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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>How to t test</title>
<meta charset="utf-8" />
<meta name="author" content="Peter Higgins" />
<link rel="stylesheet" href="xaringan-themer.css" type="text/css" />
</head>
<body>
<textarea id="source">
class: center, middle, inverse, title-slide
# How to t test
### Peter Higgins
---
### We will start with the tidy version from the {infer} package: t_test()
#### Notice that you have to state the order of the two levels of your grouping variable
---
count: false
Tidy version from the {infer} package
.panel1-ttest1-auto[
```r
*prostate
```
]
.panel2-ttest1-auto[
```
# A tibble: 316 x 20
RBC.Age.Group Median.RBC.Age Age AA FamHx PVol TVol T.Stage bGS
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 3 25 72.1 0 0 54 3 1 3
2 3 25 73.6 0 0 43.2 3 2 2
3 3 25 67.5 0 0 103. 1 1 3
4 2 15 65.8 0 0 46 1 1 1
5 2 15 63.2 0 0 60 2 1 2
6 3 25 65.4 0 0 45.9 2 1 1
7 3 25 65.5 1 0 42.6 2 1 1
8 1 10 67.1 0 0 40.7 3 1 1
9 1 10 63.9 0 0 45 2 1 1
10 2 15 63 1 0 67.6 2 1 2
# … with 306 more rows, and 11 more variables: `BN+` <dbl>,
# OrganConfined <dbl>, PreopPSA <dbl>, PreopTherapy <dbl>, Units <dbl>,
# sGS <dbl>, AnyAdjTherapy <dbl>, AdjRadTherapy <dbl>, Recurrence <dbl>,
# Censor <dbl>, TimeToRecurrence <dbl>
```
]
---
count: false
Tidy version from the {infer} package
.panel1-ttest1-auto[
```r
prostate %>%
* t_test(TVol ~ AA,
* order = c("0", "1"))
```
]
.panel2-ttest1-auto[
```
# A tibble: 1 x 6
statistic t_df p_value alternative lower_ci upper_ci
<dbl> <dbl> <dbl> <chr> <dbl> <dbl>
1 -0.683 79.2 0.496 two.sided -0.271 0.132
```
]
---
count: false
Tidy version from the {infer} package
.panel1-ttest1-auto[
```r
prostate %>%
t_test(TVol ~ AA,
order = c("0", "1"))
# Interpreting the results
# The t statistic is first
# followed by degrees of freedom
# then the p value
# the default alternative: two.sided
# then the confidence bounds
# output is a tibble so that it is
# easy to use these results
```
]
.panel2-ttest1-auto[
```
# A tibble: 1 x 6
statistic t_df p_value alternative lower_ci upper_ci
<dbl> <dbl> <dbl> <chr> <dbl> <dbl>
1 -0.683 79.2 0.496 two.sided -0.271 0.132
```
]
<style>
.panel1-ttest1-auto {
color: black;
width: 38.6060606060606%;
hight: 32%;
float: left;
padding-left: 1%;
font-size: 80%
}
.panel2-ttest1-auto {
color: black;
width: 59.3939393939394%;
hight: 32%;
float: left;
padding-left: 1%;
font-size: 80%
}
.panel3-ttest1-auto {
color: black;
width: NA%;
hight: 33%;
float: left;
padding-left: 1%;
font-size: 80%
}
</style>
---
### Now we will use the baseR version: t.test()
#### Notice that you have to use `data = .`
#### It is not quite as pipe-friendly
#### But you do not have to specify the order of the two levels.
---
count: false
Base R Version
.panel1-ttest2-auto[
```r
*prostate
```
]
.panel2-ttest2-auto[
```
# A tibble: 316 x 20
RBC.Age.Group Median.RBC.Age Age AA FamHx PVol TVol T.Stage bGS
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 3 25 72.1 0 0 54 3 1 3
2 3 25 73.6 0 0 43.2 3 2 2
3 3 25 67.5 0 0 103. 1 1 3
4 2 15 65.8 0 0 46 1 1 1
5 2 15 63.2 0 0 60 2 1 2
6 3 25 65.4 0 0 45.9 2 1 1
7 3 25 65.5 1 0 42.6 2 1 1
8 1 10 67.1 0 0 40.7 3 1 1
9 1 10 63.9 0 0 45 2 1 1
10 2 15 63 1 0 67.6 2 1 2
# … with 306 more rows, and 11 more variables: `BN+` <dbl>,
# OrganConfined <dbl>, PreopPSA <dbl>, PreopTherapy <dbl>, Units <dbl>,
# sGS <dbl>, AnyAdjTherapy <dbl>, AdjRadTherapy <dbl>, Recurrence <dbl>,
# Censor <dbl>, TimeToRecurrence <dbl>
```
]
---
count: false
Base R Version
.panel1-ttest2-auto[
```r
prostate %>%
* t.test(TVol ~ AA, data = .)
```
]
.panel2-ttest2-auto[
```
Welch Two Sample t-test
data: TVol by AA
t = -0.6833, df = 79.184, p-value = 0.4964
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2708947 0.1324320
sample estimates:
mean in group 0 mean in group 1
2.081712 2.150943
```
]
---
count: false
Base R Version
.panel1-ttest2-auto[
```r
prostate %>%
t.test(TVol ~ AA, data = .)
# Interpreting the results
# The t statistic is first
# followed by degrees of freedom
# then the p value
# then the alternative hypothesis
# then the confidence bounds
# then the mean mpg for each group
```
]
.panel2-ttest2-auto[
```
Welch Two Sample t-test
data: TVol by AA
t = -0.6833, df = 79.184, p-value = 0.4964
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2708947 0.1324320
sample estimates:
mean in group 0 mean in group 1
2.081712 2.150943
```
]
<style>
.panel1-ttest2-auto {
color: black;
width: 38.6060606060606%;
hight: 32%;
float: left;
padding-left: 1%;
font-size: 80%
}
.panel2-ttest2-auto {
color: black;
width: 59.3939393939394%;
hight: 32%;
float: left;
padding-left: 1%;
font-size: 80%
}
.panel3-ttest2-auto {
color: black;
width: NA%;
hight: 33%;
float: left;
padding-left: 1%;
font-size: 80%
}
</style>
---
<style type="text/css">
.remark-code{line-height: 1.5; font-size: 80%}
</style>
### Which version of the t test do you like better?
Discuss.
</textarea>
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