「Table-related commands in STATA」の版間の差分

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!summarize
 
!summarize
 
|[[file:summarize_data1_bysort_disease.jpg]]
 
|[[file:summarize_data1_bysort_disease.jpg]]
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|}
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==Two-way with proportions==
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===Summary of ''factorA'' based on ''sex'' with proportions===
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{|class="wikitable"
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|-
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!rowspan="2"|table
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|[[file:table_sex_factorA_statistic(percent).jpg]]
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|This calculates proportions of cells compared to the whole without showing raw values
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|-
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|[[file:table_sex_factorA_statistic(percent_across(sex)).jpg]]
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|This calculates proportions in column (longitudinal) directions
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|-
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!tabulate
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|[[file:tabulate_sex_factorA_column.jpg]]
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|This calculates proportions in column (longitudinal) directions
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|}
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===Summary of ''sex'' based on ''factorA''===
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{|class="wikitable"
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|-
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!table
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|[[file:table_factorA_sex.jpg]]
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|rowspan="2"|Both creates the same table<br> but '''tabulate''' is better visualized
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|-
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!tabulate
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|[[file:tabulate_factorA_sex.jpg]]
 
|}
 
|}
  

2023年4月2日 (日) 18:44時点における版

Abbreviations of commands

table (no abbv.)
tabulate ta
tab
tabstat (no abbv.)
summarize su

Differences between table, tabulate, tabstat, summarize

one-way two-way options
table 
table v1

create a one-way table
of v1 with simple statistics 

table v1 v2

create a two-way table
of v1 in row† and v2 in column† 

,statistics( )
tabulate 
tabulate v1

create a one-way table
of v1 with detailed statistics 

tabulate v1 v2

create a two-way table
with v1 in row† and v2 in column† 

,chi2

Pearson's chi-squared test; *only for two-way 

,summarize(v3)

detailed statistics for v3

tabstat 
tabstat v1

create a one-way table of v1
with detailed statistics

*no two- or multiple-way table 
,statistics( )
,by(v3)

detailed statistics for each of v3

summarize 
summarize v1

detailed statistics of v1

*no two- or multiple-way summary 
,detail

† row = transverse direction, column = longitudinal direction

Sample data

Suppose we have such a dataset in STATA.

STATAsample.jpg

One-way

Summary of sex, a binary variable

table Table sex.jpg Both reports frequency
but tabulate is more detailed
tabulate Tabulate sex.jpg
tabstat Tabstat sex.jpg Both reports mean
but summarize is more detailed
summarize Summarize sex.jpg

Summary of data1, a continuous variable

table Table data1.jpg Both reports frequency of each value,
which does not make sense
tabulate Tabulate data1.jpg
tabstat Tabstat data1.jpg Both reports mean
but summarize is more detailed
summarize Summarize data1.jpg

Summary of SES, a categorical variable

table Table SES.jpg Both reports frequency
but tabulate is more detailed
tabulate Tabulate SES.jpg
tabstat Tabstat SES.jpg Both reports mean
but summarize is more detailed
summarize Summarize SES.jpg

One-way, multiple

table *Both do not create one-way multiple table
tabulate
tabstat Tabstat sex data1 SES.jpg Reports mean in row (transverse) direction
summarize Summarize sex data1 SES.jpg Reports more details in column (longitudinal) direction

Two-way

Summary of factorA based on sex

table Table sex factorA.jpg Both creates the same table
but tabulate is better visualized
tabulate Tabulate sex factorA.jpg
tabstat Tabstat factorA by(sex).jpg Both reports mean
but summarize is more detailed;
needs bysort option before the command
summarize Summarize factorA bysort sex.jpg

Summary of sex based on factorA

table Table factorA sex.jpg Both creates the same table
but tabulate is better visualized
tabulate Tabulate factorA sex.jpg
tabstat Tabstat sex by(factorA).jpg Both reports mean
but summarize is more detailed;
needs bysort option before the command
summarize Summarize sex bysort factorA.jpg

Summary of data1 based on disease

table *Both do not create a meaningful table for continuous variable
tabulate
tabstat Tabstat data1 by(disease).jpg Both reports mean
but summarize is more detailed;
needs bysort option before the command
summarize Summarize data1 bysort disease.jpg

Two-way with proportions

Summary of factorA based on sex with proportions

table Table sex factorA statistic(percent).jpg This calculates proportions of cells compared to the whole without showing raw values
Table sex factorA statistic(percent across(sex)).jpg This calculates proportions in column (longitudinal) directions
tabulate Tabulate sex factorA column.jpg This calculates proportions in column (longitudinal) directions

Summary of sex based on factorA

table Table factorA sex.jpg Both creates the same table
but tabulate is better visualized
tabulate Tabulate factorA sex.jpg

Two-way, multiple

Summary of factorA, factorB, factorC based on disease

tabstat Tabstat factorABC by(disease).jpg
Tabstat factorABC by(disease) statistic(sum).jpg factorA,B,C are binary variables so summations of values provide the positivities of factorA,B,C
Tabstat factorABC by(disease) statistic(n).jpg statistic(n) (statistic(count) is the same) only counts observations with real values, which only tell non-missing observations
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