-Note: Large chunks of code have been hidden to improve readibility. ## 1. Load Packages Used in Data Analysis -Code hidden
-Code hidden
-The code below when run illustrates that there are no administrations #With <50% accuracy. Nothing excluded here. No cues at administrations with < 50% accuracy for any timepoint
df %>%
group_by(Subject, ExperimentName, Cue) %>%
summarise(Percent_Accurate = mean(Accuracy)) %>%
filter(Percent_Accurate < 0.50) ## `summarise()` has grouped output by 'Subject', 'ExperimentName'. You can
## override using the `.groups` argument.-The code below indicates the overall # of trials and the counts/percents for 1) incorrect trials to be excluded, and 2) improbable reaction time trials (i.e., <200ms OR >1500ms) to be excluded. The code immediately following then excludes this data.
df %>%
group_by(ExperimentName) %>%
summarise(Overall_Trials = n(), #Overall trial counts
Count_Inc_Trial = sum(ifelse(Accuracy == 0,1,0)), #Incorrect trial count
Per_Inc_Trial = 1 - mean(Accuracy), #Percent of trials that are incorrect
Count_Inc_RT = sum(ifelse(RT < 200 | RT > 1500, 1, 0)),
#Count of trials whose reaction times are < 200 or > 1500
Per_Inc_RT = 1 - mean(ifelse(RT < 200 | RT > 1500, 0, 1)))-Filtering out improbable reaction times
df <- df %>%
filter(Accuracy == 1) %>% #Exclude incorrect trials
filter(RT > 200 & RT < 1500) #Exclude RT's <200ms or >1500ms-Maximum RT after exclusions
max(df$RT)## [1] 1495-Minimum RT after exclusions
min(df$RT)## [1] 273-Code for Median Absolute Deviation and SD calculations for Reaction Times hidden
-MAD & SD Calculation Results
df %>%
group_by(ExperimentName) %>%
summarise(Ovr_Trl_Aftr_Exc = n(), #Overall trial counts after excluding for above-noted criteria
Count_MAD3_Over = sum(ifelse(RT > MAD_Exclude_Score, 1,0)), #Count of trials that had RT > 3MAD above individual median
Per_MAD3_Over = mean(ifelse(RT > MAD_Exclude_Score, 1, 0)),
Count_SD3_Over = sum(ifelse(RT > SD_Exclude_Score, 1,0)), #Count of trials that had RT > 3MAD above individual median
Per_SD3_Over = mean(ifelse(RT > SD_Exclude_Score, 1, 0)))-Excluding Folks based on SD for Reaction Times
df <- df %>%
filter(RT < SD_Exclude_Score) #Exclude folks whose RT are >3SD above their individual means-Code for AB calculations hidden
-Illustration of AB output
Grouped_Output-Reading in data for ANOVA’s (code hidden)
## Rows: 28 Columns: 28
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (28): ptid, Condition, SEX, MOUD, drug_BS, drug_mean_towrd, drug_mean_aw...
##
## 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.## Contrasts set to contr.sum for the following variables: Condition## Anova Table (Type 3 tests)
##
## Response: RT_Values
## Effect df MSE F ges p.value
## 1 Condition 1, 26 918.40 1.38 .024 .251
## 2 Time_Point 1, 26 1040.86 3.44 + .066 .075
## 3 Condition:Time_Point 1, 26 1040.86 0.46 .009 .504
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1## `summarise()` has grouped output by 'Condition'. You can override using the
## `.groups` argument.## Contrasts set to contr.sum for the following variables: Condition## Anova Table (Type 3 tests)
##
## Response: RT_Values
## Effect df MSE F ges p.value
## 1 Condition 1, 26 4123.24 8.56 ** .191 .007
## 2 Time_Point 1, 26 1609.41 0.09 .001 .762
## 3 Condition:Time_Point 1, 26 1609.41 1.18 .013 .286
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1## `summarise()` has grouped output by 'Condition'. You can override using the
## `.groups` argument.## Contrasts set to contr.sum for the following variables: Condition## Anova Table (Type 3 tests)
##
## Response: RT_Values
## Effect df MSE F ges p.value
## 1 Condition 1, 26 5178.90 4.99 * .143 .034
## 2 Time_Point 1, 26 773.49 0.26 .001 .614
## 3 Condition:Time_Point 1, 26 773.49 2.28 .011 .143
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1## `summarise()` has grouped output by 'Condition'. You can override using the
## `.groups` argument.## Contrasts set to contr.sum for the following variables: Condition## Anova Table (Type 3 tests)
##
## Response: RT_Values
## Effect df MSE F ges p.value
## 1 Condition 1, 26 35044.60 5.16 * .144 .032
## 2 Time_Point 1, 26 6478.72 0.94 .006 .341
## 3 Condition:Time_Point 1, 26 6478.72 0.07 <.001 .793
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1## `summarise()` has grouped output by 'Condition'. You can override using the
## `.groups` argument.## Contrasts set to contr.sum for the following variables: Condition## Anova Table (Type 3 tests)
##
## Response: RT_Values
## Effect df MSE F ges p.value
## 1 Condition 1, 26 57895.85 5.97 * .169 .022
## 2 Time_Point 1, 26 7305.64 0.36 .002 .553
## 3 Condition:Time_Point 1, 26 7305.64 0.00 <.001 .953
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1## `summarise()` has grouped output by 'Condition'. You can override using the
## `.groups` argument.## Contrasts set to contr.sum for the following variables: Condition## Anova Table (Type 3 tests)
##
## Response: RT_Values
## Effect df MSE F ges p.value
## 1 Condition 1, 26 2823.56 7.62 * .199 .010
## 2 Time_Point 1, 26 510.11 0.66 .004 .425
## 3 Condition:Time_Point 1, 26 510.11 3.06 + .018 .092
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1## `summarise()` has grouped output by 'Condition'. You can override using the
## `.groups` argument.## Contrasts set to contr.sum for the following variables: Condition## Anova Table (Type 3 tests)
##
## Response: RT_Values
## Effect df MSE F ges p.value
## 1 Condition 1, 26 593.35 0.34 .005 .564
## 2 Time_Point 1, 26 1009.01 0.01 <.001 .906
## 3 Condition:Time_Point 1, 26 1009.01 0.20 .005 .659
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1## `summarise()` has grouped output by 'Condition'. You can override using the
## `.groups` argument.## Contrasts set to contr.sum for the following variables: Condition## Anova Table (Type 3 tests)
##
## Response: RT_Values
## Effect df MSE F ges p.value
## 1 Condition 1, 26 5364.99 5.76 * .147 .024
## 2 Time_Point 1, 26 1535.92 0.74 .006 .398
## 3 Condition:Time_Point 1, 26 1535.92 6.42 * .052 .018
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1## `summarise()` has grouped output by 'Condition'. You can override using the
## `.groups` argument.## Contrasts set to contr.sum for the following variables: Condition## Anova Table (Type 3 tests)
##
## Response: RT_Values
## Effect df MSE F ges p.value
## 1 Condition 1, 26 4457.38 6.11 * .148 .020
## 2 Time_Point 1, 26 1576.59 0.13 .001 .724
## 3 Condition:Time_Point 1, 26 1576.59 3.33 + .032 .079
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1## `summarise()` has grouped output by 'Condition'. You can override using the
## `.groups` argument.Pain_P_twrd_AOV <- ANOVA_df_long %>% filter(Time_Point %in% c("pain_peak_toward", "post_pain_peak_toward"))
afex::aov_car(formula = RT_Values ~ Time_Point*Condition + Error(ptid/(Time_Point)), data = Pain_P_twrd_AOV, type = 3)## Contrasts set to contr.sum for the following variables: Condition## Anova Table (Type 3 tests)
##
## Response: RT_Values
## Effect df MSE F ges p.value
## 1 Condition 1, 26 50428.84 5.32 * .150 .029
## 2 Time_Point 1, 26 8219.99 0.26 .001 .618
## 3 Condition:Time_Point 1, 26 8219.99 1.41 .008 .245
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1Pain_P_twrd_AOV %>% select(Condition, Time_Point, RT_Values) %>% group_by(Condition, Time_Point) %>% summarise(M = mean(RT_Values), Med = median(RT_Values), SD = sd(RT_Values))## `summarise()` has grouped output by 'Condition'. You can override using the
## `.groups` argument.Pain_P_Away_AOV <- ANOVA_df_long %>% filter(Time_Point %in% c("pain_mean_away", "post_pain_mean_away"))
afex::aov_car(formula = RT_Values ~ Time_Point*Condition + Error(ptid/(Time_Point)), data = Pain_P_Away_AOV, type = 3)## Contrasts set to contr.sum for the following variables: Condition## Anova Table (Type 3 tests)
##
## Response: RT_Values
## Effect df MSE F ges p.value
## 1 Condition 1, 26 4457.38 6.11 * .148 .020
## 2 Time_Point 1, 26 1576.59 0.13 .001 .724
## 3 Condition:Time_Point 1, 26 1576.59 3.33 + .032 .079
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1Pain_P_Away_AOV %>% select(Condition, Time_Point, RT_Values) %>% group_by(Condition, Time_Point) %>% summarise(M = mean(RT_Values), Med = median(RT_Values), SD = sd(RT_Values))## `summarise()` has grouped output by 'Condition'. You can override using the
## `.groups` argument.## Contrasts set to contr.sum for the following variables: Condition## Anova Table (Type 3 tests)
##
## Response: RT_Values
## Effect df MSE F ges p.value
## 1 Condition 1, 26 3911.63 6.03 * .154 .021
## 2 Time_Point 1, 26 1085.28 0.77 .006 .388
## 3 Condition:Time_Point 1, 26 1085.28 5.24 * .042 .030
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1## `summarise()` has grouped output by 'Condition'. You can override using the
## `.groups` argument.