# LOG result of readmode_pred

#### 2024-05-11

version of R:4.3.2 version of Rstudio: 2023.12.0+369

We run the models for each dependent variables (on FFD,SFD,GD,TRT,RPD) in the following order:

Full_mdoel=lmer(depvar~readingmodepredictability+(1+readingmodepredictability|pp)+(1+readingmode*predictability|item), datafile)

model=lmer(depvar~readingmodepredictability+(1+readingmodepredictability|pp)+(1|item), datafile)

model=lmer(depvar~readingmodepredictability+(0+readingmodepredictability|pp)+(1|item), datafile)

model=lmer(depvar~readingmode*predictability+(1+readingmode+predictability|pp)+(1|item), datafile)

model=lmer(depvar~readingmode*predictability+(0+readingmode+predictability|pp)+(1|item), datafile)

model=lmer(depvar ~readingmode*predictability+ (1+readingmode|pp) + (1|item), datafile)

model=lmer(depvar ~readingmode*predictability+ (0+readingmode|pp) + (1|item), datafile)

model=lmer(depvar ~readingmode*predictability+ (1+predictability|pp) + (1|item), datafile)

model=lmer(depvar ~readingmode*predictability+ (0+predictability|pp) + (1|item), datafile)

model=lmer(depvar ~readingmode*predictability+ (1|pp) + (1|item), datafile)

Only the models that successfully converged were retained.

    library(lmerTest) # version 3.1-3

    ## 载入需要的程序包：lme4

    ## 载入需要的程序包：Matrix

    ##
    ## 载入程序包：'lmerTest'

    ## The following object is masked from 'package:lme4':
    ##
    ##     lmer

    ## The following object is masked from 'package:stats':
    ##
    ##     step

    library(lattice)  # version 0.21-9
    library(MASS)     # version 7.3-60
    library(plyr)     # version 1.8.9
    library(plotrix)  # version 3.8-4

    rm(list=ls())
    datafile0 = read.csv("datanew.csv", sep = ",", dec = ".")
    datafile <- datafile0
    colnames(datafile)

    ##  [1] "pp"             "item"           "readingmode"    "predictability"
    ##  [5] "condition"      "TRT"            "IA_SKIP"        "FFD"
    ##  [9] "SFD"            "GD"             "RPD"            "sacinlength"
    ## [13] "accuracy"       "ifquestion"

    nrow(datafile)

    ## [1] 5472

    datafile$pp = datafile[,1]
    datafile$item = datafile[,2]
    datafile$readingmode = datafile[,3]
    datafile$predictability = datafile[,4]
    datafile$condition = datafile[,5]
    datafile$pp = as.factor(datafile$pp)
    datafile$item = as.factor(datafile$item)
    datafile$readingmode = factor(datafile$readingmode, levels = c("S","A"))
    datafile$predictability = factor(datafile$predictability, levels = c("H","L"))
    datafile$condition = factor(datafile$condition, levels = c("SH","SL","AH","AL"))

# FOR FIRST FIXATION DURATION (FFD)

    measure = "FFD"
    datafile$depvar = datafile[,measure]
    datafile$depvar = log(as.numeric(datafile$depvar))

## linear mixed effect model analysis for FFD

    contrasts(datafile$predictability) <- contr.sdif(2)
    contrasts(datafile$readingmode) <- contr.sdif(2)
    model_FFD= lmer(depvar ~readingmode*predictability+ (1+readingmode|pp) + (1|item), datafile)
    summary(model_FFD, corr = FALSE)

    ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
    ## lmerModLmerTest]
    ## Formula: depvar ~ readingmode * predictability + (1 + readingmode | pp) +
    ##     (1 | item)
    ##    Data: datafile
    ##
    ## REML criterion at convergence: 3046.4
    ##
    ## Scaled residuals:
    ##     Min      1Q  Median      3Q     Max
    ## -4.2518 -0.5659 -0.0132  0.5951  3.8702
    ##
    ## Random effects:
    ##  Groups   Name           Variance  Std.Dev. Corr
    ##  item     (Intercept)    0.0009196 0.03033
    ##  pp       (Intercept)    0.0137434 0.11723
    ##           readingmode2-1 0.0127534 0.11293  0.01
    ##  Residual                0.1165263 0.34136
    ## Number of obs: 4070, groups:  item, 88; pp, 64
    ##
    ## Fixed effects:
    ##                                   Estimate Std. Error        df t value
    ## (Intercept)                      5.461e+00  1.607e-02 6.699e+01 339.855
    ## readingmode2-1                   2.263e-01  1.818e-02 5.732e+01  12.446
    ## predictability2-1                2.351e-02  1.103e-02 3.911e+03   2.132
    ## readingmode2-1:predictability2-1 5.191e-02  2.205e-02 3.913e+03   2.354
    ##                                  Pr(>|t|)
    ## (Intercept)                        <2e-16 ***
    ## readingmode2-1                     <2e-16 ***
    ## predictability2-1                  0.0331 *
    ## readingmode2-1:predictability2-1   0.0186 *
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

    confint(model_FFD)

    ## Computing profile confidence intervals ...

    ##                                         2.5 %     97.5 %
    ## .sig01                            0.009497427 0.04678851
    ## .sig02                            0.095696817 0.14299255
    ## .sig03                           -0.312823922 0.34263216
    ## .sig04                            0.080491053 0.14828614
    ## .sigma                            0.333800189 0.34903863
    ## (Intercept)                       5.429405684 5.49288275
    ## readingmode2-1                    0.190601770 0.26264419
    ## predictability2-1                 0.001904732 0.04513392
    ## readingmode2-1:predictability2-1  0.008702056 0.09515789

## simple effect analysis for FFD

    contrast.matrix <- matrix(c(
      -1, 1, 0, 0,
      0, 0, -1, 1), 4, 2,
      dimnames=list(c("SH","SL","AH","AL"),
                    c("SL-SH", "AL-AH")))
    inv.contrast.matrix <- matrix(t(ginv(contrast.matrix)), 4, 2,
                                  dimnames=list(c("SH","SL","AH","AL"),
                                                c("SL-SH", "AL-AH")))
    (contrasts(datafile$condition) <- fractions(inv.contrast.matrix))

    ##    SL-SH AL-AH
    ## SH -1/2     0
    ## SL  1/2     0
    ## AH    0  -1/2
    ## AL    0   1/2

    depvar.lmer1 = lmer(depvar ~ condition + (1|pp)+(1|item), datafile)
    summary(depvar.lmer1, corr = FALSE)

    ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
    ## lmerModLmerTest]
    ## Formula: depvar ~ condition + (1 | pp) + (1 | item)
    ##    Data: datafile
    ##
    ## REML criterion at convergence: 3083.1
    ##
    ## Scaled residuals:
    ##     Min      1Q  Median      3Q     Max
    ## -4.1975 -0.5795 -0.0230  0.6051  3.7455
    ##
    ## Random effects:
    ##  Groups   Name        Variance  Std.Dev.
    ##  item     (Intercept) 0.0009434 0.03072
    ##  pp       (Intercept) 0.0143793 0.11991
    ##  Residual             0.1192790 0.34537
    ## Number of obs: 4070, groups:  item, 88; pp, 64
    ##
    ## Fixed effects:
    ##                  Estimate Std. Error         df t value Pr(>|t|)
    ## (Intercept)     5.465e+00  1.636e-02  6.725e+01 334.040  < 2e-16 ***
    ## conditionSL-SH -3.742e-03  1.743e-02  3.965e+03  -0.215 0.829991
    ## conditionAL-AH  5.013e-02  1.387e-02  3.930e+03   3.614 0.000305 ***
    ## condition       2.184e-01  1.127e-02  3.973e+03  19.372  < 2e-16 ***
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# FOR SINGLE FIXATION DURATION (SFD)

    measure = "SFD"
    datafile$depvar = datafile[,measure]
    datafile$depvar = log(as.numeric(datafile$depvar))

## linear mixed effect model analysis for SFD

    contrasts(datafile$predictability) <- contr.sdif(2)
    contrasts(datafile$readingmode) <- contr.sdif(2)
    model_SFD= lmer(depvar ~readingmode*predictability+ (1+readingmode|pp) + (1|item), datafile)
    summary(model_SFD, corr = FALSE)

    ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
    ## lmerModLmerTest]
    ## Formula: depvar ~ readingmode * predictability + (1 + readingmode | pp) +
    ##     (1 | item)
    ##    Data: datafile
    ##
    ## REML criterion at convergence: 2377.8
    ##
    ## Scaled residuals:
    ##     Min      1Q  Median      3Q     Max
    ## -4.3358 -0.5723 -0.0260  0.5894  3.9165
    ##
    ## Random effects:
    ##  Groups   Name           Variance  Std.Dev. Corr
    ##  item     (Intercept)    0.0006038 0.02457
    ##  pp       (Intercept)    0.0141494 0.11895
    ##           readingmode2-1 0.0119111 0.10914  -0.01
    ##  Residual                0.1120927 0.33480
    ## Number of obs: 3324, groups:  item, 88; pp, 64
    ##
    ## Fixed effects:
    ##                                   Estimate Std. Error        df t value
    ## (Intercept)                      5.472e+00  1.627e-02 6.547e+01 336.258
    ## readingmode2-1                   2.568e-01  1.827e-02 6.026e+01  14.060
    ## predictability2-1                2.954e-02  1.178e-02 3.186e+03   2.507
    ## readingmode2-1:predictability2-1 7.254e-02  2.355e-02 3.182e+03   3.080
    ##                                  Pr(>|t|)
    ## (Intercept)                       < 2e-16 ***
    ## readingmode2-1                    < 2e-16 ***
    ## predictability2-1                 0.01221 *
    ## readingmode2-1:predictability2-1  0.00209 **
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

    confint(model_SFD)

    ## Computing profile confidence intervals ...

    ##                                         2.5 %     97.5 %
    ## .sig01                            0.000000000 0.04419834
    ## .sig02                            0.096912356 0.14521368
    ## .sig03                           -0.346576313 0.33245252
    ## .sig04                            0.075526711 0.14498112
    ## .sigma                            0.326552687 0.34320007
    ## (Intercept)                       5.439479411 5.50373515
    ## readingmode2-1                    0.220923352 0.29320637
    ## predictability2-1                 0.006456219 0.05264563
    ## readingmode2-1:predictability2-1  0.026385300 0.11873524

## simple effect analysis for SFD

    contrast.matrix <- matrix(c(
      -1, 1, 0, 0,
      0, 0, -1, 1), 4, 2,
      dimnames=list(c("SH","SL","AH","AL"),
                    c("SL-SH", "AL-AH")))
    inv.contrast.matrix <- matrix(t(ginv(contrast.matrix)), 4, 2,
                                  dimnames=list(c("SH","SL","AH","AL"),
                                                c("SL-SH", "AL-AH")))
    (contrasts(datafile$condition) <- fractions(inv.contrast.matrix))

    ##    SL-SH AL-AH
    ## SH -1/2     0
    ## SL  1/2     0
    ## AH    0  -1/2
    ## AL    0   1/2

    depvar.lmer1 = lmer(depvar ~ condition + (1|pp)+(1|item), datafile)
    summary(depvar.lmer1, corr = FALSE)

    ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
    ## lmerModLmerTest]
    ## Formula: depvar ~ condition + (1 | pp) + (1 | item)
    ##    Data: datafile
    ##
    ## REML criterion at convergence: 2406.3
    ##
    ## Scaled residuals:
    ##     Min      1Q  Median      3Q     Max
    ## -4.3140 -0.5786 -0.0394  0.5862  3.8199
    ##
    ## Random effects:
    ##  Groups   Name        Variance Std.Dev.
    ##  item     (Intercept) 0.00061  0.0247
    ##  pp       (Intercept) 0.01439  0.1200
    ##  Residual             0.11487  0.3389
    ## Number of obs: 3324, groups:  item, 88; pp, 64
    ##
    ## Fixed effects:
    ##                  Estimate Std. Error         df t value Pr(>|t|)
    ## (Intercept)     5.474e+00  1.637e-02  6.516e+01 334.356  < 2e-16 ***
    ## conditionSL-SH -8.462e-03  1.780e-02  3.229e+03  -0.476    0.634
    ## conditionAL-AH  6.686e-02  1.579e-02  3.213e+03   4.235 2.35e-05 ***
    ## condition       2.519e-01  1.206e-02  3.249e+03  20.881  < 2e-16 ***
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# FOR GAZE DURATION

    measure = "GD"
    datafile$depvar = datafile[,measure]
    datafile$depvar = log(as.numeric(datafile$depvar))

## linear mixed effect model analysis for GD

    contrasts(datafile$predictability) <- contr.sdif(2)
    contrasts(datafile$readingmode) <- contr.sdif(2)
    model_GD= lmer(depvar ~readingmode*predictability+ (1+readingmode|pp) + (1|item), datafile)
    summary(model_GD, corr = FALSE)

    ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
    ## lmerModLmerTest]
    ## Formula: depvar ~ readingmode * predictability + (1 + readingmode | pp) +
    ##     (1 | item)
    ##    Data: datafile
    ##
    ## REML criterion at convergence: 4435.8
    ##
    ## Scaled residuals:
    ##     Min      1Q  Median      3Q     Max
    ## -3.7040 -0.6107 -0.0757  0.6386  3.1742
    ##
    ## Random effects:
    ##  Groups   Name           Variance Std.Dev. Corr
    ##  item     (Intercept)    0.001911 0.04371
    ##  pp       (Intercept)    0.013664 0.11689
    ##           readingmode2-1 0.021171 0.14550  -0.11
    ##  Residual                0.164115 0.40511
    ## Number of obs: 4070, groups:  item, 88; pp, 64
    ##
    ## Fixed effects:
    ##                                   Estimate Std. Error        df t value
    ## (Intercept)                      5.575e+00  1.679e-02 7.020e+01 332.039
    ## readingmode2-1                   3.769e-01  2.273e-02 6.249e+01  16.581
    ## predictability2-1                5.341e-02  1.309e-02 3.906e+03   4.080
    ## readingmode2-1:predictability2-1 8.856e-02  2.618e-02 3.907e+03   3.382
    ##                                  Pr(>|t|)
    ## (Intercept)                       < 2e-16 ***
    ## readingmode2-1                    < 2e-16 ***
    ## predictability2-1                 4.6e-05 ***
    ## readingmode2-1:predictability2-1 0.000725 ***
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

    confint(model_GD)

    ## Computing profile confidence intervals ...

    ##                                        2.5 %     97.5 %
    ## .sig01                            0.02336491 0.06240260
    ## .sig02                            0.09407346 0.14391457
    ## .sig03                           -0.41478927 0.22962695
    ## .sig04                            0.10829315 0.18707481
    ## .sigma                            0.39614108 0.41422974
    ## (Intercept)                       5.54159229 5.60790045
    ## readingmode2-1                    0.33212238 0.42207690
    ## predictability2-1                 0.02775698 0.07908104
    ## readingmode2-1:predictability2-1  0.03725598 0.13990532

## simple effect analysis for GD

    contrast.matrix <- matrix(c(
      -1, 1, 0, 0,
      0, 0, -1, 1), 4, 2,
      dimnames=list(c("SH","SL","AH","AL"),
                    c("SL-SH", "AL-AH")))
    inv.contrast.matrix <- matrix(t(ginv(contrast.matrix)), 4, 2,
                                  dimnames=list(c("SH","SL","AH","AL"),
                                                c("SL-SH", "AL-AH")))
    (contrasts(datafile$condition) <- fractions(inv.contrast.matrix))

    ##    SL-SH AL-AH
    ## SH -1/2     0
    ## SL  1/2     0
    ## AH    0  -1/2
    ## AL    0   1/2

    depvar.lmer1 = lmer(depvar ~ condition + (1|pp)+(1|item), datafile)
    summary(depvar.lmer1, corr = FALSE)

    ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
    ## lmerModLmerTest]
    ## Formula: depvar ~ condition + (1 | pp) + (1 | item)
    ##    Data: datafile
    ##
    ## REML criterion at convergence: 4488.7
    ##
    ## Scaled residuals:
    ##     Min      1Q  Median      3Q     Max
    ## -3.7417 -0.6266 -0.0822  0.6444  3.1600
    ##
    ## Random effects:
    ##  Groups   Name        Variance Std.Dev.
    ##  item     (Intercept) 0.001687 0.04107
    ##  pp       (Intercept) 0.013506 0.11621
    ##  Residual             0.169222 0.41137
    ## Number of obs: 4070, groups:  item, 88; pp, 64
    ##
    ## Fixed effects:
    ##                 Estimate Std. Error        df t value Pr(>|t|)
    ## (Intercept)    5.579e+00  1.661e-02 6.973e+01 335.913  < 2e-16 ***
    ## conditionSL-SH 6.572e-03  2.076e-02 3.965e+03   0.317    0.752
    ## conditionAL-AH 9.859e-02  1.652e-02 3.930e+03   5.967 2.63e-09 ***
    ## condition      3.704e-01  1.342e-02 3.981e+03  27.604  < 2e-16 ***
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# FOR TOTAL READING TIME (TRT)

    measure = "TRT"
    datafile$depvar = datafile[,measure]
    datafile$depvar = log(as.numeric(datafile$depvar))

## linear mixed effect model analysis for TRT

    contrasts(datafile$predictability) <- contr.sdif(2)
    contrasts(datafile$readingmode) <- contr.sdif(2)
    model_TRT= lmer(depvar ~readingmode*predictability+ (1+readingmode+predictability|pp) + (1|item), datafile)
    summary(model_TRT, corr = FALSE)

    ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
    ## lmerModLmerTest]
    ## Formula:
    ## depvar ~ readingmode * predictability + (1 + readingmode + predictability |
    ##     pp) + (1 | item)
    ##    Data: datafile
    ##
    ## REML criterion at convergence: 6219.4
    ##
    ## Scaled residuals:
    ##     Min      1Q  Median      3Q     Max
    ## -3.8264 -0.6874 -0.0506  0.6993  4.0195
    ##
    ## Random effects:
    ##  Groups   Name              Variance  Std.Dev. Corr
    ##  item     (Intercept)       0.0050055 0.07075
    ##  pp       (Intercept)       0.0184086 0.13568
    ##           readingmode2-1    0.0233309 0.15274  0.01
    ##           predictability2-1 0.0006108 0.02471  0.56 0.21
    ##  Residual                   0.2150625 0.46375
    ## Number of obs: 4563, groups:  item, 88; pp, 64
    ##
    ## Fixed effects:
    ##                                   Estimate Std. Error        df t value
    ## (Intercept)                      5.724e+00  1.986e-02 8.109e+01 288.213
    ## readingmode2-1                   3.477e-01  2.374e-02 6.065e+01  14.643
    ## predictability2-1                7.813e-02  1.428e-02 6.396e+01   5.471
    ## readingmode2-1:predictability2-1 3.196e-02  2.788e-02 4.379e+03   1.147
    ##                                  Pr(>|t|)
    ## (Intercept)                       < 2e-16 ***
    ## readingmode2-1                    < 2e-16 ***
    ## predictability2-1                7.94e-07 ***
    ## readingmode2-1:predictability2-1    0.252
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

    confint(model_TRT)

    ## Computing profile confidence intervals ...

    ##                                        2.5 %     97.5 %
    ## .sig01                            0.05211518 0.09128573
    ## .sig02                            0.11045346 0.16592715
    ## .sig03                           -0.31203900 0.32963696
    ## .sig04                           -0.84120701 1.00000000
    ## .sig05                            0.11270115 0.19698592
    ## .sig06                           -1.00000000 0.86167669
    ## .sig07                            0.00000000 0.07758223
    ## .sigma                            0.45405619 0.47367040
    ## (Intercept)                       5.68454233 5.76285317
    ## readingmode2-1                    0.30084573 0.39473315
    ## predictability2-1                 0.04980440 0.10626830
    ## readingmode2-1:predictability2-1 -0.02275828 0.08668371

## simple effect analysis for TRT

    contrast.matrix <- matrix(c(
      -1, 1, 0, 0,
      0, 0, -1, 1), 4, 2,
      dimnames=list(c("SH","SL","AH","AL"),
                    c("SL-SH", "AL-AH")))
    inv.contrast.matrix <- matrix(t(ginv(contrast.matrix)), 4, 2,
                                  dimnames=list(c("SH","SL","AH","AL"),
                                                c("SL-SH", "AL-AH")))
    (contrasts(datafile$condition) <- fractions(inv.contrast.matrix))

    ##    SL-SH AL-AH
    ## SH -1/2     0
    ## SL  1/2     0
    ## AH    0  -1/2
    ## AL    0   1/2

    depvar.lmer1 = lmer(depvar ~ condition + (1|pp)+(1|item), datafile)
    summary(depvar.lmer1, corr = FALSE)

    ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
    ## lmerModLmerTest]
    ## Formula: depvar ~ condition + (1 | pp) + (1 | item)
    ##    Data: datafile
    ##
    ## REML criterion at convergence: 6267.6
    ##
    ## Scaled residuals:
    ##     Min      1Q  Median      3Q     Max
    ## -3.6274 -0.6847 -0.0593  0.7025  4.0120
    ##
    ## Random effects:
    ##  Groups   Name        Variance Std.Dev.
    ##  item     (Intercept) 0.004829 0.06949
    ##  pp       (Intercept) 0.018514 0.13606
    ##  Residual             0.220808 0.46990
    ## Number of obs: 4563, groups:  item, 88; pp, 64
    ##
    ## Fixed effects:
    ##                 Estimate Std. Error        df t value Pr(>|t|)
    ## (Intercept)    5.727e+00  1.986e-02 8.057e+01 288.325  < 2e-16 ***
    ## conditionSL-SH 6.134e-02  2.136e-02 4.442e+03   2.872   0.0041 **
    ## conditionAL-AH 9.549e-02  1.841e-02 4.417e+03   5.186 2.25e-07 ***
    ## condition      3.439e-01  1.416e-02 4.444e+03  24.278  < 2e-16 ***
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# FOR REGRESSION PATH DURATION (RPD)

    measure = "RPD"
    datafile$depvar = datafile[,measure]
    datafile$depvar = log(as.numeric(datafile$depvar))

## linear mixed effect model analysis for RPD

    contrasts(datafile$predictability) <- contr.sdif(2)
    contrasts(datafile$readingmode) <- contr.sdif(2)
    model_RPD= lmer(depvar ~readingmode*predictability+ (0+readingmode+predictability|pp) + (1|item), datafile)
    summary(model_RPD, corr = FALSE)

    ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
    ## lmerModLmerTest]
    ## Formula:
    ## depvar ~ readingmode * predictability + (0 + readingmode + predictability |
    ##     pp) + (1 | item)
    ##    Data: datafile
    ##
    ## REML criterion at convergence: 5651.2
    ##
    ## Scaled residuals:
    ##     Min      1Q  Median      3Q     Max
    ## -3.4782 -0.6389 -0.1145  0.5959  4.5990
    ##
    ## Random effects:
    ##  Groups   Name              Variance  Std.Dev. Corr
    ##  item     (Intercept)       4.797e-03 0.069263
    ##  pp       readingmodeS      2.623e-02 0.161946
    ##           readingmodeA      2.273e-02 0.150772  0.24
    ##           predictability2-1 5.328e-05 0.007299  0.68 -0.55
    ##  Residual                   2.199e-01 0.468957
    ## Number of obs: 4070, groups:  item, 88; pp, 64
    ##
    ## Fixed effects:
    ##                                   Estimate Std. Error        df t value
    ## (Intercept)                      5.677e+00  1.884e-02 8.015e+01  301.26
    ## readingmode2-1                   3.644e-01  2.879e-02 5.980e+01   12.66
    ## predictability2-1                4.769e-02  1.519e-02 2.392e+03    3.14
    ## readingmode2-1:predictability2-1 8.461e-02  3.033e-02 3.899e+03    2.79
    ##                                  Pr(>|t|)
    ## (Intercept)                       < 2e-16 ***
    ## readingmode2-1                    < 2e-16 ***
    ## predictability2-1                 0.00171 **
    ## readingmode2-1:predictability2-1  0.00530 **
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

    confint(model_RPD)

    ## Computing profile confidence intervals ...

    ##                                        2.5 %     97.5 %
    ## .sig01                            0.04901989 0.09087896
    ## .sig02                            0.12600019 0.20391568
    ## .sig03                           -0.07982783 0.53188520
    ## .sig04                           -1.00000000 1.00000000
    ## .sig05                            0.12013715 0.18677121
    ## .sig06                           -1.00000000 1.00000000
    ## .sig07                            0.00000000 0.07895168
    ## .sigma                            0.45857288 0.47951407
    ## (Intercept)                       5.63936629 5.71361708
    ## readingmode2-1                    0.30753792 0.42121711
    ## predictability2-1                 0.01790704 0.07748175
    ## readingmode2-1:predictability2-1  0.02519505 0.14412502

## simple effect analysis for RPD

    contrast.matrix <- matrix(c(
      -1, 1, 0, 0,
      0, 0, -1, 1), 4, 2,
      dimnames=list(c("SH","SL","AH","AL"),
                    c("SL-SH", "AL-AH")))
    inv.contrast.matrix <- matrix(t(ginv(contrast.matrix)), 4, 2,
                                  dimnames=list(c("SH","SL","AH","AL"),
                                                c("SL-SH", "AL-AH")))
    (contrasts(datafile$condition) <- fractions(inv.contrast.matrix))

    ##    SL-SH AL-AH
    ## SH -1/2     0
    ## SL  1/2     0
    ## AH    0  -1/2
    ## AL    0   1/2

    depvar.lmer1 = lmer(depvar ~ condition + (1|pp)+(1|item), datafile)
    summary(depvar.lmer1, corr = FALSE)

    ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
    ## lmerModLmerTest]
    ## Formula: depvar ~ condition + (1 | pp) + (1 | item)
    ##    Data: datafile
    ##
    ## REML criterion at convergence: 5722.5
    ##
    ## Scaled residuals:
    ##     Min      1Q  Median      3Q     Max
    ## -3.3975 -0.6551 -0.1258  0.5951  4.4129
    ##
    ## Random effects:
    ##  Groups   Name        Variance Std.Dev.
    ##  item     (Intercept) 0.004248 0.06518
    ##  pp       (Intercept) 0.015583 0.12483
    ##  Residual             0.228490 0.47801
    ## Number of obs: 4070, groups:  item, 88; pp, 64
    ##
    ## Fixed effects:
    ##                 Estimate Std. Error        df t value Pr(>|t|)
    ## (Intercept)    5.678e+00  1.880e-02 7.922e+01 302.058  < 2e-16 ***
    ## conditionSL-SH 7.536e-04  2.414e-02 3.960e+03   0.031    0.975
    ## conditionAL-AH 9.178e-02  1.920e-02 3.930e+03   4.780 1.82e-06 ***
    ## condition      3.647e-01  1.559e-02 3.981e+03  23.392  < 2e-16 ***
    ## ---
    ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1