| Title: | Linear Model Functions |
|---|---|
| Description: | Functions to access and test results from a linear model. |
| Authors: | Jared Studyvin [aut, cre] |
| Maintainer: | Jared Studyvin <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.0.2 |
| Built: | 2024-10-31 21:15:41 UTC |
| Source: | CRAN |
Produces the overall ANOVA table where the model sum of squares are not partioned into their parts.
anovaTable(object, ...)anovaTable(object, ...)
object |
lm or aov model object |
... |
currently ignored |
Object of class anova and data.frame
data(depression) ## MLR model modMLR <- lm(depress~trauma+control,data=depression) anovaTable(modMLR) ## ANOVA model depression$gender <- factor(depression$gender) depression$history <- factor(depression$history) modAOV <- lm(depress~-1+gender+history+gender:history,data=depression) anovaTable(modAOV)data(depression) ## MLR model modMLR <- lm(depress~trauma+control,data=depression) anovaTable(modMLR) ## ANOVA model depression$gender <- factor(depression$gender) depression$history <- factor(depression$history) modAOV <- lm(depress~-1+gender+history+gender:history,data=depression) anovaTable(modAOV)
Contrast testing function. Designed to test contrasts of parameter estimates from a linear model.
contrastTest( estVec, n, dfModel, dfError, mse, C = NULL, test = c("scheffe", "bonferroni", "hsd", "lsd"), ... )contrastTest( estVec, n, dfModel, dfError, mse, C = NULL, test = c("scheffe", "bonferroni", "hsd", "lsd"), ... )
estVec |
numeric vector of parameter estimates for comparison |
n |
numeric vector indicating the sample size for the parameter estimates, if a single value is given it is assumed to apply to all estiamtes |
dfModel |
numeric value for the model degrees of freedom |
dfError |
numeric value for the error or residual degrees of freedom |
mse |
numeric value for the mean squared error from the model |
C |
numeric matrix, each row is a contrast that should sum to zero, see details |
test |
character, indicating which testing method should be used, see details |
... |
currently ignored |
The test argument can be one of the following: 'scheffe','bonferroni','hsd', or 'lsd'. 'hsd' is the Tukey HSD test. 'lsd' is th Fisher LSD test. The other two are the Scheffe test and Bonferroni adjustment.
The matrix C is the contrast matrix. Each row is a separate contrast. The number of columns of C must be equal to the length(estVec). Row names for C are retained in the output, but they are not required.
Object of class anova and data.frame
data(genericData) mod <- lm(Y~A,data=genericData) dfModel <- anovaTable(mod)['Model','df'] dfError <- anovaTable(mod)['Residual','df'] mse <- anovaTable(mod)['Residual','MS'] meanVec <- aggregate(Y~A,FUN=mean,data=genericData)$Y n <- aggregate(Y~A,FUN=length,data=genericData)$Y ## can add names for ease of interpretation with the output names(meanVec) <- c('group 1','group 2','group 3') contrastTest(estVec=meanVec,n=n,dfModel=dfModel,dfError=dfError,mse=mse,test='hsd') ## each group vs the mean of the other two C <- rbind(c(1,-0.5,-0.5),c(-0.5,1,-0.5),c(-0.5,-0.5,1)) ## row names are not required but are helpful row.names(C) <- c('1 vs 2+3','2 vs 1+3','3 vs 1+2') contrastTest(estVec=meanVec,n=n,dfModel=dfModel,dfError=dfError,mse=mse,C=C,test='scheffe')data(genericData) mod <- lm(Y~A,data=genericData) dfModel <- anovaTable(mod)['Model','df'] dfError <- anovaTable(mod)['Residual','df'] mse <- anovaTable(mod)['Residual','MS'] meanVec <- aggregate(Y~A,FUN=mean,data=genericData)$Y n <- aggregate(Y~A,FUN=length,data=genericData)$Y ## can add names for ease of interpretation with the output names(meanVec) <- c('group 1','group 2','group 3') contrastTest(estVec=meanVec,n=n,dfModel=dfModel,dfError=dfError,mse=mse,test='hsd') ## each group vs the mean of the other two C <- rbind(c(1,-0.5,-0.5),c(-0.5,1,-0.5),c(-0.5,-0.5,1)) ## row names are not required but are helpful row.names(C) <- c('1 vs 2+3','2 vs 1+3','3 vs 1+2') contrastTest(estVec=meanVec,n=n,dfModel=dfModel,dfError=dfError,mse=mse,C=C,test='scheffe')
Self reported level of depression and other associated metrics.
data(depression)data(depression)
An object of class data.frame with 50 rows and 13 columns.
This is a fictious dataset useful for teaching how to use and interpret linear statistical models. The variables are:
Level of Education: (1) professional degree (non-college), (2) 2 years of college, (3) 2+ years of college, but not a BS degree, (4) BS degree, (5) MS degree
Annual Income: 1 = $10,0001 to $19,999; 2 = $20,000 to $29,999; ... 9 = $90,000 to $99,999; 10 = $100,000 or more
Experience of Trauma; Percent of Life Events Viewed as Traumatic: 0 = 0%, 1 = 10%, 2= 20%, ..., 9 = 90%, 10 = 100%
Satisfied with your Life: 0 = No, 1 = Yes
Feeling of Control; How much do you feel in control: 0 = Not at all, 1 = A Little, 2 = Some, 3 = A Lot, 4 = Completely
Family History of Depression: 0 = No, 1 = Yes
Weekly Amount of Exercise: 0 = None, 1 = 1 Hour, 2 = 2 Hours, 3 = 3 Hours, 4 = 4 Hours, 5 = 5 or more Hours
3-methoxy-4-hydroxyphenylethyleneglycol, Depression Related Chemical Secreted in Urine; milligrams secreted per 24 hour period, labeled as mg/24h: 0 = 0 mg/24h, 1 = 100 mg/24h,..., 9 = 900 mg/24h, 10 = 1000+ mg/24h
Amount of Sleep Problems: 0 = None, 1 = 10% of the time, ... , 9 = 90% of the time, 10 = 100% of the time
Perceived Level of Depression: 0 = None, 1 = 10% of the time, ... , 9 = 90% of the time, 10 = 100% of the time
Do I consider myself depressed: 0 = No, 1 = Yes
Feeling of Well Being; how often do you feel good about yourself: 0 = None, 1 = 10% of the time, ... , 9 = 90% of the time, 10 = 100% of the time
Your Sex: 0 = Male, 1 = Female
Generic data set with four ratio predictors (X1,X2,X3,X4), two categorical predictors (A,B) and one ratio response variable (Y).
data(depression)data(depression)
An object of class data.frame with 60 rows and 7 columns.
This is a fictious dataset useful for teaching how to use and interpret linear statistical models.