Specific flows for each statistical method

Luke W. Johnston

2016-07-17

This vignette goes over typical ‘constuction’ projects for each of the analysis designs. First, let’s load up mason!

library(mason)

T-tests

swiss %>% 
    design('t.test') %>% 
    add_settings() %>% 
    add_variables('yvars', c('Fertility', 'Agriculture')) %>% 
    add_variables('xvars', c('Examination', 'Education')) %>% 
    construct() %>% 
    scrub()
#> # A tibble: 4 x 12
#>        Yterms      Xterms estimate estimate1 estimate2 statistic
#>         <chr>       <chr>    <dbl>     <dbl>     <dbl>     <dbl>
#> 1 Agriculture   Education 39.68085  50.65957  10.97872 11.030289
#> 2 Agriculture Examination 34.17021  50.65957  16.48936  9.731731
#> 3   Fertility   Education 59.16383  70.14255  10.97872 25.730207
#> 4   Fertility Examination 53.65319  70.14255  16.48936 24.816482
#> # ... with 6 more variables: p.value <dbl>, parameter <dbl>,
#> #   conf.low <dbl>, conf.high <dbl>, method <fctr>, alternative <fctr>

Correlations

swiss %>% 
    design('cor') %>% 
    add_settings() %>% 
    add_variables('yvars', c('Fertility', 'Agriculture')) %>% 
    add_variables('xvars', c('Examination', 'Education')) %>% 
    construct() %>% 
    scrub()
#> # A tibble: 4 x 3
#>         Vars1       Vars2 Correlations
#>         <chr>       <chr>        <dbl>
#> 1 Examination   Fertility   -0.6458827
#> 2   Education   Fertility   -0.6637889
#> 3 Examination Agriculture   -0.6865422
#> 4   Education Agriculture   -0.6395225

Linear regression

swiss %>% 
    design('glm') %>% 
    add_settings() %>% 
    add_variables('yvars', c('Fertility', 'Agriculture')) %>% 
    add_variables('xvars', c('Examination', 'Education')) %>% 
    add_variables('covariates', 'Catholic') %>% 
    construct() %>% 
    scrub()
#> # A tibble: 12 x 10
#>         Yterms      Xterms        term    estimate  std.error   statistic
#>          <chr>       <chr>       <chr>       <dbl>      <dbl>       <dbl>
#> 1  Agriculture   Education (Intercept) 59.05963234 4.62225617 12.77723048
#> 2  Agriculture   Education     <-Xterm -1.39785668 0.25409607 -5.50129212
#> 3  Agriculture   Education    Catholic  0.16883756 0.05858401  2.88197372
#> 4  Agriculture Examination (Intercept) 82.30857657 8.71604359  9.44334155
#> 5  Agriculture Examination     <-Xterm -1.93530048 0.38062966 -5.08447097
#> 6  Agriculture Examination    Catholic  0.00638899 0.07281213  0.08774622
#> 7    Fertility   Education (Intercept) 74.23368920 2.35197061 31.56233713
#> 8    Fertility   Education     <-Xterm -0.78832926 0.12929324 -6.09721944
#> 9    Fertility   Education    Catholic  0.11092095 0.02980965  3.72097417
#> 10   Fertility Examination (Intercept) 83.03565524 4.97729702 16.68288128
#> 11   Fertility Examination     <-Xterm -0.88618608 0.21735858 -4.07706962
#> 12   Fertility Examination    Catholic  0.04179341 0.04157937  1.00514762
#> # ... with 4 more variables: p.value <dbl>, conf.low <dbl>,
#> #   conf.high <dbl>, sample.size <int>

Generalized estimating equations

data.frame(state.x77, state.region) %>% 
    design('gee') %>% 
    add_settings(cluster.id = 'state.region') %>% 
    add_variables('yvars', c('Income', 'Frost')) %>% 
    add_variables('xvars', c('Population', 'Murder')) %>% 
    add_variables('covariates', c('Life.Exp', 'Area')) %>% 
    add_variables('interaction', 'Area') %>% 
    construct() %>% 
    add_variables('xvars', c('Illiteracy')) %>%
    construct() %>%
    scrub()
#> Loading required namespace: geepack
#> xvars already exists in the specs, but will be replaced.
#> # A tibble: 30 x 12
#>    Yterms     Xterms         term      estimate    std.error   statistic
#>     <chr>      <chr>        <chr>         <dbl>        <dbl>       <dbl>
#> 1   Frost     Murder  (Intercept)  1.425879e+03 4.664546e+02  9.34429563
#> 2   Frost     Murder      <-Xterm -1.301990e+01 1.900418e+00 46.93720171
#> 3   Frost     Murder     Life.Exp -1.743501e+01 6.506417e+00  7.18059704
#> 4   Frost     Murder         Area  1.836322e-04 3.072173e-04  0.35727813
#> 5   Frost     Murder <-Xterm:Area -4.352580e-06 2.748875e-05  0.02507169
#> 6   Frost Population  (Intercept) -7.260855e+02 3.995192e+02  3.30293661
#> 7   Frost Population      <-Xterm  2.136489e-04 1.705346e-03  0.01569553
#> 8   Frost Population     Life.Exp  1.171606e+01 5.661719e+00  4.28219507
#> 9   Frost Population         Area  1.493809e-04 4.707465e-05 10.06969109
#> 10  Frost Population <-Xterm:Area -3.674397e-08 7.575809e-09 23.52415329
#> # ... with 20 more rows, and 6 more variables: p.value <dbl>,
#> #   conf.low <dbl>, conf.high <dbl>, sample.total <int>, sample.max <int>,
#> #   sample.min <int>