individual);

• Term project. Due ~ week 13. 30% (25% written, 5% oral; individual).

35

Admin info (keep/ print this slide)• Class: ITWS-4963/ITWS 6965• Hours: 12:00pm-1:50pm Tuesday/ Friday• Location: SAGE 3101• Instructor: Peter Fox• Instructor contact: pfox@cs.rpi.edu, 518.276.4862 (do not

leave a msg)• Contact hours: Monday** 3:00-4:00pm (or by email appt)• Contact location: Winslow 2120 (sometimes Lally 207A

announced by email)• TA: Lakshmi Chenicheri chenil@rpi.edu • Web site: http://tw.rpi.edu/web/courses/DataAnalytics/2014

– Schedule, lectures, syllabus, reading, assignments, etc.

36