lamme

Description

Log-Analytic Methods for Multiplicative Effects

Details

The lamme package is designed to test and estimate multiplicative effects via log-analytic methods.

Usage

To access this package's tutorial, type the following line into the console:

vignette("lamme-vignette")

the ABC procedure for model selection

Description

the AIC comparison with Modified Box-Cox Transformation (ABC) is a diagnostic procedure to help select among various additive and multiplicative models

Usage

abc(y, g, x = 0)

Arguments

Details

When only 'y' and 'g' are specified, the ABC procedure compares LANOVA and ANOVA models. When 'x' is also specified, the ABC procedure compares LANCOVA, ANCOVA, ANCOHET, and ANCOVA with log-transformed y.

Value

AIC results of different models. The model with smallest AIC is preferred.

Examples

data("schoene")
attach(schoene)
abc(post_HRT,group,pre_HRT)
abc(post_HRT,group)

Boostrapped CI for Effect Size measures

Description

Compute the bias-corrected and expanded percentile boostrapped confidence intervals for effect size estimates zetas and the overall signal-to-noise ratio. Additionally, if pretest scores are provided, boostrapped CI on beta is also given.

Usage

boot.es(y, g, x = 0, nrep = 1000, alpha = 0.05)

Arguments

Value

a table of lower and upper limit from bias-corrected and accelerated and expanded percentile boostrapped confidence interval. The first row is on the geometric mean of the control group (default group of comparison). After that, zeta estimates are given of the each respective group versus the control group (default group of comparison). Then, if pretest scores are given, CI on the beta estimate is given. Lastly, CI on the signal-to-noise ratio, an overall effect size measure, is provided.

References

Efron, B. (1987). "Better Bootstrap Confidence Intervals". Journal of the American Statistical Association. Journal of the American Statistical Association, Vol. 82, No. 397. 82 (397): 171–185. doi:10.2307/2289144. JSTOR 2289144.

Examples

data("schoene")
attach(schoene)
boot.es(post_HRT,group,pre_HRT,1000,.05)

Logged ANCOVA

Description

Mathematically, LANCOVA is the ANCOVA form of a log-log model where both the dependent variable and the covariate is log-transformed. LANCOVA can test and estimate multiplicative effects.

Usage

lancova(y, g, x, plot = F)

Arguments

Value

An summary object of the LANCOVA results. In residuals, the summary statistics are of sample multiplicative errors. In the coefficients table, the estimate of the intercept is the (control group) geometric mean estimate. The estimate for the pretest scores is the power parameter beta's estimate. Other coefficient estimates are effect size measure zeta's estimates. The standard error is on the logged scale. The confidence intervals are of significance level = .05 for the control group geometric mean and for the zeta estimates, respectively, of the intercept and other coefficients The residual standard error is that of the logged scale residuals. Both R-squared and Adjusted R-squared are computed on the logged model. If 'plot=TRUE', diagnostic plots are provided.

Examples

data("schoene")
attach(schoene)
lancova(post_HRT,group,pre_HRT)

Logged ANOVA

Description

Mathematically, LANOVA is the ANOVA form of a log-log model where the dependent variable is log-transformed. LANOVA can test and estimate multiplicative effects.

Usage

lanova(y, g, plot = F)

Arguments

Value

An summary object of the LANOVA results. In residuals, the summary statistics are of sample multiplicative errors. In the coefficients table, the estimate of the intercept is the default group (control group) geometric mean estimate. Other coefficient estimates are effect size measure zeta's estimates. The standard error is on the logged scale. The confidence intervals are of significance level = .05 for the control group geometric mean and for the zeta estimates, respectively, of the intercept and other coefficients The residual standard error is that of the logged scale residuals. Both R-squared and Adjusted R-squared are computed on the logged model. If 'plot=TRUE', diagnostic plots are provided.

Examples

# generate data
y1=rnorm(1000,5,1)+rnorm(1000)
y2=rnorm(1000,5.5,1)+rnorm(1000)
y3=rnorm(1000,6,1)+rnorm(1000)
y1=exp(y1)
y2=exp(y2)
y3=exp(y3)
dep=c(y1,y2,y3)
tc=rep(c(0,1,2),each=1000)
# applying lanova with the generated data
lanova(dep,tc)

Power Calculation for LANCOVA

Description

Compute the statistical power of the LANCOVA test.

Usage

pwr.lancova(k, n, r_sqrd, rho_sqrd, alpha = 0.05)

Arguments

Value

References

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale,NJ: Lawrence Erlbaum.

Examples

pwr.lancova(3,40,.1,.4,.05)

Power Calculation for LANOVA

Description

Compute the statistical power of the LANOVA test.

Usage

pwr.lanova(k, n, r_sqrd, alpha = 0.05)

Arguments

Value

References

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale,NJ: Lawrence Erlbaum.

Examples

pwr.lanova(3,40,.4,.05)

Data on Interactive Cognitive-Motor Step Training

Description

Data from a randomized controlled trial on Interactive cognitive-motor step training. 81 observations are included. The outcome variable included is the hand reaction time. The data come from a randomzied pretest-posttest design with control and treatment groups.

Usage

data(schoene)

Format

A dataframe with 81 rows and 3 variables:

group

treatment or control group from experimental manipulation

pre_HRT

prettest hand reaction time

post_HRT

posttest hand reaction time

References

Schoene D, Valenzuela T, Toson B, Delbaere K, Severino C, Garcia J, et al. (2015) Interactive Cognitive-Motor Step Training Improves Cognitive Risk Factors of Falling in Older Adults – A Randomized Controlled Trial. PLoS ONE 10(12): e0145161.

Examples

data(schoene)
head(schoene)
table(schoene$group)

Sample Size Planning for LANCOVA

Description

Compute the required per-group sample size for the LANCOVA test.

Usage

ss.lancova(k, rho_sqrd, r_sqrd, power = 0.8, alpha = 0.05)

Arguments

Value

References

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale,NJ: Lawrence Erlbaum.

Examples

ss.lancova(3,.5,.01,.14,.05)

Sample Size Planning for LANOVA

Description

Compute the required per-group sample size for the LANOVA test.

Usage

ss.lanova(k, r_sqrd, power = 0.8, alpha = 0.05)

Arguments

Value

References

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale,NJ: Lawrence Erlbaum.

Examples

ss.lanova(3,.01,.14,.05)