#+TITLE: Rapport package team
#+AUTHOR: Crosstable
#+DATE: 2011-04-26 20:25 CET
** Description
Returning the Chi-squared test of two given variables with count,
percentages and Pearson's residuals table.
*** Variable description
Two variables specified:
- "gender" ("Gender") with /673/ valid values and
- "dwell" ("Dwelling") with /662/ valid values.
**** Introduction
[[http://en.wikipedia.org/wiki/Cross_tabulation][Crosstables]] are
applicable to show the frequencies of categorical variables in a matrix
form, with a table view.
We will present four types of these crosstables. The first of them shows
the /exact numbers of the observations/, ergo the number of the
observations each of the variables' categories commonly have.
The second also shows the possessions each of these cells have, but not
the exact numbers of the observations, rather the /percentages/ of them
from the total data.
The last two type of the crosstabs contain the so-called /row and column
percentages/ which demonstrate us the distribution of the frequencies if
we concentrate only on one variable.
After that we present the /tests/ with which we can investigate the
possible relationships, associations between the variables, like
Chi-squared test, Fisher Exact Test, Goodman and Kruskal's lambda.
In the last part there are some /charts/ presented, with that one can
visually observe the distribution of the frequencies.
*** Counts
| | city | small town | village | Missing | Sum |
|-------------+--------+--------------+-----------+-----------+-------|
| *male* | 338 | 28 | 19 | 25 | 410 |
| *female* | 234 | 3 | 9 | 17 | 263 |
| *Missing* | 27 | 2 | 2 | 5 | 36 |
| *Sum* | 599 | 33 | 30 | 47 | 709 |
#+CAPTION: Counted values: "gender" and "dwell"
Most of the cases (/338/) can be found in "male-city" categories.
Row-wise "male" holds the highest number of cases (/410/) while
column-wise "city" has the utmost cases (/599/).
*** Percentages
| | city | small town | village | Missing | Sum |
|-------------+---------+--------------+-----------+-----------+---------|
| *male* | 47.67 | 3.95 | 2.68 | 3.53 | 57.83 |
| *female* | 33 | 0.42 | 1.27 | 2.4 | 37.09 |
| *Missing* | 3.81 | 0.28 | 0.28 | 0.71 | 5.08 |
| *Sum* | 84.49 | 4.65 | 4.23 | 6.63 | 100 |
#+CAPTION: Total percentages: "gender" and "dwell"
| | city | small town | village | Missing |
|-------------+---------+--------------+-----------+-----------|
| *male* | 82.44 | 6.83 | 4.63 | 6.1 |
| *female* | 88.97 | 1.14 | 3.42 | 6.46 |
| *Missing* | 75 | 5.56 | 5.56 | 13.89 |
| *Sum* | 84.49 | 4.65 | 4.23 | 6.63 |
#+CAPTION: Row percentages: "gender" and "dwell"
| | city | small town | village | Missing | Sum |
|-------------+---------+--------------+-----------+-----------+---------|
| *male* | 56.43 | 84.85 | 63.33 | 53.19 | 57.83 |
| *female* | 39.07 | 9.09 | 30 | 36.17 | 37.09 |
| *Missing* | 4.51 | 6.06 | 6.67 | 10.64 | 5.08 |
#+CAPTION: Column percentages: "gender" and "dwell"
*** Tests of Independence
In the below tests for
[[http://en.wikipedia.org/wiki/Independence_(probability_theory)][independece]]
we assume that the row and column variables are independent of each
other. If this [[http://en.wikipedia.org/wiki/Null_hypothesis][null
hypothesis]] would be rejected by the tests, then we can say that the
assumption must have been wrong, so there is a good chance that the
variables are associated.
**** Chi-squared test
One of the most widespread independence test is the
[[http://en.wikipedia.org/wiki/Pearson%27s_chi-squared_test][Chi-squared
test]]. While using that we have the alternative hypothesis, that two
variables have an association between each other, in opposite of the
null hypothesis that the variables are independent.
We use the cell frequencies from the crosstables to calculate the test
statistic for that. The test statistic is based on the difference
between this distribution and a theoretical distribution where the
variables are independent of each other. The distribution of this test
statistic follows a
[[http://en.wikipedia.org/wiki/Chi-squared_distribution][Chi-square
distribution]].
The test was invented by Karl Pearson in 1900. It should be noted that
the Chi-squared test has the disadvantage that it is sensitive to the
sample size.
***** Criteria
Before analyzing the result of the Chi-squared test, we have to check if
our data meets some requirements. There are two widely used criteria
which have to take into consideration, both of them are related to the
so-called expected counts. These expected counts are calculated from the
marginal distributions and show how the crosstabs would look like if
there were complete independency between the variables. The Chi-squared
test calculates how different are the observed cells from the expected
ones.
The two criteria are:
- none of the expected cells could be lower than 1
- 80% of the expected cells have to be at least 5
Let's look at on expected values then:
| | city | small town | village |
|------------+--------+--------------+-----------|
| *male* | 349 | 18.91 | 17.08 |
| *female* | 223 | 12.09 | 10.92 |
We can see that the Chi-squared test met the requirements.
So now check the result of the test:
| Test statistic | df | P value |
|------------------+------+------------------|
| 12.64 | 2 | /0.001804/ * * |
#+CAPTION: Pearson's Chi-squared test: =table=
To decide if the null or the alternative hypothesis could be accepted we
need to calculate the number of degrees of freedom. The degrees of
freedom is easy to calculate, we substract one from the number of the
categories of both the row and the coloumn variables and multiply them
with each other.
To each degrees of freedom there is denoted a
[[http://en.wikipedia.org/wiki/Critical_value#Statistics][critical
value]]. The result of the Chi-square test have to be lower than that
value to be able to accept the nullhypothesis.
It seems that a real association can be pointed out between /gender/ and
/dwell/ by the /Pearson's Chi-squared test/ ($\chi$=/12.64/) at the
[[http://en.wikipedia.org/wiki/Degrees_of_freedom_(statistics)][degree
of freedom]] being /2/ at the
[[http://en.wikipedia.org/wiki/Statistical_significance][significance
level]] of /0.001804/ * *.
The association between the two variables seems to be weak based on
[[http://en.wikipedia.org/wiki/Cram%C3%A9r%27s_V][Cramer's V]]
(/0.1001/).
***** References
- Fisher, R. A. (1922): On the interpretation of chi-square from
contingency tables, and the calculation of P. /Journal of the Royal
Statistical Society/ 85 (1): 87-94.
- Fisher, R.A. (1954): /Statistical Methods for Research Workers/.
Oliver and Boyd.
***** Adjusted standardized residuals
The residuals show the contribution to reject the null hypothesis at a
cell level. An extremely high or low value indicates that the given cell
had a major effect on the resulting chi-square, so thus helps
understanding the association in the crosstable.
| | city | small town | village |
|------------+---------+--------------+-----------|
| *male* | -3.08 | 3.43 | 0.76 |
| *female* | 3.08 | -3.43 | -0.76 |
#+CAPTION: Residuals: "gender" and "dwell"
Based on Pearson's residuals the following cells seems interesting (with
values higher than =2= or lower than =-2=):
- "male - city"
- "female - city"
- "male - small town"
- "female - small town"
***** References
- Snedecor, George W. and Cochran, William G. (1989): /Statistical
Methods/. Iowa State University Press.
- Karl Pearson (1900): /Philosophical Magazine/, Series 5 50 (302):
157-175.
**** Fisher Exact Test
An other test to check the possible association/independence between two
variables, is the
[[http://en.wikipedia.org/wiki/Fisher%27_exact_test][Fisher exact
test]]. This test is especially useful with small samples, but could be
used with bigger datasets as well.
We have the advantage while using the Fisher's over the Chi-square test,
that we could get an exact significance value not just a level of it,
thus we can have an impression about the power of the test and the
association.
The test was invented by, thus named after R.A. Fisher.
The variables seems to be dependent based on Fisher's exact test at the
[[http://en.wikipedia.org/wiki/P-value][significance level]] of
/0.0008061/ * * *.
*** Direction of relationship
**** Goodman and Kruskal's lambda
With the help of the
[[http://en.wikipedia.org/wiki/Goodman_and_Kruskal%27s_lambda][Goodman
and Kruskal's lambda]] we can look for not only relationship on its own,
which have directions if we set one variable as a predictor and the
other as a criterion variable.
The computed value for
[[http://en.wikipedia.org/wiki/Goodman_and_Kruskal%27s_lambda][Goodman
and Kruskal's lambda]] is the same for both directions: /0/. For this
end, we do not know the direction of the relationship.
*** Charts
If one would like to investigate the relationships rather visually than
in a crosstable form, there are several possibilities to do that.
****** Heat map
At first we can have a look at on the so-called
[[http://en.wikipedia.org/wiki/Heat_map][heat map]]. This kind of chart
uses the same amount of cells and a similar form as the crosstable does,
but instead of the numbers there are colours to show which cell contains
the most counts (or likewise the highest total percentages).
The darker colour is one cell painted, the most counts/the higher total
percentage it has.
[[plots/Crosstable-1-hires.png][[[plots/Crosstable-1.png]]]]
There can be also shown the standardized adjusted residual of each
cells:
[[plots/Crosstable-2-hires.png][[[plots/Crosstable-2.png]]]]
****** Mosaic chart
In front of the heat map, on the /mosaic charts/, not only the colours
are important. The size of the cells shows the amount of the counts one
cell has.
The width on the axis of gender determinate one side and the height on
the axis of the dwell gives the final shape of the box. The box which
demonstrates a cell from the hypothetic crosstable. We can see on the
top of the chart which category from the dwell draw the boxes what kind
of colour.
[[plots/Crosstable-3-hires.png][[[plots/Crosstable-3.png]]]]
****** Fluctuation diagram
At last but not least have a glance on the /fluctuation diagram/. Unlike
the above two charts, here the colours does not have influence on the
chart, but the sizes of the boxes, which obviously demonstrates here as
well the cells of the crosstable.
The bigger are the boxes the higher are the numbers of the counts/the
total percentages, which that boxes denote.
[[plots/Crosstable-4-hires.png][[[plots/Crosstable-4.png]]]]
** Description
Returning the Chi-squared test of two given variables with count,
percentages and Pearson's residuals table.
*** Variable description
Two variables specified:
- "email" ("Email usage") with /672/ valid values and
- "dwell" ("Dwelling") with /662/ valid values.
**** Introduction
[[http://en.wikipedia.org/wiki/Cross_tabulation][Crosstables]] are
applicable to show the frequencies of categorical variables in a matrix
form, with a table view.
We will present four types of these crosstables. The first of them shows
the /exact numbers of the observations/, ergo the number of the
observations each of the variables' categories commonly have.
The second also shows the possessions each of these cells have, but not
the exact numbers of the observations, rather the /percentages/ of them
from the total data.
The last two type of the crosstabs contain the so-called /row and column
percentages/ which demonstrate us the distribution of the frequencies if
we concentrate only on one variable.
After that we present the /tests/ with which we can investigate the
possible relationships, associations between the variables, like
Chi-squared test, Fisher Exact Test, Goodman and Kruskal's lambda.
In the last part there are some /charts/ presented, with that one can
visually observe the distribution of the frequencies.
*** Counts
| | city | small town | village | Missing |
|-----------------+--------+--------------+-----------+-----------|
| *never* | 12 | 0 | 0 | 1 |
| *very rarely* | 30 | 1 | 3 | 2 |
| *rarely* | 41 | 3 | 1 | 1 |
| *sometimes* | 67 | 4 | 8 | 8 |
| *often* | 101 | 10 | 5 | 7 |
| *very often* | 88 | 5 | 5 | 10 |
| *always* | 226 | 9 | 7 | 17 |
| *Missing* | 34 | 1 | 1 | 1 |
| *Sum* | 599 | 33 | 30 | 47 |
#+CAPTION: Counted values: "email" and "dwell" (continued below)
| | Sum |
|-----------------+-------|
| *never* | 13 |
| *very rarely* | 36 |
| *rarely* | 46 |
| *sometimes* | 87 |
| *often* | 123 |
| *very often* | 108 |
| *always* | 259 |
| *Missing* | 37 |
| *Sum* | 709 |
Most of the cases (/226/) can be found in "always-city" categories.
Row-wise "always" holds the highest number of cases (/259/) while
column-wise "city" has the utmost cases (/599/).
*** Percentages
| | city | small town | village | Missing |
|-----------------+---------+--------------+-----------+-----------|
| *never* | 1.69 | 0 | 0 | 0.14 |
| *very rarely* | 4.23 | 0.14 | 0.42 | 0.28 |
| *rarely* | 5.78 | 0.42 | 0.14 | 0.14 |
| *sometimes* | 9.45 | 0.56 | 1.13 | 1.13 |
| *often* | 14.25 | 1.41 | 0.71 | 0.99 |
| *very often* | 12.41 | 0.71 | 0.71 | 1.41 |
| *always* | 31.88 | 1.27 | 0.99 | 2.4 |
| *Missing* | 4.8 | 0.14 | 0.14 | 0.14 |
| *Sum* | 84.49 | 4.65 | 4.23 | 6.63 |
#+CAPTION: Total percentages: "email" and "dwell" (continued below)
| | Sum |
|-----------------+---------|
| *never* | 1.83 |
| *very rarely* | 5.08 |
| *rarely* | 6.49 |
| *sometimes* | 12.27 |
| *often* | 17.35 |
| *very often* | 15.23 |
| *always* | 36.53 |
| *Missing* | 5.22 |
| *Sum* | 100 |
| | city | small town | village | Missing |
|-----------------+---------+--------------+-----------+-----------|
| *never* | 92.31 | 0 | 0 | 7.69 |
| *very rarely* | 83.33 | 2.78 | 8.33 | 5.56 |
| *rarely* | 89.13 | 6.52 | 2.17 | 2.17 |
| *sometimes* | 77.01 | 4.6 | 9.2 | 9.2 |
| *often* | 82.11 | 8.13 | 4.07 | 5.69 |
| *very often* | 81.48 | 4.63 | 4.63 | 9.26 |
| *always* | 87.26 | 3.47 | 2.7 | 6.56 |
| *Missing* | 91.89 | 2.7 | 2.7 | 2.7 |
| *Sum* | 84.49 | 4.65 | 4.23 | 6.63 |
#+CAPTION: Row percentages: "email" and "dwell"
| | city | small town | village | Missing |
|-----------------+---------+--------------+-----------+-----------|
| *never* | 2 | 0 | 0 | 2.13 |
| *very rarely* | 5.01 | 3.03 | 10 | 4.26 |
| *rarely* | 6.84 | 9.09 | 3.33 | 2.13 |
| *sometimes* | 11.19 | 12.12 | 26.67 | 17.02 |
| *often* | 16.86 | 30.3 | 16.67 | 14.89 |
| *very often* | 14.69 | 15.15 | 16.67 | 21.28 |
| *always* | 37.73 | 27.27 | 23.33 | 36.17 |
| *Missing* | 5.68 | 3.03 | 3.33 | 2.13 |
#+CAPTION: Column percentages: "email" and "dwell" (continued below)
| | Sum |
|-----------------+---------|
| *never* | 1.83 |
| *very rarely* | 5.08 |
| *rarely* | 6.49 |
| *sometimes* | 12.27 |
| *often* | 17.35 |
| *very often* | 15.23 |
| *always* | 36.53 |
| *Missing* | 5.22 |
*** Tests of Independence
In the below tests for
[[http://en.wikipedia.org/wiki/Independence_(probability_theory)][independece]]
we assume that the row and column variables are independent of each
other. If this [[http://en.wikipedia.org/wiki/Null_hypothesis][null
hypothesis]] would be rejected by the tests, then we can say that the
assumption must have been wrong, so there is a good chance that the
variables are associated.
**** Chi-squared test
One of the most widespread independence test is the
[[http://en.wikipedia.org/wiki/Pearson%27s_chi-squared_test][Chi-squared
test]]. While using that we have the alternative hypothesis, that two
variables have an association between each other, in opposite of the
null hypothesis that the variables are independent.
We use the cell frequencies from the crosstables to calculate the test
statistic for that. The test statistic is based on the difference
between this distribution and a theoretical distribution where the
variables are independent of each other. The distribution of this test
statistic follows a
[[http://en.wikipedia.org/wiki/Chi-squared_distribution][Chi-square
distribution]].
The test was invented by Karl Pearson in 1900. It should be noted that
the Chi-squared test has the disadvantage that it is sensitive to the
sample size.
***** Criteria
Before analyzing the result of the Chi-squared test, we have to check if
our data meets some requirements. There are two widely used criteria
which have to take into consideration, both of them are related to the
so-called expected counts. These expected counts are calculated from the
marginal distributions and show how the crosstabs would look like if
there were complete independency between the variables. The Chi-squared
test calculates how different are the observed cells from the expected
ones.
The two criteria are:
- none of the expected cells could be lower than 1
- 80% of the expected cells have to be at least 5
Let's look at on expected values then:
| | city | small town | village |
|-----------------+---------+--------------+-----------|
| *never* | 10.83 | 0.6134 | 0.5559 |
| *very rarely* | 30.69 | 1.738 | 1.575 |
| *rarely* | 40.62 | 2.3 | 2.085 |
| *sometimes* | 71.3 | 4.038 | 3.66 |
| *often* | 104.7 | 5.93 | 5.374 |
| *very often* | 88.45 | 5.01 | 4.54 |
| *always* | 218.4 | 12.37 | 11.21 |
We can see that the Chi-squared test met the requirements.
So now check the result of the test:
| Test statistic | df | P value |
|------------------+------+-----------|
| 14.86 | 12 | /0.249/ |
#+CAPTION: Pearson's Chi-squared test: =table=
To decide if the null or the alternative hypothesis could be accepted we
need to calculate the number of degrees of freedom. The degrees of
freedom is easy to calculate, we substract one from the number of the
categories of both the row and the coloumn variables and multiply them
with each other.
To each degrees of freedom there is denoted a
[[http://en.wikipedia.org/wiki/Critical_value#Statistics][critical
value]]. The result of the Chi-square test have to be lower than that
value to be able to accept the nullhypothesis.
The requirements of the chi-squared test was not met, so
[[http://en.wikipedia.org/wiki/Yates%27s_correction_for_continuity][Yates's
correction for continuity]] applied. The approximation may be incorrect.
It seems that no real association can be pointed out between /email/ and
/dwell/ by the /Pearson's Chi-squared test/ ($\chi$=/14.86/ at the
degree of freedom being /12/) at the significance level of /0.249/.
***** References
- Fisher, R. A. (1922): On the interpretation of chi-square from
contingency tables, and the calculation of P. /Journal of the Royal
Statistical Society/ 85 (1): 87-94.
- Fisher, R.A. (1954): /Statistical Methods for Research Workers/.
Oliver and Boyd.
***** Adjusted standardized residuals
The residuals show the contribution to reject the null hypothesis at a
cell level. An extremely high or low value indicates that the given cell
had a major effect on the resulting chi-square, so thus helps
understanding the association in the crosstable.
| | city | small town | village |
|-----------------+---------+--------------+-----------|
| *never* | 1.15 | -0.81 | -0.77 |
| *very rarely* | -0.41 | -0.59 | 1.2 |
| *rarely* | 0.2 | 0.49 | -0.8 |
| *sometimes* | -1.75 | -0.02 | 2.49 |
| *often* | -1.28 | 1.9 | -0.18 |
| *very often* | -0.17 | 0 | 0.24 |
| *always* | 2.1 | -1.26 | -1.64 |
#+CAPTION: Residuals: "email" and "dwell"
Based on Pearson's residuals the following cells seems interesting (with
values higher than =2= or lower than =-2=):
- "always - city"
- "sometimes - village"
***** References
- Snedecor, George W. and Cochran, William G. (1989): /Statistical
Methods/. Iowa State University Press.
- Karl Pearson (1900): /Philosophical Magazine/, Series 5 50 (302):
157-175.
**** Fisher Exact Test
An other test to check the possible association/independence between two
variables, is the
[[http://en.wikipedia.org/wiki/Fisher%27_exact_test][Fisher exact
test]]. This test is especially useful with small samples, but could be
used with bigger datasets as well.
We have the advantage while using the Fisher's over the Chi-square test,
that we could get an exact significance value not just a level of it,
thus we can have an impression about the power of the test and the
association.
The test was invented by, thus named after R.A. Fisher.
The test could not finish within resource limits.
*** Charts
If one would like to investigate the relationships rather visually than
in a crosstable form, there are several possibilities to do that.
****** Heat map
At first we can have a look at on the so-called
[[http://en.wikipedia.org/wiki/Heat_map][heat map]]. This kind of chart
uses the same amount of cells and a similar form as the crosstable does,
but instead of the numbers there are colours to show which cell contains
the most counts (or likewise the highest total percentages).
The darker colour is one cell painted, the most counts/the higher total
percentage it has.
[[plots/Crosstable-5-hires.png][[[plots/Crosstable-5.png]]]]
There can be also shown the standardized adjusted residual of each
cells:
[[plots/Crosstable-6-hires.png][[[plots/Crosstable-6.png]]]]
****** Mosaic chart
In front of the heat map, on the /mosaic charts/, not only the colours
are important. The size of the cells shows the amount of the counts one
cell has.
The width on the axis of email determinate one side and the height on
the axis of the dwell gives the final shape of the box. The box which
demonstrates a cell from the hypothetic crosstable. We can see on the
top of the chart which category from the dwell draw the boxes what kind
of colour.
[[plots/Crosstable-7-hires.png][[[plots/Crosstable-7.png]]]]
****** Fluctuation diagram
At last but not least have a glance on the /fluctuation diagram/. Unlike
the above two charts, here the colours does not have influence on the
chart, but the sizes of the boxes, which obviously demonstrates here as
well the cells of the crosstable.
The bigger are the boxes the higher are the numbers of the counts/the
total percentages, which that boxes denote.
[[plots/Crosstable-8-hires.png][[[plots/Crosstable-8.png]]]]
--------------
This report was generated with [[http://www.r-project.org/][R]] (3.0.1)
and [[https://rapporter.github.io/rapport/][rapport]] (0.51) in /7.099/ sec on
x86\_64-unknown-linux-gnu platform.
[[images/logo.png]]