ExtractSummaryStatisticsRandomizedExp {reproducer}

ExtractSummaryStatisticsRandomizedExp

Description

This function extracts data obtained from the lme4 package lmer function. It assumes a simple randomized experiment with each element having one or more repeated measures. It outputs the mean together with its standard error and confidence interval bounds.

Usage

ExtractSummaryStatisticsRandomizedExp(lmeRA, N, alpha = 0.05)

Arguments

lmeRA

The output from the lmer function

N

The total number of observations

alpha

the probability level to be used when constructing the confidence interval bounds.

Value

REA.Summary A dataframe holding the number of observations N, the overall mean value as its standard error reported as by the lmer function, and its confidence interval bounds.

Author(s)

Barbara Kitchenham and Lech Madeyski

Examples

ShortExperimentNames <- c("E1", "E2", "E3", "E4")
FullExperimentNames <- c("EUBAS", "R1UCLM", "R2UCLM", "R3UCLM")
Metrics <- c("Comprehension", "Modification")
Groups <- c("A", "B", "C", "D")
Type <- c(rep("4G", 4))
StudyID <- "S2"
Control <- "SC"
ReshapedData <- ExtractExperimentData(
  KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello14TOSEM,
  ExperimentNames = FullExperimentNames, idvar = "ParticipantID", timevar = "Period",
  ConvertToWide = TRUE
)
NewTable <- ConstructLevel1ExperimentRData(
  ReshapedData, StudyID, ShortExperimentNames, Groups,
  Metrics, Type, Control
)
resRe <- lme4::lmer(r ~ (1 | Id), data = NewTable)
summary(resRe)
# Linear mixed model fit by REML ['lmerMod']
# Formula: r ~ (1 | Id)
# REML criterion at convergence: 47.8
# Scaled residuals:
#    Min      1Q  Median      3Q     Max
# -1.4382 -0.9691  0.2190  0.8649  1.4761
#
# Random effects:
#  Groups   Name        Variance Std.Dev.
#   Id       (Intercept) 0.03978  0.1994
#   Residual             0.20974  0.4580
#  Number of obs: 32, groups:  Id, 16
#
#  Fixed effects:
#             Estimate Std. Error t value
#  (Intercept)  0.06175    0.09508   0.649
#  N=length(NewTable$r)
ExtractSummaryStatisticsRandomizedExp(lmeRA = resRe, N = 32, alpha = 0.05)
#      N    Mean      SE LowerBound UpperBound
#   1 32 0.06175 0.09508    -0.1319     0.2554