renaming

5/data science/r/7.rmd

@@ -0,0 +1,91 @@
+---
+title: "Lab7: Logistic regression"
+author: "Vladislav Litvinov <vlad@sek1ro>"
+output:
+  pdf_document:
+  toc_float: TRUE
+---
+# Logit
+
+```{r}
+n = 500
+breaks = 100
+odds = numeric(n)
+logit = numeric(n)
+for (i in 1:n) {
+  p = runif(1)
+  odds[i] = p / (1 - p)
+  logit[i] = log(odds[i])
+}
+hist(odds, breaks = breaks)
+hist(logit, breaks = breaks)
+
+data <- matrix(nrow=4, ncol=2, byrow=TRUE, 
+               data=c(2, 3,
+                      0, 3,
+                      0, 2,
+                      1, 2))
+
+fisher.test(data)
+```
+# Data preparation
+```{r}
+setwd('/home/sek1ro/git/public/lab/ds/25-1/r')
+surve = read.csv('survey.csv')
+
+head(survey)
+
+survey$price20 = ifelse(survey$Price == 20, 1, 0)
+survey$price30 = ifelse(survey$Price == 30, 1, 0)
+head(survey)
+```
+# Model training
+
+Residuals are the differences between what we observe and what our model predicts.
+Residuals greater than the absolute value of 3 are in the tails of a standard normal distribution and usually indicate strain in the model.
+
+https://stats.stackexchange.com/questions/48178/how-to-interpret-the-intercept-term-in-a-glm
+https://library.virginia.edu/data/articles/understanding-deviance-residuals
+```{r}
+model = glm(
+  MYDEPV ~ Income + Age + price20 + price30,
+  binomial(link = "logit"),
+  survey
+)
+summary(model)
+quantile(residuals(model))
+```
+# Predicts for the model
+```{r}
+survey$odds_ratio = exp(predict(model))
+survey$prediction = survey$odds_ratio / (1 + survey$odds_ratio)
+head(survey)
+
+sum(survey$MYDEPV)
+sum(survey$prediction)
+
+new_person = data.frame(
+  Income = 58,
+  Age = 25,
+  price20 = 1,
+  price30 = 0
+)
+
+prob = predict(model, new_person, type="response")
+prob
+```
+
+```{r}
+library(ggplot2)
+predicted <- data.frame(
+  prob=model$fitted.values,
+  MYDEPV=survey$MYDEPV)
+ 
+predicted <- predicted[order(predicted$prob, decreasing=FALSE),]
+predicted$rank <- 1:nrow(predicted)
+ 
+ggplot(data=predicted, aes(x=rank, y=prob)) +
+  geom_point(aes(color=MYDEPV), alpha=0.5, shape=4, stroke=1) +
+  xlab("Index") +
+  ylab("MYDEPV")
+```