Package 'BetterReg'

Power for Comparing Dependent Coefficients in Multiple Regression with Two or Three Predictors Requires correlations between all variables as sample size. Means, sds, and alpha are option. Also computes Power(All)

Description

Power for Comparing Dependent Coefficients in Multiple Regression with Two or Three Predictors Requires correlations between all variables as sample size. Means, sds, and alpha are option. Also computes Power(All)

Usage

depbcomp(
  data = NULL,
  y = NULL,
  x1 = NULL,
  x2 = NULL,
  x3 = NULL,
  x4 = NULL,
  x5 = NULL,
  numpred = NULL,
  comps = "abs"
)

Arguments

data	name of data file
y	dependent variable name
x1	first predictor variable name
x2	second predictor variable name
x3	third predictor variable name
x4	fourth predictor variable name
x5	fifth predictor variable name
numpred	number of predictors
comps	Type of comparison, "abs" for absolute values or "raw" for raw coefficients

Value

Comparing Dependent Coefficients in Multiple Regression

Examples

depbcomp(data=testreg,y="y",x1="x1",x2="x2",x3="x3",x4="x4",x5="x5", numpred=5,comps="abs")

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Comparing Independent Coefficients in Multiple Regression

Description

Comparing Independent Coefficients in Multiple Regression

Usage

indbcomp(model1 = NULL, model2 = NULL, comps = "abs", pred = NULL)

Arguments

model1	Summary of first model (see example for how to summarize)
model2	Summary of second model (see example for how to summarize)
comps	Type of comparison. "abs" - absolute value of coefficient
pred	Number of predictors

Value

Comparing Independent Coefficients in Multiple Regression

Examples

y_1<-rnorm(200); x1_1<-rnorm(200); x2_1<-rnorm(200)
y_2<-rnorm(200); x1_2<-rnorm(200);x2_2<-rnorm(200)
df1<-as.data.frame(cbind(y_1, x1_1,x2_1))
df2<-as.data.frame(cbind(y_2, x1_2,x2_2))
model1_2<-summary(lm(y_1~x1_1+x2_1, data=df1))
model2_2<-summary(lm(y_2~x1_2+x2_2, data=df2))
indbcomp(model1 = model1_2, model2 = model2_2, comps="abs", pred=2)

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Compute Likelihood Ratio Chi-square for Binomial Logistic Regression with up to 10 predictors

Description

Compute Likelihood Ratio Chi-square for Binomial Logistic Regression with up to 10 predictors

Usage

LRchi(
  data = NULL,
  y = NULL,
  x1 = NULL,
  x2 = NULL,
  x3 = NULL,
  x4 = NULL,
  x5 = NULL,
  x6 = NULL,
  x7 = NULL,
  x8 = NULL,
  x9 = NULL,
  x10 = NULL,
  numpred = NULL
)

Arguments

data	name of your datafile, loaded
y	dependent variable name
x1	first predictor variable name
x2	second predictor variable name
x3	third predictor variable name
x4	fourth predictor variable name
x5	fifth predictor variable name
x6	sixth predictor variable name
x7	seventh predictor variable name
x8	eighth predictor variable name
x9	ninth predictor variable name
x10	tenth predictor variable name
numpred	number of predictors

Examples

LRchi(data=testlog, y="dv", x1="iv1", x2="iv2",numpred=2)

---

Compute Mahalanobis Distance for Multiple Regression

Description

Compute Mahalanobis Distance for Multiple Regression

Usage

Mahal(model = NULL, pred = NULL, values = 5)

Arguments

model	name of model
pred	number of predictors
values	number of Mahal values to print (highest values). Default is 10

Value

Mahalanobis Distance to detect MV outliers

Examples

mymodel<-lm(y~x1+x2+x3+x4, testreg)
Mahal(model=mymodel, pred=5, values = 10)

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Compute squared semi partial correlatoins for Multiple Regression

Description

Compute squared semi partial correlatoins for Multiple Regression

Usage

parts(model = NULL, pred = NULL)

Arguments

model	name of model
pred	number of predictors

Value

Squared semipartial correlations for MRC with up to 10 predictors

Examples

mymodel<-lm(y~x1+x2+x3+x4+x5, data=testreg)
parts(model=mymodel, pred=5)

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Pseudo R-square Values for Binomial Logistic Regression

Description

Pseudo R-square Values for Binomial Logistic Regression

Usage

pseudo(model = NULL)

Arguments

model

name of model

Value

Pseudo R-square Values for Logistic Regression

Examples

mymodel<-glm(dv~iv1+iv2+iv3+iv4, testlog,family = binomial())
pseudo(model=mymodel)

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R-square change for Hierarchical Multiple Regression

Description

R-square change for Hierarchical Multiple Regression

Usage

R2change(model1 = NULL, model2 = NULL)

Arguments

model1	first regression model
model2	second regression model

Examples

mymodel1<-lm(y~x1+x2, data=testreg)
mymodel2<-lm(y~x1+x2+x3+x4, data=testreg)
R2change(model1=mymodel1, model2=mymodel2)

---

testlog

Description

A dataset to test logistic regression functions

Usage

testlog

Format

A data frame with 164 rows and 11 variables:

dv

DV

iv1

1st predictor

iv2

2nd predictor

iv3

3rd predictor

iv4

4th predictor

iv5

5th predictor

iv6

6th predictor

iv7

7th predictor

iv8

8th predictor

iv9

9th predictor

iv10

10th predictor

---

testreg

Description

A dataset to test regression functions

Usage

testreg

Format

A data frame with 1000 rows and 6 variables:

y

DV

x1

1st predictor

x2

2nd predictor

x3

3rd predictor

x4

4th predictor

x5

5th predictor

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Compute tolerance for Multiple Regression

Description

Compute tolerance for Multiple Regression

Usage

tolerance(model = NULL)

Arguments

model

name of model

Value

Tolerance for MR

Examples

mymodel<-lm(y~x1+x2+x3+x4+x5, data=testreg)
tolerance(model=mymodel)

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