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sasLM (version 0.6.6)

'SAS' Linear Model

Description

This is a core implementation of 'SAS' procedures for linear models - GLM, REG, and ANOVA. Some R packages provide type II and type III SS. However, the results of nested and complex designs are often different from those of 'SAS.' Different results does not necessarily mean incorrectness. However, many wants the same results to SAS. This package aims to achieve that. Reference: Littell RC, Stroup WW, Freund RJ (2002, ISBN:0-471-22174-0).

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Version

Install

install.packages('sasLM')

Monthly Downloads

1,004

Version

0.6.6

License

GPL-3

Maintainer

Kyun-Seop Bae

Last Published

February 19th, 2022

Functions in sasLM (0.6.6)

F Test with a Set of Contrasts

An Example Data of Bioequivalence Study

Confidence Interval Estimation

Analysis BY variable

Coefficient of Variation in percentage

Collinearity Diagnostics

Correlation test of multiple numeric columns

Analysis of Variance similar to SAS PROC ANOVA

Internal Functions

Lower Confidence Limit

Standard Deviation

Convert some columns of a data.frame to factors

Estimate Linear Functions

Univariate Descriptive Statistics

Estimability Check

Mean without NA

Table Summary 3 independent(x) variables

General Linear Model similar to SAS PROC GLM

Generalized inverse matrix of type 2, g2 inverse

Inter-Quartile Range

Median without NA

Expected Mean Square Formula

Sum of Square with a Given Contrast Set

Estimate Linear Function

Least Square Means

Plot Pairwise Differences

Regression of Linear Least Square, similar to SAS PROC REG

F Test with Slice

Standard Error of the Sample Mean

Get a Contrast Matrix for Type I SS

Get a Contrast Matrix for Type II SS

Partial Correlation test of multiple columns

Pairwise Difference

ANOVA with Type I SS

Standard Error of Skewness

Regression of Conventional Way with Rich Diagnostics

Type III Expected Mean Square Formula

Standard Error of Kurtosis

Is it a corrleation matrix?

Table Summary 1 independent(x) variable

'SAS' Linear Model

Number of observations

Table Summary 2 independent(x) variables

Linear Regression with g2 inverse

Get a Contrast Matrix for Type III SS

Simple Linear Regressions with Each Independent Variable

Satterthwaite Approximation of Pooled Variance and Degree of Freedom

Test Type III SS using error term other than MSE

Plot Confidence and Prediction Bands for Simple Linear Regression

Diagnostic Plot for Regression

ANOVA with Type II SS

Geometric Coefficient of Variation in percentage

Geometric Mean without NA

ANOVA with Type III SS

Beautify the output of knitr::kable

Upper Confidence Limit

Table Summary 0 independent(x) variable