README.md

In monotonicity/stacmr: Conjoint Monotonic Regression approach to State-Trace Analysis

stacmr

The goal of stacmr is to provide functionality for state-trace analysis via conjoint monotonic regression. The main function for this is cmr(). In addition, it provides functionality for fitting monotonic regression models with function mr().

Installation

You can install the released version of stacmr from CRAN with:

## NOT YET AVAILABLE FROM CRAN

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("monotonicity/stacmr")

Example

This is a basic example.

library(stacmr)
#> Loading required package: rJava
## load data from Exp. 1 of Dunn, Newell, & Kalish (2012)
data(delay)
str(delay, width = 78, strict.width = "cut")
#> 'data.frame':    520 obs. of  5 variables:
#>  $ participant: Factor w/ 130 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 1..
#>  $ delay      : Factor w/ 2 levels "delay","no delay": 1 2 2 1 1 2 2 1 1 1 ...
#>  $ structure  : Factor w/ 2 levels "rule-based","information-integration": 1..
#>  $ block      : Factor w/ 4 levels "B1","B2","B3",..: 1 1 1 1 1 1 1 1 1 1 ...
#>  $ pc         : num  0.338 0.287 0.525 0.35 0.237 ...

stats <- sta_stats(
  data = delay, 
  col_value = "pc", 
  col_participant = "participant",
  col_dv = "structure", 
  col_within = "block", 
  col_between = "delay"
)
stats
#>   rule.based information.integration within  between N_rule.based
#> 1      0.344                   0.284     B1    delay           34
#> 2      0.443                   0.303     B2    delay           34
#> 3      0.508                   0.318     B3    delay           34
#> 4      0.517                   0.310     B4    delay           34
#> 5      0.368                   0.331     B1 no delay           34
#> 6      0.468                   0.455     B2 no delay           34
#> 7      0.576                   0.535     B3 no delay           34
#> 8      0.612                   0.549     B4 no delay           34
#>   N_information.integration
#> 1                        32
#> 2                        32
#> 3                        32
#> 4                        32
#> 5                        30
#> 6                        30
#> 7                        30
#> 8                        30


### cmr() fits conjoint monotonic regression for state-trace analysis
## Fit and test CMR State-Trace Analysis Model
st_d1 <- cmr(
  data = delay, 
  col_value = "pc", 
  col_participant = "participant",
  col_dv = "structure", 
  col_within = "block", 
  col_between = "delay", 
  nsample = 1e4
)
st_d1  ## basic information about conjoint-monotonic model
#> 
#> CMR fit to 8 data points with call:
#> cmr(data = delay, col_value = "pc", col_participant = "participant", 
#>     col_dv = "structure", col_within = "block", col_between = "delay", 
#>     nsample = 10000)
#> 
#> DVs: rule-based & information-integration 
#> Within: block 
#> Between: delay 
#> 
#> Fit value (SSE): 1.653
#> Fit difference to MR model (SSE): 1.653
#> p-value (based on 10000 samples): 0.347

summary(st_d1)  ## basic information plus estimated cell means
#> 
#> CMR fit to 8 data points with call:
#> cmr(data = delay, col_value = "pc", col_participant = "participant", 
#>     col_dv = "structure", col_within = "block", col_between = "delay", 
#>     nsample = 10000)
#> 
#> Fit value (SSE): 1.653
#> Fit difference to MR model (SSE): 1.653
#> p-value (based on 10000 samples): 0.347 
#> 
#> Estimated cell means:
#>   rule.based information.integration within  between
#> 1     0.3352                  0.2861     B1    delay
#> 2     0.4199                  0.3149     B2    delay
#> 3     0.4834                  0.3256     B3    delay
#> 4     0.4834                  0.3178     B4    delay
#> 5     0.3751                  0.3149     B1 no delay
#> 6     0.4834                  0.4357     B2 no delay
#> 7     0.5885                  0.5165     B3 no delay
#> 8     0.6252                  0.5316     B4 no delay

### cmr() also accepts partial order. 
## CMR model is tested against MR model with partial order 
st_d3 <- cmr(
  data = delay, 
  col_value = "pc", 
  col_participant = "participant",
  col_dv = "structure", 
  col_within = "block", 
  col_between = "delay", 
  partial = "auto"
)
st_d3
#> 
#> CMR fit to 8 data points with call:
#> cmr(data = delay, col_value = "pc", col_participant = "participant", 
#>     col_dv = "structure", col_within = "block", col_between = "delay", 
#>     partial = "auto")
#> 
#> DVs: rule-based & information-integration 
#> Within: block 
#> Between: delay 
#> 
#> Fit value (SSE): 1.749
#> Fit difference to MR model (SSE): 1.577
#> p-value (based on 1000 samples): 0.161
summary(st_d3)
#> 
#> CMR fit to 8 data points with call:
#> cmr(data = delay, col_value = "pc", col_participant = "participant", 
#>     col_dv = "structure", col_within = "block", col_between = "delay", 
#>     partial = "auto")
#> 
#> Fit value (SSE): 1.749
#> Fit difference to MR model (SSE): 1.577
#> p-value (based on 1000 samples): 0.161 
#> 
#> Estimated cell means:
#>   rule.based information.integration within  between
#> 1     0.3353                  0.2856     B1    delay
#> 2     0.4186                  0.3150     B2    delay
#> 3     0.4806                  0.3227     B3    delay
#> 4     0.4850                  0.3227     B4    delay
#> 5     0.3759                  0.3150     B1 no delay
#> 6     0.4850                  0.4358     B2 no delay
#> 7     0.5898                  0.5167     B3 no delay
#> 8     0.6265                  0.5318     B4 no delay

monotonicity/stacmr documentation built on Jan. 28, 2020, 3:29 a.m.