Confidence Interval

Description

Calculates the confidence interval of a vector of data.

Usage

  CI(x, ci = 0.95)

Arguments

Value

Examples

CI(rnorm(100))

Standard Error

Description

Calculates the standard error interval of a vector of data

Usage

  STDERR(x)

Arguments

Value

Examples

STDERR(rnorm(100))

Group Confidence Interval

Description

Calculates the confidence interval of grouped data

Usage

  group.CI(x, data, ci = 0.95)

Arguments

Value

A data frame consisting of one column for each grouping factor plus three columns for the upper bound, mean and lower bound of the confidence interval for each level of the grouping factor

Examples

require(latticeExtra)
with(group.CI(weight~feed,chickwts),
 segplot(feed~weight.lower+weight.upper,center=weight.mean)
)

require(Hmisc)
with(group.CI(Temp~Month,airquality),
 xYplot(Cbind(Temp.mean,Temp.lower,Temp.upper)~numericScale(Month),type="b",ylim=c(60,90))
)

Group Standard Error Interval

Description

Calculates the standard error interval of grouped data.

Usage

  group.STDERR(x, data)

Arguments

Value

A data frame consisting of one column for each grouping factor plus three columns for the upper bound, mean and lower bound of the standard error interval for each level of the grouping factor.

Examples

require(latticeExtra)
with(group.STDERR(weight~feed,chickwts),
 segplot(feed~weight.lower+weight.upper,center=weight.mean)
)

require(Hmisc)
with(group.STDERR(Temp~Month,airquality),
 xYplot(Cbind(Temp.mean,Temp.lower,Temp.upper)~numericScale(Month),type="b",ylim=c(60,90))
)

Group Upper-Center-Lower

Description

Applies a function which calculates a parameter with lower/uper bounds to groups of data.

Usage

  group.UCL(x, data, FUN, ...)

Arguments

Value

A data frame consisting of one column for each grouping factor plus three columns for the upper bound, mean and lower bound of the standard error interval for each level of the grouping factor.

Examples

require(latticeExtra)
with(group.UCL(weight~feed,chickwts,FUN=CI),
 segplot(feed~weight.lower+weight.upper,center=weight.mean)
)

require(Hmisc)
with(group.UCL(Temp~Month,airquality,FUN=STDERR),
 xYplot(Cbind(Temp.mean,Temp.lower,Temp.upper)~numericScale(Month),type="b",ylim=c(60,90))
)

Likelihood Ratio Test

Description

Computes a likelihood ratio statistic which reflects the relative likelihood of the data given two competing models.

Usage

  lr.glover(object, ..., name = NULL)

Arguments

Value

An object of class "anova" which contains the log-likelihood, degrees of freedom, the difference in degrees of freedom, likelihood ratio, and AIC/BIC corrected likelihood ratios.

Details

lr.glover performs comparisons of models via likelihood ratio tests. The default method consecutively compares the fitted model object object with the models passed in .... Subsequently, a likelihood ratio test for each two consecutive models is carried out.

References

Glover, S. & Dixon, P. (2004). Likelihood ratios: A simple and flexible statistic for empirical psychologists. Psychonomic Bulletin & Review, 11(5), 791-806.

Examples

m1 <- lm(mpg~.,mtcars)
m2 <- step(m1,~.,trace=0)
m3 <- step(m1,~.+.^2,trace=0)
lr.glover(m1,m2,m3)

Multiple plot function

Description

Renders multiple ggplot plots in one image

Usage

  multiplot(..., plotlist = NULL, cols = 1, layout = NULL)

Arguments

Note

If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE), then plot 1 will go in the upper left, 2 will go in the upper right, and 3 will go all the way across the bottom.

References

http://www.cookbook-r.com/Graphs/Multiple_graphs_on_one_page_(ggplot2)

Normalize within-group data

Description

Norms the data within specified groups in a data frame; it normalizes each subject (identified by idvar) so that they have the same mean, within each group specified by betweenvars.

Usage

  normDataWithin(data = NULL, idvar, measurevar,
    betweenvars = NULL, na.rm = FALSE, .drop = TRUE)

Arguments

Value

a data frame with normalized data

References

http://www.cookbook-r.com/Graphs/Plotting_means_and_error_bars_(ggplot2)

Circle Drawing

Description

A panel function for drawing circles.

Usage

  panel.circle(x, y, r, segments = 50L, groups = NULL, ...)

Arguments

Examples

panel.circle(0, 0, 10)

Round to Increment

Description

Rounds a value to nearest increment

Usage

  rounder(x, inc, fun = "round")

Arguments

Value

an object of class numeric

Examples

rounder(.92, .05)
rounder(.93, .05)
rounder(.93, .05, "floor")
rounder(.93, .05, "ceiling")

Run Start Indices

Description

Find the starting indices of runs in a vector.

Usage

  rsi(x)

Arguments

Value

a vector of indices indicating starting points for runs

Examples

rsi(c(0,0,0,1,2,2,3,3,3,3,3,4))

Summarizes data

Description

Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).

Usage

  summarySE(data = NULL, measurevar, groupvars = NULL,
    na.rm = FALSE, conf.interval = 0.95, .drop = TRUE)

Arguments

Value

a data frame with count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).

References

http://www.cookbook-r.com/Graphs/Plotting_means_and_error_bars_(ggplot2)

Summarize within-subjects data

Description

Summarizes data, handling within-subjects variables by removing inter-subject variability. It will still work if there are no within-S variables. Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%). If there are within-subject variables, calculate adjusted values using method from Morey (2008).

Usage

  summarySEwithin(data = NULL, measurevar,
    betweenvars = NULL, withinvars = NULL, idvar = NULL,
    na.rm = FALSE, conf.interval = 0.95, .drop = TRUE)

Arguments

Value

a data frame with count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).

References

http://www.cookbook-r.com/Graphs/Plotting_means_and_error_bars_(ggplot2)