CORNELLSASLAB

SAS output to EXCEL converter for Cornell/MIT Language and acquisition lab

Instructions:

This python code can be used to convert SAS outputs from the lang and ac lab into data tables for regression and factorial summaries. SAS formst varies by run, therefore you may have to do a little editing before the code works.

For example, a working data chunk for this program would look something like this:

                  Differences of CHILDAGE*TASK*TR Least Squares Means

                                                     Standard
CHILDAGE  TASK  TR  _CHILDAGE  _TASK  _TR  Estimate     Error     DF  t Value  Pr > |t|

1         2     1   8          1      1     -0.4167    0.1251   1544    -3.33    0.0009
1         2     1   8          1      2     -0.2917    0.1251   1544    -2.33    0.0199
1         2     2   7          2      3     -0.4375    0.1363   1544    -3.21    0.0014
1         2     2   8          1      1     -0.4583    0.1251   1544    -3.66    0.0003

The SAS System 15:01 Sunday, August 4, 2019 139

                                 The GLIMMIX Procedure

                  Differences of CHILDAGE*TASK*TR Least Squares Means

                                                     Standard
CHILDAGE  TASK  TR  _CHILDAGE  _TASK  _TR  Estimate     Error     DF  t Value  Pr > |t|

1         2     2   8          1      2     -0.3333    0.1251   1544    -2.66    0.0078
1         2     2   8          1      3     -0.5625    0.1251   1544    -4.50    <.0001
1         2     2   8          2      1     -0.5833    0.1363   1544    -4.28    <.0001
1         2     3   7          2      3     -0.4167    0.1363   1544    -3.06    0.0023
1         2     3   8          1      1     -0.4375    0.1251   1544    -3.50    0.0005
1         2     3   8          1      2     -0.3125    0.1251   1544    -2.50    0.0126
1         2     3   8          1      3     -0.5417    0.1251   1544    -4.33    <.0001

The SAS System 15:01 Sunday, August 4, 2019 140

                                 The GLIMMIX Procedure

                  Differences of CHILDAGE*TASK*TR Least Squares Means

                                                     Standard
CHILDAGE  TASK  TR  _CHILDAGE  _TASK  _TR  Estimate     Error     DF  t Value  Pr > |t|

1         2     3   8          2      1     -0.5625    0.1363   1544    -4.13    <.0001
1         2     3   8          2      2     -0.5208    0.1363   1544    -3.82    0.0001
1         2     3   8          2      3     -0.4583    0.1363   1544    -3.36    0.0008
2         1     1   2          1      2    6.66E-16   0.07654   1544     0.00    1.0000
2         1     1   2          1      3    -0.08333   0.07654   1544    -1.09    0.2764
2         1     1   8          2      1     -0.4583    0.1251   1544    -3.66    0.0003
2         1     1   8          2      2     -0.4167    0.1251   1544    -3.33    0.0009
2         1     1   8          2      3     -0.3542    0.1251   1544    -2.83    0.0047

The SAS System 15:01 Sunday, August 4, 2019 141

As you can see, every chunk starts with the same title, and ends with a date and page number in the bottom right corner. All of the chunks have the same format and same number of columns. If the format of the pasted chunks is wrong, the code likely won't work.

NOTE: when pasting the file path to your excel document, make sure excel is not running on your computer, or else there will be an error.

SAS output to EXCEL converter for Cornell/MIT Language and acquisition lab

Related tags

Overview

CORNELLSASLAB

Owner

PyTorch implementation of "Supervised Contrastive Learning" (and SimCLR incidentally)

Wordle-solver - Wordle answer generation program in python

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The PyTorch re-implement of a 3D CNN Tracker to extract coronary artery centerlines with state-of-the-art (SOTA) performance. (paper: 'Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classiﬁer')

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The codebase for our paper "Generative Occupancy Fields for 3D Surface-Aware Image Synthesis" (NeurIPS 2021)

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SeMask: Semantically Masked Transformers for Semantic Segmentation.

MetaBalance: Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks

Unified API to facilitate usage of pre-trained "perceptor" models, a la CLIP

Python implementation of "Elliptic Fourier Features of a Closed Contour"

Robust fine-tuning of zero-shot models

Implementation of the 😇 Attention layer from the paper, Scaling Local Self-Attention For Parameter Efficient Visual Backbones

Dynamic Capacity Networks using Tensorflow

[3DV 2021] A Dataset-Dispersion Perspective on Reconstruction Versus Recognition in Single-View 3D Reconstruction Networks

Official PyTorch implementation of "AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks"