ERP Data preparation
Draft version, not for citation
Robert Lew
Updated: 2024-04-04
- 1 Read in data
- 2 Assemble data from individual files into a single table
- 3 Bringing order into the data and converting to long form
- 4 Create tables with aggregation over 3 leads for legacy anterior-posterior and left-to-right analyses
- 5 Initial modelling: legacy analyses but with varying intercepts for Participants
- 6 Now using all data available and more complex RanEff structures
1 Read in data
1.1 We are using xlsx files to recover original text files: this won’t be necessary in the future
(xl.files <- list.files('.','^Sub.*xlsx'))## [1] "Sub1.xlsx" "Sub10.xlsx" "Sub11.xlsx" "Sub12.xlsx" "Sub13.xlsx"
## [6] "Sub14.xlsx" "Sub15.xlsx" "Sub16.xlsx" "Sub18.xlsx" "Sub19.xlsx"
## [11] "Sub2.xlsx" "Sub20.xlsx" "Sub21.xlsx" "Sub22.xlsx" "Sub23.xlsx"
## [16] "Sub24.xlsx" "Sub25.xlsx" "Sub26.xlsx" "Sub27.xlsx" "Sub28.xlsx"
## [21] "Sub29.xlsx" "Sub3.xlsx" "Sub31.xlsx" "Sub32.xlsx" "Sub33.xlsx"
## [26] "Sub35.xlsx" "Sub36.xlsx" "Sub37.xlsx" "Sub39.xlsx" "Sub40.xlsx"
## [31] "Sub41.xlsx" "Sub42.xlsx" "Sub43.xlsx" "Sub44.xlsx" "Sub45.xlsx"
## [36] "Sub47.xlsx" "Sub5.xlsx" "Sub6.xlsx" "Sub7.xlsx"1.2 convert xlsx back to
original txt in a loop over xl.files
for (i in seq_along(xl.files)) write_delim(read_xlsx(xl.files[i]), file=str_replace(xl.files[i], '.xlsx', '.txt'))1.3 Here’s our list of txt files:
(txt.files <- list.files('.','^Sub.*txt'))## [1] "Sub1.txt" "Sub10.txt" "Sub11.txt" "Sub12.txt" "Sub13.txt" "Sub14.txt"
## [7] "Sub15.txt" "Sub16.txt" "Sub18.txt" "Sub19.txt" "Sub2.txt" "Sub20.txt"
## [13] "Sub21.txt" "Sub22.txt" "Sub23.txt" "Sub24.txt" "Sub25.txt" "Sub26.txt"
## [19] "Sub27.txt" "Sub28.txt" "Sub29.txt" "Sub3.txt" "Sub31.txt" "Sub32.txt"
## [25] "Sub33.txt" "Sub35.txt" "Sub36.txt" "Sub37.txt" "Sub39.txt" "Sub40.txt"
## [31] "Sub41.txt" "Sub42.txt" "Sub43.txt" "Sub44.txt" "Sub45.txt" "Sub47.txt"
## [37] "Sub5.txt" "Sub6.txt" "Sub7.txt"1.4 What are the header names? We’ll need to make sense of those
(headers <- names(read_delim(txt.files[1])))## Rows: 1 Columns: 49
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: " "
## chr (1): ERPset
## dbl (48): bin17_HighNoSw_FC1, bin17_HighNoSw_C1, bin17_HighNoSw_CP1, bin17_H...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## [1] "bin17_HighNoSw_FC1" "bin17_HighNoSw_C1" "bin17_HighNoSw_CP1"
## [4] "bin17_HighNoSw_P1" "bin17_HighNoSw_Pz" "bin17_HighNoSw_CPz"
## [7] "bin17_HighNoSw_FC2" "bin17_HighNoSw_FCz" "bin17_HighNoSw_Cz"
## [10] "bin17_HighNoSw_C2" "bin17_HighNoSw_CP2" "bin17_HighNoSw_P2"
## [13] "bin18_HighSw_FC1" "bin18_HighSw_C1" "bin18_HighSw_CP1"
## [16] "bin18_HighSw_P1" "bin18_HighSw_Pz" "bin18_HighSw_CPz"
## [19] "bin18_HighSw_FC2" "bin18_HighSw_FCz" "bin18_HighSw_Cz"
## [22] "bin18_HighSw_C2" "bin18_HighSw_CP2" "bin18_HighSw_P2"
## [25] "bin19_LowNoSw_FC1" "bin19_LowNoSw_C1" "bin19_LowNoSw_CP1"
## [28] "bin19_LowNoSw_P1" "bin19_LowNoSw_Pz" "bin19_LowNoSw_CPz"
## [31] "bin19_LowNoSw_FC2" "bin19_LowNoSw_FCz" "bin19_LowNoSw_Cz"
## [34] "bin19_LowNoSw_C2" "bin19_LowNoSw_CP2" "bin19_LowNoSw_P2"
## [37] "bin20_LowSw_FC1" "bin20_LowSw_C1" "bin20_LowSw_CP1"
## [40] "bin20_LowSw_P1" "bin20_LowSw_Pz" "bin20_LowSw_CPz"
## [43] "bin20_LowSw_FC2" "bin20_LowSw_FCz" "bin20_LowSw_Cz"
## [46] "bin20_LowSw_C2" "bin20_LowSw_CP2" "bin20_LowSw_P2"
## [49] "ERPset"2 Assemble data from individual files into a single table
raw.all <- map_df(txt.files, ~read_table(.x, show_col_types=FALSE))
kable(head(raw.all))| bin17_HighNoSw_FC1 | bin17_HighNoSw_C1 | bin17_HighNoSw_CP1 | bin17_HighNoSw_P1 | bin17_HighNoSw_Pz | bin17_HighNoSw_CPz | bin17_HighNoSw_FC2 | bin17_HighNoSw_FCz | bin17_HighNoSw_Cz | bin17_HighNoSw_C2 | bin17_HighNoSw_CP2 | bin17_HighNoSw_P2 | bin18_HighSw_FC1 | bin18_HighSw_C1 | bin18_HighSw_CP1 | bin18_HighSw_P1 | bin18_HighSw_Pz | bin18_HighSw_CPz | bin18_HighSw_FC2 | bin18_HighSw_FCz | bin18_HighSw_Cz | bin18_HighSw_C2 | bin18_HighSw_CP2 | bin18_HighSw_P2 | bin19_LowNoSw_FC1 | bin19_LowNoSw_C1 | bin19_LowNoSw_CP1 | bin19_LowNoSw_P1 | bin19_LowNoSw_Pz | bin19_LowNoSw_CPz | bin19_LowNoSw_FC2 | bin19_LowNoSw_FCz | bin19_LowNoSw_Cz | bin19_LowNoSw_C2 | bin19_LowNoSw_CP2 | bin19_LowNoSw_P2 | bin20_LowSw_FC1 | bin20_LowSw_C1 | bin20_LowSw_CP1 | bin20_LowSw_P1 | bin20_LowSw_Pz | bin20_LowSw_CPz | bin20_LowSw_FC2 | bin20_LowSw_FCz | bin20_LowSw_Cz | bin20_LowSw_C2 | bin20_LowSw_CP2 | bin20_LowSw_P2 | ERPset |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| -0.666 | -0.215 | -0.529 | 1.019 | -1.433 | -1.385 | -2.415 | 4.944 | -3.569 | -5.848 | -0.605 | -1.497 | -2.468 | -1.118 | -0.690 | -2.102 | 0.611 | -2.245 | 0.460 | -2.306 | 4.247 | -1.522 | 1.436 | 1.706 | -0.586 | -0.608 | -1.108 | -1.703 | -3.215 | 0.681 | -0.590 | -3.340 | -2.690 | 1.506 | -2.095 | -3.021 | -0.339 | 0.969 | 1.527 | 1.115 | -0.281 | 5.851 | -0.417 | -1.524 | 3.487 | -2.057 | 0.513 | -1.463 | Sub1_erpnew_filt |
| 0.118 | 2.933 | 5.043 | 3.095 | 5.433 | 3.778 | -1.042 | 0.668 | 1.088 | -3.192 | 2.838 | 5.352 | -2.309 | -0.851 | 0.972 | 4.586 | 3.721 | 1.535 | -1.607 | -2.382 | -0.043 | 2.774 | 1.105 | 0.597 | -1.688 | -0.427 | 2.826 | 3.047 | 3.124 | 1.900 | -2.837 | -4.167 | 1.650 | -0.654 | 1.378 | 3.761 | 1.540 | -1.213 | 0.143 | 2.107 | 3.779 | 1.119 | 2.701 | -0.127 | -6.058 | 6.458 | 3.880 | 4.431 | Sub10_erp_filt |
| 0.877 | 0.989 | 1.859 | -0.250 | -0.081 | -0.239 | 4.400 | 1.827 | 0.482 | 0.346 | 2.725 | 0.259 | 13.888 | 9.449 | 3.876 | 7.845 | 7.878 | 6.177 | 9.951 | 8.967 | 4.949 | 5.950 | 6.779 | 5.753 | 8.502 | 8.393 | 7.430 | 8.295 | 6.641 | 7.879 | 5.850 | 2.582 | 9.293 | 5.153 | 9.415 | 8.552 | 5.870 | 5.614 | 5.210 | 7.396 | 6.050 | 6.070 | 4.567 | 2.477 | 3.359 | 4.646 | 7.941 | 5.848 | Sub11_erp_filt |
| 5.277 | 4.107 | 0.012 | 0.905 | 4.870 | 1.596 | 4.348 | 3.748 | 1.398 | 5.098 | 4.078 | 5.982 | -0.103 | 0.214 | -6.290 | -4.834 | -2.209 | -2.600 | 15.599 | 5.060 | -0.643 | 6.616 | 2.114 | 4.982 | -6.992 | -2.180 | -3.571 | -6.358 | -10.476 | -3.197 | 5.232 | 3.233 | 0.140 | 2.978 | -3.668 | -3.945 | -0.876 | 1.685 | -1.899 | -1.028 | -3.432 | -1.756 | 4.145 | -1.154 | -5.691 | -1.860 | -1.890 | 2.906 | Sub_12_erp_new_filt |
| 0.474 | -2.394 | 1.857 | 3.376 | 3.412 | 5.498 | 1.941 | 1.737 | 2.238 | -1.154 | 2.494 | 2.967 | -2.570 | -2.362 | 1.429 | 0.542 | 1.252 | -3.318 | -1.151 | -1.345 | -0.641 | 0.416 | -1.370 | 1.335 | 2.482 | 4.231 | 3.220 | 1.826 | 1.380 | 10.056 | 4.617 | 4.422 | 2.063 | 5.424 | -0.240 | 1.591 | 0.117 | 0.188 | -2.300 | 0.670 | 0.571 | 1.438 | -1.081 | -0.123 | -0.133 | -4.800 | -2.434 | -0.442 | Sub13_erp_filt_new |
| 3.334 | 1.093 | -0.133 | -1.369 | -0.946 | 1.605 | 4.752 | 4.004 | 3.430 | 3.297 | 0.729 | -0.126 | 3.055 | 2.375 | 1.308 | 0.407 | -1.003 | 3.011 | 3.206 | 0.277 | 2.797 | 2.711 | 2.553 | -0.165 | -0.543 | 1.983 | 1.441 | 0.609 | 0.190 | 1.876 | 4.001 | 2.889 | 3.641 | 2.527 | 2.476 | 0.610 | -0.379 | 0.231 | -0.425 | -1.396 | 2.113 | -0.182 | 1.277 | 2.332 | 0.253 | 0.856 | -0.488 | 0.096 | Sub14_erp_filt_new |
3 Bringing order into the data and converting to long form
eeg.long <- raw.all %>%
mutate(ERPset = str_replace(ERPset, 'Sub_', 'Sub')) %>% # fix coding error, such as for Subject 12
mutate(Participant = str_extract(ERPset,'^.+?(?=_)'), .before=1, .keep='unused') %>% # get Participant ID from ERPse
pivot_longer(-1) %>% # convert everything to long form
mutate(name = str_replace_all(name, '(High|Low)', '\\1_')) %>% # insert underscore following expectancy string
mutate(name = str_replace_all(name, '([12z]$)', '_\\1')) %>% # insert underscore before side suffix
separate_wider_delim(name, delim='_', names=c('bin','expectancy','switch','region','side')) %>% # generate variables based on underscore pattern
mutate(across(where(is.character), as.factor)) %>% # convert characters to factors
mutate(expectancy = fct_relevel(expectancy, 'Low', 'High')) %>% # reorder factor levels
mutate(region = fct_relevel(region, 'FC', 'C', 'CP')) %>%
mutate(side = fct_relevel(side, '1', 'z', '2')) %>%
mutate(side = fct_recode(side, Left='1', Midline='z', Right='2'))
write.xlsx(eeg.long, 'EEG long format all leads.xlsx', asTable=T) # save to Excel3.1 Here’s the top of our data file
kable(head(eeg.long,9))| Participant | bin | expectancy | switch | region | side | value |
|---|---|---|---|---|---|---|
| Sub1 | bin17 | High | NoSw | FC | Left | -0.666 |
| Sub1 | bin17 | High | NoSw | C | Left | -0.215 |
| Sub1 | bin17 | High | NoSw | CP | Left | -0.529 |
| Sub1 | bin17 | High | NoSw | P | Left | 1.019 |
| Sub1 | bin17 | High | NoSw | P | Midline | -1.433 |
| Sub1 | bin17 | High | NoSw | CP | Midline | -1.385 |
| Sub1 | bin17 | High | NoSw | FC | Right | -2.415 |
| Sub1 | bin17 | High | NoSw | FC | Midline | 4.944 |
| Sub1 | bin17 | High | NoSw | C | Midline | -3.569 |
3.2 A look at the levels of all variables
summary(eeg.long) ## Participant bin expectancy switch region side
## Sub1 : 48 bin17:468 Low :936 NoSw:936 FC:468 Left :624
## Sub10 : 48 bin18:468 High:936 Sw :936 C :468 Midline:624
## Sub11 : 48 bin19:468 CP:468 Right :624
## Sub12 : 48 bin20:468 P :468
## Sub13 : 48
## Sub14 : 48
## (Other):1584
## value
## Min. :-23.2420
## 1st Qu.: -0.8135
## Median : 0.9815
## Mean : 0.6294
## 3rd Qu.: 2.6510
## Max. : 15.5990
## 3.3 Filter out data for Parietal which we won’t use
eeg.noP <- eeg.long %>% filter(region != 'P')4 Create tables with aggregation over 3 leads for legacy anterior-posterior and left-to-right analyses
(anteriority <- eeg.noP %>% summarize(value = mean(value, na.rm=T), .by=c(Participant, expectancy, switch, region)))## # A tibble: 468 × 5
## Participant expectancy switch region value
## <fct> <fct> <fct> <fct> <dbl>
## 1 Sub1 High NoSw FC 0.621
## 2 Sub1 High NoSw C -3.21
## 3 Sub1 High NoSw CP -0.840
## 4 Sub1 High Sw FC -1.44
## 5 Sub1 High Sw C 0.536
## 6 Sub1 High Sw CP -0.500
## 7 Sub1 Low NoSw FC -1.51
## 8 Sub1 Low NoSw C -0.597
## 9 Sub1 Low NoSw CP -0.841
## 10 Sub1 Low Sw FC -0.76
## # ℹ 458 more rowswrite.xlsx(anteriority, 'EEG anterio-posterior long data.xlsx', asTable=T)
(laterality <- eeg.noP %>% summarize(value = mean(value, na.rm=T), .by=c(Participant, expectancy, switch, side)))## # A tibble: 468 × 5
## Participant expectancy switch side value
## <fct> <fct> <fct> <fct> <dbl>
## 1 Sub1 High NoSw Left -0.47
## 2 Sub1 High NoSw Midline -0.00333
## 3 Sub1 High NoSw Right -2.96
## 4 Sub1 High Sw Left -1.43
## 5 Sub1 High Sw Midline -0.101
## 6 Sub1 High Sw Right 0.125
## 7 Sub1 Low NoSw Left -0.767
## 8 Sub1 Low NoSw Midline -1.78
## 9 Sub1 Low NoSw Right -0.393
## 10 Sub1 Low Sw Left 0.719
## # ℹ 458 more rowswrite.xlsx(laterality, 'EEG left-to-right long data.xlsx', asTable=T)5 Initial modelling: legacy analyses but with varying intercepts for Participants
5.1 Anteriority analysis
summary(m.anteriority <- lmer(value ~ expectancy*switch + switch*region + (1|Participant), anteriority))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ expectancy * switch + switch * region + (1 | Participant)
## Data: anteriority
##
## REML criterion at convergence: 2066.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7186 -0.5410 -0.0507 0.5178 4.7348
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 6.840 2.615
## Residual 3.745 1.935
## Number of obs: 468, groups: Participant, 39
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.23682 0.48928 64.21991 2.528 0.01395 *
## expectancyHigh -0.16972 0.25301 422.00000 -0.671 0.50272
## switchSw -1.15943 0.35781 422.00000 -3.240 0.00129 **
## regionC 0.01561 0.30988 422.00000 0.050 0.95986
## regionCP -0.14392 0.30988 422.00000 -0.464 0.64257
## expectancyHigh:switchSw 0.17184 0.35781 422.00000 0.480 0.63129
## switchSw:regionC 0.05042 0.43823 422.00000 0.115 0.90846
## switchSw:regionCP 0.17719 0.43823 422.00000 0.404 0.68617
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) expctH swtchS reginC regnCP expH:S swtS:C
## expctncyHgh -0.259
## switchSw -0.366 0.354
## regionC -0.317 0.000 0.433
## regionCP -0.317 0.000 0.433 0.500
## expctncyH:S 0.183 -0.707 -0.500 0.000 0.000
## swtchSw:rgC 0.224 0.000 -0.612 -0.707 -0.354 0.000
## swtchSw:rCP 0.224 0.000 -0.612 -0.354 -0.707 0.000 0.500car::Anova(m.anteriority, type=3)## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: value
## Chisq Df Pr(>Chisq)
## (Intercept) 6.3900 1 0.011477 *
## expectancy 0.4500 1 0.502354
## switch 10.4998 1 0.001194 **
## region 0.3222 2 0.851216
## expectancy:switch 0.2306 1 0.631043
## switch:region 0.1736 2 0.916857
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15.2 Laterality analysis
summary(m.laterality <- lmer(value ~ expectancy*switch + switch*side + (1|Participant), laterality))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ expectancy * switch + switch * side + (1 | Participant)
## Data: laterality
##
## REML criterion at convergence: 1987.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5634 -0.4910 -0.0361 0.4313 4.9109
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 6.893 2.625
## Residual 3.106 1.762
## Number of obs: 468, groups: Participant, 39
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.82661 0.47941 59.34830 1.724 0.08987 .
## expectancyHigh -0.16972 0.23042 422.00000 -0.737 0.46179
## switchSw -1.02102 0.32586 422.00000 -3.133 0.00185 **
## sideMidline 0.64762 0.28220 422.00000 2.295 0.02223 *
## sideRight 0.45469 0.28220 422.00000 1.611 0.10788
## expectancyHigh:switchSw 0.17184 0.32586 422.00000 0.527 0.59822
## switchSw:sideMidline -0.17159 0.39909 422.00000 -0.430 0.66745
## switchSw:sideRight -0.01606 0.39909 422.00000 -0.040 0.96793
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) expctH swtchS sdMdln sdRght expH:S swtS:M
## expctncyHgh -0.240
## switchSw -0.340 0.354
## sideMidline -0.294 0.000 0.433
## sideRight -0.294 0.000 0.433 0.500
## expctncyH:S 0.170 -0.707 -0.500 0.000 0.000
## swtchSw:sdM 0.208 0.000 -0.612 -0.707 -0.354 0.000
## swtchSw:sdR 0.208 0.000 -0.612 -0.354 -0.707 0.000 0.500car::Anova(m.laterality, type=3)## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: value
## Chisq Df Pr(>Chisq)
## (Intercept) 2.9729 1 0.084668 .
## expectancy 0.5425 1 0.461381
## switch 9.8178 1 0.001728 **
## side 5.5533 2 0.062247 .
## expectancy:switch 0.2781 1 0.597944
## switch:side 0.2256 2 0.893341
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 16 Now using all data available and more complex RanEff structures
6.1 Anteriority analysis
6.1.1 Linear mixed model
summary(m.anter <- lmer(value ~ expectancy*switch + switch*region + (1|Participant) + (1|Participant:side), eeg.noP))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ expectancy * switch + switch * region + (1 | Participant) +
## (1 | Participant:side)
## Data: eeg.noP
##
## REML criterion at convergence: 6598.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.7967 -0.5365 -0.0308 0.4984 5.9178
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant:side (Intercept) 0.4226 0.6501
## Participant (Intercept) 6.8565 2.6185
## Residual 5.5704 2.3602
## Number of obs: 1404, groups: Participant:side, 117; Participant, 39
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.23682 0.45952 50.34424 2.692 0.00963 **
## expectancyHigh -0.16972 0.17816 1280.00074 -0.953 0.34096
## switchSw -1.15943 0.25195 1280.00074 -4.602 4.6e-06 ***
## regionC 0.01561 0.21820 1280.00074 0.072 0.94299
## regionCP -0.14392 0.21820 1280.00074 -0.660 0.50964
## expectancyHigh:switchSw 0.17184 0.25195 1280.00074 0.682 0.49533
## switchSw:regionC 0.05042 0.30858 1280.00074 0.163 0.87024
## switchSw:regionCP 0.17719 0.30858 1280.00074 0.574 0.56592
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) expctH swtchS reginC regnCP expH:S swtS:C
## expctncyHgh -0.194
## switchSw -0.274 0.354
## regionC -0.237 0.000 0.433
## regionCP -0.237 0.000 0.433 0.500
## expctncyH:S 0.137 -0.707 -0.500 0.000 0.000
## swtchSw:rgC 0.168 0.000 -0.612 -0.707 -0.354 0.000
## swtchSw:rCP 0.168 0.000 -0.612 -0.354 -0.707 0.000 0.5006.1.2 Anova
car::Anova(m.anter, type=3)## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: value
## Chisq Df Pr(>Chisq)
## (Intercept) 7.2444 1 0.007112 **
## expectancy 0.9075 1 0.340777
## switch 21.1766 1 4.188e-06 ***
## region 0.6498 2 0.722603
## expectancy:switch 0.4652 1 0.495209
## switch:region 0.3501 2 0.839397
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 16.1.3 Plot of effects
plot(allEffects(m.anter))
6.2 Laterality analysis
6.2.1 Linear mixed model
summary(m.later <- lmer(value ~ expectancy*switch + switch*side + (1|Participant) + (1|Participant:region), eeg.noP))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: value ~ expectancy * switch + switch * side + (1 | Participant) +
## (1 | Participant:region)
## Data: eeg.noP
##
## REML criterion at convergence: 6498.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.2794 -0.4841 -0.0278 0.4637 6.0184
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant:region (Intercept) 1.219 1.104
## Participant (Intercept) 6.608 2.571
## Residual 4.962 2.228
## Number of obs: 1404, groups: Participant:region, 117; Participant, 39
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.82661 0.45620 48.91875 1.812 0.0761 .
## expectancyHigh -0.16972 0.16815 1280.00001 -1.009 0.3130
## switchSw -1.02102 0.23780 1280.00001 -4.294 1.89e-05 ***
## sideMidline 0.64762 0.20594 1280.00001 3.145 0.0017 **
## sideRight 0.45469 0.20594 1280.00001 2.208 0.0274 *
## expectancyHigh:switchSw 0.17184 0.23780 1280.00001 0.723 0.4700
## switchSw:sideMidline -0.17159 0.29125 1280.00001 -0.589 0.5559
## switchSw:sideRight -0.01606 0.29125 1280.00001 -0.055 0.9560
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) expctH swtchS sdMdln sdRght expH:S swtS:M
## expctncyHgh -0.184
## switchSw -0.261 0.354
## sideMidline -0.226 0.000 0.433
## sideRight -0.226 0.000 0.433 0.500
## expctncyH:S 0.130 -0.707 -0.500 0.000 0.000
## swtchSw:sdM 0.160 0.000 -0.612 -0.707 -0.354 0.000
## swtchSw:sdR 0.160 0.000 -0.612 -0.354 -0.707 0.000 0.5006.2.2 Anova
car::Anova(m.later, type=3)## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: value
## Chisq Df Pr(>Chisq)
## (Intercept) 3.2831 1 0.069996 .
## expectancy 1.0187 1 0.312824
## switch 18.4345 1 1.758e-05 ***
## side 10.4272 2 0.005442 **
## expectancy:switch 0.5222 1 0.469907
## switch:side 0.4236 2 0.809146
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 16.2.3 Plot of effects
plot(allEffects(m.later))