# Statistical Sins: Is Your Classification Model Any Good?

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Specifically, I talked a couple of times about binomial regression (here and here), which is used to predict (read: recreate with a set of variables significantly related to) a binary outcome. The data example I used involved my dissertation data and the binary outcome was verdict: guilty or not guilty. A regression model returns the linear correction applied to the predictor variables to reproduce the outcome, and will highlight whether a predictor was significantly related to the outcome or not. But a big question you may be asking of your binomial model is: how well does it predict the outcome? Specifically, how can you examine whether your regression model is correctly classifying cases?

We’ll start by loading/setting up the data and rerunning the binomial regression with interactions.

dissertation<-read.delim("dissertation_data.txt",header=TRUE) dissertation<-dissertation[,1:44] predictors<-c("obguilt","reasdoubt","bettertolet","libertyvorder", "jurevidence","guilt") dissertation<-subset(dissertation, !is.na(libertyvorder)) dissertation[45:50]<-lapply(dissertation[predictors], function(x) { y<-scale(x, center=TRUE, scale=TRUE) } ) pred_int<-'verdict ~ obguilt.1 + reasdoubt.1 + bettertolet.1 + libertyvorder.1 + jurevidence.1 + guilt.1 + obguilt.1*guilt.1 + reasdoubt.1*guilt.1 + bettertolet.1*guilt.1 + libertyvorder.1*guilt.1 + jurevidence.1*guilt.1' model<-glm(pred_int, family="binomial", data=dissertation) summary(model) ## ## Call: ## glm(formula = pred_int, family = "binomial", data = dissertation) ## ## Deviance Residuals: ## Min 1Q Median 3Q Max ## -2.6101 -0.5432 -0.1289 0.6422 2.2805 ## ## Coefficients: ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) -0.47994 0.16264 -2.951 0.00317 ** ## obguilt.1 0.25161 0.16158 1.557 0.11942 ## reasdoubt.1 -0.09230 0.20037 -0.461 0.64507 ## bettertolet.1 -0.22484 0.20340 -1.105 0.26899 ## libertyvorder.1 0.05825 0.21517 0.271 0.78660 ## jurevidence.1 0.07252 0.19376 0.374 0.70819 ## guilt.1 2.31003 0.26867 8.598 < 2e-16 *** ## obguilt.1:guilt.1 0.14058 0.23411 0.600 0.54818 ## reasdoubt.1:guilt.1 -0.61724 0.29693 -2.079 0.03764 * ## bettertolet.1:guilt.1 0.02579 0.30123 0.086 0.93178 ## libertyvorder.1:guilt.1 -0.27492 0.29355 -0.937 0.34899 ## jurevidence.1:guilt.1 0.27601 0.36181 0.763 0.44555 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## (Dispersion parameter for binomial family taken to be 1) ## ## Null deviance: 490.08 on 354 degrees of freedom ## Residual deviance: 300.66 on 343 degrees of freedom ## AIC: 324.66 ## ## Number of Fisher Scoring iterations: 6

The predict function, which I introduced here, can also be used for the binomial model. Let's have R generate predicted scores for everyone in the dissertation sample:

dissertation$predver<-predict(model) dissertation$predver ## [1] 0.3907097456 -4.1351129605 2.1820478279 -2.8768390246 2.5804618523 ## [6] 0.4244692909 2.3065468369 -2.7853434926 0.3504760502 -0.2747339639 ## [11] -1.8506160725 -0.6956240161 -4.7860574839 -0.3875950731 -2.4955679446 ## [16] -0.3941516951 -4.5831011509 1.6185480937 0.4971923298 4.1581842900 ## [21] -0.6320531052 -4.8447046319 -2.3974890696 1.8566258698 0.0360685822 ## [26] 2.2151040131 2.3477149003 -2.4493726369 -0.2253481404 -4.8899805287 ## [31] 1.7789459288 -0.0978703861 -3.5541042186 -3.6009218603 0.1568318789 ## [36] 3.7866003489 -0.6371816898 -0.7047761441 -0.7529742376 -0.0302759317 ## [41] -0.1108055330 1.9751810033 0.2373614802 0.0424471071 -0.4018757856 ## [46] 0.0530272726 -1.0763759980 0.0099577637 0.3128581222 1.4806679691 ## [51] -1.7468626219 0.2998282372 -3.6359162016 -2.2200774510 0.3192366472 ## [56] 3.0103216033 -2.0625775984 -6.0179845235 2.0300503627 2.3676828409 ## [61] -2.8971753746 -3.2131490026 2.1349358889 3.0215336139 1.2436192890 ## [66] 0.2885535375 0.2141821004 1.9480686936 0.0438751446 -1.9368013875 ## [71] 0.2931258287 0.5319938265 0.0177643261 3.3724920900 0.0332949791 ## [76] 2.5935500970 0.7571810150 0.7131757400 2.5411073339 2.8499853550 ## [81] 2.8063291084 -0.4500738791 1.4700679077 -0.8659309719 0.0870492258 ## [86] 0.5728074322 0.1476797509 2.4697257261 2.5935500970 -2.2200774510 ## [91] -0.0941827753 1.3708676633 1.4345235392 -0.2407209578 2.4662700339 ## [96] -1.9687731888 -6.7412580522 -0.0006224018 -4.4132951092 -2.8543032695 ## [101] 1.2295635352 2.8194173530 0.1215689324 -3.8258079371 1.8959803882 ## [106] -4.5578801595 2.3754402614 0.0826808026 1.5112359711 -3.5402060466 ## [111] 0.2556657363 0.7054183194 1.4675797244 -2.3974890696 2.6955929822 ## [116] -0.3123518919 -4.8431862346 -2.0132721372 0.4673405434 -2.3053405270 ## [121] 1.9498822386 -0.5164183930 -1.8277820872 -0.0134750769 -2.3013547136 ## [126] -0.2498730859 -4.4281010683 -0.0134750769 -0.2604532514 0.1476797509 ## [131] -2.3392939519 -2.0625775984 -3.5541042186 1.5087477879 -4.6453051124 ## [136] 2.0616474606 -3.2691362859 -7.3752231145 -1.6666447439 1.0532964013 ## [141] -2.0625775984 -0.3355312717 2.2481601983 -2.2200774510 -4.3276959075 ## [146] 0.8685972087 -0.7727065311 1.7511589809 -0.4774548995 0.0008056357 ## [151] 1.7022334970 -0.4202625135 -0.2902646169 2.4409712692 0.0008056357 ## [156] 0.0008056357 -3.6009218603 -0.8567788439 -0.4528474822 0.3517462520 ## [161] 0.1307210605 -3.7843118182 -2.8419024763 -3.5191098774 -0.1460684795 ## [166] 1.8809888141 2.8194173530 -2.4656469123 1.0589888029 0.1659840070 ## [171] 1.4345235392 2.3676828409 1.5749534339 -0.1681557545 2.6406620359 ## [176] 0.1476797509 -2.2135177411 1.9168260534 -3.4993205379 0.4557086940 ## [181] -3.8136089417 -0.1121510987 -3.9772095600 1.3849234171 0.3504760502 ## [186] 2.3807710856 -3.0667307601 2.3040586537 1.7599138086 -0.2083894500 ## [191] 0.6844579761 -0.3552635652 -1.9459392035 -0.6075281598 -2.1663310490 ## [196] 2.3676828409 -1.9205271122 -2.2334295071 -4.4265826710 -1.0117771483 ## [201] -0.0161530548 -0.3072233074 -0.0161530548 -0.7451676752 -7.0351269313 ## [206] 2.6406620359 -3.7523234832 -0.2498730859 2.0222929422 3.2886316225 ## [211] -1.6221457956 2.4749949634 1.7570711677 0.0904873650 -4.7332807307 ## [216] 0.1568318789 -0.0302759317 0.5127229828 1.3097316594 -6.9309218514 ## [221] 0.0515992352 -0.4514194447 -0.2253481404 -4.7652690656 -0.4279866041 ## [226] -4.4136563866 -3.7618312672 0.0156676181 -0.2590252139 2.6076058507 ## [231] 1.6420333133 -3.9985172969 -6.2076483227 0.1632104039 0.1829426974 ## [236] -4.7652690656 -4.4212844958 1.6001906117 0.8579971472 -3.8699110198 ## [241] 0.3022779567 -0.1679979189 1.9421248181 0.6592738895 1.6132788564 ## [246] -0.0366544567 -3.4818233673 -3.9422152187 -0.3473613776 0.4321933815 ## [251] 0.7480288869 -0.2498730859 -1.9861068488 -2.2297920164 -0.7621263656 ## [256] 1.2966434147 0.1632104039 0.2048721368 1.7789459288 0.4926393080 ## [261] 0.4096285430 -1.7794744955 -2.5822853071 2.0413250624 -6.6574350219 ## [266] -0.1277642235 -2.1972434657 -2.5075677545 -0.4482774141 -0.6943740757 ## [271] -0.7821891015 6.3289445390 0.1568318789 0.1165981835 1.4781797859 ## [276] -4.2287015488 -3.6157278195 -0.1511970641 -0.7047761441 2.0935344484 ## [281] -3.8258079371 -4.4231102471 1.3097316594 3.4081542651 -0.4996175382 ## [286] -2.0534397824 0.9783975145 -2.2562634924 3.7196170683 1.1110084017 ## [291] 2.1661785291 -4.2138955896 1.9421248181 2.3065468369 -0.7139282722 ## [296] -4.1431023472 -2.0854115837 2.9389399956 1.7711269214 -0.0302759317 ## [301] -2.6458711124 0.5856241187 -0.1199576611 1.8566258698 -2.2383553905 ## [306] 2.3807710856 -0.2838860920 3.1176953128 2.8499853550 2.8063291084 ## [311] 0.0034011417 -0.4683781352 -3.0377484314 -1.3833686805 1.7764577456 ## [316] 1.7842151661 3.4081542651 0.1165981835 -4.6988069009 -2.6013721641 ## [321] 2.0616474606 -0.2498730859 -4.2207121622 4.1705330009 5.2103776377 ## [326] -4.5406977837 -1.5080855068 -2.5232652805 -5.7259789038 2.5211393933 ## [331] -0.3487069432 -2.5035573312 -2.2764097339 -5.8364854607 -1.8694684539 ## [336] 1.3402996614 0.5728074322 0.3663267540 -0.1603491921 -2.1690805453 ## [341] -1.4105339689 3.0768201201 -5.1065624241 -4.5966850670 -4.5498907729 ## [346] -1.3078399029 -1.0882592824 0.3128581222 -0.3644156933 0.3100845191 ## [351] 2.4774831467 -1.0763759980 2.2151040131 -0.0952748801 -4.6864864366

Now, remember that the outcome variable is not guilty (0) and guilty (1), so you might be wondering - what's with these predicted values? Why aren't they 0 or 1?

Binomial regression is used for nonlinear outcomes. Since the outcome is 0/1, it's nonlinear. But binomial regression is based on the general linear model. So how can we apply the general linear model to a nonlinear outcome? Answer: by transforming scores. Specifically, it transforms the outcome into a log odds ratio; the log transform makes the outcome variable behave somewhat linearly and symmetrically. The predicted outcome, then, is also a log odds ratio.

ordvalues<-dissertation[order(dissertation$predver),] ordvalues<-ordvalues[,51] ordvalues<-data.frame(1:355,ordvalues) colnames(ordvalues)<-c("number","predver") library(ggplot2) ggplot(data=ordvalues, aes(number,predver))+geom_smooth() ## `geom_smooth()` using method = 'loess'

Log odds ratios are great for analysis, but when trying to understand how well your model is predicting values, we want to convert them into a metric that's easier to understand in isolation and when compared to the observed values. We can convert them into probabilities with the following equation:

dissertation$verdict_predicted<-exp(predict(model))/(1+exp(predict(model)))

This gives us a value ranging from 0 to 1, which is the probability that a particular person will select guilty. We can use this value in different ways to see how well our model is doing. Typically, we'll divide at the 50% mark, so anyone with a probability of 0.5 or greater is predicted to select guilty, and anyone with a probability less than 0.5 would be predicted to select not guilty. We then compare this new variable with the observed results to see how well the model did.

dissertation$vpred_rounded<-round(dissertation$verdict_predicted,digits=0) library(expss) ## Warning: package 'expss' was built under R version 3.4.4 dissertation<- apply_labels(dissertation, verdict = "Actual Verdict", verdict = c("Not Guilty" = 0, "Guilty" = 1), vpred_rounded = "Predicted Verdict", vpred_rounded = c("Not Guilty" = 0, "Guilty" = 1) ) cro(dissertation$verdict,list(dissertation$vpred_rounded, total()))

Predicted Verdict | #Total | |||
---|---|---|---|---|

Not Guilty | Guilty | |||

Actual Verdict | ||||

Not Guilty | 152 | 39 | 191 | |

Guilty | 35 | 129 | 164 | |

#Total cases | 187 | 168 | 355 |