,
Hugo Najberg [ctb],
Viliam Simko [ctb],
Sasha Epskamp [rev] (Sasha reviewed the package for JOSS, see
https://github.com/openjournals/joss-reviews/issues/470)| Title: | Efficient and Publishing-Oriented Workflow for Psychological Science |
|---|---|
| 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. |
| Authors: | 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) |
| Maintainer: | Dominique Makowski <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.6.1 |
| Built: | 2024-11-12 05:27:09 UTC |
| Source: | https://github.com/neuropsychology/psycho.r |
This is data from the French validation of the Affective Style Questionnaire.
affectiveaffective
A data frame with 1277 rows and 8 variables:
Sex (F or M)
Season of birth
Current age
Salary in euros
General life satisfaction
Concealing score
Adjusting score
Tolerating score
Compare a patient's score to a control group.
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 )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 )
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. |
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
output
result <- assess(patient = 124, mean = 100, sd = 15, n = 100) print(result) plot(result)result <- assess(patient = 124, mean = 100, sd = 15, n = 100) print(result) plot(result)
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. .
crawford_dissociation.test( case_X, case_Y, controls_X, controls_Y, verbose = TRUE )crawford_dissociation.test( case_X, case_Y, controls_X, controls_Y, verbose = TRUE )
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. |
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.
Dominique Makowski
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)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)
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. .
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 )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 )
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. |
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
Dominique Makowski
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)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)
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. .
crawford.test.freq(patient, controls)crawford.test.freq(patient, controls)
patient |
Single value (patient's score). |
controls |
Vector of values (control's scores). |
Returns a data frame containing the t-value, degrees of freedom, and p-value. If significant, the patient is different from the control group.
Dan Mirman, Dominique Makowski
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))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))
Computes Signal Detection Theory indices, including d', beta, A', B”D and c.
dprime( n_hit, n_fa, n_miss = NULL, n_cr = NULL, n_targets = NULL, n_distractors = NULL, adjusted = TRUE )dprime( n_hit, n_fa, n_miss = NULL, n_cr = NULL, n_targets = NULL, n_distractors = NULL, adjusted = TRUE )
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. |
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).
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 )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 neutral and negative pictures by healthy participants.
emotionemotion
A data frame with 912 rows and 11 variables:
Subject's number
Subject's age
Subject's sex
Picture's category
Picture's name
Trial order (1-48)
Picture's emotional category (Neutral or Negative)
Participant's rating of arousal (0-100)
Participant's rating of valence (-100: negative, 100: positive, 0: neutral)
Participant's rating of autobiographical connection (is the picture's content associated with memories)
Whether the participant recalled the picture 20min after presentation
Generate all combinations.
find_combinations(object, ...)find_combinations(object, ...)
object |
Object |
... |
Arguments passed to or from other methods. |
Generate all combinations of predictors of a formula.
## S3 method for class 'formula' find_combinations(object, interaction = TRUE, fixed = NULL, ...)## S3 method for class 'formula' find_combinations(object, interaction = TRUE, fixed = NULL, ...)
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. |
list containing all combinations.
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)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.
find_matching_string(x, y, value = TRUE, step = 0.1, ignore.case = TRUE)find_matching_string(x, y, value = TRUE, step = 0.1, ignore.case = TRUE)
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. |
library(psycho) find_matching_string("Hwo rea ouy", c("How are you", "Not this word", "Nice to meet you"))library(psycho) find_matching_string("Hwo rea ouy", c("How are you", "Not this word", "Nice to meet you"))
Returns the season of an array of dates.
find_season( dates, winter = "12-21", spring = "3-20", summer = "6-21", fall = "9-22" )find_season( dates, winter = "12-21", spring = "3-20", summer = "6-21", fall = "9-22" )
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. |
season
Josh O'Brien
https://stackoverflow.com/questions/9500114/find-which-season-a-particular-date-belongs-to
library(psycho) dates <- c("2012-02-15", "2017-05-15", "2009-08-15", "1912-11-15") find_season(dates)library(psycho) dates <- c("2012-02-15", "2017-05-15", "2009-08-15", "1912-11-15") find_season(dates)
Returns the golden ratio (1.618034...).
golden(x = 1)golden(x = 1)
x |
A number to be multiplied by the golden ratio. The default (x=1) returns the value of the golden ratio. |
library(psycho) golden() golden(8)library(psycho) golden() golden(8)
Creates or tests for objects of mode "psychobject".
is.psychobject(x)is.psychobject(x)
x |
an arbitrary R object. |
Check if a dataframe is standardized.
is.standardized(df, tol = 0.1)is.standardized(df, tol = 0.1)
df |
A dataframe. |
tol |
The error treshold. |
bool.
library(psycho) library(effectsize) df <- psycho::affective is.standardized(df) dfZ <- effectsize::standardize(df) is.standardized(dfZ)library(psycho) library(effectsize) df <- psycho::affective is.standardized(df) dfZ <- effectsize::standardize(df) is.standardized(dfZ)
Test for comparing post-test to baseline for a single participant.
mellenbergh.test(t0, t1, controls)mellenbergh.test(t0, t1, controls)
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. |
Returns a data frame containing the z-value and p-value. If significant, the difference between pre and post tests is significant.
Dominique Makowski
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)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.
percentile(z_score)percentile(z_score)
z_score |
Z score. |
library(psycho) percentile(-1.96)library(psycho) percentile(-1.96)
Transform a percentile to a z score.
percentile_to_z(percentile)percentile_to_z(percentile)
percentile |
Percentile |
library(psycho) percentile_to_z(95)library(psycho) percentile_to_z(95)
Plot the results.
## S3 method for class 'psychobject' plot(x, ...)## S3 method for class 'psychobject' plot(x, ...)
x |
A psychobject class object. |
... |
Arguments passed to or from other methods. |
Compute the n models based on n sampling of data.
power_analysis( fit, n_max, n_min = NULL, step = 1, n_batch = 1, groups = NULL, verbose = TRUE, CI = 90 )power_analysis( fit, n_max, n_min = NULL, step = 1, n_batch = 1, groups = NULL, verbose = TRUE, CI = 90 )
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. |
A dataframe containing the summary of all models for all iterations.
## 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)## 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.
## S3 method for class 'psychobject' print(x, ...)## S3 method for class 'psychobject' print(x, ...)
x |
A psychobject class object. |
... |
Further arguments passed to or from other methods. |
Removes all columns containing ony NaNs.
remove_empty_cols(df)remove_empty_cols(df)
df |
Dataframe. |
Print the results.
## S3 method for class 'psychobject' summary(object, round = NULL, ...)## S3 method for class 'psychobject' summary(object, round = NULL, ...)
object |
A psychobject class object. |
round |
Round the ouput. |
... |
Further arguments passed to or from other methods. |
Extract values as list.
values(x)values(x)
x |
A psychobject class object. |