wjhopper

hwanalysis <- function(hwnumber = NULL, base_graphing=FALSE) { 

  #   A standalone R function, so to use it, use source("hwanalysis.R") from the command line and then call hwanalysis() with 
  #     1). the numeric number of the HW you want to analyze grades for and 
  #     2). the graphing style you want. The default base_graphing= FALSE uses ggplot (requires ggplot2 and dplyr be
  #         installed). base_graphing=TRUE gives you old school base R plots, thus no additional dependencies. (but we should all
  #         have ggplot2 and dplyr anyway right?)

  if (is.null(hwnumber)) {
    stop('hwnumber must be an integer which defines which hw you want to analyze grades for!')

wjhopper

function resps = getSubjectInfo(varargin)

<<<<<<< HEAD
% Calling
% c=struct('radio',struct('eeg',struct('label','EEG Experiment?')), ...
%     'textinput',struct('subNum',struct('label','Subject Number')),...
%     'dropdown',struct('session',struct('label','Session','val',[1 2])));
=======
% Calling Example 
% c = { struct('name','sub_num','type','textinput','label','Subject Number','classcheck',@(x) (isnumeric(x) && ~isnan(x))), ...

wjhopper

function factor_cell_mat = factor_magic(nrow, factors)
% Calling example: 
% factors = {[1,2],[.25,.75] ;
%     [1,2],[.5,.5] ;
%     [1,2,3],[1/3,1/3,1/3] }
% factor_magic(2,48,factors)
factor_cell_mat = cell(nrow,size(factors,1));

for i=1:size(factors,1)
    nFactors = length(unique(factors{i,1}));

wjhopper

matlab -r "cd('C:\Users\will\Documents\CEMNL\Testing Differentially Strengthens Recovery\Runfiles\');path(path,pwd);main"

wjhopper

    function onset = draw(cue,msg,string,drawExtra)
        drawExtra(params{:}) 
        DrawFormattedText(windowPtr,cue,'right', 'center',[],[],[],[],[],[],left-[0 0 spacing 0]);
        DrawFormattedText(windowPtr,msg, 'center','center');
        DrawFormattedText(windowPtr,string,right(1)+spacing, 'center');
        [~, onset] = Screen('Flip', windowPtr);
    end

wjhopper

ratingsROC <- function(Fcount =c(2,8,37,23,30), Hcount = c(61,15,15,5,4)) { 
  #R script to fit ROC data from confidence rating task
  #CMR + WJH Mar 26 2015

  #data from Macmillan & Creelman 2005 Example 3b
  #Low Frequency words, confidence rating recognition task
  # Counts are ordered from 'sure old' to 'sure new' response for each stim class
  
  fa_probs <- cumsum(Fcount)/sum(Fcount)
  h_probs <- cumsum(Hcount)/sum(Hcount)

wjhopper

library(dplyr)
library(reshape2)
library(ggplot2)

n <- 500
null <- .75
obs_n <- seq(1,n-1)
binom_probs_low <- pbinom(obs_n, n, null)
binom_probs_upp <- pbinom(obs_n, n, null,lower.tail = FALSE)
binom_probs <- pmin(binom_probs_low, binom_probs_upp)

wjhopper

 cat /var/log/dpkg.log |awk '/ removed / {printf "%s\n",$0}'

wjhopper

## Some sort of list comprehension using only functional programming 
## techniques that returns a list holding the coefficients of variation
## (i.e. the mean divided by the standard deviation) for each column in the 
## mtcars data set. 

## This isn't the clearest code (i.e. its really hard to tell its dividing
## the mean by the standard deviation) but it uses absolutely no imperative control 
## flow structures and no vectorized R functions, so if you can read and understand
## this you can do you some functional programming for great good =). I can't believe I
## actually figured this out, much less that I figured it out in like 5 minutes, 

wjhopper

%--------------------------------------------------------%
% Onscreen script to record race/ethnic/sex demographics %
% for Matlab                                             %
% Updated 09/21/2015                                     %
%--------------------------------------------------------%

%{
Purpose:
A Matlab script which will generate a set of dialog boxes that ask 
participants an assortment of questions regarding demographics (in