| Type: | Package |
| Title: | Efficient and Publishing-Oriented Workflow for Psychological Science |
| Version: | 0.6.1 |
| Maintainer: | Dominique Makowski <dom.makowski@gmail.com> |
| URL: | https://github.com/neuropsychology/psycho.R |
| BugReports: | https://github.com/neuropsychology/psycho.R/issues |
| Description: | The main goal of the psycho package is to provide tools for psychologists, neuropsychologists and neuroscientists, to facilitate and speed up the time spent on data analysis. It aims at supporting best practices and tools to format the output of statistical methods to directly paste them into a manuscript, ensuring statistical reporting standardization and conformity. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.1.1 |
| Depends: | R (≥ 3.5.0) |
| Imports: | stats, scales, utils, dplyr, stringr, ggplot2, insight, bayestestR, parameters, effectsize |
| Suggests: | knitr, rmarkdown, testthat, covr, GPArotation |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2021-01-19 01:55:59 UTC; domma |
| Author: | Dominique Makowski
 [aut, cre,
cph],
Hugo Najberg [ctb],
Viliam Simko [ctb],
Sasha Epskamp [rev] (Sasha reviewed the package for JOSS, see
https://github.com/openjournals/joss-reviews/issues/470) |
| Repository: | CRAN |
| Date/Publication: | 2021-01-19 06:40:10 UTC |
Data from the Affective Style Questionnaire (ASQ - French Validation)
Description
This is data from the French validation of the Affective Style Questionnaire.
Usage
affective
Format
A data frame with 1277 rows and 8 variables:
- Sex
Sex (F or M)
- Birth_Season
Season of birth
- Age
Current age
- Salary
Salary in euros
- Life_Satisfaction
General life satisfaction
- Concealing
Concealing score
- Adjusting
Adjusting score
- Tolerating
Tolerating score
Compare a patient's score to a control group
Description
Compare a patient's score to a control group.
Usage
assess(
patient,
mean = 0,
sd = 1,
n = NULL,
controls = NULL,
CI = 95,
treshold = 0.05,
iter = 10000,
color_controls = "#2196F3",
color_CI = "#E91E63",
color_score = "black",
color_size = 2,
alpha_controls = 1,
alpha_CI = 0.8,
verbose = TRUE
)
Arguments
patient |
Single value (patient's score). |
mean |
Mean of the control sample. |
sd |
SD of the control sample. |
n |
Size of the control sample. |
controls |
Vector of values (control's scores). |
CI |
Credible interval bounds. |
treshold |
Significance treshold. |
iter |
Number of iterations. |
color_controls |
Color of the controls distribution. |
color_CI |
Color of CI distribution. |
color_score |
Color of the line representing the patient's score. |
color_size |
Size of the line representing the patient's score. |
alpha_controls |
Alpha of the CI distribution. |
alpha_CI |
lpha of the controls distribution. |
verbose |
Print possible warnings. |
Details
Until relatively recently the standard way of testing for a difference between a case and controls was to convert the case’s score to a z score using the control sample mean and standard deviation (SD). If z was less than -1.645 (i.e., below 95
Value
output
Author(s)
Examples
result <- assess(patient = 124, mean = 100, sd = 15, n = 100)
print(result)
plot(result)
Crawford-Garthwaite (2007) Bayesian test for single-case analysis.
Description
Neuropsychologists often need to compare a single case to a small control group. However, the standard two-sample t-test does not work because the case is only one observation. Crawford and Garthwaite (2007) demonstrate that the Bayesian test is a better approach than other commonly-used alternatives. .
Usage
crawford.test(
patient,
controls = NULL,
mean = NULL,
sd = NULL,
n = NULL,
CI = 95,
treshold = 0.1,
iter = 10000,
color_controls = "#2196F3",
color_CI = "#E91E63",
color_score = "black",
color_size = 2,
alpha_controls = 1,
alpha_CI = 0.8
)
Arguments
patient |
Single value (patient's score). |
controls |
Vector of values (control's scores). |
mean |
Mean of the control sample. |
sd |
SD of the control sample. |
n |
Size of the control sample. |
CI |
Credible interval bounds. |
treshold |
Significance treshold. |
iter |
Number of iterations. |
color_controls |
Color of the controls distribution. |
color_CI |
Color of CI distribution. |
color_score |
Color of the line representing the patient's score. |
color_size |
Size of the line representing the patient's score. |
alpha_controls |
Alpha of the CI distribution. |
alpha_CI |
lpha of the controls distribution. |
Details
The p value obtained when this test is used to test significance also simultaneously provides a point estimate of the abnormality of the patient’s score; for example if the one-tailed probability is .013 then we know that the patient’s score is significantly (p < .05) below the control mean and that it is estimated that 1.3
Author(s)
Dominique Makowski
Examples
library(psycho)
crawford.test(patient = 125, mean = 100, sd = 15, n = 100)
plot(crawford.test(patient = 80, mean = 100, sd = 15, n = 100))
crawford.test(patient = 10, controls = c(0, -2, 5, 2, 1, 3, -4, -2))
test <- crawford.test(patient = 7, controls = c(0, -2, 5, -6, 0, 3, -4, -2))
plot(test)
Crawford-Howell (1998) frequentist t-test for single-case analysis.
Description
Neuropsychologists often need to compare a single case to a small control group. However, the standard two-sample t-test does not work because the case is only one observation. Crawford and Garthwaite (2012) demonstrate that the Crawford-Howell (1998) t-test is a better approach (in terms of controlling Type I error rate) than other commonly-used alternatives. .
Usage
crawford.test.freq(patient, controls)
Arguments
patient |
Single value (patient's score). |
controls |
Vector of values (control's scores). |
Value
Returns a data frame containing the t-value, degrees of freedom, and p-value. If significant, the patient is different from the control group.
Author(s)
Dan Mirman, Dominique Makowski
Examples
library(psycho)
crawford.test.freq(patient = 10, controls = c(0, -2, 5, 2, 1, 3, -4, -2))
crawford.test.freq(patient = 7, controls = c(0, -2, 5, 2, 1, 3, -4, -2))
Crawford-Howell (1998) modified t-test for testing difference between a patient’s performance on two tasks.
Description
Assessing dissociation between processes is a fundamental part of clinical neuropsychology. However, while the detection of suspected impairments is a fundamental feature of single-case studies, evidence of an impairment on a given task usually becomes of theoretical interest only if it is observed in the context of less impaired or normal performance on other tasks. Crawford and Garthwaite (2012) demonstrate that the Crawford-Howell (1998) t-test for dissociation is a better approach (in terms of controlling Type I error rate) than other commonly-used alternatives. .
Usage
crawford_dissociation.test(
case_X,
case_Y,
controls_X,
controls_Y,
verbose = TRUE
)
Arguments
case_X |
Single value (patient's score on test X). |
case_Y |
Single value (patient's score on test Y). |
controls_X |
Vector of values (control's scores of X). |
controls_Y |
Vector of values (control's scores of Y). |
verbose |
True or False. Prints the interpretation text. |
Value
Returns a data frame containing the t-value, degrees of freedom, and p-value. If significant, the dissociation between test X and test Y is significant.
Author(s)
Dominique Makowski
Examples
library(psycho)
case_X <- 142
case_Y <- 7
controls_X <- c(100, 125, 89, 105, 109, 99)
controls_Y <- c(7, 8, 9, 6, 7, 10)
crawford_dissociation.test(case_X, case_Y, controls_X, controls_Y)
Dprime (d') and Other Signal Detection Theory indices.
Description
Computes Signal Detection Theory indices, including d', beta, A', B”D and c.
Usage
dprime(
n_hit,
n_fa,
n_miss = NULL,
n_cr = NULL,
n_targets = NULL,
n_distractors = NULL,
adjusted = TRUE
)
Arguments
n_hit |
Number of hits. |
n_fa |
Number of false alarms. |
n_miss |
Number of misses. |
n_cr |
Number of correct rejections. |
n_targets |
Number of targets (n_hit + n_miss). |
n_distractors |
Number of distractors (n_fa + n_cr). |
adjusted |
Should it use the Hautus (1995) adjustments for extreme values. |
Value
Calculates the d', the beta, the A' and the B”D based on the signal detection theory (SRT). See Pallier (2002) for the algorithms.
Returns a list containing the following indices:
dprime (d'): The sensitivity. Reflects the distance between the two distributions: signal, and signal+noise and corresponds to the Z value of the hit-rate minus that of the false-alarm rate.
beta: The bias (criterion). The value for beta is the ratio of the normal density functions at the criterion of the Z values used in the computation of d'. This reflects an observer's bias to say 'yes' or 'no' with the unbiased observer having a value around 1.0. As the bias to say 'yes' increases (liberal), resulting in a higher hit-rate and false-alarm-rate, beta approaches 0.0. As the bias to say 'no' increases (conservative), resulting in a lower hit-rate and false-alarm rate, beta increases over 1.0 on an open-ended scale.
c: Another index of bias. the number of standard deviations from the midpoint between these two distributions, i.e., a measure on a continuum from "conservative" to "liberal".
aprime (A'): Non-parametric estimate of discriminability. An A' near 1.0 indicates good discriminability, while a value near 0.5 means chance performance.
bppd (B”D): Non-parametric estimate of bias. A B”D equal to 0.0 indicates no bias, positive numbers represent conservative bias (i.e., a tendency to answer 'no'), negative numbers represent liberal bias (i.e. a tendency to answer 'yes'). The maximum absolute value is 1.0.
Note that for d' and beta, adjustement for extreme values are made following the recommandations of Hautus (1995).
Author(s)
Examples
library(psycho)
n_hit <- 9
n_fa <- 2
n_miss <- 1
n_cr <- 7
indices <- psycho::dprime(n_hit, n_fa, n_miss, n_cr)
df <- data.frame(
Participant = c("A", "B", "C"),
n_hit = c(1, 2, 5),
n_fa = c(6, 8, 1)
)
indices <- psycho::dprime(
n_hit = df$n_hit,
n_fa = df$n_fa,
n_targets = 10,
n_distractors = 10,
adjusted = FALSE
)
Emotional Ratings of Pictures
Description
Emotional ratings of neutral and negative pictures by healthy participants.
Usage
emotion
Format
A data frame with 912 rows and 11 variables:
- Participant_ID
Subject's number
- Participant_Age
Subject's age
- Participant_Sex
Subject's sex
- Item_Category
Picture's category
- Item_Name
Picture's name
- Trial_Order
Trial order (1-48)
- Emotion_Condition
Picture's emotional category (Neutral or Negative)
- Subjective_Arousal
Participant's rating of arousal (0-100)
- Subjective_Valence
Participant's rating of valence (-100: negative, 100: positive, 0: neutral)
- Autobiographical_Link
Participant's rating of autobiographical connection (is the picture's content associated with memories)
- Recall
Whether the participant recalled the picture 20min after presentation
Generate all combinations.
Description
Generate all combinations.
Usage
find_combinations(object, ...)
Arguments
object |
Object |
... |
Arguments passed to or from other methods. |
Author(s)
Generate all combinations of predictors of a formula.
Description
Generate all combinations of predictors of a formula.
Usage
## S3 method for class 'formula'
find_combinations(object, interaction = TRUE, fixed = NULL, ...)
Arguments
object |
Formula. |
interaction |
Include interaction term. |
fixed |
Additional formula part to add at the beginning of each combination. |
... |
Arguments passed to or from other methods. |
Value
list containing all combinations.
Author(s)
Examples
library(psycho)
f <- as.formula("Y ~ A + B + C + D")
f <- as.formula("Y ~ A + B + C + D + (1|E)")
f <- as.formula("Y ~ A + B + C + D + (1|E) + (1|F)")
find_combinations(f)
Fuzzy string matching.
Description
Fuzzy string matching.
Usage
find_matching_string(x, y, value = TRUE, step = 0.1, ignore.case = TRUE)
Arguments
x |
Strings. |
y |
List of strings to be matched. |
value |
Return value or the index of the closest string. |
step |
Step by which decrease the distance. |
ignore.case |
if FALSE, the pattern matching is case sensitive and if TRUE, case is ignored during matching. |
Author(s)
Examples
library(psycho)
find_matching_string("Hwo rea ouy", c("How are you", "Not this word", "Nice to meet you"))
Find season of dates.
Description
Returns the season of an array of dates.
Usage
find_season(
dates,
winter = "12-21",
spring = "3-20",
summer = "6-21",
fall = "9-22"
)
Arguments
dates |
Array of dates. |
winter |
month-day of winter solstice. |
spring |
month-day of spring equinox. |
summer |
month-day of summer solstice. |
fall |
month-day of fall equinox. |
Value
season
Author(s)
Josh O'Brien
See Also
https://stackoverflow.com/questions/9500114/find-which-season-a-particular-date-belongs-to
Examples
library(psycho)
dates <- c("2012-02-15", "2017-05-15", "2009-08-15", "1912-11-15")
find_season(dates)
Golden Ratio.
Description
Returns the golden ratio (1.618034...).
Usage
golden(x = 1)
Arguments
x |
A number to be multiplied by the golden ratio. The default (x=1) returns the value of the golden ratio. |
Author(s)
Examples
library(psycho)
golden()
golden(8)
Creates or tests for objects of mode "psychobject".
Description
Creates or tests for objects of mode "psychobject".
Usage
is.psychobject(x)
Arguments
x |
an arbitrary R object. |
Check if a dataframe is standardized.
Description
Check if a dataframe is standardized.
Usage
is.standardized(df, tol = 0.1)
Arguments
df |
A dataframe. |
tol |
The error treshold. |
Value
bool.
Author(s)
Examples
library(psycho)
library(effectsize)
df <- psycho::affective
is.standardized(df)
dfZ <- effectsize::standardize(df)
is.standardized(dfZ)
Mellenbergh & van den Brink (1998) test for pre-post comparison.
Description
Test for comparing post-test to baseline for a single participant.
Usage
mellenbergh.test(t0, t1, controls)
Arguments
t0 |
Single value (pretest or baseline score). |
t1 |
Single value (posttest score). |
controls |
Vector of scores of the control group OR single value corresponding to the control SD of the score. |
Value
Returns a data frame containing the z-value and p-value. If significant, the difference between pre and post tests is significant.
Author(s)
Dominique Makowski
Examples
library(psycho)
mellenbergh.test(t0 = 4, t1 = 12, controls = c(0, -2, 5, 2, 1, 3, -4, -2))
mellenbergh.test(t0 = 8, t1 = 2, controls = 2.6)
Transform z score to percentile.
Description
Transform z score to percentile.
Usage
percentile(z_score)
Arguments
z_score |
Z score. |
Author(s)
Examples
library(psycho)
percentile(-1.96)
Transform a percentile to a z score.
Description
Transform a percentile to a z score.
Usage
percentile_to_z(percentile)
Arguments
percentile |
Percentile |
Author(s)
Examples
library(psycho)
percentile_to_z(95)
Plot the results.
Description
Plot the results.
Usage
## S3 method for class 'psychobject'
plot(x, ...)
Arguments
x |
A psychobject class object. |
... |
Arguments passed to or from other methods. |
Author(s)
Power analysis for fitted models.
Description
Compute the n models based on n sampling of data.
Usage
power_analysis(
fit,
n_max,
n_min = NULL,
step = 1,
n_batch = 1,
groups = NULL,
verbose = TRUE,
CI = 90
)
Arguments
fit |
A lm or stanreg model. |
n_max |
Max sample size. |
n_min |
Min sample size. If null, take current nrow. |
step |
Increment of the sequence. |
n_batch |
Number of iterations at each sample size. |
groups |
Grouping variable name (string) to preserve proportions. Can be a list of strings. |
verbose |
Print progress. |
CI |
Confidence level. |
Value
A dataframe containing the summary of all models for all iterations.
Author(s)
Examples
## Not run:
library(dplyr)
library(psycho)
fit <- lm(Sepal.Length ~ Sepal.Width, data = iris)
results <- power_analysis(fit, n_max = 300, n_min = 100, step = 5, n_batch = 20)
results %>%
filter(Variable == "Sepal.Width") %>%
select(n, p) %>%
group_by(n) %>%
summarise(
p_median = median(p),
p_mad = mad(p)
)
## End(Not run)
Print the results.
Description
Print the results.
Usage
## S3 method for class 'psychobject'
print(x, ...)
Arguments
x |
A psychobject class object. |
... |
Further arguments passed to or from other methods. |
Author(s)
Remove empty columns.
Description
Removes all columns containing ony NaNs.
Usage
remove_empty_cols(df)
Arguments
df |
Dataframe. |
Author(s)
Print the results.
Description
Print the results.
Usage
## S3 method for class 'psychobject'
summary(object, round = NULL, ...)
Arguments
object |
A psychobject class object. |
round |
Round the ouput. |
... |
Further arguments passed to or from other methods. |
Author(s)
Extract values as list.
Description
Extract values as list.
Usage
values(x)
Arguments
x |
A psychobject class object. |