README.md
In emjcampos/pERPred: Principle 'ERP' Reduction
pERPred 
The pERPred package is a tool for conducting ERP analyses using the Principle ERP Reduction algorithm put forth in Campos and Hazlett et al (2020). This package contains the functions necessary to derive the principle ERPs discussed in that publication as well as the tools for performing "pERP-space analysis". Let this serve as a guide to our approach to ERP analysis in R.
Installation
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("emjcampos/pERPred")
Example
Simulated Data Description
The generation model for the simulated data is described in the publication as follows. The observed signal is assumed to be a linear combination of the principal ERP components (pERPs). Let Yi, v, e(t) denote the ERP signal observed for subject i at task v and electrode e. The data generation model in the simulation will be: $Y_{i,v,e}(t) = \sum_{c=1}^C k_{c,v,e}\phi^{\star}_c(t) + \sum_{c=1}^C \xi_{c,i,v,e}\phi^{\star}_c(t) + \zeta_{i,v,e}(t).$
In the simulation, N = 100, V = 9, C = 5, T = 384, and each subject has a common E = 40. The first term reflects that each task and electrode is composed of a weighted average of pERPs, where ϕc⋆(t) are the ``true'' pERPs for purposes of simulation. To simulate a single ϕc⋆(t), we draw a function that is a superposition of Gaussian kernels centered at different time points to produce a smooth, low-frequency function intended to mimic the type of source signals we would expect to contribute to ERP waveforms. These simulated signals are then rotated by ICA to form maximally independent bases and are plotted here.
The coefficients, kc, v, e, are drawn from a normal distribution with mean zero and variance σk2 = 0.25.
The second term reflects the structured stochastic canonical component with dependencies within a subject across tasks and electrodes. The subject specific scores ξc, i, v, e will be simulated from a Matrix Normal (to create a depedence structure within tasks and across electrodes) with mean zero and covariance matrices Σc, v and Σc, e of dimension V * V and E * E (identically and independently drawn over subjects only). The covariance matrices are created in the same way for the electrode and task dimensions. A matrix with 0.5 on the diagonal and 0.1 elsewhere created. Each of these is multiplied by a factor (0.1, 0.2, 0.3, 0.4, 0.5), so that each true ERP component is represented differently. In the results, we expect the component with the highest multiplier to explain the most variation in the observed signals.
The last term is the independent and identically distributed (iid) measurement error. The measurement error ζi, v, e(t) will be simulated as independent draws (over i, v, and e) from a normal distribution with mean zero and variance σerror2 equal to some proportion of the signal variance. Define the variance of the simulated signal terms $\sum_{c=1}^C k_{c,v,e}\phi^{\star}_c(t) + \sum_{c=1}^C \xi_{c,i,v,e}\phi^{\star}_c(t)$ to be σsignal2. For the purposes of this vignette, we will only explore the low noise case in which 1/3 of the simulated ERP will be noise so $\sigma^2_\text{error} = \sqrt{0.5}\times\sigma_\text{signal}$.
The data to be used in the pERPred function must be averaged over trials. Then there must be one column for the "Task", "Subject", and "Time", and the remaining columns are the named electrodes that were observed. All subjects must have the same number of time points observed as well as the same number of tasks. However, there may be a different number of electrodes (use NA for the missing electrodes). For example, our simulated data looks like this:
head(simulated_data[, 1:7])
#> Task Subject Time AF3 AF4 AFZ C3
#> 1 Task_1 Subject_001 0.0026042 -0.8315047 1.6557474 -1.4637006 -1.2174339
#> 2 Task_1 Subject_001 0.0052083 0.9028223 1.1525489 -2.3697299 -3.4455528
#> 3 Task_1 Subject_001 0.0078125 -0.0770906 0.5224889 -0.8622194 -0.6448022
#> 4 Task_1 Subject_001 0.0104167 -0.2268654 0.0654785 -2.8212200 -3.6817255
#> 5 Task_1 Subject_001 0.0130208 -1.1375707 1.3043730 -1.1123895 -1.5617383
#> 6 Task_1 Subject_001 0.0156250 -1.8166604 0.7649564 -0.9293482 -2.6024852
pERP-RED
Begin by loading the pERPred package.
library(pERPred)
Now using the pERPred function, you can estimate the pERPs. There are two choices that are chosen by the user:
1. the number of pERPs to estimate; and
2. the proportion of variation each of the PCA steps must explain.
By default, the percent of variation is set to 80. Raising this value means more components will be kept in each of the PCA steps and the computation time will rise accordingly. Also, the number of pERPs is chosen based on an R2 value defined as: $R_{test}^2 = 1 - \frac{|| Y_j - \Phi \beta_j ||_{\mathcal{F}}^2}{|| Y_j ||_{\mathcal{F}}^2}$ and can be calculated using the R2_test function.
When estimating the pERPs, split the data into a training and test set, then calculate the Rtest2. After the number of pERPs is chosen, you can re-estimate the pERPs using the whole dataset. If preferred, it is possible to parallelize the estimation to speed up this process using the future package (see commented code).
subject_list <- unique(simulated_data$Subject)
electrode_list <- names(simulated_data)[-c(1:3)]
task_list <- unique(simulated_data$Task)
train_subjects <-
sort(sample(subject_list, round(2 * length(subject_list) / 3)))
pERPs <- map(
3:7,
~ pERPred(
simulated_data[simulated_data$Subject %in% train_subjects, ],
num_pERPs = .x,
percent_variation_electrode = 80,
percent_variation_subject = 80
)
)
# library(future)
# plan(multiprocess)
# pERPs <- future_map(
# 3:7,
# ~ pERPred(
# simulated_data[simulated_data$Subject %in% train_subjects, ],
# num_pERPs = .x,
# percent_variation_electrode = 80,
# percent_variation_subject = 80
# ),
# .progress = TRUE)
Based on the Rtest2 values, we would choose 5 as the appropriate number of pERPs since it is the point at which the Rtest2 tapers off.
R2 <- map_dfr(pERPs, ~R2_test(simulated_data, .x))
#> # A tibble: 5 x 2
#> R2 pERPs
#> <dbl> <int>
#> 1 0.468 3
#> 2 0.575 4
#> 3 0.671 5
#> 4 0.672 6
#> 5 0.673 7
pERPs5 <- pERPred(
simulated_data,
num_pERPs = 5,
percent_variation_electrode = 80,
percent_variation_subject = 80
)
pERPs5 %>%
mutate(Time = unique(simulated_data$Time)) %>%
gather(pERP, Amplitude, -Time) %>%
ggplot() +
geom_line(aes(x = Time, y = Amplitude)) +
facet_wrap(~ pERP, ncol = 2)
pERP-space Analysis
There are many analyses we can perform using these pERPs. In the paper, we discuss a few. First, we want to compute the weights ωj for each record using the pERP_scorer function.
individual_scores <- pERP_scorer(simulated_data, pERPs5)
head(individual_scores)
#> # A tibble: 6 x 8
#> # Groups: Task, Subject, Electrode [2]
#> Task Subject Electrode term estimate std.error statistic p.value
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Task_1 Subject_001 AF3 pERP 01 0.562 0.0456 12.3 1.64e-29
#> 2 Task_1 Subject_001 AF3 pERP 02 -0.153 0.0456 -3.35 8.83e- 4
#> 3 Task_1 Subject_001 AF3 pERP 03 -0.383 0.0456 -8.38 1.02e-15
#> 4 Task_1 Subject_001 AF3 pERP 04 0.682 0.0456 15.0 4.69e-40
#> 5 Task_1 Subject_001 AF3 pERP 05 0.734 0.0456 16.1 9.10e-45
#> 6 Task_1 Subject_001 AF4 pERP 01 0.0114 0.0480 0.238 8.12e- 1
Now we can reconstruct each record using their weights and the pERPs. Here is an example using subject 1, electrode CZ, task 1.
record <- simulated_data %>%
filter(Subject == "Subject_001",
Task == "Task_1") %>%
select(Task, Time, "CZ")
scores <- individual_scores %>%
filter(Subject == "Subject_001",
Electrode == "CZ",
Task == "Task_1") %>%
pull(estimate)
projected <- data.frame("Projected" = as.matrix(pERPs5) %*%
as.matrix(scores)) %>%
as.data.frame() %>%
mutate(Time = record$Time) %>%
mutate(Projected = Projected + mean(record$CZ))
ggplot() +
geom_line(data = record,
aes(x = Time, y = CZ, color = "Observed")) +
geom_line(data = projected,
aes(x = Time, y = Projected, color = "Reconstructed")) +
theme(legend.title = element_blank()) +
labs(y = TeX("$\\mu V$"))
Keeping in mind that this is simulated data, we can visualize how these weights are represented across the scalp for all subjects using the coefficient_headmap function. Create a dataframe of the average scores where the column of averages is named average. Split the dataframe by Task to create a list of dataframes. This will allow the scale to remain the same over all tasks. If you want the scale to stay the same over only selected Tasks, only include those tasks in the list of dataframes.
average_scores <- individual_scores %>%
group_by(Task, Electrode, term) %>%
summarise(average = mean(estimate, na.rm = TRUE)) %>%
split(.$Task)
# keep scale the same across all tasks
coefficient_headmap("Task_1", average_scores)
# keep scale the same across only tasks 1 and 2
coefficient_headmap("Task_1", average_scores[c("Task_1", "Task_2")])
Here is an example of how one would conduct tests for differences among groups. We will test if there is a difference in scores for Task_1 and Task_2 at CZ.
pERP_difference(
scores = individual_scores,
electrode = "CZ",
task1 = "Task_1",
task2 = "Task_2"
)
#> pERP Task_1 Overall Mean Task_1 Overall APSD Task_1 Overall SE
#> 1 pERP 01 0.3421022 0.3522178 0.03522178
#> 2 pERP 02 0.2765889 0.2360526 0.02360526
#> 3 pERP 03 -0.2785253 0.2056983 0.02056983
#> 4 pERP 04 0.2168410 0.3839734 0.03839734
#> 5 pERP 05 -0.7411287 0.3180623 0.03180623
#> Task_1 Overall t Task_1 Overall N Task_2 Overall Mean Task_2 Overall APSD
#> 1 9.712802 100 -0.01720551 0.3367229
#> 2 11.717256 100 -0.74303513 0.2540876
#> 3 -13.540475 100 -0.53359646 0.2073085
#> 4 5.647292 100 -0.26786325 0.3917388
#> 5 -23.301369 100 0.38377263 0.3675706
#> Task_2 Overall SE Task_2 Overall t Task_2 Overall N Mean Difference
#> 1 0.03367229 -0.5109693 100 0.3593077
#> 2 0.02540876 -29.2432679 100 1.0196240
#> 3 0.02073085 -25.7392477 100 0.2550712
#> 4 0.03917388 -6.8378031 100 0.4847042
#> 5 0.03675706 10.4407882 100 -1.1249013
#> SE Difference t signif
#> 1 0.04872778 7.373774 *
#> 2 0.03468160 29.399566 *
#> 3 0.02920421 8.734053 *
#> 4 0.05485388 8.836280 *
#> 5 0.04860779 -23.142408 *
If we had another grouping variable, such as diagnosis, we could add that to the individual scores dataframe.
set.seed(1234)
group <- data.frame(
Subject = distinct(simulated_data, Subject),
group_member = sample(
c("Control", "Treatment"),
100,
replace = TRUE,
prob = c(.5, .5)
)
)
individual_scores_groups <- full_join(individual_scores, group, by = "Subject")
pERP_difference(scores = individual_scores_groups,
electrode = "CZ",
task1 = "Task_1",
group1 = "Control",
group2 = "Treatment")
#> pERP Task_1 Control Mean Task_1 Control APSD Task_1 Control SE
#> 1 pERP 01 0.3023001 0.3446887 0.05138316
#> 2 pERP 02 0.2997375 0.2228006 0.03321315
#> 3 pERP 03 -0.2922279 0.2063993 0.03076820
#> 4 pERP 04 0.2160037 0.3539055 0.05275711
#> 5 pERP 05 -0.7609320 0.3456268 0.05152300
#> Task_1 Control t Task_1 Control N Task_1 Treatment Mean Task_1 Treatment APSD
#> 1 5.883251 45 0.3746675 0.3580919
#> 2 9.024661 45 0.2576492 0.2467701
#> 3 -9.497727 45 -0.2673141 0.2063382
#> 4 4.094306 45 0.2175260 0.4101740
#> 5 -14.768784 45 -0.7249260 0.2958655
#> Task_1 Treatment SE Task_1 Treatment t Task_1 Treatment N Mean Difference
#> 1 0.04828510 7.759486 55 -0.072367483
#> 2 0.03327447 7.743148 55 0.042088294
#> 3 0.02782264 -9.607788 55 -0.024913886
#> 4 0.05530784 3.933005 55 -0.001522269
#> 5 0.03989450 -18.171076 55 -0.036006055
#> SE Difference t signif
#> 1 0.07051014 -1.02634145
#> 2 0.04701387 0.89523136
#> 3 0.04148230 -0.60059070
#> 4 0.07643475 -0.01991593
#> 5 0.06516280 -0.55255539
The user can insert these tables directly into their manuscript using kable, xtable or any other of a number of tools in R.
Shiny app
In our paper we reference a Shiny application that we used to explore all of the results from the pERP-space analysis easily (https://perpred.shinyapps.io/asd_exploration). The function shiny_pERPs will allow you to create a very similiar application.
shiny_pERPs(simulated_data, group, pERPs5, individual_scores)
emjcampos/pERPred documentation built on June 23, 2021, 6:18 p.m.
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pERPred
The pERPred package is a tool for conducting ERP analyses using the Principle ERP Reduction algorithm put forth in Campos and Hazlett et al (2020). This package contains the functions necessary to derive the principle ERPs discussed in that publication as well as the tools for performing "pERP-space analysis". Let this serve as a guide to our approach to ERP analysis in R.
Installation
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("emjcampos/pERPred")
Example
Simulated Data Description
The generation model for the simulated data is described in the publication as follows. The observed signal is assumed to be a linear combination of the principal ERP components (pERPs). Let Yi, v, e(t) denote the ERP signal observed for subject i at task v and electrode e. The data generation model in the simulation will be: $Y_{i,v,e}(t) = \sum_{c=1}^C k_{c,v,e}\phi^{\star}_c(t) + \sum_{c=1}^C \xi_{c,i,v,e}\phi^{\star}_c(t) + \zeta_{i,v,e}(t).$
In the simulation, N = 100, V = 9, C = 5, T = 384, and each subject has a common E = 40. The first term reflects that each task and electrode is composed of a weighted average of pERPs, where ϕc⋆(t) are the ``true'' pERPs for purposes of simulation. To simulate a single ϕc⋆(t), we draw a function that is a superposition of Gaussian kernels centered at different time points to produce a smooth, low-frequency function intended to mimic the type of source signals we would expect to contribute to ERP waveforms. These simulated signals are then rotated by ICA to form maximally independent bases and are plotted here.
The coefficients, kc, v, e, are drawn from a normal distribution with mean zero and variance σk2 = 0.25.
The second term reflects the structured stochastic canonical component with dependencies within a subject across tasks and electrodes. The subject specific scores ξc, i, v, e will be simulated from a Matrix Normal (to create a depedence structure within tasks and across electrodes) with mean zero and covariance matrices Σc, v and Σc, e of dimension V * V and E * E (identically and independently drawn over subjects only). The covariance matrices are created in the same way for the electrode and task dimensions. A matrix with 0.5 on the diagonal and 0.1 elsewhere created. Each of these is multiplied by a factor (0.1, 0.2, 0.3, 0.4, 0.5), so that each true ERP component is represented differently. In the results, we expect the component with the highest multiplier to explain the most variation in the observed signals.
The last term is the independent and identically distributed (iid) measurement error. The measurement error ζi, v, e(t) will be simulated as independent draws (over i, v, and e) from a normal distribution with mean zero and variance σerror2 equal to some proportion of the signal variance. Define the variance of the simulated signal terms $\sum_{c=1}^C k_{c,v,e}\phi^{\star}_c(t) + \sum_{c=1}^C \xi_{c,i,v,e}\phi^{\star}_c(t)$ to be σsignal2. For the purposes of this vignette, we will only explore the low noise case in which 1/3 of the simulated ERP will be noise so $\sigma^2_\text{error} = \sqrt{0.5}\times\sigma_\text{signal}$.
The data to be used in the pERPred function must be averaged over trials. Then there must be one column for the "Task", "Subject", and "Time", and the remaining columns are the named electrodes that were observed. All subjects must have the same number of time points observed as well as the same number of tasks. However, there may be a different number of electrodes (use NA for the missing electrodes). For example, our simulated data looks like this:
head(simulated_data[, 1:7])
#> Task Subject Time AF3 AF4 AFZ C3
#> 1 Task_1 Subject_001 0.0026042 -0.8315047 1.6557474 -1.4637006 -1.2174339
#> 2 Task_1 Subject_001 0.0052083 0.9028223 1.1525489 -2.3697299 -3.4455528
#> 3 Task_1 Subject_001 0.0078125 -0.0770906 0.5224889 -0.8622194 -0.6448022
#> 4 Task_1 Subject_001 0.0104167 -0.2268654 0.0654785 -2.8212200 -3.6817255
#> 5 Task_1 Subject_001 0.0130208 -1.1375707 1.3043730 -1.1123895 -1.5617383
#> 6 Task_1 Subject_001 0.0156250 -1.8166604 0.7649564 -0.9293482 -2.6024852
pERP-RED
Begin by loading the pERPred package.
library(pERPred)
Now using the pERPred function, you can estimate the pERPs. There are two choices that are chosen by the user:
1. the number of pERPs to estimate; and
2. the proportion of variation each of the PCA steps must explain.
By default, the percent of variation is set to 80. Raising this value means more components will be kept in each of the PCA steps and the computation time will rise accordingly. Also, the number of pERPs is chosen based on an R2 value defined as: $R_{test}^2 = 1 - \frac{|| Y_j - \Phi \beta_j ||_{\mathcal{F}}^2}{|| Y_j ||_{\mathcal{F}}^2}$ and can be calculated using the R2_test function.
When estimating the pERPs, split the data into a training and test set, then calculate the Rtest2. After the number of pERPs is chosen, you can re-estimate the pERPs using the whole dataset. If preferred, it is possible to parallelize the estimation to speed up this process using the future package (see commented code).
subject_list <- unique(simulated_data$Subject)
electrode_list <- names(simulated_data)[-c(1:3)]
task_list <- unique(simulated_data$Task)
train_subjects <-
sort(sample(subject_list, round(2 * length(subject_list) / 3)))
pERPs <- map(
3:7,
~ pERPred(
simulated_data[simulated_data$Subject %in% train_subjects, ],
num_pERPs = .x,
percent_variation_electrode = 80,
percent_variation_subject = 80
)
)
# library(future)
# plan(multiprocess)
# pERPs <- future_map(
# 3:7,
# ~ pERPred(
# simulated_data[simulated_data$Subject %in% train_subjects, ],
# num_pERPs = .x,
# percent_variation_electrode = 80,
# percent_variation_subject = 80
# ),
# .progress = TRUE)
Based on the Rtest2 values, we would choose 5 as the appropriate number of pERPs since it is the point at which the Rtest2 tapers off.
R2 <- map_dfr(pERPs, ~R2_test(simulated_data, .x))
#> # A tibble: 5 x 2
#> R2 pERPs
#> <dbl> <int>
#> 1 0.468 3
#> 2 0.575 4
#> 3 0.671 5
#> 4 0.672 6
#> 5 0.673 7
pERPs5 <- pERPred(
simulated_data,
num_pERPs = 5,
percent_variation_electrode = 80,
percent_variation_subject = 80
)
pERPs5 %>%
mutate(Time = unique(simulated_data$Time)) %>%
gather(pERP, Amplitude, -Time) %>%
ggplot() +
geom_line(aes(x = Time, y = Amplitude)) +
facet_wrap(~ pERP, ncol = 2)
pERP-space Analysis
There are many analyses we can perform using these pERPs. In the paper, we discuss a few. First, we want to compute the weights ωj for each record using the pERP_scorer function.
individual_scores <- pERP_scorer(simulated_data, pERPs5)
head(individual_scores)
#> # A tibble: 6 x 8
#> # Groups: Task, Subject, Electrode [2]
#> Task Subject Electrode term estimate std.error statistic p.value
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Task_1 Subject_001 AF3 pERP 01 0.562 0.0456 12.3 1.64e-29
#> 2 Task_1 Subject_001 AF3 pERP 02 -0.153 0.0456 -3.35 8.83e- 4
#> 3 Task_1 Subject_001 AF3 pERP 03 -0.383 0.0456 -8.38 1.02e-15
#> 4 Task_1 Subject_001 AF3 pERP 04 0.682 0.0456 15.0 4.69e-40
#> 5 Task_1 Subject_001 AF3 pERP 05 0.734 0.0456 16.1 9.10e-45
#> 6 Task_1 Subject_001 AF4 pERP 01 0.0114 0.0480 0.238 8.12e- 1
Now we can reconstruct each record using their weights and the pERPs. Here is an example using subject 1, electrode CZ, task 1.
record <- simulated_data %>%
filter(Subject == "Subject_001",
Task == "Task_1") %>%
select(Task, Time, "CZ")
scores <- individual_scores %>%
filter(Subject == "Subject_001",
Electrode == "CZ",
Task == "Task_1") %>%
pull(estimate)
projected <- data.frame("Projected" = as.matrix(pERPs5) %*%
as.matrix(scores)) %>%
as.data.frame() %>%
mutate(Time = record$Time) %>%
mutate(Projected = Projected + mean(record$CZ))
ggplot() +
geom_line(data = record,
aes(x = Time, y = CZ, color = "Observed")) +
geom_line(data = projected,
aes(x = Time, y = Projected, color = "Reconstructed")) +
theme(legend.title = element_blank()) +
labs(y = TeX("$\\mu V$"))
Keeping in mind that this is simulated data, we can visualize how these weights are represented across the scalp for all subjects using the coefficient_headmap function. Create a dataframe of the average scores where the column of averages is named average. Split the dataframe by Task to create a list of dataframes. This will allow the scale to remain the same over all tasks. If you want the scale to stay the same over only selected Tasks, only include those tasks in the list of dataframes.
average_scores <- individual_scores %>%
group_by(Task, Electrode, term) %>%
summarise(average = mean(estimate, na.rm = TRUE)) %>%
split(.$Task)
# keep scale the same across all tasks
coefficient_headmap("Task_1", average_scores)
# keep scale the same across only tasks 1 and 2
coefficient_headmap("Task_1", average_scores[c("Task_1", "Task_2")])
Here is an example of how one would conduct tests for differences among groups. We will test if there is a difference in scores for Task_1 and Task_2 at CZ.
pERP_difference(
scores = individual_scores,
electrode = "CZ",
task1 = "Task_1",
task2 = "Task_2"
)
#> pERP Task_1 Overall Mean Task_1 Overall APSD Task_1 Overall SE
#> 1 pERP 01 0.3421022 0.3522178 0.03522178
#> 2 pERP 02 0.2765889 0.2360526 0.02360526
#> 3 pERP 03 -0.2785253 0.2056983 0.02056983
#> 4 pERP 04 0.2168410 0.3839734 0.03839734
#> 5 pERP 05 -0.7411287 0.3180623 0.03180623
#> Task_1 Overall t Task_1 Overall N Task_2 Overall Mean Task_2 Overall APSD
#> 1 9.712802 100 -0.01720551 0.3367229
#> 2 11.717256 100 -0.74303513 0.2540876
#> 3 -13.540475 100 -0.53359646 0.2073085
#> 4 5.647292 100 -0.26786325 0.3917388
#> 5 -23.301369 100 0.38377263 0.3675706
#> Task_2 Overall SE Task_2 Overall t Task_2 Overall N Mean Difference
#> 1 0.03367229 -0.5109693 100 0.3593077
#> 2 0.02540876 -29.2432679 100 1.0196240
#> 3 0.02073085 -25.7392477 100 0.2550712
#> 4 0.03917388 -6.8378031 100 0.4847042
#> 5 0.03675706 10.4407882 100 -1.1249013
#> SE Difference t signif
#> 1 0.04872778 7.373774 *
#> 2 0.03468160 29.399566 *
#> 3 0.02920421 8.734053 *
#> 4 0.05485388 8.836280 *
#> 5 0.04860779 -23.142408 *
If we had another grouping variable, such as diagnosis, we could add that to the individual scores dataframe.
set.seed(1234)
group <- data.frame(
Subject = distinct(simulated_data, Subject),
group_member = sample(
c("Control", "Treatment"),
100,
replace = TRUE,
prob = c(.5, .5)
)
)
individual_scores_groups <- full_join(individual_scores, group, by = "Subject")
pERP_difference(scores = individual_scores_groups,
electrode = "CZ",
task1 = "Task_1",
group1 = "Control",
group2 = "Treatment")
#> pERP Task_1 Control Mean Task_1 Control APSD Task_1 Control SE
#> 1 pERP 01 0.3023001 0.3446887 0.05138316
#> 2 pERP 02 0.2997375 0.2228006 0.03321315
#> 3 pERP 03 -0.2922279 0.2063993 0.03076820
#> 4 pERP 04 0.2160037 0.3539055 0.05275711
#> 5 pERP 05 -0.7609320 0.3456268 0.05152300
#> Task_1 Control t Task_1 Control N Task_1 Treatment Mean Task_1 Treatment APSD
#> 1 5.883251 45 0.3746675 0.3580919
#> 2 9.024661 45 0.2576492 0.2467701
#> 3 -9.497727 45 -0.2673141 0.2063382
#> 4 4.094306 45 0.2175260 0.4101740
#> 5 -14.768784 45 -0.7249260 0.2958655
#> Task_1 Treatment SE Task_1 Treatment t Task_1 Treatment N Mean Difference
#> 1 0.04828510 7.759486 55 -0.072367483
#> 2 0.03327447 7.743148 55 0.042088294
#> 3 0.02782264 -9.607788 55 -0.024913886
#> 4 0.05530784 3.933005 55 -0.001522269
#> 5 0.03989450 -18.171076 55 -0.036006055
#> SE Difference t signif
#> 1 0.07051014 -1.02634145
#> 2 0.04701387 0.89523136
#> 3 0.04148230 -0.60059070
#> 4 0.07643475 -0.01991593
#> 5 0.06516280 -0.55255539
The user can insert these tables directly into their manuscript using kable, xtable or any other of a number of tools in R.
Shiny app
In our paper we reference a Shiny application that we used to explore all of the results from the pERP-space analysis easily (https://perpred.shinyapps.io/asd_exploration). The function shiny_pERPs will allow you to create a very similiar application.
shiny_pERPs(simulated_data, group, pERPs5, individual_scores)
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