surv.mean: Mean survival curves

In matsukik/RTsurvival: Survival Analaysis and Divergence Point Estimation Methods for Reaction Time Data

Description

Obtain survival proportion per condition for each participant then calculate the means of each condition and difference btween the conditions.

Usage

surv.mean(subject, latency, condition, binsize = 1, window = 600)

Arguments

subject	a vector speficying participant number or ID
latency	a numeric vector containing the latency measures such as reaction time or fixation in millisecond.
condition	a vector or factor specifying the experimental conditions. The conditions should be ordered such that the one that is expected to have shorter latencies (i.e., faster condition) comes first.
binsize	numeric. The size of each timing bins in millisecond. Default to 1.
window	numeric. The maximum window of the timing bin in millisecond. Default to 600.

Value

A list of class RTsurvival containing

curve	Mean survival proportion at each timing bin for each condition in a data frame
differnece	differnce in mean survival proportion between slower vs faster condition at each timing bins
binsize	The same value supplied in the argument binsize
window	The same value supplied in the argument window

References

Reingold, E. M. & Sheridan, H. (2014). Estimating the divergence point: A novel distributional analysis procedure for determining the onset of the influence of experimental variables. Frontiers in Psychology. doi: 10.3389/fpsyg.2014.01432.

Reingold, E. M., Reichle, E. D., Glaholt, M. G., & Sheridan, H. (2012). Direct lexical control of eye movements in reading: Evidence from a survival analysis of fixation durations. Cognitive Psychology, 65, 177-206.

See Also

surv.curv, DPA.orig

Examples

data(DPAsample)
msc1 <- surv.mean(DPAsample$subject, DPAsample$duration, DPAsample$condition)
plot(msc1)

matsukik/RTsurvival documentation built on May 21, 2019, 12:57 p.m.