---
title: "8- Hypothesis testing with qualitative variables NB"
author: "Alex Sanchez, Miriam Mota, Ricardo Gonzalo and \n Santiago Perez-Hoyos"
date: "Statistics and Bioinformatics Unit. \n Vall d'Hebron Institut de Recerca"
output:
html_notebook:
code_folding: hide
editor_options:
chunk_output_type: inline
---
```{r setLicense, child = 'license.Rmd'}
```
```{r setup, include=FALSE}
knitr::opts_chunk$set(message = FALSE, warning=FALSE)
```
```{r echo=FALSE}
knitr::knit_hooks$set(mysize = function(before, options, envir) {
if (before)
return(options$size)
})
```
```{r}
sex <- factor(c("Female", "Male"))
blood_group <- factor(c("A", "B", "AB", "O"))
blood_group
```
- Besides, factors can be forced to be "ordereded"
```{r}
tumorstage <- factor(1:4, ordered=TRUE)
```
- Be careful with the names of factors, by default, _levels_ assigned in alphabetical order.
```{r}
levels(blood_group)
```
# Creating factors
- Factors can be created ...
- automatically, when reading a file or
- Not all functions for reading data from file will create a factor!!!
- Usually levels will be defined from alphabetic order
- using the `factor` or the `as.factor` commands.
- more flexible
# Create factors automatically
- This is achieved by
- Using the `read.table` or `read.delim` functions for reading
- Setting the "character variables as.factors" to TRUE
- Example
- Load the `diabetes` dataset using the `Import Dataset` feature of Rstudio
- From text (base) (use the file `diabetes.csv`)
- From text (readr) (use the file `diabetes.csv`)
- From Excel (use the file `diabetes.xls`)
- What is the class of the variable `mort`
```{r}
library(readxl)
diabetes1 <- read_excel("datasets/diabetes.xls")
class(diabetes1$mort)
summary(diabetes1$mort)
diabetes1$mort <- as.factor(diabetes1$mort)
class(diabetes1$mort)
summary(diabetes1$mort)
diabetes2 <- read.csv("datasets/diabetes.csv", stringsAsFactors=TRUE)
class(diabetes2$mort)
summary(diabetes2$mort)
library(haven)
diabetes3 <- read_sav("datasets/diabetes.sav")
class(diabetes3$MORT)
summary(diabetes3$MORT)
```
# Exercise 1
- Select one of the datasets that you have worked with during the course
- diabetes.xls
- osteoporosis.csv
- demora.xls
- Read the dataset into R and check that the categorical variables you are interested in are converted into factors.
- Confirm the conversion by summarizing the variables
# Exercise 2
- Use the diabetes.sav file and import it into R with the "Import from SPSS" feature.
- What is the class of the "MORT" variable.
- Turn it into one factor so that it has the same levels as when you read it using `read.csv`
# Example
Consider the following study relating smoking and cancer.
```{r, echo=FALSE, out.width="80%", fig.cap="", eval=FALSE}
knitr::include_graphics("images/cancerAndSmoking1png.png")
```
Our goal here would be to determine if there is an association between smoking and cancer.
# Crosstabulating a dataset
- Data may come from a table (aggregated) or disagregated in a data file.
- In this case we need to build the table applying "cross-tabulation"
```{r}
dadescancer <- read.csv("datasets/dadescancer.csv",
stringsAsFactors = TRUE)
```
```{r}
#attach(dadescancer)
mytable <-table(dadescancer$cancer, dadescancer$fumar)
mytable
```
# There are many ways to do crosstabulation
```{r}
with(dadescancer, table(cancer, fumar) )
```
```{r}
myXtable <- xtabs (~ cancer + fumar, data = dadescancer)
myXtable
```
# Crosstabulation (2): Marginal tables
Marginal values are important to understand the structure of the data:
```{r}
margin.table(mytable, 1) # A frequencies (summed over B)
margin.table(mytable, 2) # B frequencies (summed over A)
```
---
```{r}
mytable<- addmargins(mytable)
```
# Crosstabulation (3): In percentages
Showing tables as percentages is useful for comparisons
```{r}
prop.table(mytable) # cell percentages
prop.table(mytable, 1) # row percentages
# prop.table(mytable, 2) # column percentages
```
# Crosstabulation (4): In percentages
```{r}
require(gmodels)
gmodels::CrossTable(dadescancer$cancer, dadescancer$fumar, expected=TRUE, prop.chisq=FALSE)
```
# Exercise 3
- With the osteoporosis dataset repeat the crosstabulation done above using
- Two categorical variables
- Variable "MENOP" and a newly created variable "catBUA" created by properly categorizing variable BUA.
# Proportion tests with R
Alternative "NOT EQUAL". This is set by default.
```{r}
prop.test(x=142, n=723, p=0.15)
```
Alternative "GREATER"
```{r}
prop.test(x=142, n=723, p=0.15, alternative="g")
```
Alternative "LESS THAN"
```{r}
prop.test(x=142, n=723, p=0.15, alternative="l")
```
Notice that _choosing the wrong alternative may yield unreasonable conclusions_.
# Estimation comes with proportion test
- `prop.test` does __three__ distinct calculations
- A test for the hypothesis $H_0: p=p_0$ is performed
- A confidence interval for $p$ is built based on the sample
- A point estimate for $p$ is also provided.
```{r, echo=FALSE, out.width="100%", fig.cap="", eval=FALSE}
knitr::include_graphics("images/propTestsAndCI.png")
```
# Exercise 4
- In the osteoporosis dataset.
- Test the hypothesis that the proportion of women with `osteoporosis` is higher than 7%
- In the global population of the study
- Only in women with osteoporosis
- Select a sample of size 100 and repeat the test. How do the results change?
- What sample size should we have taken so that th precision of the confidence intervals would have been at most 3% with a probability of 95%?
# Contingency tables
- A contingency table (a.k.a cross tabulation or cross tab) is a matrix-like table that displays the (multivariate) frequency distribution of the variables.
- It is bidimensional, and classifies all observations according to two categorical variables
(A and B, rows and columns).
```{r, echo=FALSE, out.width="80%", fig.cap="", eval=FALSE}
knitr::include_graphics("images/contingencytables1.png")
```
# Chi squared tests with R
```{r}
mytable<- with(dadescancer, table(cancer, fumar) )
chisq.test (mytable)
```
# Fisher test. an assumptions-free alternative
Chi-squared test require that sample sizes are "big" and expected frequencies are, at least greater than 5.
Fisher test can be an alternative if these assumptions are not met, especially for two times two tables.
```{r}
fisher.test(mytable)
```
# Exercise 5
- Use the osteoporosis dataset to study if it can be detected an association between the variables `menop` and `classific` in the osteoporosis dataset.
- Do not start with a test but with an appropriate summarization and visualization!