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sasLM (version 0.9.8)

'SAS' Linear Model

Description

This is a core implementation of 'SAS' procedures for linear models - GLM, REG, ANOVA, FREQ, and UNIVARIATE. 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

730

Version

0.9.8

License

GPL-3

Maintainer

Kyun-Seop Bae

Last Published

May 6th, 2023

Functions in sasLM (0.9.8)

Mean without NA

Generalized inverse matrix of type 2, g2 inverse

General Linear Model similar to SAS PROC GLM

Standard Error of Kurtosis

Odds Ratio and Score CI of two groups without strata by the MN method

Odds Ratio of two groups with strata by CMH method

Odds Ratio of two groups

Pairwise Difference

Estimate Linear Function

Median without NA

Risk Difference between two groups

Risk Difference between two groups with strata by inverse variance method

Relative Risk and Score CI of two groups with strata by the MN method

Odds Ratio and Score CI of two groups with strata by MN method

Relative Risk of two groups with strata by inverse variance method

Odds Ratio of two groups with strata by inverse variance method

Test with Random Effects

Relative Risk and Score CI of two groups without strata by by MN method

Number of observations

Regression of Linear Least Square, similar to SAS PROC REG

Relative Risk of the two groups

Risk Difference and Score CI between two groups with strata by the MN method

Risk Difference and Score CI between two groups without strata by the MN method

Standard Error of the Sample Mean

Upper Confidence Limit

Univariate Descriptive Statistics

F Test with Slice

Correlation test by Fisher's Z transformation

Get a Contrast Matrix for Type I SS

ANOVA with Type I SS

ANOVA with Type II SS

Partial Correlation test of multiple columns

Convert some columns of a data.frame to factors

Beautify the output of knitr::kable

Estimability Check

Estimate Linear Functions

Standard Deviation

Standard Error of Skewness

White's Model Specification Test

Plot Confidence and Prediction Bands for Simple Linear Regression

Sum of Square with a Given Contrast Set

Test Type III SS using error term other than MSE

Type III Expected Mean Square Formula

Residual Diagnostic Plot for Regression

Inter-Quartile Range

Get a Contrast Matrix for Type II SS

Table Summary 0 independent(x) variable

Regression of Conventional Way with Rich Diagnostics

Table Summary 1 independent(x) variable

Diagnostic Plot for Regression

'SAS' Linear Model

Satterthwaite Approximation of Variance and Degree of Freedom

Geometric Coefficient of Variation in percentage

Geometric Mean without NA

Table Summary 2 independent(x) variables

Table Summary 3 independent(x) variables

Linear Regression with g2 inverse

Simple Linear Regressions with Each Independent Variable

Get a Contrast Matrix for Type III SS

An example data for meta-analysis - aspirin in coronary heart disease

ANOVA with Type III SS

Score Confidence Interval for a Proportion or a Binomial Distribution

Is it a correlation matrix?

Example Datasets

Confidence Interval Estimation

Plot Pairwise Differences

Analysis BY variable

Coefficient of Variation in percentage

Collinearity Diagnostics

F Test with a Set of Contrasts

Correlation test of multiple numeric columns

Internal Functions

An Example Data of Bioequivalence Study

Lower Confidence Limit

Expected Mean Square Formula

Least Square Means