Package 'suddengains' reference manual

Title:	Identify Sudden Gains in Longitudinal Data
Description:	Identify sudden gains based on the three criteria outlined by Tang and DeRubeis (1999) <doi:10.1037/0022-006X.67.6.894> to a selection of repeated measures. Sudden losses, defined as the opposite of sudden gains can also be identified. Two different datasets can be created, one including all sudden gains/losses and one including one selected sudden gain/loss for each case. It can extract scores around sudden gains/losses. It can plot the average change around sudden gains/losses and trajectories of individual cases.
Authors:	Milan Wiedemann [aut, cre] , Graham M Thew [aut] , Richard Stott [ctb] , Anke Ehlers [ctb, ths] , Mental Health Research UK [fnd], Wellcome Trust [fnd]
Maintainer:	Milan Wiedemann <[email protected]>
License:	MIT + file LICENSE
Version:	0.7.2
Built:	2024-11-20 03:20:18 UTC
Source:	https://github.com/milanwiedemann/suddengains

Check if a given interval is a sudden gain/loss

Description

Checks if a specific session to session interval is a sudden gain/loss.

Usage

check_interval(
  pre_values,
  post_values,
  sg_crit1_cutoff,
  sg_crit2_pct = 0.25,
  sg_crit3 = TRUE,
  sg_crit3_alpha = 0.05,
  sg_crit3_adjust = TRUE,
  sg_crit3_critical_value = 2.776,
  identify = c("sg", "sl"),
  details = TRUE
)
check_interval(
  pre_values,
  post_values,
  sg_crit1_cutoff,
  sg_crit2_pct = 0.25,
  sg_crit3 = TRUE,
  sg_crit3_alpha = 0.05,
  sg_crit3_adjust = TRUE,
  sg_crit3_critical_value = 2.776,
  identify = c("sg", "sl"),
  details = TRUE
)

Arguments

`pre_values`	Vector, three pre gain/loss values to be checked for a sudden gain/loss (n-2, n-1, n)
`post_values`	Vector, three post gain/loss values to be checked for a sudden gain/loss (n+1, n+2, n+3)
`sg_crit1_cutoff`	Numeric, specifying the cut-off value to be used for the first sudden gains criterion. The function `define_crit1_cutoff` can be used to calculate a cutoff value based on the Reliable Change Index (RCI; Jacobson & Truax, 1991). If set to `NULL` the first criterion wont be applied.
`sg_crit2_pct`	Numeric, specifying the percentage change to be used for the second sudden gains/losses criterion. If set to `NULL` the second criterion wont be applied.
`sg_crit3`	If set to `TRUE` the third criterion will be applied automatically adjusting the critical value for missingness. If set to `FALSE` the third criterion wont be applied.
`sg_crit3_alpha`	Numeric, alpha for the student t-test (two-tailed) to determine the critical value to be used for the third criterion. Degrees of freedom are based on the number of available data in the three sessions preceding the gain and the three sessions following the gain.
`sg_crit3_adjust`	Logical, specify whether critical value gets adjusted for missingness, see Lutz et al. (2013) and the documentation of this R package for further details. This argument is set to `TRUE` by default adjusting the critical value for missingness as described in the package documentation and Lutz et al. (2013): A critical value of 2.776 is used when all three data points before and after a potential gain are available, where one data point is missing either before or after a potential gain a critical value of 3.182 is used, and where one data point is missing both before and after the gain a critical value of 4.303 is used. If set to `FALSE` a critical value of 2.776 will instead be used for all comparisons, regardless of missingnes in the sequence of data points that are investigated for sudden gains.
`sg_crit3_critical_value`	Numeric, specifying the critical value to instead be used for all comparisons, regardless of missingnes in the sequence of data points that are investigated for potential sudden gains.
`identify`	String, specifying whether to identify sudden gains (`"sg"`) or sudden losses (`"sl"`).
`details`	Logical, details yes no?

Value

Information on whether a given interval is a sudden gain/loss

Examples

# Check interval for sudden gain using all 3 criteria
# No missing data, alpha = 0.05
check_interval(pre_values = c(32, 31, 33),
               post_values = c(5, 6, 7),
               sg_crit1_cutoff = 7,
               sg_crit2_pct = .25,
               sg_crit3 = TRUE,
               sg_crit3_alpha = .05,
               identify = "sg")

# No missing data, alpha = 0.01
check_interval(pre_values = c(32, 31, 33),
               post_values = c(5, 6, 7),
               sg_crit1_cutoff = 7,
               sg_crit2_pct = .25,
               sg_crit3 = TRUE,
               sg_crit3_alpha = .01,
               identify = "sg")

# Check interval for sudden gain using only third criterion
# Some missing data, alpha = 0.01
check_interval(pre_values = c(NA,31,33),
               post_values = c(5, NA, 7),
               sg_crit1_cutoff = NULL,
               sg_crit2_pct = NULL,
               sg_crit3 = TRUE,
               sg_crit3_alpha = .01,
               identify = "sg")

# Check interval for sudden loss using all three criteria
# Some missing data, alpha = 0.05
check_interval(pre_values = c(5, NA, 7),
               post_values = c(16, 12, 14),
               sg_crit1_cutoff = -7,
               sg_crit2_pct = .25,
               sg_crit3 = TRUE,
               sg_crit3_alpha = .05,
               identify = "sl")

# Check interval for sudden gain using all 3 criteria
# No missing data, alpha = 0.05
check_interval(pre_values = c(32, 31, 33),
               post_values = c(5, 6, 7),
               sg_crit1_cutoff = 7,
               sg_crit2_pct = .25,
               sg_crit3 = TRUE,
               sg_crit3_alpha = .05,
               identify = "sg")

# No missing data, alpha = 0.01
check_interval(pre_values = c(32, 31, 33),
               post_values = c(5, 6, 7),
               sg_crit1_cutoff = 7,
               sg_crit2_pct = .25,
               sg_crit3 = TRUE,
               sg_crit3_alpha = .01,
               identify = "sg")

# Check interval for sudden gain using only third criterion
# Some missing data, alpha = 0.01
check_interval(pre_values = c(NA,31,33),
               post_values = c(5, NA, 7),
               sg_crit1_cutoff = NULL,
               sg_crit2_pct = NULL,
               sg_crit3 = TRUE,
               sg_crit3_alpha = .01,
               identify = "sg")

# Check interval for sudden loss using all three criteria
# Some missing data, alpha = 0.05
check_interval(pre_values = c(5, NA, 7),
               post_values = c(16, 12, 14),
               sg_crit1_cutoff = -7,
               sg_crit2_pct = .25,
               sg_crit3 = TRUE,
               sg_crit3_alpha = .05,
               identify = "sl")

Count number of between-session intervals available to identify sudden gains

Description

Calculates the number of total between-session intervals present in the data set and the number of between-session intervals that are available to identify sudden gains talking into account the pattern of missing data.

Usage

count_intervals(data, id_var_name, sg_var_list, identify_sg_1to2 = FALSE)
count_intervals(data, id_var_name, sg_var_list, identify_sg_1to2 = FALSE)

Arguments

`data`	A data set in wide format including an ID variable and variables for each measurement point.
`id_var_name`	String, specifying the name of the ID variable. Each row should have a unique value.
`sg_var_list`	Vector, specifying the variable names of each measurement point sequentially.
`identify_sg_1to2`	Logical, indicating whether to identify sudden losses from measurement point 1 to 2. If set to TRUE, this implies that the first variable specified in `sg_var_list` represents a baseline measurement point, e.g. pre-intervention assessment.

Value

List with values for:

total_between_sess_intervals: The total number of between-session intervals present in the data set, NAs are also included here. This multiplies the number of cases (rows) with the number of specified between-session intervals: nrows * (length(sg_var_list) - 1).
total_between_sess_intervals_sg: The total number of between-session intervals where sudden gains can theoretically, NAs are also included here. This multiplies the number of cases (rows) with the number of between-session intervals where sudden gains can be identified using the 3 original criteria: nrows * (length(sg_var_list) - 3).
available_between_sess_intervals_sg: The total number of between-session intervals that can be analysed for sudden gains taking into account the pattern of missing data.
not_available_between_sess_intervals_sg: The total number of between-session intervals that can not be analysed for sudden gains due to the pattern of missing data.

Examples

# Count between session intervals in "sgdata"
count_intervals(data = sgdata,
                id_var_name = "id",
                sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3", "bdi_s4",
                                "bdi_s5", "bdi_s6", "bdi_s7", "bdi_s8",
                                "bdi_s9", "bdi_s10", "bdi_s11", "bdi_s12"))
# Count between session intervals in "sgdata"
count_intervals(data = sgdata,
                id_var_name = "id",
                sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3", "bdi_s4",
                                "bdi_s5", "bdi_s6", "bdi_s7", "bdi_s8",
                                "bdi_s9", "bdi_s10", "bdi_s11", "bdi_s12"))

Create a data set with one gain per person

Description

This function returns a wide data set with one row for each case in data. The data set includes variables indicating whether each case experienced a sudden gain/loss or not, values around the period of each gain/loss, and descriptives. For cases with no sudden gain/loss the descriptive variables are coded as missing (NA).

Usage

create_byperson(
  data,
  sg_crit1_cutoff,
  id_var_name,
  sg_var_list,
  tx_start_var_name,
  tx_end_var_name,
  sg_measure_name,
  multiple_sg_select = c("first", "last", "smallest", "largest"),
  data_is_bysg = FALSE,
  identify = c("sg", "sl"),
  sg_crit2_pct = 0.25,
  sg_crit3 = TRUE,
  sg_crit3_alpha = 0.05,
  sg_crit3_adjust = TRUE,
  sg_crit3_critical_value = 2.776,
  identify_sg_1to2 = FALSE
)
create_byperson(
  data,
  sg_crit1_cutoff,
  id_var_name,
  sg_var_list,
  tx_start_var_name,
  tx_end_var_name,
  sg_measure_name,
  multiple_sg_select = c("first", "last", "smallest", "largest"),
  data_is_bysg = FALSE,
  identify = c("sg", "sl"),
  sg_crit2_pct = 0.25,
  sg_crit3 = TRUE,
  sg_crit3_alpha = 0.05,
  sg_crit3_adjust = TRUE,
  sg_crit3_critical_value = 2.776,
  identify_sg_1to2 = FALSE
)

Arguments

`data`	A data set in wide format including an ID variable and variables for each measurement point.
`sg_crit1_cutoff`	Numeric, specifying the cut-off value to be used for the first sudden gains criterion. The function `define_crit1_cutoff` can be used to calculate a cutoff value based on the Reliable Change Index (RCI; Jacobson & Truax, 1991). If set to `NULL` the first criterion wont be applied.
`id_var_name`	String, specifying the name of the ID variable.
`sg_var_list`	Vector, specifying the variable names of each measurement point sequentially.
`tx_start_var_name`	String, specifying the variable name of the first measurement point of the intervention.
`tx_end_var_name`	String, specifying the variable name of the last measurement point of the intervention.
`sg_measure_name`	String, specifying the name of the measure used to identify sudden gains/losses.
`multiple_sg_select`	String, specifying which sudden gain/loss to select for this data set if more than one gain/loss was identified per case. Options are: `"first"`, `"last"`, `"smallest"`, or `"largest"`. The default is to select the first sudden gain (`"first"`) if someone experienced multiple gains.
`data_is_bysg`	Logical, specifying whether the data set in the `data` argument is a bysg datasets created using the `create_bysg` function.
`identify`	String, specifying whether to identify sudden gains (`"sg"`) using `identify_sg` or sudden losses (`"sl"`) using `identify_sl`. The default is to identify sudden gains (`"sg"`).
`sg_crit2_pct`	Numeric, specifying the percentage change to be used for the second sudden gains/losses criterion. If set to `NULL` the second criterion wont be applied.
`sg_crit3`	If set to `TRUE` the third criterion will be applied automatically adjusting the critical value for missingness. If set to `FALSE` the third criterion wont be applied.
`sg_crit3_alpha`	Numeric, alpha for the student t-test (two-tailed) to determine the critical value to be used for the third criterion. Degrees of freedom are based on the number of available data in the three sessions preceding the gain and the three sessions following the gain.
`sg_crit3_adjust`	Logical, specify whether critical value gets adjusted for missingness, see Lutz et al. (2013) and the documentation of this R package for further details. This argument is set to `TRUE` by default adjusting the critical value for missingness as described in the package documentation and Lutz et al. (2013): A critical value of 2.776 is used when all three data points before and after a potential gain are available, where one datapoint is missing either before or after a potential gain a critical value of 3.182 is used, and where one datapoint is missing both before and after the gain a critical value of 4.303 is used (for sg_crit3_alpha = 0.05). If set to `FALSE` the critical value specified in `sg_crit3_critical_value` will instead be used for all comparisons, regardless of missingnes in the sequence of data points that are investigated for potential sudden gains.
`sg_crit3_critical_value`	Numeric, specifying the critical value to instead be used for all comparisons, regardless of missingnes in the sequence of data points that are investigated for potential sudden gains.
`identify_sg_1to2`	Logical, indicating whether to identify sudden losses from measurement point 1 to 2. If set to TRUE, this implies that the first variable specified in `sg_var_list` represents a baseline measurement point, e.g. pre-intervention assessment.

Value

A wide data set with one row per case (id_var_name) in data.

References

Tang, T. Z., & DeRubeis, R. J. (1999). Sudden gains and critical sessions in cognitive-behavioral therapy for depression. Journal of Consulting and Clinical Psychology, 67(6), 894–904. doi:10.1037/0022-006X.67.6.894.

Examples

# Create byperson data set, selecting the largest gain in case of muliple gains
create_byperson(data = sgdata,
                sg_crit1_cutoff = 7,
                id_var_name = "id",
                tx_start_var_name = "bdi_s1",
                tx_end_var_name = "bdi_s12",
                sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3",
                                "bdi_s4", "bdi_s5", "bdi_s6",
                                "bdi_s7", "bdi_s8", "bdi_s9",
                                "bdi_s10", "bdi_s11", "bdi_s12"),
                sg_measure_name = "bdi",
                multiple_sg_select = "largest")
# Create byperson data set, selecting the largest gain in case of muliple gains
create_byperson(data = sgdata,
                sg_crit1_cutoff = 7,
                id_var_name = "id",
                tx_start_var_name = "bdi_s1",
                tx_end_var_name = "bdi_s12",
                sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3",
                                "bdi_s4", "bdi_s5", "bdi_s6",
                                "bdi_s7", "bdi_s8", "bdi_s9",
                                "bdi_s10", "bdi_s11", "bdi_s12"),
                sg_measure_name = "bdi",
                multiple_sg_select = "largest")

Create a data set with one row for each sudden gain/loss

Description

This function returns a wide data set with one row for each sudden gain/loss and assigns a unique identifier to each. The data set includes variables indicating values around the period of each gain/loss, and calculates descriptives of each gain/loss.

Usage

create_bysg(
  data,
  sg_crit1_cutoff,
  id_var_name,
  sg_var_list,
  tx_start_var_name,
  tx_end_var_name,
  sg_measure_name,
  sg_crit2_pct = 0.25,
  sg_crit3 = TRUE,
  sg_crit3_alpha = 0.05,
  sg_crit3_adjust = TRUE,
  sg_crit3_critical_value = 2.776,
  identify = c("sg", "sl"),
  identify_sg_1to2 = FALSE
)
create_bysg(
  data,
  sg_crit1_cutoff,
  id_var_name,
  sg_var_list,
  tx_start_var_name,
  tx_end_var_name,
  sg_measure_name,
  sg_crit2_pct = 0.25,
  sg_crit3 = TRUE,
  sg_crit3_alpha = 0.05,
  sg_crit3_adjust = TRUE,
  sg_crit3_critical_value = 2.776,
  identify = c("sg", "sl"),
  identify_sg_1to2 = FALSE
)

Arguments

`data`	A data set in wide format including an ID variable and variables for each measurement point.
`sg_crit1_cutoff`	Numeric, specifying the cut-off value to be used for the first sudden gains criterion. The function `define_crit1_cutoff` can be used to calculate a cutoff value based on the Reliable Change Index (RCI; Jacobson & Truax, 1991). If set to `NULL` the first criterion wont be applied.
`id_var_name`	String, specifying the name of the ID variable.
`sg_var_list`	Vector, specifying the variable names of each measurement point sequentially.
`tx_start_var_name`	String, specifying the variable name of the first measurement point of the intervention.
`tx_end_var_name`	String, specifying the variable name of the last measurement point of the intervention.
`sg_measure_name`	String, specifying the name of the measure used to identify sudden gains/losses.
`sg_crit2_pct`	Numeric, specifying the percentage change to be used for the second sudden gains/losses criterion. If set to `NULL` the second criterion wont be applied.
`sg_crit3`	If set to `TRUE` the third criterion will be applied automatically adjusting the critical value for missingness. If set to `FALSE` the third criterion wont be applied.
`sg_crit3_alpha`	Numeric, alpha for the student t-test (two-tailed) to determine the critical value to be used for the third criterion. Degrees of freedom are based on the number of available data in the three sessions preceding the gain and the three sessions following the gain.
`sg_crit3_adjust`	Logical, specify whether critical value gets adjusted for missingness, see Lutz et al. (2013) and the documentation of this R package for further details. This argument is set to `TRUE` by default adjusting the critical value for missingness as described in the package documentation and Lutz et al. (2013): A critical value of 2.776 is used when all three data points before and after a potential gain are available, where one datapoint is missing either before or after a potential gain a critical value of 3.182 is used, and where one datapoint is missing both before and after the gain a critical value of 4.303 is used (for sg_crit3_alpha = 0.05). If set to `FALSE` the critical value set in `sg_crit3_critical_value` will instead be used for all comparisons, regardless of missingnes in the sequence of data points that are investigated for potential sudden gains.
`sg_crit3_critical_value`	Numeric, specifying the critical value to instead be used for all comparisons, regardless of missingnes in the sequence of data points that are investigated for potential sudden gains.#'
`identify`	String, specifying whether to identify sudden gains (`"sg"`) using `identify_sg` or sudden losses (`"sl"`) using `identify_sl`. The default is to identify sudden gains (`"sg"`).
`identify_sg_1to2`	Logical, indicating whether to identify sudden losses from measurement point 1 to 2. If set to TRUE, this implies that the first variable specified in `sg_var_list` represents a baseline measurement point, e.g. pre-intervention assessment.

Value

A wide data set with one row per sudden gain/loss.

References

Examples

# Create bypsg data set
create_bysg(data = sgdata,
            sg_crit1_cutoff = 7,
            id_var_name = "id",
            tx_start_var_name = "bdi_s1",
            tx_end_var_name = "bdi_s12",
            sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3",
                            "bdi_s4", "bdi_s5", "bdi_s6",
                            "bdi_s7", "bdi_s8", "bdi_s9",
                            "bdi_s10", "bdi_s11", "bdi_s12"),
            sg_measure_name = "bdi")

# Create bypsg data set
create_bysg(data = sgdata,
            sg_crit1_cutoff = 7,
            id_var_name = "id",
            tx_start_var_name = "bdi_s1",
            tx_end_var_name = "bdi_s12",
            sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3",
                            "bdi_s4", "bdi_s5", "bdi_s6",
                            "bdi_s7", "bdi_s8", "bdi_s9",
                            "bdi_s10", "bdi_s11", "bdi_s12"),
            sg_measure_name = "bdi")

Define cut-off value for first SG criterion

Description

Define a cut-off value for the first sudden gains criterion based on the Reliable Change Index (RCI; Jacobson & Truax, 1991) using an estimate for the standard deviation (sd) of the normal population and the reliability of the scale. These values can be entered manually using the arguments sd and reliability or extracted from data using the arguments data_sd and data_reliability. This function calculates the standard error of measurement (se), the standard error of the difference (sdiff) and a value that classifies as reliable change (reliable_change_value) based on the Reliable Change Index (RCI; Jacobson & Truax, 1991).

$se = sd * \sqrt{(1 - reliability)}$

$sdiff = \sqrt{(2 * se^2)}$

$reliable change value = 1.96 * sdiff$

Usage

define_crit1_cutoff(
  sd = NULL,
  reliability = NULL,
  data_sd = NULL,
  data_reliability = NULL
)
define_crit1_cutoff(
  sd = NULL,
  reliability = NULL,
  data_sd = NULL,
  data_reliability = NULL
)

Arguments

`sd`	Numeric, standard deviation of normal population or standard deviation at baseline. This argument is not needed if a vector with pretreatment scores is specified in the `data_sd` argument.
`reliability`	Numeric, between 0 and 1 indicating reliability of the scale. This argument is not needed if item-by-item data is specified in the `data_reliability` argument.
`data_sd`	A vector with pretreatment values. This argument is not needed if the standard deviation is specified in the `sd` argument.
`data_reliability`	A dataset in wide format (one row for each individual and one column for each item) including only the item-by-item scores of the SG measure (no ID variable). According to Jacobson & Truax (1991) the test-retest reliability should be used. Martinovich et al. (1996) suggest that the internal consistency (Cronbach's alpha) can be used instead of the test-retest reliability and may be more appropriate for estimating the standard error in some cases. This argument is not needed if the reliability is specified in the `reliability` argument.

Value

A list with estimates the for standard error of measurement (se), the standard error of the difference (sdiff) and a value that classifies as reliable change (reliable_change_value).

References

Jacobson, N. S., & Truax, P. A. (1991). Clinical significance: A statistical approach to defining meaningful change in psychotherapy research. Journal of Consulting and Clinical Psychology, 59 (1), 12-19. doi:10.1037/0022-006X.59.1.12.

Martinovich, Z., Saunders, S., & Howard, K. (1996). Some Comments on “Assessing Clinical Significance”. Psychotherapy Research, 6(2), 124–132. doi:10.1080/10503309612331331648.

Stiles et al. (2003). Early sudden gains in psychotherapy under routine clinic conditions: Practice-based evidence. Journal of Consulting and Clinical Psychology, 71 (1), 14-21. doi:10.1037/0022-006X.71.1.14.

Examples

# Define cut-off value for first SG criterion
# In this example the standard deviation and the reliability are specified manually
define_crit1_cutoff(sd = 10.5,
                    reliability = 0.931)

# In this example the reliability is specified manually
# The standard deviation of the variable "bdi_s0" in the dataset "sgdata" gets calculated
define_crit1_cutoff(data_sd = sgdata$bdi_s0,
                    reliability = 0.931)
# Define cut-off value for first SG criterion
# In this example the standard deviation and the reliability are specified manually
define_crit1_cutoff(sd = 10.5,
                    reliability = 0.931)

# In this example the reliability is specified manually
# The standard deviation of the variable "bdi_s0" in the dataset "sgdata" gets calculated
define_crit1_cutoff(data_sd = sgdata$bdi_s0,
                    reliability = 0.931)

Show descriptives for the sudden gains datasets

Description

Descriptives might differ between the bysg and byperson data sets depending on whether multiple gains are present.

Usage

describe_sg(data, sg_data_structure = c("bysg", "byperson"))
describe_sg(data, sg_data_structure = c("bysg", "byperson"))

Arguments

`data`	A `bysg` or `byperson` dataset created using the function `create_bysg` or `create_byperson`.
`sg_data_structure`	String, indicating whether the input data is a `bysg` or `byperson` dataset.

Value

A list, showing basic descriptive statistics for sudden gains within the dataset specified. Note that some numbers (e.g. percentages) will be different depending which dataset is selected, because where a participant has multiple gains, only one is selected for the byperson dataset. The list includes values for:

total_n: number of rows in input dataset
sg_total_n: total number of sudden gains
sg_n: number of people who experienced a sudden gain (byperson dataset only)
sg_pct: percentage of people in the input dataset who experienced a sudden gain
sg_multiple_n: number of people who experienced a sudden gain (byperson dataset only)
sg_multiple_pct: percentage of people in the input dataset who experienced more than one sudden gain
sg_reversal_n: number of sudden gains that later meet the criteria for a reversal
sg_reversal_pct: percentage of sudden gains that later meet the criteria for a reversal
sg_magnitude_m: mean magnitude of the sudden gains observed
sg_magnitude_sd: standard deviation of the magnitude of the sudden gains observed

Examples

# Create bysg dataset
bysg <- create_bysg(data = sgdata,
                    sg_crit1_cutoff = 7,
                    id_var_name = "id",
                    tx_start_var_name = "bdi_s1",
                    tx_end_var_name = "bdi_s12",
                    sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3",
                                    "bdi_s4", "bdi_s5", "bdi_s6",
                                    "bdi_s7", "bdi_s8", "bdi_s9",
                                    "bdi_s10", "bdi_s11", "bdi_s12"),
                    sg_measure_name = "bdi")

# Describe bysg dataset
describe_sg(data = bysg,
            sg_data_structure = "bysg")
# Create bysg dataset
bysg <- create_bysg(data = sgdata,
                    sg_crit1_cutoff = 7,
                    id_var_name = "id",
                    tx_start_var_name = "bdi_s1",
                    tx_end_var_name = "bdi_s12",
                    sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3",
                                    "bdi_s4", "bdi_s5", "bdi_s6",
                                    "bdi_s7", "bdi_s8", "bdi_s9",
                                    "bdi_s10", "bdi_s11", "bdi_s12"),
                    sg_measure_name = "bdi")

# Describe bysg dataset
describe_sg(data = bysg,
            sg_data_structure = "bysg")

Extract values around the sudden gain

Description

Extract values of measures around the time of a sudden gain.

Usage

extract_values(
  data,
  id_var_name,
  extract_var_list,
  sg_session_n_var_name = "sg_session_n",
  extract_measure_name = "x",
  start_numbering = 1,
  add_to_data = TRUE
)
extract_values(
  data,
  id_var_name,
  extract_var_list,
  sg_session_n_var_name = "sg_session_n",
  extract_measure_name = "x",
  start_numbering = 1,
  add_to_data = TRUE
)

Arguments

`data`	A `bysg` or `byperson` data set in wide format with the variable `sg_session_n` and all variables specified in `extract_var_list`.
`id_var_name`	String, specifying the name of the ID variable.
`extract_var_list`	List or vector, specifying the variable names of session to session scores to extract from. If this is a list, the name of each element will be used when creating new variables. Note that each element of this list must have the same number of repeated measures as specified in `sg_var_list` when creating the sudden gains data set.
`sg_session_n_var_name`	String, specifying variable name that contains information about the pregain session number. If the sudden gains data set was created using the suddengains R package, the default argument "sg_session_n" should be used.
`extract_measure_name`	String, if `extract_var_list` is a vector, this string will be used as the when creating new variables of the extracted measures.
`start_numbering`	Numeric, set to by default 1. Change to 0 if a pre-treatment (e.g. baseline assessment) measurement point is included in `extract_var_list`.
`add_to_data`	Logical, if set to `TRUE`, the extracted values are added as new variables to the input dataset. If set to false, only the ID variable and all extracted values will be returned.

Value

A wide dataset with values for extract_measure_name around the sudden gain.

Examples

# Create bysg dataset
bysg <- create_bysg(data = sgdata,
                    sg_crit1_cutoff = 7,
                    id_var_name = "id",
                    tx_start_var_name = "bdi_s1",
                    tx_end_var_name = "bdi_s12",
                    sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3",
                                    "bdi_s4", "bdi_s5", "bdi_s6",
                                    "bdi_s7", "bdi_s8", "bdi_s9",
                                    "bdi_s10", "bdi_s11", "bdi_s12"),
                    sg_measure_name = "bdi")

# For bysg dataset select "id" and "rq" variables first
sgdata_rq <- sgdata %>%
  dplyr::select(id, rq_s0:rq_s12)

# Join them with the sudden gains data set, here "bysg"
bysg_rq <- bysg %>%
  dplyr::left_join(sgdata_rq, by = "id")

# Extract "rq" scores around sudden gains on "bdi" in the bysg dataset
bysg_rq <- extract_values(data = bysg_rq,
                          id_var_name = "id_sg",
                          extract_var_list = c("rq_s1", "rq_s2", "rq_s3", "rq_s4",
                                               "rq_s5", "rq_s6", "rq_s7", "rq_s8",
                                               "rq_s9", "rq_s10", "rq_s11", "rq_s12"),
                          extract_measure_name = "rq",
                          add_to_data = TRUE)
# Create bysg dataset
bysg <- create_bysg(data = sgdata,
                    sg_crit1_cutoff = 7,
                    id_var_name = "id",
                    tx_start_var_name = "bdi_s1",
                    tx_end_var_name = "bdi_s12",
                    sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3",
                                    "bdi_s4", "bdi_s5", "bdi_s6",
                                    "bdi_s7", "bdi_s8", "bdi_s9",
                                    "bdi_s10", "bdi_s11", "bdi_s12"),
                    sg_measure_name = "bdi")

# For bysg dataset select "id" and "rq" variables first
sgdata_rq <- sgdata %>%
  dplyr::select(id, rq_s0:rq_s12)

# Join them with the sudden gains data set, here "bysg"
bysg_rq <- bysg %>%
  dplyr::left_join(sgdata_rq, by = "id")

# Extract "rq" scores around sudden gains on "bdi" in the bysg dataset
bysg_rq <- extract_values(data = bysg_rq,
                          id_var_name = "id_sg",
                          extract_var_list = c("rq_s1", "rq_s2", "rq_s3", "rq_s4",
                                               "rq_s5", "rq_s6", "rq_s7", "rq_s8",
                                               "rq_s9", "rq_s10", "rq_s11", "rq_s12"),
                          extract_measure_name = "rq",
                          add_to_data = TRUE)

Identify sudden gains.

Description

Function to identify sudden gains in longitudinal data structured in wide format.

Usage

identify_sg(
  data,
  id_var_name,
  sg_var_list,
  sg_crit1_cutoff,
  sg_crit2_pct = 0.25,
  sg_crit3 = TRUE,
  sg_crit3_alpha = 0.05,
  sg_crit3_adjust = TRUE,
  sg_crit3_critical_value = 2.776,
  identify_sg_1to2 = FALSE,
  crit123_details = FALSE
)
identify_sg(
  data,
  id_var_name,
  sg_var_list,
  sg_crit1_cutoff,
  sg_crit2_pct = 0.25,
  sg_crit3 = TRUE,
  sg_crit3_alpha = 0.05,
  sg_crit3_adjust = TRUE,
  sg_crit3_critical_value = 2.776,
  identify_sg_1to2 = FALSE,
  crit123_details = FALSE
)

Arguments

`data`	A data set in wide format including an ID variable and variables for each measurement point.
`id_var_name`	String, specifying the name of the ID variable. Each row should have a unique value.
`sg_var_list`	Vector, specifying the variable names of each measurement point sequentially.
`sg_crit1_cutoff`	Numeric, specifying the cut-off value to be used for the first sudden gains criterion. The function `define_crit1_cutoff` can be used to calculate a cutoff value based on the Reliable Change Index (RCI; Jacobson & Truax, 1991). If set to `NULL` the first criterion wont be applied.
`sg_crit2_pct`	Numeric, specifying the percentage change to be used for the second sudden gains criterion. If set to `NULL` the second criterion wont be applied.
`sg_crit3`	If set to `TRUE` the third criterion will be applied automatically adjusting the critical value for missingness. If set to `FALSE` the third criterion wont be applied.
`sg_crit3_alpha`	Numeric, alpha for the two-tailed student t-test to determine the critical value to be used for the third criterion. Degrees of freedom are based on the number of available data in the three sessions preceding the gain and the three sessions following the gain.
`sg_crit3_adjust`	Logical, specify whether critical value gets adjusted for missingness, see Lutz et al. (2013) and the documentation of this R package for further details. This argument is set to `TRUE` by default adjusting the critical value for missingness as described in the package documentation and Lutz et al. (2013): A critical value of 2.776 is used when all three data points before and after a potential gain are available, where one data point is missing either before or after a potential gain a critical value of 3.182 is used, and where one data point is missing both before and after the gain a critical value of 4.303 is used (for sg_crit3_alpha = 0.05). If set to `FALSE` the critical value set in `sg_crit3_critical_value` will instead be used for all comparisons, regardless of missingnes in the sequence of data points that are investigated for potential sudden gains.
`sg_crit3_critical_value`	Numeric, specifying the critical value to instead be used for all comparisons, regardless of missingnes in the sequence of data points that are investigated for potential sudden gains.
`identify_sg_1to2`	Logical, indicating whether to identify sudden gains from measurement point 1 to 2. If set to TRUE, this implies that the first variable specified in `sg_var_list` represents a baseline measurement point, e.g. pre-intervention assessment.
`crit123_details`	Logical, if set to `TRUE` this function returns information about which of the three criteria (e.g. "sg_crit1_2to3", "sg_crit2_2to3", and "sg_crit3_2to3") are met for each session to session interval for all cases. Variables named "sg_2to3", "sg_3to4" summarise all criteria that were selected to identify sudden gains.

Value

A wide data set indicating whether sudden gains are present for each session to session interval for all cases in data.

References

Lutz, W., Ehrlich, T., Rubel, J., Hallwachs, N., Röttger, M.-A., Jorasz, C., … Tschitsaz-Stucki, A. (2013). The ups and downs of psychotherapy: Sudden gains and sudden losses identified with session reports. Psychotherapy Research, 23(1), 14–24. doi:10.1080/10503307.2012.693837.

Examples

# Identify sudden gains
identify_sg(data = sgdata,
            sg_crit1_cutoff = 7,
            id_var_name = "id",
            sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3",
                            "bdi_s4", "bdi_s5", "bdi_s6",
                            "bdi_s7", "bdi_s8", "bdi_s9",
                            "bdi_s10", "bdi_s11", "bdi_s12"))
# Identify sudden gains
identify_sg(data = sgdata,
            sg_crit1_cutoff = 7,
            id_var_name = "id",
            sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3",
                            "bdi_s4", "bdi_s5", "bdi_s6",
                            "bdi_s7", "bdi_s8", "bdi_s9",
                            "bdi_s10", "bdi_s11", "bdi_s12"))

Identify sudden losses.

Description

Function to identify sudden losses in longitudinal data structured in wide format.

Usage

identify_sl(
  data,
  id_var_name,
  sg_var_list,
  sg_crit1_cutoff,
  sg_crit2_pct = 0.25,
  sg_crit3 = TRUE,
  sg_crit3_alpha = 0.05,
  sg_crit3_adjust = TRUE,
  sg_crit3_critical_value = 2.776,
  identify_sg_1to2 = FALSE,
  crit123_details = FALSE
)
identify_sl(
  data,
  id_var_name,
  sg_var_list,
  sg_crit1_cutoff,
  sg_crit2_pct = 0.25,
  sg_crit3 = TRUE,
  sg_crit3_alpha = 0.05,
  sg_crit3_adjust = TRUE,
  sg_crit3_critical_value = 2.776,
  identify_sg_1to2 = FALSE,
  crit123_details = FALSE
)

Arguments

`data`	A data set in wide format including an ID variable and variables for each measurement point.
`id_var_name`	String, specifying the name of the ID variable. Each row should have a unique value.
`sg_var_list`	Vector, specifying the variable names of each measurement point sequentially.
`sg_crit1_cutoff`	Numeric, specifying the negative cut-off value to be used for the first sudden losses criterion, see examples below. The function `define_crit1_cutoff` can be used to calculate a cutoff value based on the Reliable Change Index (RCI; Jacobson & Truax, 1991). If set to `NULL` the first criterion wont be applied.
`sg_crit2_pct`	Numeric, specifying the percentage change to be used for the second sudden losses criterion. If set to `NULL` the first criterion wont be applied.
`sg_crit3`	Logical, if set to `TRUE` the third criteria will be applied automatically adjusting the critical value for missingness. If set to `FALSE` the third criterion wont be applied.
`sg_crit3_alpha`	Numeric, alpha for the student t-test (two-tailed) to determine the critical value to be used for the third criterion. Degrees of freedom are based on the number of available data in the three sessions preceding the loss and the three sessions following the loss.
`sg_crit3_adjust`	Logical, specify whether critical value gets adjusted for missingness, see Lutz et al. (2013) and the documentation of this R package for further details. This argument is set to `TRUE` by default adjusting the critical value for missingness as described in the package documentation and Lutz et al. (2013): A critical value of 2.776 is used when all three data points before and after a potential gain are available, where one data point is missing either before or after a potential gain a critical value of 3.182 is used, and where one data point is missing both before and after the gain a critical value of 4.303 is used (for sg_crit3_alpha = 0.05). If set to `FALSE` the critical value set in `sg_crit3_critical_value` will instead be used for all comparisons, regardless of missingnes in the sequence of data points that are investigated for potential sudden gains.
`sg_crit3_critical_value`	Numeric, if the argument `sg_crit3_adjust = FALSE`, specifying the critical value to instead be used for all comparisons, regardless of missingnes in the sequence of data points that are investigated for potential sudden gains.
`identify_sg_1to2`	Logical, indicating whether to identify sudden losses from measurement point 1 to 2. If set to TRUE, this implies that the first variable specified in `sg_var_list` represents a baseline measurement point, e.g. pre-intervention assessment.
`crit123_details`	Logical, if set to `TRUE` this function returns information about which of the three criteria (e.g. "sg_crit1_2to3", "sg_crit2_2to3", and "sg_crit3_2to3") are met for each session to session interval for all cases. Variables named "sg_2to3", "sg_3to4" summarise all criteria that were selected to identify sudden gains.

Value

A wide data set indicating whether sudden losses are present for each session to session interval for all cases in data.

References

Examples

# Identify sudden losses
identify_sl(data = sgdata,
            # Negative cut-off value to identify sudden losses
            sg_crit1_cutoff = -7,
            id_var_name = "id",
            sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3",
                            "bdi_s4", "bdi_s5", "bdi_s6",
                            "bdi_s7", "bdi_s8", "bdi_s9",
                            "bdi_s10", "bdi_s11", "bdi_s12"))
# Identify sudden losses
identify_sl(data = sgdata,
            # Negative cut-off value to identify sudden losses
            sg_crit1_cutoff = -7,
            id_var_name = "id",
            sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3",
                            "bdi_s4", "bdi_s5", "bdi_s6",
                            "bdi_s7", "bdi_s8", "bdi_s9",
                            "bdi_s10", "bdi_s11", "bdi_s12"))

Plot average change in variables around the sudden gain

Description

Generates a plot of the mean values around the sudden gain using ggplot. This can be used to plot the primary outcome or secondary measures. The parameters starting with "group" allow to plot the average gain magnitude by group. Further ggplot2 components can be added using + following this function.

Usage

plot_sg(
  data,
  id_var_name,
  tx_start_var_name,
  tx_end_var_name,
  sg_pre_post_var_list,
  ylab,
  xlab = "Session",
  colour_single = "#239b89ff",
  colour_group = c("viridis", "ggplot", "grey"),
  viridis_option = c("D", "A", "B", "C"),
  viridis_begin = 0,
  viridis_end = 1,
  group_var_name = NULL,
  group_levels = NULL,
  group_labels = NULL,
  group_title = NULL,
  apaish = TRUE
)
plot_sg(
  data,
  id_var_name,
  tx_start_var_name,
  tx_end_var_name,
  sg_pre_post_var_list,
  ylab,
  xlab = "Session",
  colour_single = "#239b89ff",
  colour_group = c("viridis", "ggplot", "grey"),
  viridis_option = c("D", "A", "B", "C"),
  viridis_begin = 0,
  viridis_end = 1,
  group_var_name = NULL,
  group_levels = NULL,
  group_labels = NULL,
  group_title = NULL,
  apaish = TRUE
)

Arguments

`data`	A `bysg` or `byperson` dataset created using the function `create_bysg` or `create_byperson`.
`id_var_name`	String, specifying the name of the ID variable.
`tx_start_var_name`	String, specifying the variable name of the first measurement point of the intervention.
`tx_end_var_name`	String, specifying the variable name of the last measurement point of the intervention.
`sg_pre_post_var_list`	Vector, specifying the variable names of the 3 measurement points before, and the 3 after the sudden gain, for the measure being plotted.
`ylab`	String, specifying the label for the y axis i.e. the name of the measure being plotted.
`xlab`	String, specifying the label for the x axis, e.g. `"Session"`.
`colour_single`	String, specifying the colour of the plot for one group.
`colour_group`	String, specifying the discrete colour palette to be used for the groups.
`viridis_option`	String specifying the colour option for discrete viridis palette, see `scale_fill_viridis_d`.
`viridis_begin`	Numeric, specifying hue between 0 and 1 at which the viridis colormap begins, see `scale_fill_viridis_d`.
`viridis_end`	Numeric, specifying hue between 0 and 1 at which the viridis colormap ends, see `scale_fill_viridis_d`.
`group_var_name`	String, specifying the variable name of the group variable.
`group_levels`	Vector, specifying the levels as numeric for the groups in `group_var_name`.
`group_labels`	Vector, specifying the label names as strings for the groups in `group_var_name`.
`group_title`	String, specifying the title that will be used for the groups specified in `group_labels`.
`apaish`	Logical, make plot APA publishable.

Value

A plot of the mean values around the sudden gain, for the measure specified.

Examples

# First create a bysg (or byperson) dataset
bysg <- create_bysg(data = sgdata,
                    sg_crit1_cutoff = 7,
                    id_var_name = "id",
                    tx_start_var_name = "bdi_s1",
                    tx_end_var_name = "bdi_s12",
                    sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3",
                                    "bdi_s4", "bdi_s5", "bdi_s6",
                                    "bdi_s7", "bdi_s8", "bdi_s9",
                                    "bdi_s10", "bdi_s11", "bdi_s12"),
                    sg_measure_name = "bdi")

# Plot average change of BDI values around the period of the sudden gain
plot_sg(data = bysg,
        id_var_name = "id",
        tx_start_var_name = "bdi_s1",
        tx_end_var_name = "bdi_s12",
        sg_pre_post_var_list = c("sg_bdi_2n", "sg_bdi_1n", "sg_bdi_n",
                                 "sg_bdi_n1", "sg_bdi_n2", "sg_bdi_n3"),
        ylab = "BDI", xlab = "Session")
# First create a bysg (or byperson) dataset
bysg <- create_bysg(data = sgdata,
                    sg_crit1_cutoff = 7,
                    id_var_name = "id",
                    tx_start_var_name = "bdi_s1",
                    tx_end_var_name = "bdi_s12",
                    sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3",
                                    "bdi_s4", "bdi_s5", "bdi_s6",
                                    "bdi_s7", "bdi_s8", "bdi_s9",
                                    "bdi_s10", "bdi_s11", "bdi_s12"),
                    sg_measure_name = "bdi")

# Plot average change of BDI values around the period of the sudden gain
plot_sg(data = bysg,
        id_var_name = "id",
        tx_start_var_name = "bdi_s1",
        tx_end_var_name = "bdi_s12",
        sg_pre_post_var_list = c("sg_bdi_2n", "sg_bdi_1n", "sg_bdi_n",
                                 "sg_bdi_n1", "sg_bdi_n2", "sg_bdi_n3"),
        ylab = "BDI", xlab = "Session")

Plot summary of available data per time point and analysed session to session intervals

Description

Plot summary of available data per time point and analysed session to session intervals

Usage

plot_sg_intervals(data, id_var_name, sg_var_list, identify_sg_1to2 = FALSE)
plot_sg_intervals(data, id_var_name, sg_var_list, identify_sg_1to2 = FALSE)

Arguments

`data`	A data set in wide format including an ID variable and variables for each measurement point.
`id_var_name`	String, specifying the name of the ID variable. Each row should have a unique value.
`sg_var_list`	Vector, specifying the variable names of each measurement point sequentially.
`identify_sg_1to2`	Logical, indicating whether to identify sudden losses from measurement point 1 to 2. If set to TRUE, this implies that the first variable specified in `sg_var_list` represents a baseline measurement point, e.g. pre-intervention assessment.

Value

Plot showing percentage of available data per time point and percentage of session to session intervals that were analysed for sudden gains.

Examples

# Create plot
plot_sg_intervals(data = sgdata,
                  id_var_name = "id",
                  sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3",
                                  "bdi_s4", "bdi_s5", "bdi_s6",
                                  "bdi_s7", "bdi_s8", "bdi_s9",
                                  "bdi_s10", "bdi_s11", "bdi_s12"))

# Create plot
plot_sg_intervals(data = sgdata,
                  id_var_name = "id",
                  sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3",
                                  "bdi_s4", "bdi_s5", "bdi_s6",
                                  "bdi_s7", "bdi_s8", "bdi_s9",
                                  "bdi_s10", "bdi_s11", "bdi_s12"))

Plot individual trajectories

Description

Plot individual trajectories of selected cases using ggplot. This function can be combined with a filter command to explore the trajectories of individual or groups of cases. Further ggplot2 components can be added using + following this function.

Usage

plot_sg_trajectories(
  data,
  id_var,
  var_list,
  select_id_list = NULL,
  select_n = NULL,
  show_id = TRUE,
  show_legend = TRUE,
  legend_title = "ID",
  id_label_size = 2,
  connect_missing = TRUE,
  colour = c("viridis", "ggplot", "grey"),
  viridis_option = c("D", "A", "B", "C"),
  viridis_begin = 0,
  viridis_end = 1,
  line_alpha = 1,
  point_alpha = 1,
  xlab = "X",
  ylab = "Y",
  scale_x_num = FALSE,
  scale_x_num_start = 1,
  apaish = TRUE,
  ...
)
plot_sg_trajectories(
  data,
  id_var,
  var_list,
  select_id_list = NULL,
  select_n = NULL,
  show_id = TRUE,
  show_legend = TRUE,
  legend_title = "ID",
  id_label_size = 2,
  connect_missing = TRUE,
  colour = c("viridis", "ggplot", "grey"),
  viridis_option = c("D", "A", "B", "C"),
  viridis_begin = 0,
  viridis_end = 1,
  line_alpha = 1,
  point_alpha = 1,
  xlab = "X",
  ylab = "Y",
  scale_x_num = FALSE,
  scale_x_num_start = 1,
  apaish = TRUE,
  ...
)

Arguments

`data`	Dataset in wide format.
`id_var`	String, specifying ID variable.
`var_list`	Vector, specifying variable names to be plotted in sequential order.
`select_id_list`	Vector, specifying case IDs to be plotted.
`select_n`	Numeric, specifying number of randomly selected cases to be plotted.
`show_id`	Logical, specifying whether or not to show ID variables inside the plot near the first measurement point.
`show_legend`	Logical, specifying whether or not a legend of all IDs.
`legend_title`	String, specifying the title of legend, by default the variable name of `id_var` will be shown.
`id_label_size`	Numeric, specifying the size of the ID label, if `show_id = TRUE`.
`connect_missing`	Logical, specifying whether to connect points across missing values.
`colour`	String, specifying the discrete colour palette to be used.
`viridis_option`	String, specifying the colour option for discrete viridis palette, if `colour = "viridis"`. See `scale_fill_viridis_d` for more details.
`viridis_begin`	Numeric, specifying hue between 0 and 1 at which the viridis colormap begins, if `colour = "viridis"`. See `scale_fill_viridis_d` for more details.
`viridis_end`	Numeric, specifying hue between 0 and 1 at which the viridis colormap ends, if `colour = "viridis"`. See `scale_fill_viridis_d` for more details.
`line_alpha`	Numeric, specifying alpha (transparency) of lines.
`point_alpha`	Numeric, specifying alpha (transparency) of points.
`xlab`	String for x axis label.
`ylab`	String for y axis label.
`scale_x_num`	Logical, if `TRUE` print sequential numbers starting from 1 as x axis labels, if `FALSE` use variable names.
`scale_x_num_start`	Numeric, specifying the starting value of the x axis, if `scale_x_num = TRUE`.
`apaish`	Logical, if `TRUE` aligns plot with APA guidelines.
`...`	Further arguments to be passed on to `geom_label_repel`.

Value

ggplot2 object

Examples

# Plot individual trajectories of IDs 2, 4, 5, and 9
plot_sg_trajectories(data = sgdata,
                     id_var = "id",
                     select_id_list = c("2", "4", "5", "9"),
                     var_list = c("bdi_s1", "bdi_s2", "bdi_s3", "bdi_s4",
                                  "bdi_s5", "bdi_s6", "bdi_s7", "bdi_s8",
                                  "bdi_s9", "bdi_s10", "bdi_s11", "bdi_s12"),
                     show_id = TRUE,
                     id_label_size = 4,
                     label.padding = .2,
                     show_legend = FALSE,
                     colour = "viridis",
                     viridis_option = "D",
                     viridis_begin = 0,
                     viridis_end = .8,
                     connect_missing = FALSE,
                     scale_x_num = TRUE,
                     scale_x_num_start = 1,
                     apaish = TRUE,
                     xlab = "Session",
                     ylab = "BDI")

# Create byperson dataset to use for plotting
byperson <- create_byperson(data = sgdata,
                            sg_crit1_cutoff = 7,
                            id_var_name = "id",
                            tx_start_var_name = "bdi_s1",
                            tx_end_var_name = "bdi_s12",
                            sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3",
                                            "bdi_s4", "bdi_s5", "bdi_s6",
                                            "bdi_s7", "bdi_s8", "bdi_s9",
                                            "bdi_s10", "bdi_s11", "bdi_s12"),
                            sg_measure_name = "bdi")

# First, filter byperson dataset to only include cases with more than one sudden gain
# Next, plot BDI trajectory of 3 randomly selected cases with with more than one sudden gain
byperson %>%
  dplyr::filter(sg_freq_byperson > 1) %>%
    plot_sg_trajectories(id_var = "id_sg",
                         var_list = c("bdi_s1", "bdi_s2", "bdi_s3", "bdi_s4",
                                      "bdi_s5", "bdi_s6", "bdi_s7", "bdi_s8",
                                      "bdi_s9", "bdi_s10", "bdi_s11", "bdi_s12"),
                         select_n = 3,
                         show_id = TRUE,
                         show_legend = TRUE,
                         scale_x_num = TRUE,
                         scale_x_num_start = 1,
                         xlab = "Session",
                         ylab = "BDI")
# Plot individual trajectories of IDs 2, 4, 5, and 9
plot_sg_trajectories(data = sgdata,
                     id_var = "id",
                     select_id_list = c("2", "4", "5", "9"),
                     var_list = c("bdi_s1", "bdi_s2", "bdi_s3", "bdi_s4",
                                  "bdi_s5", "bdi_s6", "bdi_s7", "bdi_s8",
                                  "bdi_s9", "bdi_s10", "bdi_s11", "bdi_s12"),
                     show_id = TRUE,
                     id_label_size = 4,
                     label.padding = .2,
                     show_legend = FALSE,
                     colour = "viridis",
                     viridis_option = "D",
                     viridis_begin = 0,
                     viridis_end = .8,
                     connect_missing = FALSE,
                     scale_x_num = TRUE,
                     scale_x_num_start = 1,
                     apaish = TRUE,
                     xlab = "Session",
                     ylab = "BDI")

# Create byperson dataset to use for plotting
byperson <- create_byperson(data = sgdata,
                            sg_crit1_cutoff = 7,
                            id_var_name = "id",
                            tx_start_var_name = "bdi_s1",
                            tx_end_var_name = "bdi_s12",
                            sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3",
                                            "bdi_s4", "bdi_s5", "bdi_s6",
                                            "bdi_s7", "bdi_s8", "bdi_s9",
                                            "bdi_s10", "bdi_s11", "bdi_s12"),
                            sg_measure_name = "bdi")

# First, filter byperson dataset to only include cases with more than one sudden gain
# Next, plot BDI trajectory of 3 randomly selected cases with with more than one sudden gain
byperson %>%
  dplyr::filter(sg_freq_byperson > 1) %>%
    plot_sg_trajectories(id_var = "id_sg",
                         var_list = c("bdi_s1", "bdi_s2", "bdi_s3", "bdi_s4",
                                      "bdi_s5", "bdi_s6", "bdi_s7", "bdi_s8",
                                      "bdi_s9", "bdi_s10", "bdi_s11", "bdi_s12"),
                         select_n = 3,
                         show_id = TRUE,
                         show_legend = TRUE,
                         scale_x_num = TRUE,
                         scale_x_num_start = 1,
                         xlab = "Session",
                         ylab = "BDI")

Rename variable names to a generic and consistent format

Description

Rename variable names to a generic and consistent format that can be used by other functions of the suddengains package.

Usage

rename_sg_vars(
  data,
  rename_var_list,
  new_var_str = "temp_var_",
  start_numbering = 1
)
rename_sg_vars(
  data,
  rename_var_list,
  new_var_str = "temp_var_",
  start_numbering = 1
)

Arguments

`data`	Dataframe in wide format.
`rename_var_list`	Vector of variables to be renamed.
`new_var_str`	String, new name for variables.
`start_numbering`	Numeric, first number to be used as suffix for renaming variables specified in "rename_var_list".

Value

Dataframe in wide format with renamed variables.

Select sample providing enough data to identify sudden gains

Description

Select sample for further sudden gains analyses depending on specified methods.

The following table shows the different data patterns that get selected when method = "pattern". This function goes through the data and selects all cases with at least one of the following data patterns.

Pattern	x1	x2	x3	x4	x5	x6
1.	x	N	x	x	.	.
2.	x	N	x	.	x	.
3.	x	.	N	x	x	.
4.	x	.	N	x	.	x

Note. x1 to x6 are consecutive data points of the primary outcome measure. 'x' = Available data; '.' = Missing data. 'N' represents available data to be examined as a possible pregain session.

Usage

select_cases(
  data,
  id_var_name,
  sg_var_list,
  method = c("pattern", "min_sess"),
  min_sess_num = NULL,
  return_id_lgl = FALSE
)
select_cases(
  data,
  id_var_name,
  sg_var_list,
  method = c("pattern", "min_sess"),
  min_sess_num = NULL,
  return_id_lgl = FALSE
)

Arguments

`data`	A dataset in wide format with an id variable and the sudden gains variables.
`id_var_name`	String, specifying the name of the ID variable. Each row should have a unique value.
`sg_var_list`	Vector, specifying the variable names of each measurement point sequentially.
`method`	String, specifying the method used to select cases: `pattern` or `min_sess`.
`min_sess_num`	Numeric, minimum number of available sessions to be selected. This argument needs to be specified if `method = min_sess`.
`return_id_lgl`	Logical, if `TRUE` the function returns the ID variable and a new variable `sg_select` indicating whether there is enough data available to identify sudden gains. If set to `FALSE` this function returns the input data together with the new variable `sg_select`.

Value

A wide dataset indicating with all cases and a variable indicating whether each cases provides enough data to identify sudden gains.

Examples

# 1. method = "pattern"
select_cases(data = sgdata,
             id_var_name = "id",
             sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3", "bdi_s4",
                             "bdi_s5", "bdi_s6", "bdi_s7", "bdi_s8",
                             "bdi_s9", "bdi_s10", "bdi_s11", "bdi_s12"),
             method = "pattern",
             return_id_lgl = FALSE)

# 2. method = "min_sess"
select_cases(data = sgdata,
             id_var_name = "id",
             sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3", "bdi_s4",
                             "bdi_s5", "bdi_s6", "bdi_s7", "bdi_s8",
                             "bdi_s9", "bdi_s10", "bdi_s11", "bdi_s12"),
             method = "min_sess",
             min_sess_num = 9,
             return_id_lgl = TRUE)
# 1. method = "pattern"
select_cases(data = sgdata,
             id_var_name = "id",
             sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3", "bdi_s4",
                             "bdi_s5", "bdi_s6", "bdi_s7", "bdi_s8",
                             "bdi_s9", "bdi_s10", "bdi_s11", "bdi_s12"),
             method = "pattern",
             return_id_lgl = FALSE)

# 2. method = "min_sess"
select_cases(data = sgdata,
             id_var_name = "id",
             sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3", "bdi_s4",
                             "bdi_s5", "bdi_s6", "bdi_s7", "bdi_s8",
                             "bdi_s9", "bdi_s10", "bdi_s11", "bdi_s12"),
             method = "min_sess",
             min_sess_num = 9,
             return_id_lgl = TRUE)

Example dataset dataset with repeated measures of depression and rumination

Description

Example dataset with a measure of depression symptoms (BDI) and a secondary process measure (RQ; Rumination Questionnaire) to illustrate how the package works.

Usage

data(sgdata)
data(sgdata)

Format

A longitudinal dataset in wide format, i.e one row per person, one column per variable.

id: ID variable, unique identifier for each person
bdi_s0: BDI value, baseline assessment
bdi_s1: BDI value, session 1
bdi_s2: BDI value, session 2
bdi_s3: BDI value, session 3
bdi_s4: BDI value, session 4
bdi_s5: BDI value, session 5
bdi_s6: BDI value, session 6
bdi_s7: BDI value, session 7
bdi_s8: BDI value, session 8
bdi_s9: BDI value, session 9
bdi_s10: BDI value, session 10
bdi_s11: BDI value, session 11
bdi_s12: BDI value, session 12
bdi_fu1: BDI value, follow-up measure 1
bdi_fu2: BDI value, follow-up measure 2
rq_s0: RQ value, baseline assessment
rq_s1: RQ value, session 1
rq_s2: RQ value, session 2
rq_s3: RQ value, session 3
rq_s4: RQ value, session 4
rq_s5: RQ value, session 5
rq_s6: RQ value, session 6
rq_s7: RQ value, session 7
rq_s8: RQ value, session 8
rq_s9: RQ value, session 9
rq_s10: RQ value, session 10
rq_s11: RQ value, session 11
rq_s12: RQ value, session 12
rq_fu1: RQ value, follow-up measure 1
rq_fu2: RQ value, follow-up measure 2

Examples

# Load data into global environment
data(sgdata)
# Load data into global environment
data(sgdata)

Example dataset dataset with repeated measures of depression and rumination (bad variable names)

Description

Same as sgdata but with bad variable names to illustrate that the package can also work with inconsistent names.

Usage

data(sgdata_bad)
data(sgdata_bad)

Format

A longitudinal dataset in wide format, i.e one row per person, one column per variable.

id: ID variable, unique identifier for each person
bdi_intake: BDI value, baseline assessment
bdi1: BDI value, session 1
wk2bdi: BDI value, session 2
bdi.S3: BDI value, session 3
bdis4: BDI value, session 4
bdi_session5: BDI value, session 5
bdi_weekSix: BDI value, session 6
beck.dep.inv_sess7: BDI value, session 7
weeeek8bdi: BDI value, session 8
bdi_nine: BDI value, session 9
bdii10: BDI value, session 10
bDi11: BDI value, session 11
bdi_s12_end: BDI value, session 12
bdi_fu1: BDI value, follow-up measure 1
bdi_followup2: BDI value, follow-up measure 2
rq_intake: RQ value, baseline assessment
rqi1: RQ value, session 1
wk2rq: RQ value, session 2
rq.S3: RQ value, session 3
rq_s4: RQ value, session 4
rq_session5: RQ value, session 5
rq_weekSix: RQ value, session 6
rq_sess7: RQ value, session 7
weeeek8rq: RQ value, session 8
rqnine: RQ value, session 9
rq10: RQ value, session 10
rqi11: RQ value, session 11
rq_s12_end: RQ value, session 12
prq_fu1: RQ value, follow-up measure 1
rqq_followup2: RQ value, follow-up measure 2

Examples

# Load data into global environment
data(sgdata_bad)
# Load data into global environment
data(sgdata_bad)

Write a sudden gains data frame (byperson) to CSV, SPSS, STATA or Excel files

Description

Writes a data frame as a specified file type.

Usage

write_byperson(
  data,
  sg_crit1_cutoff,
  id_var_name,
  sg_var_list,
  tx_start_var_name,
  tx_end_var_name,
  sg_measure_name,
  sg_crit2_pct = 0.25,
  sg_crit3 = TRUE,
  sg_crit3_alpha = 0.05,
  sg_crit3_adjust = TRUE,
  sg_crit3_critical_value = 2.776,
  identify = c("sg", "sl"),
  identify_sg_1to2 = FALSE,
  multiple_sg_select = c("first", "last", "smallest", "largest"),
  data_is_bysg = FALSE,
  format = c("CSV", "SPSS", "STATA", "Excel"),
  path,
  stata_version = 14,
  ...
)
write_byperson(
  data,
  sg_crit1_cutoff,
  id_var_name,
  sg_var_list,
  tx_start_var_name,
  tx_end_var_name,
  sg_measure_name,
  sg_crit2_pct = 0.25,
  sg_crit3 = TRUE,
  sg_crit3_alpha = 0.05,
  sg_crit3_adjust = TRUE,
  sg_crit3_critical_value = 2.776,
  identify = c("sg", "sl"),
  identify_sg_1to2 = FALSE,
  multiple_sg_select = c("first", "last", "smallest", "largest"),
  data_is_bysg = FALSE,
  format = c("CSV", "SPSS", "STATA", "Excel"),
  path,
  stata_version = 14,
  ...
)

Arguments

`data`	A data set in wide format including an ID variable and variables for each measurement point.
`sg_crit1_cutoff`	Numeric, specifying the cut-off value to be used for the first sudden gains criterion. The function `define_crit1_cutoff` can be used to calculate a cutoff value based on the Reliable Change Index (RCI; Jacobson & Truax, 1991). If set to `NULL` the first criterion wont be applied.
`id_var_name`	String, specifying the name of the ID variable. Each row should have a unique value.
`sg_var_list`	Vector, specifying the variable names of each measurement point sequentially.
`tx_start_var_name`	String, specifying the variable name of the first measurement point of the intervention.
`tx_end_var_name`	String, specifying the variable name of the last measurement point of the intervention.
`sg_measure_name`	String, specifying the name of the measure used to identify sudden gains/losses.
`sg_crit2_pct`	Numeric, specifying the percentage change to be used for the second sudden gains/losses criterion. If set to `NULL` the second criterion wont be applied.
`sg_crit3`	If set to `TRUE` the third criterion will be applied automatically adjusting the critical value for missingness. If set to `FALSE` the third criterion wont be applied.
`sg_crit3_alpha`	Numeric, alpha for the student t-test (two-tailed) to determine the critical value to be used for the third criterion. Degrees of freedom are based on the number of available data in the three sessions preceding the gain and the three sessions following the gain.
`sg_crit3_adjust`	Logical, specify whether critical value gets adjusted for missingness, see Lutz et al. (2013) and the documentation of this R package for further details. This argument is set to `TRUE` by default adjusting the critical value for missingness as described in the package documentation and Lutz et al. (2013): A critical value of 2.776 is used when all three data points before and after a potential gain are available, where one data point is missing either before or after a potential gain a critical value of 3.182 is used, and where one data point is missing both before and after the gain a critical value of 4.303 is used (for sg_crit3_alpha = 0.05). If set to `FALSE` the critical value set in `sg_crit3_critical_value` will instead be used for all comparisons, regardless of missingnes in the sequence of data points that are investigated for potential sudden gains.
`sg_crit3_critical_value`	Numeric, specifying the critical value to instead be used for all comparisons, regardless of missingnes in the sequence of data points that are investigated for potential sudden gains.#'
`identify`	String, specifying whether to identify sudden gains (`"sg"`) using `identify_sg` or sudden losses (`"sl"`) using `identify_sl`. The default is to identify sudden gains (`"sg"`).
`identify_sg_1to2`	Logical, indicating whether to identify sudden losses from measurement point 1 to 2.
`multiple_sg_select`	String, specifying which sudden gain/loss to select for this data set if more than one gain/loss was identified per case. Options are: `"first"`, `"last"`, `"smallest"`, or `"largest"`, `create_byperson`.
`data_is_bysg`	Logical, specifying whether the data set in the `data` argument is a bysg data set created using the `create_bysg` function.
`format`	String, specifying the format of the data file, `"CSV"`, `"SPSS"`, `"STATA"` or `"Excel"`.
`path`	String, specifying the file name ending with the matching file extension, `".csv"`, `".sav"`, `".dta"` or `".xlsx"`.
`stata_version`	Numeric, specifying STATA version number.
`...`	Additional parameters to be passed on to the specified write function, see `write_csv` for `"CSV"`, `write_sav` for `"SPSS"`, `write_dta` for `"STATA"` or `write_xlsx` for `"Excel"` for more information.

Value

A csv file containing a wide data set with one row per case (id_var_name) in data.

References

Examples

# Adjust "path" argument before running
# Create character string name for temporary "byperson.csv" file
temp <- tempfile(pattern = "byperson", fileext = ".csv")

# Write byperson dataset (CSV file)
# To write a different format change the 'format' argument ...
# ... as well as the file extension in the 'path' argument
write_byperson(data = sgdata,
               sg_crit1_cutoff = 7,
               id_var_name = "id",
               tx_start_var_name = "bdi_s1",
               tx_end_var_name = "bdi_s12",
               sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3", "bdi_s4",
                               "bdi_s5", "bdi_s6", "bdi_s7", "bdi_s8",
                               "bdi_s9", "bdi_s10", "bdi_s11", "bdi_s12"),
               sg_measure_name = "bdi",
               identify_sg_1to2 = FALSE,
               multiple_sg_select = "largest",
               format = "CSV",
               path = temp)
# Adjust "path" argument before running
# Create character string name for temporary "byperson.csv" file
temp <- tempfile(pattern = "byperson", fileext = ".csv")

# Write byperson dataset (CSV file)
# To write a different format change the 'format' argument ...
# ... as well as the file extension in the 'path' argument
write_byperson(data = sgdata,
               sg_crit1_cutoff = 7,
               id_var_name = "id",
               tx_start_var_name = "bdi_s1",
               tx_end_var_name = "bdi_s12",
               sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3", "bdi_s4",
                               "bdi_s5", "bdi_s6", "bdi_s7", "bdi_s8",
                               "bdi_s9", "bdi_s10", "bdi_s11", "bdi_s12"),
               sg_measure_name = "bdi",
               identify_sg_1to2 = FALSE,
               multiple_sg_select = "largest",
               format = "CSV",
               path = temp)

Write a sudden gains data frame (bysg) to CSV, SPSS, STATA or Excel files

Description

Writes a data frame as a specified file type.

Usage

write_bysg(
  data,
  sg_crit1_cutoff,
  id_var_name,
  sg_var_list,
  tx_start_var_name,
  tx_end_var_name,
  sg_measure_name,
  sg_crit2_pct = 0.25,
  sg_crit3 = TRUE,
  sg_crit3_alpha = 0.05,
  sg_crit3_adjust = TRUE,
  sg_crit3_critical_value = 2.776,
  identify = c("sg", "sl"),
  identify_sg_1to2 = FALSE,
  format = c("CSV", "SPSS", "STATA", "Excel"),
  path,
  stata_version = 14,
  ...
)
write_bysg(
  data,
  sg_crit1_cutoff,
  id_var_name,
  sg_var_list,
  tx_start_var_name,
  tx_end_var_name,
  sg_measure_name,
  sg_crit2_pct = 0.25,
  sg_crit3 = TRUE,
  sg_crit3_alpha = 0.05,
  sg_crit3_adjust = TRUE,
  sg_crit3_critical_value = 2.776,
  identify = c("sg", "sl"),
  identify_sg_1to2 = FALSE,
  format = c("CSV", "SPSS", "STATA", "Excel"),
  path,
  stata_version = 14,
  ...
)

Arguments

`data`	A data set in wide format including an ID variable and variables for each measurement point.
`sg_crit1_cutoff`	Numeric, specifying the cut-off value to be used for the first sudden gains criterion. The function `define_crit1_cutoff` can be used to calculate a cutoff value based on the Reliable Change Index (RCI; Jacobson & Truax, 1991). If set to `NULL` the first criterion wont be applied.
`id_var_name`	String, specifying the name of the ID variable. Each row should have a unique value.
`sg_var_list`	Vector, specifying the variable names of each measurement point sequentially.
`tx_start_var_name`	String, specifying the variable name of the first measurement point of the intervention.
`tx_end_var_name`	String, specifying the variable name of the last measurement point of the intervention.
`sg_measure_name`	String, specifying the name of the measure used to identify sudden gains/losses.
`sg_crit2_pct`	Numeric, specifying the percentage change to be used for the second sudden gains/losses criterion. If set to `NULL` the second criterion wont be applied.
`sg_crit3`	If set to `TRUE` the third criterion will be applied automatically adjusting the critical value for missingness. If set to `FALSE` the third criterion wont be applied.
`sg_crit3_alpha`	Numeric, alpha for the student t-test (two-tailed) to determine the critical value to be used for the third criterion. Degrees of freedom are based on the number of available data in the three sessions preceding the gain and the three sessions following the gain.
`sg_crit3_adjust`	Logical, specify whether critical value gets adjusted for missingness, see Lutz et al. (2013) and the documentation of this R package for further details. This argument is set to `TRUE` by default adjusting the critical value for missingness as described in the package documentation and Lutz et al. (2013): A critical value of 2.776 is used when all three data points before and after a potential gain are available, where one data point is missing either before or after a potential gain a critical value of 3.182 is used, and where one data point is missing both before and after the gain a critical value of 4.303 is used (for sg_crit3_alpha = 0.05). If set to `FALSE` the critical value set in `sg_crit3_critical_value` will instead be used for all comparisons, regardless of missingnes in the sequence of data points that are investigated for potential sudden gains.
`sg_crit3_critical_value`	Numeric, specifying the critical value to instead be used for all comparisons, regardless of missingnes in the sequence of data points that are investigated for potential sudden gains.
`identify`	String, specifying whether to identify sudden gains (`"sg"`) using `identify_sg` or sudden losses (`"sl"`) using `identify_sl`. The default is to identify sudden gains (`"sg"`).
`identify_sg_1to2`	Logical, indicating whether to identify sudden losses from measurement point 1 to 2.
`format`	String, specifying the format of the data file, `"CSV"`, `"SPSS"`, `"STATA"` or `"Excel"`.
`path`	String, specifying the file name ending with the matching file extension, `".csv"`, `".sav"`, `".dta"` or `".xlsx"`.
`stata_version`	Numeric, specifying STATA version number.
`...`	Additional parameters to be passed on to the specified write function, see `write_csv` for `"CSV"`, `write_sav` for `"SPSS"`, `write_dta` for `"STATA"` or `write_xlsx` for `"Excel"` for more information.

Value

A csv file containing a wide data set with one row per sudden gain/loss.

References

Examples

# Adjust "path" argument before running
# Create character string name for temporary "bysg.csv" file
temp <- tempfile(pattern = "bysg", fileext = ".csv")

# Write bysg dataset (CSV file)
# To write a different format change the 'format' argument
# as well as the file extension in the 'path' argument
write_bysg(data = sgdata,
           sg_crit1_cutoff = 7,
           id_var_name = "id",
           tx_start_var_name = "bdi_s1",
           tx_end_var_name = "bdi_s12",
           sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3", "bdi_s4",
                           "bdi_s5", "bdi_s6", "bdi_s7", "bdi_s8",
                           "bdi_s9", "bdi_s10", "bdi_s11", "bdi_s12"),
           sg_measure_name = "bdi",
           identify_sg_1to2 = FALSE,
           format = "CSV",
           path = temp)
# Adjust "path" argument before running
# Create character string name for temporary "bysg.csv" file
temp <- tempfile(pattern = "bysg", fileext = ".csv")

# Write bysg dataset (CSV file)
# To write a different format change the 'format' argument
# as well as the file extension in the 'path' argument
write_bysg(data = sgdata,
           sg_crit1_cutoff = 7,
           id_var_name = "id",
           tx_start_var_name = "bdi_s1",
           tx_end_var_name = "bdi_s12",
           sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3", "bdi_s4",
                           "bdi_s5", "bdi_s6", "bdi_s7", "bdi_s8",
                           "bdi_s9", "bdi_s10", "bdi_s11", "bdi_s12"),
           sg_measure_name = "bdi",
           identify_sg_1to2 = FALSE,
           format = "CSV",
           path = temp)

Package 'suddengains'

Help Index

Check if a given interval is a sudden gain/loss

Description

Usage

Arguments

Value

Examples

Count number of between-session intervals available to identify sudden gains

Description

Usage

Arguments

Value

Examples

Create a data set with one gain per person

Description

Usage

Arguments

Value

References

Examples

Create a data set with one row for each sudden gain/loss

Description

Usage

Arguments

Value

References

Examples

Define cut-off value for first SG criterion

Description

Usage

Arguments

Value

References

Examples

Show descriptives for the sudden gains datasets

Description

Usage

Arguments

Value

Examples

Extract values around the sudden gain

Description

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Identify sudden gains.

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Identify sudden losses.

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Plot average change in variables around the sudden gain

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Plot summary of available data per time point and analysed session to session intervals

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Plot individual trajectories

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Rename variable names to a generic and consistent format