# Multiple Regression, good P-value, but Low R2

I am trying to build a model in R to predict Conversion Rate.

So, the model below:

library(caret)
set.seed(100)
TrainIndex <- sample(1:nrow(data), 0.8*nrow(data))
data.train <- data[TrainIndex,]
data.test <- data[-TrainIndex,]
nrow(data.test)
model <- lm(CR ~ age+ gender + fact_interest + fact_xyz_campaign_id + age:gender , data=data.train)


will not have a good adjusted r-squared (0.04):

Call:
lm(formula = CR ~ age + gender + fact_interest + fact_xyz_campaign_id +
age:gender, data = data.train)

Residuals:
Min      1Q  Median      3Q     Max
-27.327 -10.606  -4.389   1.712  90.329

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)               15.7126     6.7187   2.339 0.019581 *
age35-39                   2.4407     2.8559   0.855 0.393002
age40-44                  -3.8775     2.8716  -1.350 0.177274
age45-49                  -2.4614     2.6736  -0.921 0.357494
genderM                    3.4636     2.3296   1.487 0.137436
fact_interest7            -5.0387     7.1947  -0.700 0.483909
fact_interest10            3.4767     6.1014   0.570 0.568950
fact_interest15            5.3734     6.4681   0.831 0.406342
fact_interest16           -0.3113     5.8971  -0.053 0.957913
fact_interest18            1.0352     6.7091   0.154 0.877405
fact_interest19            5.1792     6.7937   0.762 0.446059
fact_interest20            1.4886     6.4909   0.229 0.818663
fact_interest21            0.9513     6.7828   0.140 0.888497
fact_interest22           -1.4520     6.8222  -0.213 0.831511
fact_interest23           -5.5979     7.2095  -0.776 0.437690
fact_interest24            2.8532     7.1828   0.397 0.691303
fact_interest25           -1.6134     7.2790  -0.222 0.824633
fact_interest26           -4.4335     6.6683  -0.665 0.506314
fact_interest27           -0.7451     6.2913  -0.118 0.905751
fact_interest28            5.0277     6.5291   0.770 0.441486
fact_interest29            2.9637     6.1361   0.483 0.629225
fact_interest30           -5.5094     7.1835  -0.767 0.443317
fact_interest31            8.1505     7.2712   1.121 0.262630
fact_interest32            9.3286     6.9570   1.341 0.180308
fact_interest36           -1.9311     7.4236  -0.260 0.794821
fact_interest63            2.6956     6.6486   0.405 0.685253
fact_interest64           -0.8879     6.4072  -0.139 0.889811
fact_interest65           10.6108     8.0674   1.315 0.188767
fact_interest66           -0.3091     8.4337  -0.037 0.970773
fact_interest100          -1.0102    10.9944  -0.092 0.926811
fact_interest101          21.0835    10.2688   2.053 0.040356 *
fact_interest102          10.0960     9.7438   1.036 0.300425
fact_interest103           1.2078    16.0312   0.075 0.939960
fact_interest104          20.0581    11.0045   1.823 0.068689 .
fact_interest105          -4.4382    13.4331  -0.330 0.741184
fact_interest106           0.1643    11.9851   0.014 0.989068
fact_interest107           0.3466     9.7365   0.036 0.971608
fact_interest108           1.2449    12.0004   0.104 0.917398
fact_interest109           0.6469    11.9954   0.054 0.957004
fact_interest110           2.8295    10.9817   0.258 0.796733
fact_interest111           0.2209    10.9821   0.020 0.983960
fact_interest112           1.1079    10.2833   0.108 0.914232
fact_interest113           1.9598    10.9944   0.178 0.858567
fact_interest114          -0.4277    12.0009  -0.036 0.971578
fact_xyz_campaign_id936   -4.7058     3.6280  -1.297 0.194952
fact_xyz_campaign_id1178 -13.5117     3.6365  -3.716 0.000216 ***
age35-39:genderM          -1.3838     3.9013  -0.355 0.722908
age40-44:genderM           3.6620     4.0497   0.904 0.366112
age45-49:genderM          -2.0268     3.8028  -0.533 0.594175
---
Signif. codes:  0 *** 0.001 ** 0.01 * 0.05 . 0.1   1

Residual standard error: 21.21 on 865 degrees of freedom
Multiple R-squared:  0.09209,   Adjusted R-squared:  0.04171
F-statistic: 1.828 on 48 and 865 DF,  p-value: 0.0006598


So, based on the provided information, is there any way to improve the model? what other models shall I try besides linear models to make sure that I have a good predictive model?

UPDATE1: you spend money to FB to show the ad. Impressions are the number of time the ad was shown, Clicks are the number of clicks on a specific ad. the company xyz has run 3 campaigns in total (xyz_campaign_id helps to identify between them). Interests are categorical variables (e.g. interest 2 might mean that the ad is targeting people who are interested in apple). Now, I myself am not too sure about the Approved_Conversion vs CR. The goal is to predict (Approved_Conversions/Clicks) CR. CR gives you a rate, it makes more sense to predict such a rate to know which audience segment to focus on (e.g. younger men with specific interests, etc.). If we predict approved_conversion, we also need to consider clicks, impressions or spent, but considering how the process works, it might not make sense (e.g. we dont know the clicks or spent before we run the ad)

• Can you provide some more exploratory overview with some better graphs? You should be able to plot CR as a function of campain_ID (is this a factor variable or a numerical variable?) and/or interest for the 6 different combinations of age (3 categories) and gender (two genders?). The current single boxplot is not very informative and tells little about any possible pattern (except that there is possibly some strong non linearity, e.g. the relationship might be logarithmic, this makes it important to also carefully consider different ways to plot the graphs, e.g. a logarithmic scale may help). – Martijn Weterings Sep 23 at 11:24
• The new graphs help a lot. They make me think about the following question. What is conversion rate? How did you obtain the data for this. It is not a straightforward variable. E.g. why are there so many values at 100 and 50 but hardly anything in between? If you explain the process (the mechanism) how the data is created then people can obtain a better idea what kind of model might suit the data. (sidenote: also plot the y-scale logarithmic since nothing detailed is visible about the lower 50% of the data) – Martijn Weterings Sep 23 at 12:51
• The data doesn't look complicated now, but do you have a link that explains all the terms for the statisticians that do no know about facebook and conversion, or could you add such explanation? E.g. there is a bunch of terms like interest, campaign id, impressions, etc. that are unexplained. So, again, what is the process that creates this dataset? Starting to write this down clearly will already help a lot and provide half the answer. – Martijn Weterings Sep 23 at 13:55
• @MartijnWeterings, please read the UPDATE – towi_parallelism Sep 23 at 15:41
• towi - what I meant by count being important is that a 50% conversion rate wtih 2 clicks is less reliable than a 50% conversion rate with 1000 clicks. - you are not providing this to your model if you just use CR for each subgroup instead of using a logistic regression model and passing the number of clicks with approved conversions and number of clicks with unapproved conversions. what approximations are you making in applying logistic regression - there should be none?!! – seanv507 Sep 23 at 17:24

From the challenge information on the data it appears that there are various success metrics for the data, depending on whether you are interested in the number of impressions, click, or conversions. In your question you seem to be focussing on a particular metric involving the rate of conversions-per-click. Since this is a challenge, I will avoid doing the same analysis you are doing, and instead illustrate how you can model count-ratios using generalised linear modelling (GLM).

The first thing to note is that regardless of which particular metric you wish to focus on, it is good practice to avoid modelling using variables constructed as count-ratios. Instead of doing this, it is much better to model the counts directly, usually by using some kind of GLM with a link function that relates the logarithms of the count variables. This can be done in a way that fixes the model coefficients on the logarithms, thus dealing implicitly with a fixed ratio of counts. I will show you an example of this using a model that attempts to predict the clicks-per-impression from an advertising campaign. You can of course adapt this to deal with your preferred metric.

An example - analysing clicks-per-impression with a quasi-Poisson model: For this analysis, an initial scatterplot matrix of the training data (omitted here) establishes that there are rough linear relationships between the log-clicks, the log-impressions and the log-spend. That relationships is common with count data, and it is suggestive of a log-linear model form.

To model clicks-per-impression, you can instead use the log-clicks as your response variable and use the log-impressions as the first explanatory variable, with a fixed unit coefficient. (This gives you a model formula that can be rearranged to yield an expression for the clicks-per-impression.) An obvious place to start would be with either a negative binomial model or a quasi-Poisson model, since these are models that deal well with count data; they have the correct support, and they allow for variable levels of dispersion in the data. The model should include log-impressions as a fixed offset, log-spending as an explanatory variable, and then the factor variables of interest.$$^\dagger$$ (Since there was zero spending on some of the campaigns I adjusted with some padding for this variable so that it would make sense in a logarithm.$$^{\dagger \dagger}$$) The model form, with the log-link made explicit, is:

log(Clicks) ~ offset(log(Impressions))
+ I(log(Spent+0.01))
+ factor(age):factor(gender)
+ factor(interest) + factor(xyz_campaign_id)


In the example here I have used a quasi-Poisson model, but the negative binomial model gives similar results. This model leads to a decent predictive model for Clicks, and implicitly for Clicks-per-Impression. It also gives a reasonable residual plot for the fitted model. It also gives an implicit estimate of the clicks-per-impressions for any input values of the other explanatory variables. The fitted model on the training data, excluding the coefficients for interest, yields:

                                  Estimate Std. Error t value Pr(>|t|)
(Intercept)                      -7.896839   0.094512 -83.554  < 2e-16 ***
I(log(Spent + 0.01))              0.086426   0.004984  17.340  < 2e-16 ***
...
factor(xyz_campaign_id)936       -0.410562   0.079494  -5.165 2.99e-07 ***
factor(xyz_campaign_id)1178      -0.839471   0.079674 -10.536  < 2e-16 ***
factor(age)30-34:factor(gender)F -0.111433   0.015510  -7.185 1.45e-12 ***
factor(age)35-39:factor(gender)F  0.145936   0.016643   8.769  < 2e-16 ***
factor(age)40-44:factor(gender)F  0.272116   0.016361  16.632  < 2e-16 ***
factor(age)45-49:factor(gender)F  0.292668   0.014253  20.534  < 2e-16 ***
factor(age)30-34:factor(gender)M -0.415893   0.016504 -25.199  < 2e-16 ***
factor(age)35-39:factor(gender)M -0.243519   0.019479 -12.502  < 2e-16 ***
factor(age)40-44:factor(gender)M -0.082256   0.018197  -4.520 7.03e-06 ***

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

(Dispersion parameter for quasipoisson family taken to be 0.4400126)

Null deviance: 2909.78  on 913  degrees of freedom
Residual deviance:  398.51  on 864  degrees of freedom


The ANOVA for the model is:

                           Df Deviance Resid. Df Resid. Dev
NULL                                         913    2909.78
I(log(Spent + 0.01))        1   199.72       912    2710.05
factor(interest)           39   435.64       873    2274.41
factor(xyz_campaign_id)     2   501.94       871    1772.47
factor(age):factor(gender)  7  1373.96       864     398.51


The predictions from applying the fitted model to the test data yield RMSE = 6.6457, and the prediction plots shows that there is some difficulty in predicting outcomes for ads with high numbers of predicted clicks. The residual and prediction plots are shown below. As can be seen from the plots and the summary output for the model, there is a substantial difference between the three campaigns in the data.

How does this help in understanding the clicks-per-impression? I noted above that this model form, which works with the raw count values, gives you an implicit predictive equation for the rate variable formed by the taking the ratio of Clicks on Impressions. To see why this is the case, we observe that (using the above coefficients) our fitted regression equation is:

$$\ln(\widehat{\text{Clicks}}) = \ln(\text{Impressions}) - 7.896839 + 0.086426 \ln(\text{Spent}) + \cdots .$$

Rearranging this equation yields:

$$\frac{\widehat{\text{Clicks}}}{\text{Impressions}} = \exp(- 7.896839) \times \text{Spent}^{0.086426} \times \cdots .$$

The left-hand-side of this equation functions as a rough estimator of the clicks-per-impression, and thus, your model allows you to make estimates of this rate measure. (There are a few technical issues with using this as an estimator. It is notable that non-linear functions of the predicted response are biased estimators of the corresponding non-linear function of the actual response, so the above equation gives a biased estimator of the clicks-per-impression. With non-linear transforms it is better to deal with the transformed response by looking at confidence intervals formed from the fitted model and then back-transforming these intervals. A detailed discussion of this issue is beyond the scope of this post, but the point here is that you can get an estimator of the rate measure from a model that operates on the raw count values.)

This shows you the method by which you can use a GLM for count values to obtain an implicit estimator of a rate measure formed as the ratio of two count values. The advantage of this method is that models designed to deal with count values often fit this data particularly well, and naturally take account of the heteroscedasticity that occurs with respect to the count variables. As noted, the present answer uses a different set of count variables than the ones you are interested in, but the method could be adapted to the rate measure of interest to you.

R code: Here is the R code used for the above plots and output:

######################### GET DATA #########################

library(MASS);
library(ggplot2);

FILE <- 'http://rhokat.wdfiles.com/local--files/home%3Ahome/conversion_data.csv';
DATA <- read.csv(FILE, sep = ',')

#Split into training and testing data
#Replicates analysis in the question
#PROP is the proportion of data put in the training set
set.seed(100);
PROP       <- 0.8;
INDEX      <- sample(1:nrow(DATA), PROP*nrow(DATA));
DATA_TRAIN <- DATA[INDEX,  ];
DATA_TEST  <- DATA[-INDEX, ];

######################### FIT MODEL ########################

#Fit a quasi-Poisson model to the training data
MODEL_FORM <- as.formula(Clicks ~ offset(log(Impressions))
+ I(log(Spent+0.01))
+ factor(age):factor(gender)
+ factor(interest)
+ factor(xyz_campaign_id));
MODEL      <- glm(MODEL_FORM, family = 'quasipoisson', data = DATA_TRAIN);

#Add fitted values and deviance residuals to data
DATA_TRAIN$$Fitted <- fitted(MODEL); DATA_TRAIN$$Residual <- residuals(MODEL, 'deviance');

#Generate the residual plot
RES_PLOT <- ggplot(data = DATA_TRAIN,
aes(x = Fitted, y = Residual,
colour = factor(xyz_campaign_id))) +
geom_point(alpha = 0.4, size = 3) +
scale_x_log10() +
scale_colour_discrete(name = 'Company No.') +
theme(plot.title = element_text(hjust = 0.5, face = 'bold'),
plot.subtitle = element_text(hjust = 0.5)) +
ggtitle('Residual Plot') +
labs(subtitle = '(Quasi-Poisson Model – Training data)') +
xlab('Predicted Clicks') + ylab('Deviance Residuals');
RES_PLOT;

#Generate ANOVA for model
anova(MODEL);

##################### MAKE PREDICTIONS #####################

#Apply the model to make predications on the test data
DATA_TEST$$Clicks_Pred <- exp(predict(MODEL, newdata = DATA_TEST)); RMSE <- sqrt(mean((DATA_TEST$$Clicks - DATA_TEST$Clicks_Pred)^2)); #Generate the prediction plot SUBTITLE <- paste0('(Quasi-Poisson Model – Test data – RMSE = ', round(RMSE,4), ')'); PRED_PLOT <- ggplot(data = DATA_TEST, aes(x = Clicks_Pred, y = Clicks, colour = factor(xyz_campaign_id))) + geom_point(alpha = 0.4, size = 3) + geom_abline(slope = 1, linetype = 'dashed') + scale_colour_discrete(name = 'Company No.') + scale_x_continuous() + scale_y_continuous() + theme(plot.title = element_text(hjust = 0.5, face = 'bold'), plot.subtitle = element_text(hjust = 0.5)) + ggtitle('Prediction Plot') + labs(subtitle = SUBTITLE) + xlab('Predicted Clicks') + ylab('Actual Clicks'); PRED_PLOT;  $$^\dagger$$ Some preliminary exploratory analysis shows that the variable fb_campaign_id is nested within xyz_campaign_id and this is too detailed, since it perfectly predicts several values in combination with the other factors. Using this variable would overfit the model so we omit it. $$^{\dagger \dagger}$$ If I were doing a proper analysis I would try to avoid variable-padding for a log variable, since this has the side-effect of making your analysis depend on the arbitrary padding value. In this case the output is reasonably robust to this value, and I am only fitting an example model. • thanks. there are few points though. 1- Clicks/Impressions is the Click-through Rate, which is another part of the challenge. So, CR should definitely be related to the Conversions. 2- How did you come up with quasi-Poisson model? can you tell me what exploration made you choose this? 3- I used lm() without logs to predict clicks from the formula you provided and got the Adj R2 of 99%, so why you choose quasi? 4- so you are saying that A ~ log(B) +x+y+z is equivalent to A/B ~ x+y+z? 5- we won't have the Spent to predict with, but I understand we need it somehow – towi_parallelism Sep 26 at 18:01 • @towi: With count data, you frequently find that there is a close association of the logarithms, so the first thing I did was to look at a scatterplot matrix of log-clicks, log-impressions, and log-spend. This showed that these three variables are linearly related, which suggested the model formula. As far as the choice of distributional family goes, the quasi-Poisson and negative binomial distributions are standard places to start for count data. I fitted both, but the above analysis just shows one. – Ben Sep 26 at 22:18 • Please note: I have made a major edit to this answer to model the clicks-per-impression, and explain this modelling more clearly. The results have changed relative to the initial post, but they still show you one way you can model this type of data. – Ben Sep 26 at 23:04 • Thanks @Ben. 1- I still don't understand how predicting count in a way you did it helps with predicting the ratio. Do you have a reference for that? Also, in the case of Conversions/Clicks, that method cannot be applied, simply because Clicks are unknown before running the ad. So, I cannot use the same approach for my CR model. 2- that aside, can you please suggest a way to model my CR? can we just assume there are not enough predictors available for that? – towi_parallelism Sep 27 at 7:49 • @towi_parallelism: To explain this a bit better I had edited to add an additional section showing specifically how the predictions on the count values can be transformed to implicit predictions on the rate measures formed from the count values. It should not matter that you do not observe clicks before running the ad in your new data, since you do have all the variables in your training/testing data, so you should still be able to fit the model and obtain a predictive equation. – Ben Sep 27 at 7:53 I downloaded the data from your link and attempted to recreate your problem. The first thing I did is to convert the campaign id to a factor: data$$xyz_campaign_id <- as.factor(data$$xyz_campaign_id)  Next I ran a linear model to predict Total Conversion using the variables you specified in your example. simple_linear <- lm(Total_Conversion ~ gender + interest + Clicks + Spent + Impressions + xyz_campaign_id, data = data)  I then checked the model output by running summary(simple_linear), and got a high adjusted R-squared value of 0.73 showing that this model explains approximately 73% of the variation in the data. To improve the model I explored the effect that interactions could have on model fit. See this wikipedia article for more information on interactions. Example code on constructing various models with varying interaction terms: simple_linear.1int <- lm(Total_Conversion ~ gender + interest + Clicks * Spent + Impressions + xyz_campaign_id, data = data) simple_linear.2int <- lm(Total_Conversion ~ gender + interest + Clicks * Spent * Impressions + xyz_campaign_id, data = data) simple_linear.3int <- lm(Total_Conversion ~ gender + interest * Clicks * Spent * Impressions + xyz_campaign_id, data = data) simple_linear.4int <- lm(Total_Conversion ~ gender * interest * Clicks * Spent * Impressions + xyz_campaign_id, data = data)  We can now compare these models using the Akaike Information Criterion which gives us an estimate of the quality of a statistical model (the lower the value the better the model when comparing it against others). # AIC criterion calculation for every model AIC(simple_linear) AIC(simple_linear.1int) AIC(simple_linear.2int) AIC(simple_linear.3int) AIC(simple_linear.4int)  When now running summary() again for every model, the one with 4 interaction terms now has the highest R-squared value and the lowest AIC value, and therefore represents an improvement over the simple linear model. • Thanks for the effort. If you include Clicks and Impressions, etc. how useful would that be? You cannot suggest to the team that they focus on campaigns with specific Impressions. We need to create a model based on the age, gender, interest (and possibly campaign id). How would your model help the marketing team? Also, the question is asking to model and predict conversion rate (I assume approved/clicks), while you have predicted the conversion (or total_conversion) – towi_parallelism Sep 22 at 12:16 • My background isn't in digital marketing however I arrived at the conclusion of incorporating clicks and the amount of spending as when I visualized the data there was a very clear positive relationship between spending/clicks and total conversion. If you haven't done so already I would visualize the data (a good tool is ggplot2) and you may come across some new relationships you maybe haven't seen before. Check ourcodingclub.github.io/2017/02/28/modelling.html for more info on linear models in R – sakell Sep 22 at 12:36 • I did so. the correlations are also clear from a correlation map. If you look at the last questions, it is looking for a sort of objective segmentation. Your link is useful though. thanks for sharing. – towi_parallelism Sep 22 at 17:26 ### Good p but low R2 This is not a strange situation since you have a lot of data points. You are dealing with a sort of law of large numbers (decreasing probability for small deviation of the mean for large number of data). For larger number of data small deviations of the trend line (R2 is deviation of trend line divided by deviation of the data) become less probable. See the images below for example. set.seed(1) layout(matrix(1:3,1)) for (i in 1:3) { n <- c(5,10,100)[i] p <- 0 r <- 0 pmod <- 0 for (t in 1:5000) { y <- rnorm(n,0,1) x <- seq(0,10,length.out=n) mod <- lm(y~x) anv <- anova(mod) if (abs(anv$$Pr(>F)[1]-0.01) < abs(p-0.01)) { pmod <- mod p <- anv$$Pr(>F)[1] r <- var(predict(mod)/var(y)) } } plot(x,y, xlim=c(0,10),ylim=c(-2.5,2.5)) lines(x,predict(mod)) Lines <- list(bquote(paste("n = ",.(n))),bquote(paste("p = ",.(round(p,5)))),bquote(paste("R"^2," = ",.(round(r,3))))) mtext(do.call(expression, Lines),side=3,line=2:0, cex=0.7) }  ### Improvement of model Your data is basically data of success and failure. For each click/impression you may or may not get a click/conversion. This can be modeled as binomial distributed data (you model the probability to get a success as a function of the variables like campaign gender and interest). Then it is better to use a generalized linear model instead of an ordinary linear regression (it allows a link function but most important of all it allows to model probability of the erro differently, as binary data instead of gaussian distributed data). Below is an example how to put it in code: data$$y <- cbind(data$$Total_Conversion,data$$Impressions-data$$Total_Conversion) data[1,] mod <- glm(y ~ as.factor(xyz_campaign_id) + as.factor(age) + as.factor(gender) + as.factor(interest) , family = binomial(), data=data) summary(mod) > summary(mod) Call: glm(formula = y ~ as.factor(xyz_campaign_id) + as.factor(age) + as.factor(gender) + as.factor(interest), family = binomial(), data = data) Deviance Residuals: Min 1Q Median 3Q Max -4.8862 -0.4765 0.3319 1.0702 5.7332 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -8.35436 0.21102 -39.590 < 2e-16 *** as.factor(xyz_campaign_id)936 -0.31878 0.14020 -2.274 0.02299 * as.factor(xyz_campaign_id)1178 -2.33740 0.13557 -17.241 < 2e-16 *** as.factor(age)35-39 -0.31129 0.04921 -6.326 2.52e-10 *** as.factor(age)40-44 -0.47718 0.05351 -8.917 < 2e-16 *** as.factor(age)45-49 -0.74920 0.04900 -15.288 < 2e-16 *** as.factor(gender)M 0.24957 0.03892 6.413 1.42e-10 *** as.factor(interest)7 -0.16451 0.20515 -0.802 0.42260 as.factor(interest)10 -0.41185 0.17030 -2.418 0.01559 * as.factor(interest)15 -0.38375 0.17425 -2.202 0.02764 * as.factor(interest)16 -0.84690 0.16783 -5.046 4.51e-07 *** as.factor(interest)18 -0.77352 0.19269 -4.014 5.96e-05 *** as.factor(interest)19 -0.25322 0.18817 -1.346 0.17840 as.factor(interest)20 -0.23804 0.18131 -1.313 0.18923 as.factor(interest)21 0.14531 0.19889 0.731 0.46502 as.factor(interest)22 -0.65382 0.21273 -3.073 0.00212 ** as.factor(interest)23 -0.19944 0.24408 -0.817 0.41386 as.factor(interest)24 -0.20747 0.22375 -0.927 0.35378 as.factor(interest)25 -0.33910 0.19485 -1.740 0.08181 . as.factor(interest)26 -0.49123 0.19806 -2.480 0.01313 * as.factor(interest)27 -0.45564 0.17210 -2.648 0.00811 ** as.factor(interest)28 -0.57311 0.17881 -3.205 0.00135 ** as.factor(interest)29 -0.33412 0.16888 -1.978 0.04788 * as.factor(interest)30 -0.20310 0.21990 -0.924 0.35569 as.factor(interest)31 0.37680 0.23585 1.598 0.11013 as.factor(interest)32 -0.49900 0.19566 -2.550 0.01076 * as.factor(interest)36 0.20341 0.24657 0.825 0.40940 as.factor(interest)63 -0.48914 0.18826 -2.598 0.00937 ** as.factor(interest)64 -0.34273 0.19310 -1.775 0.07591 . as.factor(interest)65 -0.27561 0.23590 -1.168 0.24269 as.factor(interest)66 -0.47645 0.31082 -1.533 0.12531 as.factor(interest)100 0.36159 0.22934 1.577 0.11488 as.factor(interest)101 0.38336 0.19991 1.918 0.05515 . as.factor(interest)102 -0.30425 0.31133 -0.977 0.32843 as.factor(interest)103 -0.49026 0.27085 -1.810 0.07029 . as.factor(interest)104 0.67505 0.22212 3.039 0.00237 ** as.factor(interest)105 -0.35516 0.23820 -1.491 0.13596 as.factor(interest)106 -0.22747 0.27586 -0.825 0.40961 as.factor(interest)107 -0.19347 0.20202 -0.958 0.33823 as.factor(interest)108 -0.18805 0.23327 -0.806 0.42015 as.factor(interest)109 -0.17986 0.22852 -0.787 0.43126 as.factor(interest)110 -0.13102 0.23032 -0.569 0.56945 as.factor(interest)111 0.05861 0.24320 0.241 0.80955 as.factor(interest)112 0.25045 0.21119 1.186 0.23565 as.factor(interest)113 -0.16450 0.25586 -0.643 0.52027 as.factor(interest)114 -0.15016 0.33078 -0.454 0.64986 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 3212.1 on 1142 degrees of freedom Residual deviance: 1619.0 on 1097 degrees of freedom AIC: 4579.6 Number of Fisher Scoring iterations: 6  The actual implementation in the GLM function is not so important for this particular case. For low probabilities of success and high number of trials the binomial distribution is almost the same as the Poisson distribution, and also the link functions are similar in the tail, e.g. $$log(p) \approx log(\frac{p}{1-p})$$ ### How good is the model? I would not use $$R^2$$ for this type of data (instead use $$\chi^2$$ to measure goodness of fit). This is because you naturally have a variation in the data because it is Poisson/Binomial distributed or according to some other distribution of counts that is a sum of Bernoulli trials. For instance, imagine you have to model coins that have probability for heads and tails between 0.49 and 0.51. Then your model may very accurately model this small variation (between coins) in the probability, but the data may have a large variation (within repeated flips of a single coin). E.g. if you flip a coin 10 times then it will have a mean close to 5 times heads, but the variance will be 2.5 and you will have a large $$R^2$$ but your model can not be much better (the data varies with 2.5 but your model only differs between 0.49 and 0.51 for the probability of heads and tails). Something similar occurs when you will model the probability of an impression turning into a click. The model will be over-fitted (even though the $$R^2$$ is only around 0.25) See the images below (generated with the code below) • left a plot of predicted clicks vs observed clicks • right a plot of modeled data (average of ten repetitions) using binomial distribution with the predicted value for the probability of v click and the observed number of impressions as the number of trials. Note that the observations are more close to the predictions than the modeled data. This indicates over-fitting (or under-dispersion, making us model the data with too large variance). The imaged below show the results of a Monte Carlo simulation (10 000 times) to estimate the distribution for $$\chi^2$$ and $$R^2$$. The values for the model are: > # compute chi-sq > sum(((npred-data$$Clicks)^2/npred)) [1] 586.4344 > # compute R^2 > var(predict(mod, type='response')) / var(data$$Clicks/data$Impressions)
[1] 0.275814


The low chi-square value (and also the relatively high $$R^2$$) indicate that the fit is too good to be true.

It would be much more plausible to observe $$R^2$$ around 0.10 and it should not be expected that a model with a higher value is truly a better model. To expect a low $$R^2$$ is simply due to the variability of the measurements, that are comparable to the variability in the example with the coin flips.

Small note: when you model the conversions/impressions then you do get a lower $$R^2$$ and higher $$\chi^2$$ than expected.

# create data column for binomial model
data$$y <- cbind(data$$Clicks,data$$Impressions-data$$Clicks)

# model
mod <- glm(y ~ as.factor(xyz_campaign_id) +
(as.factor(age) +
as.factor(gender) +
as.factor(interest)) , family = binomial(link='logit'), data=data)

# print results
summary(mod)

# compare deviation of predictions with monte carlo
set.seed(1)
layout(matrix(1:2,1))

# prediction of counts
npred <- predict(mod, type='response')*data$Impressions # plot vs observations plot(npred[which(as.factor(data$$xyz_campaign_id)!=1)],data$$Clicks[which(as.factor(data$xyz_campaign_id)!=1)],
pch=21,col=rgb(0,0,0,0.1),bg=rgb(0,0,0,0.1),cex=0.7,log="",
xlim = c(0,365),ylim=c(0,365),
xlab = "n predicted", ylab = "n observed")

# plot vs modelled data
plot(-100,-100,
pch=21,col=rgb(0,0,0,0.01),bg=rgb(0,0,0,0.01),cex=0.7,log="",
xlim=c(0,365),ylim=c(0,365),
xlab = "n predicted", ylab = "y modeled")
for (i in 1:10) {
test <- rbinom(length(npred),data$Impressions,predict(mod,type='response')) points(npred,test, pch=21,col=rgb(0,0,0,0.01),bg=rgb(0,0,0,0.01),cex=0.7) } # compute chi-sq sum(((npred-data$$Clicks)^2/npred)) # compute R^2 var(predict(mod, type='response')) / var(data$$Clicks/data$Impressions)

# compare with monte carlo simulation
v_chi <- rep(0,10000)
v_p <- rep(0,10000)
for (i in 1:10000) {
test <- rbinom(length(npred),data$$Impressions,predict(mod,type='response')) v_chi[i] <- sum(((npred-test)^2/npred)) v_p[i] <- var(predict(mod, type='response'))/var(test/data$$Impressions)
}
hist(v_chi, xlab=expression(chi^2),main=expression(paste("Histogram of ",chi^2," for modeled data")))
hist(v_p, xlab=expression(R^2),main=expression(paste("Histogram of ",R^2," for modeled data")))

• thanks Martin, that's an interesting way of looking at it. how do I interpret this? e.g. what does a positive coefficient mean here? (since we have 2 columns for y) I also considered glm(CR/100 ~ fact_xyz_campaign_id + gender + age + fact_interest , data=data.train, family="binomial") which considers the CR values in [0,1] . The reason I mentioning it is because the psuedo R2 (McFadden) value I get using that model is similar to what you get here (~25%) – towi_parallelism Sep 29 at 9:00
• @towi_parallelism The standard link function for the binomial model in glm is the logit function and you are doing logistic regression ( en.wikipedia.org/wiki/Logistic_regression ). The values that you are modelling relate to the logarithm of the odds (rather than the probability) $$\log \left( \frac{p}{1-p} \right)$$ – Martijn Weterings Sep 29 at 9:09
• thanks, I got the point about your model. Now about the comment you made on my binomial model, I'm a bit confused. I just tried family=binomial(link='logit') and family=binomial and they give me the same result. I predict using the type="response". you are saying I'm getting log of odds rather than the predicted CR values in my prediction? – towi_parallelism Sep 29 at 9:32
• Those function like predict(mod, type='response') have all kinds of features. If you do not use response the you get the log odds. If you use response then it gives you the value for $p$, the probability for a conversion. The values that you see in the table of my answer, e.g. -8.35436 for the intercept, relate to the log-odds. When you are making a prediction then you can convert them to probability as is done by selecting type='response'. So you get $$log(\frac{p}{1-p}) = X \beta$$ and $$p= \frac{ 1}{1+exp( -X \beta)}$$ – Martijn Weterings Sep 29 at 9:36
• I don't believe that the link functions and using poisson vs binomial actually matter that much here. Since the probabilities are so small you get that (1) the Poisson and binomial distributions are much the same, and (2) you are using mostly the tails of the link functions which are very similar as well. (one different application that you may consider is a quasi-binomial/poisson model that allows different relations between mean and variance ie overdispersion) – Martijn Weterings Sep 29 at 9:48

Consider what look like CR outliers to address in different possible ways (cross-validation, downweight/transform/delete). Prior comments on interactions would seem helpful, and squared terms might be worth exploring.

• thanks. there is only one CR =200 and I converted it back to 100. Cannot think of more changes. Looked at the high leverage points too, tried to remove them to see if the results change, but more or less the same – towi_parallelism Sep 23 at 8:05