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ARM Models Sorted by Name

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Peter Li edited this page Aug 11, 2014 · 2 revisions

8 Schools

  • 8_schools: multi-level linear model with redundant parameterization

Congress

  • congress: linear model with two predictors
lm (vote_88 ~ vote_86   incumbency_88)

Dogs

  • dogs: multi-level logit regression model

  • dogs_log: multi-level model using binomial distribution

  • dogs_check: multi-level model using binomial distribution

Earnings

  • earnings: linear model with one predictor
lm (earnings ~ height)
  • earnings: linear model with one predictor and natural log transformation
lm (log_earnings ~ height)
  • earnings: linear model with one predictor and log10 transformation
lm (log10_earnings ~ height)
  • earnings: linear model with two predictors and natural log transformation
lm (log_earnings ~ height   male)
  • earnings: linear model with two predictors and interaction and natural log transformation
lm (log_earnings ~ height   male   height:male)
  • earnings: linear model with two predictors and interaction and natural log transformation centered using z-score
lm (log_earnings ~ z_height   male   z_height:male)
  • earnings: linear model with two predictors and log log transformation
lm (log_earnings ~ log_height   male)
  • earnings: generalized linear model with logit link function and two predictors
glm (earn_pos ~ height   male, family=binomial(link="logit"))
  • earnings: linear model with two predictors and log transformation
lm (log_earn ~ height   male, subset=earn>0)

lmer (y ~ x (1   x | ethn))

lmer (y ~ x.centered   (1   x.centered | eth)   (1   x.centered | age)   (1   x.centered | eth:age))
  • earnings: linear model with ten predictors
lm (earnings ~ male   over65   white   immig   educ_r   workmos   workhrs_top   any_ssi   any_welfare 
                 any_charity)
  • earnings_pt1: logistic regression model with eight predictors
glm (earnings ~ male   over65   white   immig   educ_r   any_ssi   any_welfare   any_charity,
     family=binomial(link="logit"))

lm (earnings ~ male   over65   white   immig   educ_r   any_ssi   any_welfare   any_charity)
  • earnings2: mlinear model with eleven predictors
lm (earnings ~ interest   male   over65   white   immig   educ_r   workmos   workhrs_top   any_ssi 
                 any_welfare   any_charity)

Election88

  • election88: multi-level logistic regression model with group level predictors
lmer (y ~ black   female   (1 | state), family=binomial(link="logit"))
  • election88_full: multi-level logistic regression model with group level predictors
lmer (y ~ black   female   black:female   v.prev.full   (1 | age)   (1 | edu)   (1 | age.edu) 
            (1 | state)   (1 | region.full), family=binomial(link="logit"))
  • election88: multi-level logistic regression model with redundant parameterization
lmer (y ~ female   black   female:black   (1 | age)   (1 | edu)   (1 | age_edu)   (1 | state), 
      family=binomial(link="logit"))

lmer (y ~ female   black   female:black   (1 | age)   (1 | edu)   (1 | age_edu)   (1 | state), 
      family=binomial(link="logit"))

Electric

  • electric: linear model with one predictor
lm (post_test ~ treatment)
  • electric: linear model with two predictors
lm (post_test ~ pre_test   treatment)
  • electric: linear model with two predictors and interaction
lm (post_test ~ pre_test   treatment   pre_test:treatment)
  • electric_1a: multi-level linear model with varying intercept and slope
lmer (y ~ 1   (1 | pair)   (treatment | grade))
  • electric_1b: multi-level linear model with varying intercept and slope
lmer (y ~ treatment   pre_test   (1 | pair))
  • electric_1c: multi-level linear model with group level factors
lmer (y ~ 1   (1 | pair)   (treatment | grade)   (pre_test | grade))
  • electric: multi-level linear model with varying intercept
lmer (y ~ treatment   (1 | pair))

Grades

  • grades: linear model with one predictor
lm (final ~ midterm)

HIV

  • hiv: multi-level linear model with varying slope and intercept
lmer (y ~ time   (1   time | person)
  • hiv_inter: multi-level linear model with interaction and varying slope and intercept
lmer (y ~ time:treatment   (1   time | person)

Ideo

  • ideo: linear model with two predictors
lm (score1 ~ party   x, subset=overlap)
  • ideo: linear model with two predictors
lm (score1 ~ party   x, subset=incs)
  • ideo: linear model with two predictors and reparamaterization
lm (score1 ~ party   I(z*(party==0))   I(z*(party==1)), subset=incs)
  • ideo: linear model with two predictors and interaction
lm (score1 ~ party   x   party:x, subset=incs)

Item Response

  • item_response: multi-level logistic regression model with parameter expansion
lmer (y ~ a:g   (a:g | k,j)   (g:b | k), family=binomial(link="logit"))

Kid IQ

  • kid_iq: linear model with one predictor
lm (kid_score ~ mom_hs)
  • kid_iq: linear model with one predictor
lm (kid_score ~ mom_iq)
  • kid_iq: linear model with two predictors
lm (kid_score ~ mom_hs   mom_iq)
  • kid_iq: linear model with two predictors and interaction
lm (kid_score ~ mom_hs   mom_iq   mom_hs:mom_iq)
  • kid_iq: linear model with two predictors
lm (ppvt ~ hs   afqt)
  • kid_iq: linear model with two predictors and interaction centered using mean
lm (kid_score ~ c_mom_hs   c_mom_iq   c_mom_hs:c_mom_iq)
  • kid_iq: linear model with two predictors and interaction centered using conventional points
lm (kid_score ~ c2_mom_hs   c2_mom_iq   c2_mom_hs:c2_mom_iq)
  • kid_iq: linear model with two predictors and interaction centered using z-score
lm (kid_score ~ z_mom_hs   z_mom_iq   z_mom_hs:z_mom_iq)
  • kid_iq: linear model with one factor
lm (kid_score ~ as.factor(mom_work))

Lightspeed

lm (y ~ 1)

Mesquite

  • mesquite: linear model with six predictors
lm (weight~ diam1   diam2   canopy_height   total_height   density   group)
  • mesquite: linear model with six predictors and log transformation
lm (log_weight~ log_diam1   log_diam2   log_canopy_height   log_total_height   log_density   group)
  • mesquite: linear model with one transformed predictor and log transformation
lm (log_weight ~ log_canopy_volume)
  • mesquite: linear model with three predictors and three transformed predictors and log transformation
lm (log_weight ~ log_canopy_volume   log_canopy_area   log_canopy_shape   log_total_height   log_density   group)
  • mesquite: linear model with one predictor and two transformed predictors and log transformation
lm (log_weight ~ log_canopy_volume   log_canopy_area   group)
  • mesquite: linear model with two predictors and three transformed predictors and log transformation
lm (log_weight ~ log_canopy_volume   log_canopy_area   log_canopy_shape   log_total_height   group)

NES

  • nes: linear model with eight predictors
lm (partyid7 ~ real_ideo   race_adj   age30_44   age45_64   age65up   educ1   gender   income)
  • nes: generalized linear model with logit link function and one predictor
glm (vote ~ income, family=binomial(link="logit"))

Pilots

  • pilots: non-nested multi-level linear model with group level predictors
lmer (y ~ 1   (1 | group.id) (1 | scenario.id))
  • pilots: multi-level linear model with varying intercept and redundant parameterization
lmer (y ~ 1   (1 | treatment)   (1 | airport))
  • pilots_expansion: multi-level linear model with varying intercept and parameter expansion
lmer (y ~ 1   (1 | treatment)   (1 | airport))

* [17.3_flight_simulator](https://github.com/stan-dev/example-models/blob/master/ARM/Ch.17/17.3_flight_simulator.stan): varying intercept model      

lmer(y ~ 1   (1 | treatment)   (1 | airport))

Radon

lmer (y ~ 1   (1 | county))

lm (y ~ x)

lmer (y ~ x   (1 | county))
  • radon_group: multi-level linear model with group level predictor and individual level predictors
lmer (y ~ x   u   (1 | county))
  • radon_vary_si: multi-level linear model with group level predictors
lmer (y ~ x (1   x | county))

lmer (y ~ x   u.full   x:u.full   (1   x | county))
  • radon_redundant: multi-level liner model with varying intercept and redundant parameterization
lmer (y ~ 1   (1 | county))

lmer (y ~ x   (1 | county))

lmer (y ~ x   (1 | county))
  • anova_radon_nopred: multi-level linear model with varying intercept and set up for ANOVA
lmer (y ~ 1   (1 | county))

lmer (y ~ u   x   (1 | county))

lmer (y ~ u   x   x_mean   (1 | county))

lmer (y ~ u   (1 | county))

Roaches

  • roaches: poisson regression model with exposure and three predictors
glm (y ~ roach1   treatment   senior, family=poisson, offset=log(exposure2))

glm(y ~ roach1   treatment   senior, family=quasipoisson, offset=log(exposure2))

Sesame

  • sesame: linear model with one predictor
lm (watched ~ encouraged)
  • sesame: linear model with one predictor
lm (y ~ encouraged)
  • sesame: linear model with one predictor
lm (y ~ watched_hat)
  • sesame: linear model with three predictors and one factor
lm (watched ~ encouraged   pretest   as.factor(site)   setting)
  • sesame: linear model with three predictors and one factor
lm (y ~ watched_hat   pretest   as.factor(site)   setting)

Statistical Calculations

lmer (y ~ theta (theta | j))
  • r_sqr: multi-level linear model with appropriate calculations for R^2
lmer (y ~ 1   (1   x | county))
  • finite_populations: linear model with appropriate calculations for calculating the standard deviation of a finite population
lm (g ~ u_1   u)

Unemployment

lm (y ~ y_lag)

Weight

  • weight: centered linear model
lm (y ~ c_height   1)

Wells

  • wells: generalized linear model with logit link function and one predictor
glm (switc ~ dist, family=binomial(link="logit"))
  • wells: generalized linear model with logit link function and one predictor
glm (switc ~ dist100, family=binomial(link="logit"))
  • wells: generalized linear model with logit link function and two predictors and interaction
glm (switc ~ dist100   arsenic   dist100:arsenic, family=binomial(link="logit"))
  • wells: generalized linear model with logit link function with two predictors and interaction centered using mean
glm (switc ~ c_dist100   c_arsenic   c_dist100:c_arsenic, family=binomial(link="logit"))
  • wells: generalized linear model with logit link function and four predictors and interaction centered using mean
glm (switc ~ c_dist100   c_arsenic   c_dist100:c_arsenic   assoc   educ4, family=binomial(link="logit"))
  • wells: generalized linear model with logit link function and three predictors and interaction centered using mean
glm (switc ~ c_dist100   c_arsenic   c_dist100:c_arsenic   educ4, family=binomial(link="logit"))
  • wells: generalized linear model with logit link function and three predictors and interaction centered using mean
glm (switc ~ c_dist100   c_arsenic   c_educ4   c_dist100:c_arsenic   c_dist100:c_educ4   c_arsenic:c_educ4, 
     family=binomial(link="logit"))
  • wells: generalized linear model with logit link function with three predictors and interaction with log transform and centered using mean
glm (switc ~ c_dist100   c_log_arsenic   c_educ4   c_dist100:c_log_arsenic   c_dist100:c_educ4     
               c_log_arsenic:c_educ4, 
     family=binomial(link="logit"))
  • wells: generalized linear model with logit link function with three predictors and interaction with log transform and centered using mean
glm (switc ~ dist100   log_arsenic   educ4   dist100:log_arsenic   dist100:educ4   log_arsenic:educ4, 
     family=binomial(link="logit"))
  • wells: generalized linear model with logit link function and three predictors
glm (switc ~ dist100   arsenic   educ4, family=binomial(link="logit"))
  • wells: generalized linear model with logit link function and three predictors with interaction
glm (switc ~ dist100   arsenic   educ4   dist100:arsenic, family=binomial(link="logit"))
  • wells: generalized linear model with probit link function and one predictor
glm (switc ~ dist100, family=binomial(link="probit"))
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