experiment1fix.R
maggie
2023-10-02
#Load packages
library(lme4)## Loading required package: Matrixlibrary(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ tidyr::expand() masks Matrix::expand()
## ✖ dplyr::filter() masks stats::filter()
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## ✖ tidyr::pack() masks Matrix::pack()
## ✖ tidyr::unpack() masks Matrix::unpack()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(afex)## ************
## Welcome to afex. For support visit: http://afex.singmann.science/
## - Functions for ANOVAs: aov_car(), aov_ez(), and aov_4()
## - Methods for calculating p-values with mixed(): 'S', 'KR', 'LRT', and 'PB'
## - 'afex_aov' and 'mixed' objects can be passed to emmeans() for follow-up tests
## - Get and set global package options with: afex_options()
## - Set sum-to-zero contrasts globally: set_sum_contrasts()
## - For example analyses see: browseVignettes("afex")
## ************
##
## Attaching package: 'afex'
##
## The following object is masked from 'package:lme4':
##
## lmergetwd()## [1] "/Users/maggie/Downloads/bookR"setwd("/Users/maggie/Downloads/bookR")
#Load data, and name that object "Sheet 1-stim_IA_full.csv"
stimIA_data <- read_csv("Sheet 1-stim_IA_full.csv")## Rows: 12183 Columns: 183
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (143): RECORDING_SESSION_LABEL, TRIAL_LABEL, IA_LABEL, IA_FIRST_RUN_DWEL...
## dbl (36): TRIAL_INDEX, IA_ID, IA_DWELL_TIME, IA_FIXATION_COUNT, IP_INDEX, A...
## lgl (4): IA_DYNAMIC, IP_END_EVENT_MATCHED, IP_START_EVENT_MATCHED, Trial_R...
##
## 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.#View the first six rows of the data frame
head(stimIA_data)## # A tibble: 6 × 183
## RECORDING_SESSION_LABEL TRIAL_INDEX TRIAL_LABEL IA_ID IA_LABEL IA_DWELL_TIME
## <chr> <dbl> <chr> <dbl> <chr> <dbl>
## 1 01LYX 1 Trial: 1 5 Her 0
## 2 01LYX 1 Trial: 1 6 collar 0
## 3 01LYX 1 Trial: 1 7 is 0
## 4 01LYX 1 Trial: 1 8 ___ 0
## 5 01LYX 1 Trial: 1 9 all 0
## 6 01LYX 1 Trial: 1 10 the 0
## # ℹ 177 more variables: IA_FIRST_RUN_DWELL_TIME <chr>,
## # IA_SECOND_RUN_DWELL_TIME <chr>, `IA_DWELL_TIME_%` <chr>,
## # IA_FIRST_FIXATION_DURATION <chr>, IA_FIRST_FIXATION_INDEX <chr>,
## # IA_FIRST_FIXATION_X <chr>, IA_FIRST_FIXATION_Y <chr>,
## # IA_FIRST_SACCADE_AMPLITUDE <chr>, IA_FIRST_SACCADE_ANGLE <chr>,
## # IA_FIRST_SACCADE_INDEX <chr>, `IA_FIXATION_%` <chr>,
## # IA_FIXATION_COUNT <dbl>, IP_INDEX <dbl>, IP_LABEL <chr>, ACC <dbl>, …#Select variables
glimpse(stimIA_data)## Rows: 12,183
## Columns: 183
## $ RECORDING_SESSION_LABEL <chr> "01LYX", "01LYX", "01LYX", "01LY…
## $ TRIAL_INDEX <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ TRIAL_LABEL <chr> "Trial: 1", "Trial: 1", "Trial: …
## $ IA_ID <dbl> 5, 6, 7, 8, 9, 10, 11, 41, 42, 4…
## $ IA_LABEL <chr> "Her", "collar", "is", "___", "a…
## $ IA_DWELL_TIME <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ IA_FIRST_RUN_DWELL_TIME <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_SECOND_RUN_DWELL_TIME <chr> ".", ".", ".", ".", ".", ".", ".…
## $ `IA_DWELL_TIME_%` <chr> "0.0000", "0.0000", "0.0000", "0…
## $ IA_FIRST_FIXATION_DURATION <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FIRST_FIXATION_INDEX <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FIRST_FIXATION_X <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FIRST_FIXATION_Y <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FIRST_SACCADE_AMPLITUDE <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FIRST_SACCADE_ANGLE <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FIRST_SACCADE_INDEX <chr> ".", ".", ".", ".", ".", ".", ".…
## $ `IA_FIXATION_%` <chr> "0.0000", "0.0000", "0.0000", "0…
## $ IA_FIXATION_COUNT <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ IP_INDEX <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ IP_LABEL <chr> "Stimuli", "Stimuli", "Stimuli",…
## $ ACC <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ ChoiceMade <chr> "B", "B", "B", "B", "B", "B", "B…
## $ PositionList <chr> "[(146, 437), (528, 437), (146, …
## $ RT <dbl> 6146.020, 6146.020, 6146.020, 61…
## $ conditionid <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,…
## $ cnPassedLength <dbl> 316, 316, 316, 316, 316, 316, 31…
## $ currentItemIndex <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,…
## $ enPassedLength <dbl> 372, 372, 372, 372, 372, 372, 37…
## $ enlist <chr> "[Her collar, is neat, all the t…
## $ groupid <chr> "A", "A", "A", "A", "A", "A", "A…
## $ type <chr> "p", "p", "p", "p", "p", "p", "p…
## $ IA_FIRST_SACCADE_END_TIME <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FIRST_SACCADE_START_TIME <chr> ".", ".", ".", ".", ".", ".", ".…
## $ DATA_FILE <chr> "01LYX.edf", "01LYX.edf", "01LYX…
## $ EYE_USED <chr> "LEFT", "LEFT", "LEFT", "LEFT", …
## $ GROUPING_VARIABLES <chr> "trialid", "trialid", "trialid",…
## $ IA_AREA <dbl> 4300, 5600, 2400, 3200, 3200, 32…
## $ IA_AVERAGE_FIX_PUPIL_SIZE <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_BOTTOM <dbl> 225, 225, 225, 225, 225, 225, 22…
## $ IA_DYNAMIC <lgl> FALSE, FALSE, FALSE, FALSE, FALS…
## $ IA_END_TIME <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FIRST_FIXATION_PREVIOUS_FIX_IA <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FIRST_FIXATION_PREVIOUS_IAREAS <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FIRST_FIXATION_RUN_INDEX <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FIRST_FIXATION_TIME <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FIRST_FIXATION_VISITED_IA_COUNT <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FIRST_FIX_PROGRESSIVE <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FIRST_RUN_END_TIME <chr> ".", ".", ".", ".", ".", ".", ".…
## $ `IA_FIRST_RUN_FIXATION_%` <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FIRST_RUN_FIXATION_COUNT <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FIRST_RUN_LANDING_POSITION <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FIRST_RUN_LAUNCH_SITE <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FIRST_RUN_START_TIME <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FSA_COUNT_5 <chr> "0", "0", "0", "0", "0", "0", "0…
## $ IA_FSA_COUNT_6 <chr> "0", "0", "0", "0", "0", "0", "0…
## $ IA_FSA_COUNT_7 <chr> "0", "0", "0", "0", "0", "0", "0…
## $ IA_FSA_COUNT_8 <chr> "0", "0", "0", "0", "0", "0", "0…
## $ IA_FSA_COUNT_9 <chr> "0", "0", "0", "0", "0", "0", "0…
## $ IA_FSA_COUNT_10 <chr> "0", "0", "0", "0", "0", "0", "0…
## $ IA_FSA_COUNT_11 <chr> "0", "0", "0", "0", "0", "0", "0…
## $ IA_FSA_COUNT_41 <chr> "0", "0", "0", "0", "0", "0", "0…
## $ IA_FSA_COUNT_42 <chr> "0", "0", "0", "0", "0", "0", "0…
## $ IA_FSA_COUNT_43 <chr> "0", "0", "0", "0", "0", "0", "0…
## $ IA_FSA_COUNT_44 <chr> "0", "0", "0", "0", "0", "0", "0…
## $ IA_FSA_COUNT_51 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ IA_FSA_COUNT_52 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ IA_FSA_COUNT_53 <chr> "0", "0", "0", "0", "0", "0", "0…
## $ IA_FSA_COUNT_61 <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FSA_COUNT_62 <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FSA_COUNT_63 <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FSA_COUNT_54 <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FSA_COUNT_64 <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FSA_DURATION_5 <chr> "0", "0", "0", "0", "0", "0", "0…
## $ IA_FSA_DURATION_6 <chr> "0", "0", "0", "0", "0", "0", "0…
## $ IA_FSA_DURATION_7 <chr> "0", "0", "0", "0", "0", "0", "0…
## $ IA_FSA_DURATION_8 <chr> "0", "0", "0", "0", "0", "0", "0…
## $ IA_FSA_DURATION_9 <chr> "0", "0", "0", "0", "0", "0", "0…
## $ IA_FSA_DURATION_10 <chr> "0", "0", "0", "0", "0", "0", "0…
## $ IA_FSA_DURATION_11 <chr> "0", "0", "0", "0", "0", "0", "0…
## $ IA_FSA_DURATION_41 <chr> "0", "0", "0", "0", "0", "0", "0…
## $ IA_FSA_DURATION_42 <chr> "0", "0", "0", "0", "0", "0", "0…
## $ IA_FSA_DURATION_43 <chr> "0", "0", "0", "0", "0", "0", "0…
## $ IA_FSA_DURATION_44 <chr> "0", "0", "0", "0", "0", "0", "0…
## $ IA_FSA_DURATION_51 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ IA_FSA_DURATION_52 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ IA_FSA_DURATION_53 <chr> "0", "0", "0", "0", "0", "0", "0…
## $ IA_FSA_DURATION_61 <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FSA_DURATION_62 <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FSA_DURATION_63 <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FSA_DURATION_54 <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_FSA_DURATION_64 <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_GROUP <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_INSTANCES_COUNT <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_LAST_FIXATION_DURATION <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_LAST_FIXATION_RUN <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_LAST_FIXATION_TIME <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_LAST_FIXATION_X <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_LAST_FIXATION_Y <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_LAST_RUN_DWELL_TIME <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_LAST_RUN_END_TIME <chr> ".", ".", ".", ".", ".", ".", ".…
## $ `IA_LAST_RUN_FIXATION_%` <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_LAST_RUN_FIXATION_COUNT <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_LAST_RUN_LANDING_POSITION <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_LAST_RUN_LAUNCH_SITE <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_LAST_RUN_START_TIME <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_LAST_SACCADE_AMPLITUDE <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_LAST_SACCADE_ANGLE <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_LAST_SACCADE_END_TIME <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_LAST_SACCADE_INDEX <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_LAST_SACCADE_START_TIME <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_LEFT <dbl> 70, 156, 268, 316, 380, 444, 508…
## $ IA_LEGAL <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ IA_LEGAL_IMMEDIATE <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ IA_MAX_FIX_PUPIL_SIZE <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_MIN_FIX_PUPIL_SIZE <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_POINTS <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_REGRESSION_IN <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_REGRESSION_IN_COUNT <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_REGRESSION_OUT <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_REGRESSION_OUT_COUNT <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_REGRESSION_OUT_FULL <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_REGRESSION_OUT_FULL_COUNT <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_REGRESSION_PATH_DURATION <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_RIGHT <dbl> 156, 268, 316, 380, 444, 508, 62…
## $ IA_RUN_COUNT <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ IA_SECOND_FIXATION_DURATION <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_SECOND_FIXATION_RUN <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_SECOND_FIXATION_TIME <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_SECOND_FIXATION_X <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_SECOND_FIXATION_Y <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_SECOND_RUN_END_TIME <chr> ".", ".", ".", ".", ".", ".", ".…
## $ `IA_SECOND_RUN_FIXATION_%` <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_SECOND_RUN_FIXATION_COUNT <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_SECOND_RUN_LANDING_POSITION <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_SECOND_RUN_LAUNCH_SITE <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_SECOND_RUN_START_TIME <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_SELECTIVE_REGRESSION_PATH_DURATION <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_SKIP <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ IA_SPILLOVER <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_START_TIME <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_THIRD_FIXATION_DURATION <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_THIRD_FIXATION_RUN <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_THIRD_FIXATION_TIME <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_THIRD_FIXATION_X <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_THIRD_FIXATION_Y <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_THIRD_RUN_DWELL_TIME <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_THIRD_RUN_END_TIME <chr> ".", ".", ".", ".", ".", ".", ".…
## $ `IA_THIRD_RUN_FIXATION_%` <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_THIRD_RUN_FIXATION_COUNT <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_THIRD_RUN_LANDING_POSITION <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_THIRD_RUN_LAUNCH_SITE <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_THIRD_RUN_START_TIME <chr> ".", ".", ".", ".", ".", ".", ".…
## $ IA_TOP <dbl> 175, 175, 175, 175, 175, 175, 17…
## $ IA_TYPE <chr> "RECTANGLE", "RECTANGLE", "RECTA…
## $ INTEREST_AREA_FIXATION_SEQUENCE <chr> "[0, 51, 0, 52, 0, 53]", "[0, 51…
## $ IP_END_EVENT_MATCHED <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TR…
## $ IP_END_TIME <dbl> 655766, 655766, 655766, 655766, …
## $ IP_START_EVENT_MATCHED <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TR…
## $ IP_START_TIME <dbl> 652825, 652825, 652825, 652825, …
## $ REPORTING_METHOD <chr> "Fixations", "Fixations", "Fixat…
## $ TIME_SCALE <chr> "Trial Relative", "Trial Relativ…
## $ TRIAL_DWELL_TIME <dbl> 2615, 2615, 2615, 2615, 2615, 26…
## $ TRIAL_FIXATION_COUNT <dbl> 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,…
## $ TRIAL_IA_COUNT <dbl> 14, 14, 14, 14, 14, 14, 14, 14, …
## $ TRIAL_START_TIME <dbl> 652739, 652739, 652739, 652739, …
## $ TRIAL_TOTAL_VISITED_IA_COUNT <dbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,…
## $ RandomPostitonIndex <chr> "[3, 0, 2, 1]", "[3, 0, 2, 1]", …
## $ Session_Name_ <chr> "01LYX", "01LYX", "01LYX", "01LY…
## $ Trial_Index_ <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ Trial_Recycled_ <lgl> FALSE, FALSE, FALSE, FALSE, FALS…
## $ audiofile <chr> "p2.wav", "p2.wav", "p2.wav", "p…
## $ choicea <chr> "tidy", "tidy", "tidy", "tidy", …
## $ choiceb <chr> "clean", "clean", "clean", "clea…
## $ choicec <chr> "white", "white", "white", "whit…
## $ choiced <chr> "broken", "broken", "broken", "b…
## $ currentWait <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ discript <chr> "单语", "单语", "单语", "单语", …
## $ fontSize <dbl> 20, 20, 20, 20, 20, 20, 20, 20, …
## $ listLength <dbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,…
## $ questionstring <chr> "Her collar is ___ all the time.…
## $ timelist <chr> "[1033, 882, 960]", "[1033, 882,…
## $ trialid <chr> "p2", "p2", "p2", "p2", "p2", "p…
## $ wordsPass <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…variables <- c("RECORDING_SESSION_LABEL", "trialid","IA_ID",
"IA_DWELL_TIME_%","IA_FIXATION_%","RT",
"ACC","IA_DWELL_TIME","IA_FIRST_FIXATION_DURATION",
"IA_FIRST_FIXATION_INDEX","currentItemIndex","discript",
"conditionid","groupid")
stim4 <- stimIA_data %>%
select(any_of(variables) )
# change column names
df <- stim4 %>% rename(subj = "RECORDING_SESSION_LABEL",
trial = "trialid", ia = "IA_ID",
dtrate = "IA_DWELL_TIME_%",
fixrate = "IA_FIXATION_%", rt = "RT",
acc = "ACC", dt = "IA_DWELL_TIME",
ffd = "IA_FIRST_FIXATION_DURATION",
ff = "IA_FIRST_FIXATION_INDEX",
item = "currentItemIndex",
lang = "discript", cond = "conditionid",
gr = "groupid")
glimpse(df)## Rows: 12,183
## Columns: 14
## $ subj <chr> "01LYX", "01LYX", "01LYX", "01LYX", "01LYX", "01LYX", "01LYX",…
## $ trial <chr> "p2", "p2", "p2", "p2", "p2", "p2", "p2", "p2", "p2", "p2", "p…
## $ ia <dbl> 5, 6, 7, 8, 9, 10, 11, 41, 42, 43, 44, 51, 52, 53, 5, 6, 7, 8,…
## $ dtrate <chr> "0.0000", "0.0000", "0.0000", "0.0000", "0.0000", "0.0000", "0…
## $ fixrate <chr> "0.0000", "0.0000", "0.0000", "0.0000", "0.0000", "0.0000", "0…
## $ rt <dbl> 6146.020, 6146.020, 6146.020, 6146.020, 6146.020, 6146.020, 61…
## $ acc <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ dt <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 604, 256, 175, 0, 0, 0, 0, 0,…
## $ ffd <chr> ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", "338", …
## $ ff <chr> ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", "2", "5…
## $ item <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,…
## $ lang <chr> "单语", "单语", "单语", "单语", "单语", "单语", "单语", "单语"…
## $ cond <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4,…
## $ gr <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A…#cross tabulate
table(df$subj,df$lang)##
## 单语 双语
## 01LYX 137 256
## 02PQJ 250 143
## 03LSY 137 256
## 04CMY 250 143
## 05MXJ 137 256
## 06HC 250 143
## 07GXJ 137 256
## 08ZYQ 250 143
## 09MZW 137 256
## 10WLH 250 143
## 11LDP 137 256
## 12SCC 250 143
## 13WSR 137 256
## 15ZMZ 137 256
## 16QY 250 143
## 17DSL 137 256
## 18BXY 250 143
## 19WLL 137 256
## 20ZZY 250 143
## 21WYQ 137 256
## 22SYC 250 143
## 23SYH 137 256
## 24MBH 250 143
## 25HYX 137 256
## 26CJ 250 143
## 27YSH 137 256
## 28HYX 250 143
## 29HRE 137 256
## 30YAL 250 143
## 31SWY 137 256
## ZWL 14.00 250 143table(df$subj,df$cond) ##
## 1 2 3 4
## 01LYX 67 70 129 127
## 02PQJ 126 124 70 73
## 03LSY 70 67 127 129
## 04CMY 124 126 73 70
## 05MXJ 67 70 129 127
## 06HC 126 124 70 73
## 07GXJ 70 67 127 129
## 08ZYQ 124 126 73 70
## 09MZW 67 70 129 127
## 10WLH 126 124 70 73
## 11LDP 70 67 127 129
## 12SCC 124 126 73 70
## 13WSR 67 70 129 127
## 15ZMZ 70 67 127 129
## 16QY 124 126 73 70
## 17DSL 67 70 129 127
## 18BXY 126 124 70 73
## 19WLL 70 67 127 129
## 20ZZY 124 126 73 70
## 21WYQ 67 70 129 127
## 22SYC 126 124 70 73
## 23SYH 70 67 127 129
## 24MBH 124 126 73 70
## 25HYX 67 70 129 127
## 26CJ 126 124 70 73
## 27YSH 70 67 127 129
## 28HYX 124 126 73 70
## 29HRE 67 70 129 127
## 30YAL 126 124 70 73
## 31SWY 70 67 127 129
## ZWL 14.00 126 124 70 73table(df$ia,df$lang)##
## 单语 双语
## 5 62 62
## 6 62 62
## 7 62 62
## 8 62 62
## 9 62 62
## 10 62 62
## 11 62 62
## 41 62 62
## 42 62 62
## 43 62 62
## 44 62 62
## 51 1116 1116
## 52 1116 1116
## 53 1084 1086
## 54 279 279
## 61 510 606
## 62 510 606
## 63 510 606
## 64 135 144table(df$ia,df$cond)##
## 1 2 3 4
## 5 31 31 31 31
## 6 31 31 31 31
## 7 31 31 31 31
## 8 31 31 31 31
## 9 31 31 31 31
## 10 31 31 31 31
## 11 31 31 31 31
## 41 31 31 31 31
## 42 31 31 31 31
## 43 31 31 31 31
## 44 31 31 31 31
## 51 558 558 558 558
## 52 558 558 558 558
## 53 542 542 542 544
## 54 140 139 139 140
## 61 255 255 303 303
## 62 255 255 303 303
## 63 255 255 303 303
## 64 68 67 72 72#EDA
library(skimr)
summary(df)## subj trial ia dtrate
## Length:12183 Length:12183 Min. : 5.00 Length:12183
## Class :character Class :character 1st Qu.:51.00 Class :character
## Mode :character Mode :character Median :53.00 Mode :character
## Mean :51.59
## 3rd Qu.:61.00
## Max. :64.00
## fixrate rt acc dt
## Length:12183 Min. : 1082 Min. :0.0000 Min. : 0.0
## Class :character 1st Qu.: 2863 1st Qu.:1.0000 1st Qu.: 0.0
## Mode :character Median : 3884 Median :1.0000 Median : 396.0
## Mean : 4568 Mean :0.8749 Mean : 479.6
## 3rd Qu.: 5461 3rd Qu.:1.0000 3rd Qu.: 801.0
## Max. :23218 Max. :1.0000 Max. :2723.0
## ffd ff item lang
## Length:12183 Length:12183 Min. :1.000 Length:12183
## Class :character Class :character 1st Qu.:2.000 Class :character
## Mode :character Mode :character Median :2.000 Mode :character
## Mean :2.265
## 3rd Qu.:3.000
## Max. :3.000
## cond gr
## Min. :1.000 Length:12183
## 1st Qu.:2.000 Class :character
## Median :3.000 Mode :character
## Mean :2.525
## 3rd Qu.:4.000
## Max. :4.000skim(df)| Name | df |
| Number of rows | 12183 |
| Number of columns | 14 |
| _______________________ | |
| Column type frequency: | |
| character | 8 |
| numeric | 6 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| subj | 0 | 1 | 4 | 9 | 0 | 31 | 0 |
| trial | 0 | 1 | 1 | 2 | 0 | 72 | 0 |
| dtrate | 0 | 1 | 1 | 6 | 0 | 3873 | 0 |
| fixrate | 0 | 1 | 1 | 6 | 0 | 95 | 0 |
| ffd | 0 | 1 | 1 | 4 | 0 | 527 | 0 |
| ff | 0 | 1 | 1 | 2 | 0 | 22 | 0 |
| lang | 0 | 1 | 2 | 2 | 0 | 2 | 0 |
| gr | 0 | 1 | 1 | 1 | 0 | 4 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| ia | 0 | 1 | 51.59 | 13.16 | 5.00 | 51.00 | 53.00 | 61.00 | 64.0 | ▁▁▁▆▇ |
| rt | 0 | 1 | 4567.98 | 2546.85 | 1081.93 | 2862.82 | 3884.38 | 5460.82 | 23217.7 | ▇▂▁▁▁ |
| acc | 0 | 1 | 0.87 | 0.33 | 0.00 | 1.00 | 1.00 | 1.00 | 1.0 | ▁▁▁▁▇ |
| dt | 0 | 1 | 479.60 | 483.74 | 0.00 | 0.00 | 396.00 | 801.00 | 2723.0 | ▇▃▂▁▁ |
| item | 0 | 1 | 2.26 | 0.46 | 1.00 | 2.00 | 2.00 | 3.00 | 3.0 | ▁▁▇▁▃ |
| cond | 0 | 1 | 2.52 | 1.12 | 1.00 | 2.00 | 3.00 | 4.00 | 4.0 | ▇▇▁▇▇ |
df %>% skim(dt)| Name | Piped data |
| Number of rows | 12183 |
| Number of columns | 14 |
| _______________________ | |
| Column type frequency: | |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| dt | 0 | 1 | 479.6 | 483.74 | 0 | 0 | 396 | 801 | 2723 | ▇▃▂▁▁ |
stim4%>%
skim() %>%
dplyr::filter(n_missing > 0)## # A tibble: 0 × 17
## # ℹ 17 variables: skim_type <chr>, skim_variable <chr>, n_missing <int>,
## # complete_rate <dbl>, character.min <int>, character.max <int>,
## # character.empty <int>, character.n_unique <int>,
## # character.whitespace <int>, numeric.mean <dbl>, numeric.sd <dbl>,
## # numeric.p0 <dbl>, numeric.p25 <dbl>, numeric.p50 <dbl>, numeric.p75 <dbl>,
## # numeric.p100 <dbl>, numeric.hist <chr>library(DataExplorer)
library(ggplot2)
create_report(stim4)##
##
## processing file: report.rmd##
|
| | 0%
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|. | 2%
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|.. | 5% [global_options]
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|... | 7%
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|.... | 10% [introduce]
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|.... | 12%
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|..... | 14% [plot_intro]
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|...... | 17%
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|....... | 19% [data_structure]
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|........ | 21%
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|......... | 24% [missing_profile]
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|.......... | 26%
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|........... | 29% [univariate_distribution_header]
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|........... | 31%
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|............ | 33% [plot_histogram]
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|.............. | 38% [plot_density]
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|............... | 40%
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|................ | 43% [plot_frequency_bar]
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|................. | 45%
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|.................. | 48% [plot_response_bar]
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|.................. | 50%
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|................... | 52% [plot_with_bar]
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|.............................. | 81% [bivariate_distribution_header]
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|............................... | 83%
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|................................ | 86% [plot_response_boxplot]
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|................................. | 88%
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|................................. | 90% [plot_by_boxplot]
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|.................................. | 93%
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|................................... | 95% [plot_response_scatterplot]
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|.................................... | 98%
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|.....................................| 100% [plot_by_scatterplot] ## output file: /Users/maggie/Downloads/bookR/report.knit.md## /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/pandoc +RTS -K512m -RTS /Users/maggie/Downloads/bookR/report.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /Users/maggie/Downloads/bookR/report.html --lua-filter /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/rmarkdown/rmarkdown/lua/latex-div.lua --embed-resources --standalone --variable bs3=TRUE --section-divs --table-of-contents --toc-depth 6 --template /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=yeti --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /var/folders/b2/49rr6ps17r128kg5_hklt5br0000gn/T//Rtmp2NIiT9/rmarkdown-str221c7830fe27.html##
## Output created: report.html# delete outliers by subj
df1 <- df %>%
group_by(subj) %>%
filter(dt>150&abs(scale(dt))<=3)## Warning: Using one column matrices in `filter()` was deprecated in dplyr 1.1.0.
## ℹ Please use one dimensional logical vectors instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.df1 %>%
group_by(ia,lang) %>%
summarise(mdt=mean(dt),
dt_SD=sd(dt))## `summarise()` has grouped output by 'ia'. You can override using the `.groups`
## argument.## # A tibble: 29 × 4
## # Groups: ia [19]
## ia lang mdt dt_SD
## <dbl> <chr> <dbl> <dbl>
## 1 5 双语 200 NA
## 2 6 单语 397. 198.
## 3 6 双语 368. 156.
## 4 7 单语 356. 223.
## 5 7 双语 252. 62.0
## 6 8 单语 426 18.3
## 7 8 双语 774 264.
## 8 9 单语 611 251.
## 9 9 双语 526 547.
## 10 10 单语 191 55.2
## # ℹ 19 more rows# do we need to log transform?
qqnorm(df1$dt) 
qqnorm(log(df1$dt))
# no need to log
# if yes
df1$dt = log(df1$dt)
#geom_boxplot
ggplot(df1,aes(ia,dt,fill=lang))+
geom_boxplot(notch = TRUE)+
facet_wrap(~lang)
#mixed model
library(lme4)
summary(mix.m0 <- lmer(dt~ia*lang*cond+
(1+lang*cond|subj)+
(ia|gr),
data=df1),cor=F)## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dt ~ ia * lang * cond + (1 + lang * cond | subj) + (ia | gr)
## Data: df1
##
## REML criterion at convergence: 11485.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.8193 -0.6799 0.0619 0.7386 2.5698
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subj (Intercept) 0.0228225 0.15107
## lang双语 0.1013764 0.31840 -0.23
## cond 0.0013640 0.03693 -0.66 -0.32
## lang双语:cond 0.0015374 0.03921 0.61 -0.90 0.03
## gr (Intercept) 0.3279733 0.57269
## ia 0.0001204 0.01097 -1.00
## Residual 0.2544902 0.50447
## Number of obs: 7666, groups: subj, 31; gr, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.340e+00 6.023e-01 5.897e+01 8.866 1.92e-12 ***
## ia 2.537e-02 1.155e-02 5.983e+01 2.196 0.03196 *
## lang双语 2.001e+00 7.362e-01 6.307e+03 2.718 0.00659 **
## cond 6.339e-01 4.671e-01 7.574e+03 1.357 0.17483
## ia:lang双语 -4.478e-02 1.386e-02 6.559e+03 -3.232 0.00124 **
## ia:cond -1.229e-02 8.962e-03 7.577e+03 -1.371 0.17032
## lang双语:cond -5.728e-01 4.886e-01 7.600e+03 -1.172 0.24108
## ia:lang双语:cond 1.128e-02 9.346e-03 7.598e+03 1.207 0.22745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(mix.m1 <- lmer(dt~ia*lang*cond+
(1+lang+cond|subj)+
(ia|gr),
data=df1),cor=F)## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dt ~ ia * lang * cond + (1 + lang + cond | subj) + (ia | gr)
## Data: df1
##
## REML criterion at convergence: 11488.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.8218 -0.6803 0.0614 0.7382 2.5490
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subj (Intercept) 0.0176521 0.13286
## lang双语 0.0596471 0.24423 0.14
## cond 0.0020657 0.04545 -0.41 -0.78
## gr (Intercept) 0.6117129 0.78212
## ia 0.0002244 0.01498 -1.00
## Residual 0.2544881 0.50447
## Number of obs: 7666, groups: subj, 31; gr, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.330e+00 6.590e-01 9.957e+00 8.087 1.1e-05 ***
## ia 2.559e-02 1.264e-02 1.010e+01 2.025 0.070058 .
## lang双语 2.077e+00 7.346e-01 7.162e+03 2.828 0.004696 **
## cond 6.398e-01 4.673e-01 7.602e+03 1.369 0.171016
## ia:lang双语 -4.640e-02 1.384e-02 7.124e+03 -3.352 0.000806 ***
## ia:cond -1.242e-02 8.966e-03 7.599e+03 -1.385 0.166110
## lang双语:cond -5.995e-01 4.885e-01 7.603e+03 -1.227 0.219762
## ia:lang双语:cond 1.185e-02 9.346e-03 7.601e+03 1.267 0.205022
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#mix.m1和0无显著差别,继续简化
summary(mix.m2 <- lmer(dt~ia*lang*cond+
(1+lang|subj)+
(ia|gr),
data=df1),cor=F)## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dt ~ ia * lang * cond + (1 + lang | subj) + (ia | gr)
## Data: df1
##
## REML criterion at convergence: 11492.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.7443 -0.6880 0.0549 0.7416 2.5719
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subj (Intercept) 1.761e-02 0.132689
## lang双语 3.643e-02 0.190869 -0.41
## gr (Intercept) 2.220e-01 0.471155
## ia 8.502e-05 0.009221 -1.00
## Residual 2.549e-01 0.504879
## Number of obs: 7666, groups: subj, 31; gr, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.314e+00 5.793e-01 1.491e+02 9.174 3.47e-16 ***
## ia 2.590e-02 1.115e-02 1.435e+02 2.323 0.021575 *
## lang双语 2.288e+00 7.318e-01 7.627e+03 3.126 0.001780 **
## cond 6.630e-01 4.673e-01 7.604e+03 1.419 0.155997
## ia:lang双语 -5.064e-02 1.380e-02 7.605e+03 -3.671 0.000244 ***
## ia:cond -1.287e-02 8.966e-03 7.604e+03 -1.435 0.151272
## lang双语:cond -6.742e-01 4.885e-01 7.606e+03 -1.380 0.167527
## ia:lang双语:cond 1.334e-02 9.344e-03 7.606e+03 1.427 0.153481
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')anova(mix.m1,mix.m2)## refitting model(s) with ML (instead of REML)## Warning in optwrap(optimizer, devfun, x@theta, lower = x@lower, calc.derivs =
## TRUE, : convergence code 1 from bobyqa: bobyqa -- maximum number of function
## evaluations exceeded## Data: df1
## Models:
## mix.m2: dt ~ ia * lang * cond + (1 + lang | subj) + (ia | gr)
## mix.m1: dt ~ ia * lang * cond + (1 + lang + cond | subj) + (ia | gr)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## mix.m2 15 11463 11567 -5716.4 11433
## mix.m1 18 11465 11590 -5714.3 11429 4.1491 3 0.2458#mix.m1和2无显著差别，仍然存在singular，继续简化
summary(mix.m3 <- lmer(dt~ia*lang*cond+
(1+lang|subj)+
(ia|gr),
data=df1),cor=F)## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dt ~ ia * lang * cond + (1 + lang | subj) + (ia | gr)
## Data: df1
##
## REML criterion at convergence: 11492.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.7443 -0.6880 0.0549 0.7416 2.5719
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subj (Intercept) 1.761e-02 0.132689
## lang双语 3.643e-02 0.190869 -0.41
## gr (Intercept) 2.220e-01 0.471155
## ia 8.502e-05 0.009221 -1.00
## Residual 2.549e-01 0.504879
## Number of obs: 7666, groups: subj, 31; gr, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.314e+00 5.793e-01 1.491e+02 9.174 3.47e-16 ***
## ia 2.590e-02 1.115e-02 1.435e+02 2.323 0.021575 *
## lang双语 2.288e+00 7.318e-01 7.627e+03 3.126 0.001780 **
## cond 6.630e-01 4.673e-01 7.604e+03 1.419 0.155997
## ia:lang双语 -5.064e-02 1.380e-02 7.605e+03 -3.671 0.000244 ***
## ia:cond -1.287e-02 8.966e-03 7.604e+03 -1.435 0.151272
## lang双语:cond -6.742e-01 4.885e-01 7.606e+03 -1.380 0.167527
## ia:lang双语:cond 1.334e-02 9.344e-03 7.606e+03 1.427 0.153481
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(mix.m4 <- lmer(dt~ia*lang*cond+
(1|subj)+
(1|gr),
data=df1),cor=F)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dt ~ ia * lang * cond + (1 | subj) + (1 | gr)
## Data: df1
##
## REML criterion at convergence: 11730.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3664 -0.6752 0.0611 0.7332 2.5817
##
## Random effects:
## Groups Name Variance Std.Dev.
## subj (Intercept) 0.014797 0.12164
## gr (Intercept) 0.002111 0.04594
## Residual 0.265487 0.51525
## Number of obs: 7666, groups: subj, 31; gr, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.197e+00 5.306e-01 7.422e+03 9.795 < 2e-16 ***
## ia 2.817e-02 1.017e-02 7.631e+03 2.769 0.00564 **
## lang双语 2.904e+00 7.412e-01 7.631e+03 3.918 9.00e-05 ***
## cond 7.485e-01 4.749e-01 7.630e+03 1.576 0.11504
## ia:lang双语 -6.281e-02 1.398e-02 7.631e+03 -4.491 7.18e-06 ***
## ia:cond -1.452e-02 9.112e-03 7.630e+03 -1.593 0.11120
## lang双语:cond -7.945e-01 4.971e-01 7.630e+03 -1.598 0.11006
## ia:lang双语:cond 1.566e-02 9.510e-03 7.630e+03 1.647 0.09963 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mix.m5 <- lmer(dt~ia*lang+cond+
(1|subj)+
(1|gr),
data=df1),cor=F)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dt ~ ia * lang + cond + (1 | subj) + (1 | gr)
## Data: df1
##
## REML criterion at convergence: 11710.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3229 -0.6769 0.0590 0.7305 2.5891
##
## Random effects:
## Groups Name Variance Std.Dev.
## subj (Intercept) 0.014745 0.12143
## gr (Intercept) 0.002108 0.04591
## Residual 0.265513 0.51528
## Number of obs: 7666, groups: subj, 31; gr, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.001e+00 1.343e-01 7.998e+02 44.680 < 2e-16 ***
## ia 1.234e-02 2.502e-03 7.642e+03 4.933 8.29e-07 ***
## lang双语 1.921e+00 1.524e-01 7.638e+03 12.600 < 2e-16 ***
## cond 5.229e-03 1.178e-02 7.631e+03 0.444 0.657
## ia:lang双语 -4.299e-02 2.855e-03 7.639e+03 -15.056 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#M4 没有报错，但和M3有显著差异
anova(mix.m4,mix.m5)## refitting model(s) with ML (instead of REML)## Data: df1
## Models:
## mix.m5: dt ~ ia * lang + cond + (1 | subj) + (1 | gr)
## mix.m4: dt ~ ia * lang * cond + (1 | subj) + (1 | gr)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## mix.m5 8 11686 11742 -5835.1 11670
## mix.m4 11 11688 11765 -5833.3 11666 3.6611 3 0.3005# m4和m5没有显著差异
#model diagnosis
qqnorm(residuals(mix.m4))
qqline(residuals(mix.m4))
qqnorm(residuals(mix.m5))
qqline(residuals(mix.m5))
hist(residuals(mix.m4))
hist(residuals(mix.m5))
#fixed and interaction effect obtained
afex::mixed(mix.m5 <- lmer(dt~ia*lang+cond+
(1|subj) + (1|gr),
data=df1),
REML=FALSE,
method="LRT",
data=df1)## Contrasts set to contr.sum for the following variables: lang, subj, gr
## Formula (the first argument) converted to formula.
## Numerical variables NOT centered on 0: ia, cond
## If in interactions, interpretation of lower order (e.g., main) effects difficult.## Mixed Model Anova Table (Type 3 tests, LRT-method)
##
## Model: dt ~ ia * lang + cond + (1 | subj) + (1 | gr)
## Data: df1
## Df full model: 8
## Effect df Chisq p.value
## 1 ia 1 40.54 *** <.001
## 2 lang 1 157.39 *** <.001
## 3 cond 1 0.20 .657
## 4 ia:lang 1 223.69 *** <.001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1emmeans::emmeans(mix.m5,specs = pairwise~ia*lang)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 7666' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 7666)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 7666' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 7666)' or larger];
## but be warned that this may result in large computation time and memory use.## $emmeans
## ia lang emmean SE df asymp.LCL asymp.UCL
## 52.9 单语 6.67 0.0354 Inf 6.60 6.74
## 52.9 双语 6.31 0.0344 Inf 6.25 6.38
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio
## ia52.888859900861 单语 - ia52.888859900861 双语 0.353 0.0265 Inf 13.318
## p.value
## <.0001
##
## Degrees-of-freedom method: asymptotic#load se and lp data ?
se_data <- read_csv("SE data.csv")## Rows: 31 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): subj
## dbl (2): lp, se
##
## 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.# join two data frames by ID (subject ID variable)
sedf <- inner_join (df, se_data, by = "subj")
glimpse(sedf)## Rows: 11,790
## Columns: 16
## $ subj <chr> "01LYX", "01LYX", "01LYX", "01LYX", "01LYX", "01LYX", "01LYX",…
## $ trial <chr> "p2", "p2", "p2", "p2", "p2", "p2", "p2", "p2", "p2", "p2", "p…
## $ ia <dbl> 5, 6, 7, 8, 9, 10, 11, 41, 42, 43, 44, 51, 52, 53, 5, 6, 7, 8,…
## $ dtrate <chr> "0.0000", "0.0000", "0.0000", "0.0000", "0.0000", "0.0000", "0…
## $ fixrate <chr> "0.0000", "0.0000", "0.0000", "0.0000", "0.0000", "0.0000", "0…
## $ rt <dbl> 6146.020, 6146.020, 6146.020, 6146.020, 6146.020, 6146.020, 61…
## $ acc <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ dt <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 604, 256, 175, 0, 0, 0, 0, 0,…
## $ ffd <chr> ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", "338", …
## $ ff <chr> ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", "2", "5…
## $ item <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,…
## $ lang <chr> "单语", "单语", "单语", "单语", "单语", "单语", "单语", "单语"…
## $ cond <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4,…
## $ gr <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A…
## $ lp <dbl> 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30…
## $ se <dbl> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,…### Fit a mixed-effects model
summary(mix.m6 <- lmer(dt~lp*ia*lang+
(1|subj)+
(1|gr),
data=sedf),cor=F)## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dt ~ lp * ia * lang + (1 | subj) + (1 | gr)
## Data: sedf
##
## REML criterion at convergence: 178890.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4212 -0.8936 -0.1519 0.6537 4.8322
##
## Random effects:
## Groups Name Variance Std.Dev.
## subj (Intercept) 4930 70.21
## gr (Intercept) 0 0.00
## Residual 226671 476.10
## Number of obs: 11790, groups: subj, 30; gr, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -40.6125 167.7670 557.1284 -0.242 0.808810
## lp 12.4578 4.7484 553.0652 2.624 0.008941 **
## ia 13.3773 2.9417 11770.2281 4.547 5.48e-06 ***
## lang双语 383.5383 208.7169 11781.9589 1.838 0.066146 .
## lp:ia -0.3061 0.0830 11770.6987 -3.688 0.000227 ***
## lp:lang双语 -12.8527 5.9170 11781.9051 -2.172 0.029862 *
## ia:lang双语 -14.2484 3.9953 11733.3344 -3.566 0.000364 ***
## lp:ia:lang双语 0.3916 0.1132 11729.8536 3.459 0.000544 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#model diagnosis
qqnorm(residuals(mix.m6))
qqline(residuals(mix.m6))
hist(residuals(mix.m6))
#fixed and interaction effect obtained
afex::mixed(mix.m6 <- lmer(dt~lp*ia*lang+
(1|subj)+
(1|gr),
data=sedf),
REML=FALSE,
method="LRT",
data=sedf)## boundary (singular) fit: see help('isSingular')
## Contrasts set to contr.sum for the following variables: lang, subj, gr
## Formula (the first argument) converted to formula.
## Numerical variables NOT centered on 0: lp, ia
## If in interactions, interpretation of lower order (e.g., main) effects difficult.## Warning: Some predictor variables are on very different scales: consider
## rescaling## boundary (singular) fit: see help('isSingular')## Warning: Some predictor variables are on very different scales: consider
## rescaling## Warning: Some predictor variables are on very different scales: consider
## rescaling## boundary (singular) fit: see help('isSingular')## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescaling## boundary (singular) fit: see help('isSingular')## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescaling## boundary (singular) fit: see help('isSingular')## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescaling## boundary (singular) fit: see help('isSingular')## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescaling## boundary (singular) fit: see help('isSingular')## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescaling## boundary (singular) fit: see help('isSingular')## Warning: Some predictor variables are on very different scales: consider
## rescaling## boundary (singular) fit: see help('isSingular')## Warning: lme4 reported (at least) the following warnings for 'full':
## * boundary (singular) fit: see help('isSingular')## Warning: lme4 reported (at least) the following warnings for 'lp':
## * boundary (singular) fit: see help('isSingular')## Warning: lme4 reported (at least) the following warnings for 'ia':
## * boundary (singular) fit: see help('isSingular')## Warning: lme4 reported (at least) the following warnings for 'lang':
## * boundary (singular) fit: see help('isSingular')## Warning: lme4 reported (at least) the following warnings for 'lp:ia':
## * boundary (singular) fit: see help('isSingular')## Warning: lme4 reported (at least) the following warnings for 'lp:lang':
## * boundary (singular) fit: see help('isSingular')## Warning: lme4 reported (at least) the following warnings for 'ia:lang':
## * boundary (singular) fit: see help('isSingular')## Warning: lme4 reported (at least) the following warnings for 'lp:ia:lang':
## * boundary (singular) fit: see help('isSingular')## Mixed Model Anova Table (Type 3 tests, LRT-method)
##
## Model: dt ~ lp * ia * lang + (1 | subj) + (1 | gr)
## Data: sedf
## Df full model: 11
## Effect df Chisq p.value
## 1 lp 1 2.85 + .092
## 2 ia 1 10.20 ** .001
## 3 lang 1 3.37 + .066
## 4 lp:ia 1 3.96 * .047
## 5 lp:lang 1 4.72 * .030
## 6 ia:lang 1 12.71 *** <.001
## 7 lp:ia:lang 1 11.97 *** <.001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1# Fit the mixed-effects model with rescaled predictors
mix.m7 <- mixed(lmer(dt ~ lp * ia * lang + (1 | subj) + (1 | gr), data = sedf),
REML = FALSE,
method = "LRT",
data = sedf)## boundary (singular) fit: see help('isSingular')
## Contrasts set to contr.sum for the following variables: lang, subj, gr
## Formula (the first argument) converted to formula.
## Numerical variables NOT centered on 0: lp, ia
## If in interactions, interpretation of lower order (e.g., main) effects difficult.## Warning: Some predictor variables are on very different scales: consider
## rescaling## boundary (singular) fit: see help('isSingular')## Warning: Some predictor variables are on very different scales: consider
## rescaling## Warning: Some predictor variables are on very different scales: consider
## rescaling## boundary (singular) fit: see help('isSingular')## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescaling## boundary (singular) fit: see help('isSingular')## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescaling## boundary (singular) fit: see help('isSingular')## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescaling## boundary (singular) fit: see help('isSingular')## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescaling## boundary (singular) fit: see help('isSingular')## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescaling## boundary (singular) fit: see help('isSingular')## Warning: Some predictor variables are on very different scales: consider
## rescaling## boundary (singular) fit: see help('isSingular')# Print the model summary
summary(mix.m7)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: dt ~ lp * ia * lang + (1 | subj) + (1 | gr)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 178925.7 179006.8 -89451.8 178903.7 11779
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4199 -0.8944 -0.1519 0.6543 4.8311
##
## Random effects:
## Groups Name Variance Std.Dev.
## subj (Intercept) 4557 67.5
## gr (Intercept) 0 0.0
## Residual 226556 476.0
## Number of obs: 11790, groups: subj, 30; gr, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.512e+02 1.257e+02 2.239e+02 1.203 0.230209
## lp 6.030e+00 3.563e+00 2.235e+02 1.693 0.091943 .
## ia 6.252e+00 1.957e+00 1.177e+04 3.195 0.001404 **
## lang1 -1.917e+02 1.043e+02 1.179e+04 -1.837 0.066199 .
## lp:ia -1.103e-01 5.543e-02 1.177e+04 -1.989 0.046677 *
## lp:lang1 6.426e+00 2.957e+00 1.179e+04 2.173 0.029804 *
## ia:lang1 7.121e+00 1.997e+00 1.174e+04 3.566 0.000363 ***
## lp:ia:lang1 -1.958e-01 5.658e-02 1.173e+04 -3.461 0.000541 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) lp ia lang1 lp:ia lp:ln1 i:lng1
## lp -0.985
## ia -0.796 0.784
## lang1 0.039 -0.038 -0.072
## lp:ia 0.785 -0.796 -0.985 0.068
## lp:lang1 -0.038 0.036 0.068 -0.985 -0.065
## ia:lang1 -0.058 0.056 0.106 -0.967 -0.101 0.952
## lp:ia:lang1 0.056 -0.053 -0.101 0.953 0.097 -0.967 -0.985
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# Assuming 'sedf' is your data frame
# Rescale the predictor variables
sedf$lp <- scale(sedf$lp)
sedf$ia <- scale(sedf$ia)
# Transform the "lang" variable into a numerical format using dummy coding
sedf$lang_binary <- as.numeric(sedf$lang == "双语")
# Create a new variable 'lang_centered' with values -1 and 1
sedf$lang_centered <- ifelse(sedf$lang == "双语", 1, -1)
# Fit the mixed-effects model with the transformed "lang" variable
mix.m8 <- mixed(lmer(dt ~ lp * ia * lang_centered +
(1 | subj) +
(1 | gr),
data = sedf),
REML = FALSE,
method = "LRT",
data = sedf)## boundary (singular) fit: see help('isSingular')
## Contrasts set to contr.sum for the following variables: subj, gr
## Formula (the first argument) converted to formula.
## Numerical variables NOT centered on 0: lang_centered
## If in interactions, interpretation of lower order (e.g., main) effects difficult.
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')summary(mix.m8)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: dt ~ lp * ia * lang_centered + (1 | subj) + (1 | gr)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 178925.7 179006.8 -89451.8 178903.7 11779
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4199 -0.8944 -0.1519 0.6543 4.8311
##
## Random effects:
## Groups Name Variance Std.Dev.
## subj (Intercept) 4557 67.5
## gr (Intercept) 0 0.0
## Residual 226556 476.0
## Number of obs: 11790, groups: subj, 30; gr, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 485.583 13.085 29.961 37.109 < 2e-16 ***
## lp 2.079 13.110 30.179 0.159 0.875079
## ia 31.879 4.393 11761.238 7.257 4.20e-13 ***
## lang_centered -48.068 4.572 11482.318 -10.515 < 2e-16 ***
## lp:ia -8.829 4.438 11767.280 -1.989 0.046677 *
## lp:lang_centered 22.352 4.615 11583.918 4.844 1.29e-06 ***
## ia:lang_centered -4.217 4.502 11700.094 -0.937 0.349035
## lp:ia:lang_centered 15.679 4.531 11734.233 3.461 0.000541 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) lp ia lng_cn lp:ia lp:ln_ i:lng_
## lp -0.002
## ia 0.002 -0.004
## lang_centrd -0.013 0.018 -0.030
## lp:ia -0.004 0.010 -0.009 0.041
## lp:lng_cntr 0.018 -0.051 0.041 -0.009 -0.119
## i:lng_cntrd -0.010 0.014 -0.025 0.065 0.034 -0.007
## lp::lng_cnt 0.014 -0.041 0.034 -0.007 -0.097 0.079 -0.006
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# Check for missing values in 'dt' and other predictor variables
any(is.na(sedf$dt))## [1] FALSEany(is.na(sedf$lp))## [1] FALSEany(is.na(sedf$ia))## [1] FALSEany(is.na(sedf$lang_centered))## [1] FALSE# Divide participants into high and low SE groups (using lp_mean as an example)
mean_se <- mean(sedf$lp)
high_se <- subset(sedf, lp > mean_se)
low_se <- subset(sedf, lp <= mean_se)
mix.hm0 <- mixed(lmer(dt ~ lp * ia * lang_centered +
(1 | subj) +
(1 | gr),
data = high_se),
REML = FALSE,
method = "LRT",
data = high_se)## Contrasts set to contr.sum for the following variables: subj, gr
## Formula (the first argument) converted to formula.
## Numerical variables NOT centered on 0: lp, lang_centered
## If in interactions, interpretation of lower order (e.g., main) effects difficult.summary(mix.hm0)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: dt ~ lp * ia * lang_centered + (1 | subj) + (1 | gr)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 101729.7 101804.6 -50853.8 101707.7 6670
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2150 -0.9706 -0.1410 0.6817 4.0775
##
## Random effects:
## Groups Name Variance Std.Dev.
## subj (Intercept) 1411.3 37.57
## gr (Intercept) 698.3 26.43
## Residual 238457.6 488.32
## Number of obs: 6681, groups: subj, 17; gr, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 514.099 24.727 10.793 20.791 4.72e-10 ***
## lp -21.394 24.625 16.006 -0.869 0.397795
## ia 27.842 10.853 6664.556 2.565 0.010329 *
## lang_centered -2.390 11.228 5933.136 -0.213 0.831412
## lp:ia 2.654 12.404 6664.837 0.214 0.830562
## lp:lang_centered -44.926 12.820 5618.657 -3.504 0.000461 ***
## ia:lang_centered 31.795 11.084 6398.928 2.868 0.004138 **
## lp:ia:lang_centered -36.471 12.658 6252.772 -2.881 0.003974 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) lp ia lng_cn lp:ia lp:ln_ i:lng_
## lp -0.720
## ia 0.000 -0.001
## lang_centrd 0.003 0.012 0.031
## lp:ia -0.001 0.002 -0.834 -0.012
## lp:lng_cntr 0.002 -0.025 -0.011 -0.832 -0.016
## i:lng_cntrd 0.002 0.009 0.025 0.055 -0.010 -0.044
## lp::lng_cnt 0.002 -0.020 -0.009 -0.044 -0.013 0.054 -0.833mix.hm1 <- mixed(lmer(dt ~ lp * ia * lang_centered +
(1 | subj) +
(1 | gr),
data = high_se),
REML = FALSE,
method = "LRT",
data = low_se)## Contrasts set to contr.sum for the following variables: subj, gr
## Formula (the first argument) converted to formula.
## Numerical variables NOT centered on 0: lp, lang_centered
## If in interactions, interpretation of lower order (e.g., main) effects difficult.
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')summary(mix.hm1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: dt ~ lp * ia * lang_centered + (1 | subj) + (1 | gr)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 77061.8 77133.7 -38519.9 77039.8 5098
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9240 -0.8083 -0.1876 0.6175 5.0022
##
## Random effects:
## Groups Name Variance Std.Dev.
## subj (Intercept) 7.473e+03 86.4451
## gr (Intercept) 4.225e-05 0.0065
## Residual 2.058e+05 453.5975
## Number of obs: 5109, groups: subj, 13; gr, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 440.3742 45.2384 13.0225 9.735 2.42e-07 ***
## lp -42.7150 39.6743 13.1237 -1.077 0.301
## ia 0.5582 11.6383 5096.5695 0.048 0.962
## lang_centered 28.2369 12.1301 5086.1765 2.328 0.020 *
## lp:ia -44.8545 10.3759 5098.1570 -4.323 1.57e-05 ***
## lp:lang_centered 100.6451 10.7793 5104.4138 9.337 < 2e-16 ***
## ia:lang_centered 50.9720 11.9244 5105.6596 4.275 1.95e-05 ***
## lp:ia:lang_centered 73.1394 10.5437 5108.9755 6.937 4.51e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) lp ia lng_cn lp:ia lp:ln_ i:lng_
## lp 0.836
## ia 0.004 0.008
## lang_centrd -0.016 -0.027 -0.050
## lp:ia 0.008 0.016 0.835 -0.082
## lp:lng_cntr -0.027 -0.051 -0.082 0.835 -0.155
## i:lng_cntrd -0.013 -0.022 -0.041 0.073 -0.067 0.069
## lp::lng_cnt -0.022 -0.041 -0.067 0.070 -0.127 0.096 0.835
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')