Package 'umx'

Description

Print function for "money" objects, e.g. fin_interest().

Usage

bucks(
  x,
  symbol = "$",
  big.mark = ",",
  decimal.mark = ".",
  trim = TRUE,
  largest_with_cents = 1e+05,
  negative_parens = c("hyphen", "minus", "parens"),
  ...
)

Arguments

Value

• invisible

See Also

• fin_percent(), fin_interest(), scales::dollar()

Examples

bucks(100 * 1.05^32)
fin_interest(deposits = 20e3, interest = 0.07, yrs = 20)

Description

A helper to convert degrees (360 in a circle) to Rad ($2\pi$ in a circle).

note: R's trig functions, e.g. sin() use Radians for input!

The formula is radians = $deg x 180/\pi$.

• 180 Degrees is equal to $\pi$ radians.

• 1 Rad = $180/\pi$ degrees = ~ 57.296 degrees.

Usage

deg2rad(deg)

Arguments

Value

• value in radians

References

https://en.wikipedia.org/wiki/Radian

See Also

• rad2deg(), sin()

Other Miscellaneous Functions: fin_JustifiedPE(), fin_NI(), fin_interest(), fin_percent(), fin_ticker(), fin_valuation(), rad2deg(), umxBrownie()

Examples

deg2rad(180) == pi # TRUE!

Description

Download a file from Dropbox, given either the url, or the name and key

Usage

dl_from_dropbox(x, key = NULL)

Arguments

Details

Improvements would include error handling...

Value

None

References

- https://thebiobucket.blogspot.kr/2013/04/download-files-from-dropbox.html

See Also

Other File Functions: umx, umx_file_load_pseudo(), umx_make_sql_from_excel(), umx_move_file(), umx_open(), umx_rename_file(), umx_write_to_clipboard()

Examples

## Not run: 
dl_from_dropbox("https://dl.dropboxusercontent.com/s/7kauod48r9cfhwc/tinytwinData.rda")
dl_from_dropbox("tinytwinData.rda", key = "7kauod48r9cfhwc")

## End(Not run)

Description

A dataset containing indicators for two traits varA and varB, each measured in MZ and DZ twins.

Usage

data(docData)

Format

A data frame 6 manifests for each of two twins in 1400 families of MZ and DZ twins

Details

It is designed to show off umxDoC() testing the hypothesis varA causes varB, varB causes varA, both cause each other.

• zygosity "MZFF", "DZFF", "MZMM", or "DZMM"

• varA1_T1 Twin one's manifest 1 for varA

• varA2_T1 Twin one's manifest 2 for varA

• varA3_T1 Twin one's manifest 3 for varA

• varB1_T1 Twin one's manifest 1 for varB

• varB2_T1 Twin one's manifest 2 for varB

• varB3_T1 Twin one's manifest 3 for varB

• varA1_T2 Twin two's manifest 1 for varA

• varA2_T2 Twin two's manifest 2 for varA

• varA3_T2 Twin two's manifest 3 for varA

• varB1_T2 Twin two's manifest 1 for varB

• varB2_T2 Twin two's manifest 2 for varB

• varB3_T2 Twin two's manifest 3 for varB

References

• N.A. Gillespie and N.G. Martin (2005). Direction of Causation Models. In Encyclopedia of Statistics in Behavioral Science, 1, 496–499. Eds. Brian S. Everitt & David C. Howell

See Also

• umxDoC(), plot.MxModelDoC(), umxSummary.MxModelDoC(), umxModify()

Other datasets: Fischbein_wt, GFF, iqdat, umx, us_skinfold_data

Examples

data(docData)
str(docData)
mzData = subset(docData, zygosity %in% c("MZFF", "MZMM"))
dzData = subset(docData, zygosity %in% c("DZFF", "DZMM"))
par(mfrow = c(1, 2))  # 1 rows and 3 columns
plot(varA1_T2 ~varA1_T1, ylim = c(-4, 4), data = mzData, main="MZ")
tmp = round(cor.test(~varA1_T1 + varA1_T2, data = mzData)$estimate, 2)
text(x=-4, y=3, labels = paste0("r = ", tmp))
plot(varA1_T2 ~varA1_T1, ylim = c(-4, 4), data = dzData, main="DZ")
tmp = round(cor.test(~varA1_T1 + varA1_T2, data = dzData)$estimate, 2)
text(x=-4, y=3, labels = paste0("r = ", tmp))
par(mfrow = c(1, 1))  # back to as it was

Description

Returns the AIC for an OpenMx model. Original Author: Brandmaier

Usage

## S3 method for class 'MxModel'
extractAIC(fit, scale, k, ...)

Arguments

Value

• AIC value

See Also

• AIC(), umxCompare(), logLik()

Other Reporting functions: RMSEA(), RMSEA.MxModel(), RMSEA.summary.mxmodel(), loadings(), loadings.MxModel(), residuals.MxModel(), tmx_show(), tmx_show.MxMatrix(), umxCI(), umxCI_boot(), umxConfint(), umxExpCov(), umxExpMeans(), umxFitIndices(), umxRotate()

Examples

## Not run: 
require(umx)
data(demoOneFactor)
manifests = names(demoOneFactor)
m1 = umxRAM("One Factor", data = demoOneFactor, type = "cov",
	umxPath("G", to = manifests),
	umxPath(var = manifests),
	umxPath(var = "G", fixedAt = 1)
)
extractAIC(m1)
# -2.615998
AIC(m1)

## End(Not run)

Description

Allows you to determine the final value of an initial principal (with optional periodic deposits), over a number of years (yrs) at a given rate of interest. Principal and deposits are optional. You control compounding periods each year (n) and whether deposits occur at the beginning or end of the year. The function outputs a nice table of annual returns, formats the total using a user-settable currency symbol. Can also report using a web table.

notes: Graham valuation: fair P/E = 9 + (1.5 * growth%). e.g. $INTEL fair P/E = 9+.53 = 10.5 up to 9+210 = 29 Can move the weighting between a conservative .5 and an optimistic 2 (in terms of how long the growth will last and how low the hurdle rate is)

Usage

fin_interest(
  principal = 100,
  deposits = 0,
  inflate = 0,
  interest = 0.05,
  yrs = 10,
  final = NULL,
  n = 12,
  when = "beginning",
  symbol = NULL,
  largest_with_cents = 0,
  baseYear = as.numeric(format(Sys.time(), "%Y")),
  table = TRUE,
  report = c("markdown", "html")
)

Arguments

Value

• Value of balance after yrs of investment.

References

• https://en.wikipedia.org/wiki/Compound_interest

See Also

• umx_set_dollar_symbol(), fin_percent(), fin_NI(), fin_valuation()

Other Miscellaneous Functions: deg2rad(), fin_JustifiedPE(), fin_NI(), fin_percent(), fin_ticker(), fin_valuation(), rad2deg(), umxBrownie()

Examples

## Not run: 
# 1. Value of a principal after yrs years at 5% return, compounding monthly.
# Report in browser as a nice table of annual returns and formatted totals.
fin_interest(principal = 5000, interest = 0.05, rep= "html")

## End(Not run)

# Report as a nice markdown table
fin_interest(principal = 5000, interest = 0.05, yrs = 10)

umx_set_dollar_symbol("£")
# 2 What rate is needed to increase principal to final value in yrs time?
fin_interest(final = 1.4, yrs=5)
fin_interest(principal = 50, final=200, yrs = 5)

# 3. What's the value of deposits of $100/yr after 10 years at 7% return?
fin_interest(deposits = 100, interest = 0.07, yrs = 10, n = 12)

# 4. What's the value of $20k + $100/yr over 10 years at 7% return?
fin_interest(principal= 20e3, deposits= 100, interest= .07, yrs= 10, symbol="$")

# 5. What is $10,000 invested at the end of each year for 5 years at 6%?
fin_interest(deposits = 10e3, interest = 0.06, yrs = 5, n=1, when= "end")

# 6. What will $20k be worth after 10 years at 15% annually (n=1)?
fin_interest(deposits=20e3, interest = 0.15, yrs = 10, n=1, baseYear=1)
# $466,986

# manual equivalent
sum(20e3*(1.15^(10:1))) # 466985.5

# 7. Annual (rather than monthly) compounding (n=1)
fin_interest(deposits = 100, interest = 0.07, yrs = 10, n=1)

# 8 Interest needed to increase principal to final value in yrs time.
fin_interest(principal = 100, final=200, yrs = 5)

Description

Compute the Justified P/E of a stock. Justified P/E = ( (DPS / EPS) * (1 + g)) / (k – g) DPS is the dividend per share, EPS is the earnings per share, g is the sustainable growth rate, and k is the required rate of return.

Usage

fin_JustifiedPE(
  Dividend = 0.02,
  EPS = 1,
  growthRate = 0.08,
  discountRate = 0.12,
  basePE = 20,
  yrs = 10
)

Arguments

Value

• A PE that is justified for this stock.

See Also

• fin_interest(), fin_percent(), fin_NI()

Other Miscellaneous Functions: deg2rad(), fin_NI(), fin_interest(), fin_percent(), fin_ticker(), fin_valuation(), rad2deg(), umxBrownie()

Examples

# fin_JustifiedPE(Dividend= .8, EPS = 2, growthRate = .06, discountRate = .1)

Description

Employees pay contributions at 12%% on annual earnings between £9,568 and £50,270. Above that you pay at 2%%. Employers pay at 13.8%% on all annual earnings of more than £8,840, although there are different thresholds for those under the age of 21 and for apprentices under the age of 25.

Usage

fin_NI(annualEarnings, symbol = "£")

Arguments

Value

• NI

References

• https://www.telegraph.co.uk/tax/tax-hacks/politicians-running-scared-long-overdue-national-insurance-overhaul/

See Also

• fin_interest(), fin_percent(), fin_valuation()

Other Miscellaneous Functions: deg2rad(), fin_JustifiedPE(), fin_interest(), fin_percent(), fin_ticker(), fin_valuation(), rad2deg(), umxBrownie()

Examples

fin_NI(42e3)
fin_NI(142000)

Description

Determine the percent change needed to "undo" an initial percent change. Has a plot function as well. If an amount of $100 has 20% added, what percent do we need to drop it by to return to the original value?

fin_percent(20) yields $100 increased by 20% = $120 (Percent to reverse = -17%)

Usage

fin_percent(
  percent,
  value = 100,
  symbol = "$",
  digits = 2,
  plot = TRUE,
  logY = TRUE
)

Arguments

Value

• new value and change required to return to baseline.

See Also

• fin_interest()

Other Miscellaneous Functions: deg2rad(), fin_JustifiedPE(), fin_NI(), fin_interest(), fin_ticker(), fin_valuation(), rad2deg(), umxBrownie()

Examples

# Percent needed to return to original value after 10% taken off
fin_percent(-10)

# Percent needed to return to original value after 10% added on
fin_percent(10)

# Percent needed to return to original value after 50% off 34.50
fin_percent(-50, value = 34.5)

Description

Open a stock ticker, currently in yahoo finance

Usage

fin_ticker(ticker = "INTC")

Arguments

Value

• Open a ticker in a finance site online

See Also

• fin_interest(), fin_percent(), fin_NI()

Other Miscellaneous Functions: deg2rad(), fin_JustifiedPE(), fin_NI(), fin_interest(), fin_percent(), fin_valuation(), rad2deg(), umxBrownie()

Examples

# Open $INTC in yahoo finance.
## Not run: 
fin_ticker("INTC")

## End(Not run)

Description

fin_valuation uses the revenue, operating margin, expenses and PE to compute a market capitalization. Better to use a more powerful online site.

Usage

fin_valuation(
  revenue = 6e+06 * 30000,
  opmargin = 0.08,
  expenses = 0.2,
  PE = 30,
  symbol = "$",
  use = c("B", "M")
)

Arguments

Details

Revenue is multiplied by opmargin to get a gross profit. From this the proportion specified in expenses is subtracted and the resulting earnings turned into a price via the PE

Value

• value

See Also

• fin_interest(), fin_NI(), fin_percent()

Other Miscellaneous Functions: deg2rad(), fin_JustifiedPE(), fin_NI(), fin_interest(), fin_percent(), fin_ticker(), rad2deg(), umxBrownie()

Examples

fin_valuation(rev=7e9, opmargin=.1, PE=33)
# Market cap =  $18,480,000,000
# (Based on PE= 33, operating Income of $0.70 B, and net income =$0.56B

Description

A dataframe containing correlations of weight for 66 females measured 6 times at 6-month intervals.

Usage

data(Fischbein_wt)

Format

A 6*6 correlation matrix based on n = 66 female subjects.

Details

• Weight1: Weight at time 1 (t0)

• Weight2: Weight at time 2 (t0 + 6 months)

• Weight3: Weight at time 3 (t0 + 12 months)

• Weight4: Weight at time 4 (t0 + 18 months)

• Weight5: Weight at time 5 (t0 + 24 months)

• Weight6: Weight at time 6 (t0 + 32 months)

Created as follows:

Fischbein_wt = umx_read_lower(file = "", diag = TRUE, names = paste0("Weight", 1:6), ensurePD= TRUE)
1.000
0.985	1.000
0.968	0.981	1.000
0.957	0.970	0.985	1.000
0.932	0.940	0.964	0.975	1.000
0.890	0.897	0.927	0.949	0.973	1.000

References

Fischbein, S. (1977). Intra-pair similarity in physical growth of monozygotic and of dizygotic twins during puberty. Annals of Human Biology, 4. 417-430. doi:10.1080/03014467700002401

See Also

Other datasets: GFF, docData, iqdat, umx, us_skinfold_data

Examples

## Not run: 
data(Fischbein_wt) # load the data
str(Fischbein_wt) # data.frame
as.matrix(Fischbein_wt) # convert to matrix

## End(Not run)

Description

FishersMethod implements R.A. Fisher's (1925) method for creating a meta-analytic p-value by combining a set of p-values from tests of the same hypothesis in independent samples. See also Stouffer's method for combining Z scores, which allows weighting.

Usage

FishersMethod(pvalues, ...)

Arguments

Value

• A meta-analytic p-value

References

• Fisher, R.A. (1925). Statistical Methods for Research Workers. Oliver and Boyd (Edinburgh). ISBN 0-05-002170-2.

• Fisher, R. A (1948). "Questions and answers #14". The American Statistician. 2: 30–31. doi:10.2307/2681650.

• Stouffer, S. A. and Suchman, E. A. and DeVinney, L. C. and Star, S. A. and Williams, R. M. Jr. (1949) The American Soldier, Vol. 1 - Adjustment during Army Life. Princeton, Princeton University Press.

See Also

Other Miscellaneous Stats Functions: SE_from_p(), geometric_mean(), harmonic_mean(), oddsratio(), reliability(), umx, umxCov2cor(), umxHetCor(), umxParan(), umxWeightedAIC(), umx_apply(), umx_cor(), umx_means(), umx_r_test(), umx_round(), umx_scale(), umx_var()

Examples

FishersMethod(c(.041, .378))

Description

The Geometric mean is the nth-root of the product of n input values. Common uses include computing economic utility. For example, the geometric mean utility of c(1, 2, 10) is

$(1 * 2 * 10)^\frac{1}{3}$

= 2.7 not 4.3 (the arithmetic mean of utility).

Usage

geometric_mean(x, na.rm = c(TRUE, FALSE))

Arguments

Value

• Geometric mean of x

References

• https://en.wikipedia.org/wiki/Geometric_mean

See Also

• harmonic_mean(), mean()

Other Miscellaneous Stats Functions: FishersMethod(), SE_from_p(), harmonic_mean(), oddsratio(), reliability(), umx, umxCov2cor(), umxHetCor(), umxParan(), umxWeightedAIC(), umx_apply(), umx_cor(), umx_means(), umx_r_test(), umx_round(), umx_scale(), umx_var()

Examples

geometric_mean(c(50, 100))

# For a given sum, geometric mean is maximised when all values are equal:
geometric_mean(c(75,75))

v = c(1, 149); c(sum(v), geometric_mean(v), mean(v), median(v))
# 150.00000  12.20656  75.00000  75.00000

# Underlying logic
sqrt(50 * 100)

# Alternate form using logs
exp(mean(log(c(50 *100))))

# Reciprocal duality
1/geometric_mean(c(100, 50))
geometric_mean(c(1/100, 1/50))

Description

Measures of family functioning, happiness and related variables in twins, and their brothers and sisters. (see details)

Usage

data(GFF)

Format

A data frame with 1000 rows of twin-family data columns.

Details

Several scales in the data are described in van der Aa et al. (2010). General Family Functioning (GFF) refers to adolescents' evaluations general family health vs. pathology. It assesses problem solving, communication, roles within the household, affection, and control. GFF was assessed with a Dutch translation of the General Functioning sub-scale of the McMaster Family Assessment Device (FAD) (Epstein et al., 1983).

Family Conflict (FC) refers to adolescents' evaluations of the amount of openly expressed anger, aggression, and conflict among family members. Conflict sub-scale of the Family Environment Scale (FES) (Moos, 1974)

Quality of life in general (QLg) was assessed with the 10-step Cantril Ladder from best- to worst-possible life (Cantril, 1965).

• zyg_6grp: Six-level zygosity: MZMM, DZMM, MZFF, DZFF, DZMF, DZFM

• zyg_2grp: Two-level zygosity measure: 'MZ', 'DZ'

• divorce: Parental divorce status: 0 = No, 1 = Yes

• sex_T1: Sex of twin 1: 0 = "male", 1 = "female"

• age_T1: Age of twin 1 (years)

• gff_T1: General family functioning for twin 1

• fc_T1: Family conflict sub-scale of the FES

• qol_T1: Quality of life for twin 1

• hap_T1: General happiness for twin 1

• sat_T1: Satisfaction with life for twin 1

• AD_T1: Anxiety and Depression for twin 1

• SOMA_T1: Somatic complaints for twin 1

• SOC_T1: Social problems for twin 1

• THOU_T1: Thought disorder problems for twin 1

• sex_T2: Sex of twin 2

• age_T2: Age of twin 2

• gff_T2: General family functioning for twin 2

• fc_T2: Family conflict sub-scale of the FES

• qol_T2: Quality of life for twin 2

• hap_T2: General happiness for twin 2

• sat_T2: Satisfaction with life for twin 2

• AD_T2: Anxiety and Depression for twin 2

• SOMA_T2: Somatic complaints for twin 2

• SOC_T2: Social problems for twin 2

• THOU_T2: Thought disorder problems for twin 2

• sex_Ta: Sex of sib 1

• age_Ta: Age of sib 1

• gff_Ta: General family functioning for sib 1

• fc_Ta: Family conflict sub-scale of the FES

• qol_Ta: Quality of life for sib 1

• hap_Ta: General happiness for sib 1

• sat_Ta: Satisfaction with life for sib 1

• AD_Ta: Anxiety and Depression for sib 1

• SOMA_Ta: Somatic complaints for sib 1

• SOC_Ta: Social problems for sib 1

• THOU_Ta: Thought disorder problems for sib 1

• sex_Ts: Sex of sib 2

• age_Ts: Age of sib 2

• gff_Ts: General family functioning for sib 2

• fc_Ts: Family conflict sub-scale of the FES

• qol_Ts: Quality of life for sib 2

• hap_Ts: General happiness for sib 2

• sat_Ts: Satisfaction with life for sib 2

• AD_Ts: Anxiety and Depression for sib 2

• SOMA_Ts: Somatic complaints for sib 2

• SOC_Ts: Social problems for sib 2

• THOU_Ts: Thought disorder problems for sib 2

References

van der Aa, N., Boomsma, D. I., Rebollo-Mesa, I., Hudziak, J. J., & Bartels, M. (2010). Moderation of genetic factors by parental divorce in adolescents' evaluations of family functioning and subjective wellbeing. Twin Research and Human Genetics, 13, 143-162. doi:10.1375/twin.13.2.143

See Also

Other datasets: Fischbein_wt, docData, iqdat, umx, us_skinfold_data

Examples

## Not run: 
# Twin 1 variables (end in '_T1')
data(GFF)
umx_names(GFF, "1$") # Just variables ending in 1 (twin 1)
str(GFF) # first few rows


m1 = umxACE(selDVs= "gff", sep = "_T",
	mzData = subset(GFF, zyg_2grp == "MZ"), 
	dzData = subset(GFF, zyg_2grp == "DZ")
)

## End(Not run)

Description

Add a fit statistic to a ggplot

Usage

ggAddR(model, effect = NA, xloc = 8, yloc = 10)

Arguments

Value

• plot

See Also

• umxPlot(), umxPlotFun()

Other Plotting functions: plot.MxLISRELModel(), plot.MxModel(), plot.MxModelTwinMaker(), umx, umxPlot(), umxPlotACE(), umxPlotACEcov(), umxPlotACEv(), umxPlotCP(), umxPlotDoC(), umxPlotFun(), umxPlotGxE(), umxPlotGxEbiv(), umxPlotIP(), umxPlotSexLim(), umxPlotSimplex()

Examples

## Not run: 
m1 = lm(mpg ~ wt, data = mtcars)
p = ggplot2::ggplot(data = mtcars, aes(x = wt, y = mpg))+ geom_point() +geom_smooth()+
ggAddR(m1, effect = NA, xloc=2, yloc= 10); p

## End(Not run)

Description

The harmonic mean is the reciprocal of the arithmetic mean of the reciprocals of the input values. Common uses include computing the mean of ratios, for instance the average P/E ratio in a portfolio. Also it is the correct mean for averaging speeds weighted for distance.

Usage

harmonic_mean(x, weights = NULL, na.rm = c(TRUE, FALSE))

Arguments

Value

• Harmonic mean of x

References

• https://en.wikipedia.org/wiki/Harmonic_mean

See Also

• geometric_mean(), aggregate()

Other Miscellaneous Stats Functions: FishersMethod(), SE_from_p(), geometric_mean(), oddsratio(), reliability(), umx, umxCov2cor(), umxHetCor(), umxParan(), umxWeightedAIC(), umx_apply(), umx_cor(), umx_means(), umx_r_test(), umx_round(), umx_scale(), umx_var()

Examples

# Harmonic means are suitable for ratios
tmp = c(33/1, 23/1)
harmonic_mean(tmp)

geometric_mean(tmp)
mean(tmp)

# Example with weights
harmonic_mean(c(33/1, 23/1), weights= c(.2, .8))
# If Jack travels outbound at 1 mph, and returns at 10 miles an hour, what is his average speed?
harmonic_mean(c(1,10)) # 1.81 mph

Description

You can install OpenMx, including the latest NPSOL-enabled build of OpenMx. Options are:

1. "NPSOL": Install from our repository (default): This is where we maintain binaries supporting parallel processing and NPSOL.

2. "travis": Install the latest travis built (MacOS only).

3. "CRAN": Install from CRAN.

4. "open travis build page": Open the list of travis builds in a browser window.

Usage

install.OpenMx(
  loc = c("NPSOL", "travis", "CRAN", "open travis build page", "UVa"),
  url = NULL,
  lib,
  repos = getOption("repos")
)

Arguments

Value

None

References

• https://github.com/tbates/umx, https://tbates.github.io

See Also

umxVersion()

Other Miscellaneous Utility Functions: libs(), qm(), umx, umxLav2RAM(), umxModelNames(), umxRAM2Lav(), umxVersion(), umx_array_shift(), umx_find_object(), umx_lower.tri(), umx_msg(), umx_open_CRAN_page(), umx_pad(), umx_print()

Examples

## Not run: 
install.OpenMx() # gets the NPSOL version
install.OpenMx("NPSOL") # gets the NPSOL version explicitly
install.OpenMx("CRAN") # Get the latest CRAN version
install.OpenMx("open travis build page") # Open web page of travis builds

## End(Not run)

Description

Measures of IQ across four ages in 261 pairs of identical twins and 301 pairs of fraternal (DZ) twins. (see details). It is used as data for the [umxSimplex()] examples.

Usage

data(iqdat)

Format

A data frame with 562 rows (twin families). Nine measures on each twin.

Details

• zygosity Zygosity (MZ or DZ)

• IQ_age1_T1 T1 IQ measured at age 1

• IQ_age2_T1 T1 IQ measured at age 2

• IQ_age3_T1 T1 IQ measured at age 3

• IQ_age4_T1 T1 IQ measured at age 4

• IQ_age1_T2 T2 IQ measured at age 1

• IQ_age2_T2 T2 IQ measured at age 2

• IQ_age3_T2 T2 IQ measured at age 3

• IQ_age4_T2 T2 IQ measured at age 4

References

Boomsma, D. I., Martin, N. G., & Molenaar, P. C. (1989). Factor and simplex models for repeated measures: application to two psychomotor measures of alcohol sensitivity in twins. *Behavior Genetics*, **19**, 79-96. Retrieved from <https://www.ncbi.nlm.nih.gov/pubmed/2712815>

See Also

[umxSimplex()]

Other datasets: Fischbein_wt, GFF, docData, umx, us_skinfold_data

Examples

## Not run: 
data(iqdat)
str(iqdat)
par(mfrow = c(1, 3))  # 1 rows and 3 columns
plot(IQ_age4_T1 ~ IQ_age4_T2, ylim = c(50, 150), data = subset(iqdat, zygosity == "MZ"))
plot(IQ_age4_T1 ~ IQ_age4_T2, ylim = c(50, 150), data = subset(iqdat, zygosity == "DZ"))
plot(IQ_age1_T1 ~ IQ_age4_T2, data = subset(iqdat, zygosity == "MZ"))
par(mfrow = c(1, 1))  # back to as it was

## End(Not run)

Description

libs allows loading multiple libraries in one call

Usage

libs(..., force.update = FALSE)

Arguments

Value

• nothing.

See Also

• library(), install.packages(), remove.packages()

Other Miscellaneous Utility Functions: install.OpenMx(), qm(), umx, umxLav2RAM(), umxModelNames(), umxRAM2Lav(), umxVersion(), umx_array_shift(), umx_find_object(), umx_lower.tri(), umx_msg(), umx_open_CRAN_page(), umx_pad(), umx_print()

Examples

## Not run: 
libs("umx", "OpenMx", "car")
libs("umx", c("OpenMx", "car"))
remove.packages()

## End(Not run)

Description

See loadings.MxModel to access the loadings of OpenMx EFA models.

Usage

loadings(x, ...)

Arguments

Details

Base loadings handles factanal() objects.

Value

• matrix of loadings

References

• https://tbates.github.io, https://github.com/tbates/umx

See Also

Other Reporting functions: RMSEA(), RMSEA.MxModel(), RMSEA.summary.mxmodel(), extractAIC.MxModel(), loadings.MxModel(), residuals.MxModel(), tmx_show(), tmx_show.MxMatrix(), umxCI(), umxCI_boot(), umxConfint(), umxExpCov(), umxExpMeans(), umxFitIndices(), umxRotate()

Description

loadings extracts the factor loadings from an EFA (factor analysis) model. It behaves equivalently to stats::loadings, returning the loadings from an EFA (factor analysis). However it does not store the rotation matrix.

Usage

## S3 method for class 'MxModel'
loadings(x, ...)

Arguments

Value

• loadings matrix

References

• https://github.com/tbates/umx, https://tbates.github.io

See Also

• factanal(), loadings()

Other Reporting functions: RMSEA(), RMSEA.MxModel(), RMSEA.summary.mxmodel(), extractAIC.MxModel(), loadings(), residuals.MxModel(), tmx_show(), tmx_show.MxMatrix(), umxCI(), umxCI_boot(), umxConfint(), umxExpCov(), umxExpMeans(), umxFitIndices(), umxRotate()

Examples

## Not run: 
myVars = c("mpg", "disp", "hp", "wt", "qsec")
m1 = umxEFA(name = "test", factors = 2, data = mtcars[, myVars])
loadings(m1)

## End(Not run)

Description

Succinctly select complete rows from a dataframe.

Usage

noNAs(df, rows = NULL, cols = NULL, drop = TRUE)

Arguments

Value

• Complete rows and (optionally) selected columns

See Also

Other Data Functions: prolific_anonymize(), prolific_check_ID(), prolific_read_demog(), umx, umxFactor(), umxHetCor(), umx_as_numeric(), umx_cont_2_quantiles(), umx_lower2full(), umx_make_MR_data(), umx_make_TwinData(), umx_make_fake_data(), umx_make_raw_from_cov(), umx_merge_randomized_columns(), umx_polychoric(), umx_polypairwise(), umx_polytriowise(), umx_read_lower(), umx_rename(), umx_reorder(), umx_score_scale(), umx_select_valid(), umx_stack(), umx_strings2numeric()

Examples

tmp = mtcars
tmp[2,1] = NA
noNAs(tmp, cols="mpg")
noNAs(tmp, cols="mpg", drop = FALSE)
noNAs(tmp) # no Mazda RX4 Wag

Description

Returns the odds in each group, and the odds ratio. Takes the cases (n) and total N as a list of two numbers for each of two groups.

Usage

oddsratio(grp1 = c(n = 3, N = 10), grp2 = c(n = 1, N = 10), alpha = 0.05)

Arguments

Details

Returns a list of odds1, odds2, and OR + CI. Has a pretty-printing method so displays as:

Group 1 odds = 0.43
Group 2 odds = 0.11
          OR = 3.86 CI95[0.160, 3.64]

Value

• List of odds in group 1 and group2, and the resulting OR and CI

References

• https://stats.oarc.ucla.edu/r/dae/logit-regression/, https://tbates.github.io

See Also

• umx_r_test()

Other Miscellaneous Stats Functions: FishersMethod(), SE_from_p(), geometric_mean(), harmonic_mean(), reliability(), umx, umxCov2cor(), umxHetCor(), umxParan(), umxWeightedAIC(), umx_apply(), umx_cor(), umx_means(), umx_r_test(), umx_round(), umx_scale(), umx_var()

Examples

oddsratio(grp1 = c(1, 10), grp2 = c(3, 10))
oddsratio(grp1 = 0.111, grp2 = 0.429)
oddsratio(grp1 = c(3, 10), grp2 = c(1, 10))
oddsratio(grp1 = c(3, 10), grp2 = c(1, 10), alpha = .01)

Description

plot.MxLISRELModel produces SEM diagrams using [DiagrammeR::DiagrammeR() to create the image.

Usage

## S3 method for class 'MxLISRELModel'
plot(
  x = NA,
  std = FALSE,
  fixed = TRUE,
  means = TRUE,
  digits = 2,
  file = "name",
  labels = c("none", "labels", "both"),
  resid = c("circle", "line", "none"),
  strip_zero = TRUE,
  ...
)

Arguments

Details

Note: By default, plots open in your browser (or plot pane if using RStudio).

Opening in an external editor/app

The underlying format is graphviz. If you use umx_set_plot_format("graphviz"), figures will open in a graphviz helper app (if installed). If you use graphviz, we try and use that app, but YOU HAVE TO INSTALL IT!

On MacOS, you may need to associate the ‘.gv’ extension with your graphviz app. Find the .gv file made by plot, get info (cmd-I), then choose “open with”, select graphviz.app (or OmniGraffle professional), then set “change all”.

The commercial application “OmniGraffle” is great for editing these images.

References

• https://github.com/tbates/umx, https://en.wikipedia.org/wiki/DOT_(graph_description_language)

See Also

• umx_set_plot_format(), umx_set_auto_plot(), umx_set_plot_format(), plot.MxModel(), umxPlotACE(), umxPlotCP(), umxPlotIP(), umxPlotGxE()

Other Plotting functions: ggAddR(), plot.MxModel(), plot.MxModelTwinMaker(), umx, umxPlot(), umxPlotACE(), umxPlotACEcov(), umxPlotACEv(), umxPlotCP(), umxPlotDoC(), umxPlotFun(), umxPlotGxE(), umxPlotGxEbiv(), umxPlotIP(), umxPlotSexLim(), umxPlotSimplex()

Examples

# plot()
# TODO get LISREL example model
# Figure out how to map its matrices to plot. Don't do without establishing demand.

Description

plot() produces SEM diagrams in graphviz format, and relies on DiagrammeR::DiagrammeR() to create the image.

Usage

## S3 method for class 'MxModel'
plot(
  x = NA,
  std = FALSE,
  fixed = TRUE,
  means = TRUE,
  digits = 2,
  file = "name",
  labels = c("none", "labels", "both"),
  resid = c("circle", "line", "none"),
  strip_zero = FALSE,
  splines = c("TRUE", "FALSE", "compound", "ortho", "polyline"),
  min = NULL,
  same = NULL,
  max = NULL,
  ...
)

Arguments

Details

Note: DiagrammeR::DiagrammeR() is supported out of the box. By default, plots open in your browser. Other options include pdf SVG etc.

If you use umx_set_plot_format("graphviz"), graphs will open in a graphviz helper app (if installed).

The commercial application “OmniGraffle” is great for editing these images. On unix and windows, plot() will create a pdf and open it in your default pdf reader.

If you use graphviz, we try and use that app, but YOU HAVE TO INSTALL IT!

MacOS note: On Mac, we will try and open an app: you may need to associate the ‘.gv’ extension with the graphviz app. Find the .gv file made by plot, get info (cmd-I), then choose “open with”, select graphviz.app (or OmniGraffle professional), then set “change all”.

References

• https://github.com/tbates/umx, https://en.wikipedia.org/wiki/DOT_(graph_description_language)

See Also

• umx_set_plot_format(), plot.MxModel(), umxPlotACE(), umxPlotCP(), umxPlotIP(), umxPlotGxE()

Other Plotting functions: ggAddR(), plot.MxLISRELModel(), plot.MxModelTwinMaker(), umx, umxPlot(), umxPlotACE(), umxPlotACEcov(), umxPlotACEv(), umxPlotCP(), umxPlotDoC(), umxPlotFun(), umxPlotGxE(), umxPlotGxEbiv(), umxPlotIP(), umxPlotSexLim(), umxPlotSimplex()

Examples

## Not run: 
require(umx)
data(demoOneFactor)
manifests = names(demoOneFactor)
m1 = umxRAM("One Factor", data = demoOneFactor, type = "cov",
	umxPath("G", to = manifests),
	umxPath(var = manifests),
	umxPath(var = "G", fixedAt = 1)
)
plot(m1)
plot(m1, std = TRUE, resid = "line", digits = 3, strip_zero = FALSE)

# ============================================================
# = With a growth model, demonstrate splines= false to force =
# = straight lines, and move "rank" of intercept object      =
# ============================================================

m1 = umxRAM("grow", data = myGrowthMixtureData,
	umxPath(var = manifests, free = TRUE), 
	umxPath(means = manifests, fixedAt = 0), 
	umxPath(v.m. = c("int","slope")),
	umxPath("int", with = "slope"),
	umxPath("int", to = manifests, fixedAt = 1), 
	umxPath("slope", to = manifests, arrows = 1, fixedAt = c(0,1,2,3,4))
)

plot(m1, means=FALSE, strip=TRUE, splines="FALSE", max="int")

## End(Not run) # end dontrun

Description

Assumes the model has a group called "MZ" inside.

Usage

## S3 method for class 'MxModelTwinMaker'
plot(
  x = NA,
  std = FALSE,
  fixed = TRUE,
  means = TRUE,
  oneTwin = TRUE,
  sep = "_T",
  digits = 2,
  file = "name",
  labels = c("none", "labels", "both"),
  resid = c("circle", "line", "none"),
  strip_zero = FALSE,
  splines = TRUE,
  min = NULL,
  same = NULL,
  max = NULL,
  ...
)

Arguments

Details

If you use umx_set_plot_format("graphviz"), they will open in a graphviz helper app (if installed). The commercial application “OmniGraffle” is great for editing these images. On unix and windows, plot() will create a pdf and open it in your default pdf reader.

See Also

• umx_set_plot_format(), plot.MxModel(), umxPlotACE(), umxPlotCP(), umxPlotIP(), umxPlotGxE()

Other Plotting functions: ggAddR(), plot.MxLISRELModel(), plot.MxModel(), umx, umxPlot(), umxPlotACE(), umxPlotACEcov(), umxPlotACEv(), umxPlotCP(), umxPlotDoC(), umxPlotFun(), umxPlotGxE(), umxPlotGxEbiv(), umxPlotIP(), umxPlotSexLim(), umxPlotSimplex()

Examples

## Not run: 
require(umx)
# 
# =====================
# = Make an ACE model =
# =====================
# 1. Clean data: Add separator and scale
data(twinData)
tmp = umx_make_twin_data_nice(data=twinData, sep="", zygosity="zygosity", numbering=1:2)
tmp = umx_scale_wide_twin_data(varsToScale= c("wt", "ht"), sep= "_T", data= tmp)
mzData = subset(tmp, zygosity %in%  c("MZFF", "MZMM"))
dzData = subset(tmp, zygosity %in%  c("DZFF", "DZMM"))

# 2. Define paths: You only need the paths for one person:
paths = c(
umxPath(v1m0 = c("a1", 'c1', "e1")),
umxPath(means = c("wt")),
umxPath(c("a1", 'c1', "e1"), to = "wt", values=.2)
)
m1 = umxTwinMaker("test", paths, mzData = mzData, dzData= dzData)
plot(m1, std= TRUE, means= FALSE)
plot(m1, means=FALSE, std=TRUE, strip=TRUE, splines="FALSE", max="intercept")

## End(Not run) # end dontrun

# =================
# = An ACEv model =
# =================
# Not complete

paths = c(
umxPath(v1m0 = c("A1", 'C1', "E1")),
umxPath(v1m0 = c("A2", 'C2', "E2")),
umxPath(v.m0 = c("l1", 'l2')),
umxPath(v.m. = c("wt", "ht")),
umxPath(c("A1", 'C1', "E1"), to = "l1", values= .2),
umxPath(c("A2", 'C2', "E2"), to = "l2", values= .2),
umxPath(c("l1", 'l2'), to = c("wt", "ht"), values= .2)
)

Description

Plot method for "percent" objects: e.g. fin_percent().

Usage

## S3 method for class 'percent'
plot(x, ...)

Arguments

Value

• invisible

See Also

• fin_percent()

Examples

# Percent needed to return to original value after 10% off
fin_percent(-10)
# Percent needed to return to original value after 10% on
tmp = fin_percent(10)
plot(tmp)

# Percent needed to return to original value after 50% off 34.50
fin_percent(-50, value = 34.5, logY = FALSE)

Description

power.ACE.test simulates a univariate ACE model. It computes power to detect dropping one or more paths (a, c, or a after dropping c), specified in ⁠drop=⁠.

The interface and functionality of this service are experimental and subject to change.

Usage

power.ACE.test(
  AA = 0.5,
  CC = 0,
  EE = NULL,
  DD = NULL,
  update = c("a", "c", "a_after_dropping_c", "d"),
  value = 0,
  n = NULL,
  MZ_DZ_ratio = 1,
  sig.level = 0.05,
  power = 0.8,
  method = c("ncp", "empirical"),
  search = FALSE,
  tryHard = c("yes", "no", "ordinal", "search"),
  digits = 2,
  optimizer = NULL,
  nSim = 4000
)

Arguments

Details

Statistical power is the proportion of studies that, over the long run, one should expect to yield a statistically significant result given certain study characteristics such as sample size (N), the expected effect size ($\beta$), and the criterion for statistical significance ($\alpha$).

(with nMZpairs= 2000 and MZ_DZ_ratio*nMZpairs DZ twins.

A typical target for power is 80%. Much as the accepted critical p-value is .05, this has emerged as a trade off, in this case of resources required for more powerful studies against the cost of missing a true effect. People interested in truth discourage running studies with low power: A study with 20 percent power will fail to detect real effects 80% of the time. But even with zero power, the Type-I error rate remains a nominal 5% (and with any researcher degrees of freedom, perhaps much more than that). Low powered research, then, fails to detect true effects, and generates support for random false theories about as often. This sounds silly, but empirical rates are often as low as 20% (Button, et al., 2013).

Illustration of $\alpha$, $\beta$, and power (1-$\beta$):

Value

OpenMx::mxPower() object

References

• Visscher, P.M., Gordon, S., Neale, M.C. (2008). Power of the classical twin design revisited: II detection of common environmental variance. Twin Res Hum Genet, 11: 48-54. doi:10.1375/twin.11.1.48.

• Button, K. S., Ioannidis, J. P., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S., and Munafo, M. R. (2013). Power failure: why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14, 365-376. doi:10.1038/nrn3475

See Also

• umxPower(), OpenMx::mxPower(), umxACE()

Other Twin Modeling Functions: umx, umxACE(), umxACEcov(), umxACEv(), umxCP(), umxDiffMZ(), umxDiscTwin(), umxDoC(), umxDoCp(), umxGxE(), umxGxE_window(), umxGxEbiv(), umxIP(), umxMRDoC(), umxReduce(), umxReduceACE(), umxReduceGxE(), umxRotate.MxModelCP(), umxSexLim(), umxSimplex(), umxSummarizeTwinData(), umxSummaryACE(), umxSummaryACEv(), umxSummaryDoC(), umxSummaryGxEbiv(), umxSummarySexLim(), umxSummarySimplex(), umxTwinMaker()

Examples

# =====================================================
# = N for .8 power to detect a^2 = .5 equal MZ and DZ =
# =====================================================
power.ACE.test(AA = .5, CC = 0, update = "a")
# Suggests n = 84 MZ and 94 DZ pairs.

## Not run: 
# ================================
# = Show power across range of N =
# ================================
power.ACE.test(AA= .5, CC= 0, update = "a", search = TRUE)

# Salutary note: You need well fitting models with correct betas in the data
# for power to be valid.
# tryHard helps ensure this, as does the default nSim= 4000 pair data.
# Power is important to get right, so I recommend using tryHard = "yes" (the default)

# =====================
# = Power to detect C =
# =====================

# 102 of each of MZ and DZ pairs for 80% power (default).
power.ACE.test(AA= .5, CC= .3, update = "c")

# ==========================================
# = Set 'a' to a fixed, but non-zero value =
# ==========================================

power.ACE.test(update= "a", value= sqrt(.2), AA= .5, CC= 0)

# ========================================
# = Drop More than one parameter (A & C) =
# ========================================
# E vs AE: the hypothesis that twins show no familial similarity.
power.ACE.test(update = "a_after_dropping_c", AA= .5, CC= .3)

# ===================================================
# = More power to detect A > 0 when more C present  =
# ===================================================

power.ACE.test(update = "a", AA= .5, CC= .0)
power.ACE.test(update = "a", AA= .5, CC= .3)

# ====================================================
# = More power to detect C > 0 when more A present?  =
# ====================================================

power.ACE.test(update = "c", AA= .0, CC= .5)
power.ACE.test(update = "c", AA= .3, CC= .5)


# ===================================
# = Power with more DZs or more MZs =
# ===================================

# Power about the same: total pairs with 2 MZs per DZ
power.ACE.test(MZ_DZ_ratio= 2/1, update= "a", AA= .3, CC= 0, method="ncp", tryHard="yes")
power.ACE.test(MZ_DZ_ratio= 1/2, update= "a", AA= .3, CC= 0, method="ncp", tryHard="yes")
power.ACE.test(update= "a", AA= .3, CC= 0, method="ncp", tryHard="yes")


# =====================================
# = Compare ncp and empirical methods =
# =====================================

power.ACE.test(update= "a", AA= .5, CC= 0, method = "ncp")
# method = "ncp": For 80% power, you need 166 MZ and 166 DZ pairs
power.ACE.test(update= "a", AA= .5, CC= 0, method= "empirical")
# method= "empirical": For 80% power, you need 154 MZ and 154 DZ pairs

# ====================
# = Show off options =
# ====================
# 1. tryHard

power.ACE.test(update = "a", AA= .5, CC= 0, tryHard= "no")

# 2. toggle optimizer

power.ACE.test(update= "a", AA= .5, CC= 0, optimizer= "SLSQP")

# 3. You can raise or lower the number of pairs used in the true model
#    by varying nSim (twin pairs in the simulated data).

power.ACE.test(update = "a", AA= .5, CC= 0, nSim= 20)


## End(Not run)

Description

Print method for the oddsratio() function.

Usage

## S3 method for class 'oddsratio'
print(x, digits = 3, ...)

Arguments

Value

• invisible oddsratio object (x).

See Also

• print(), oddsratio(),

Examples

oddsratio(grp1 = c(1, 10), grp2 = c(3, 10))
oddsratio(grp1 = c(3, 10), grp2 = c(1, 10))
oddsratio(grp1 = c(3, 10), grp2 = c(1, 10), alpha = .01)

Description

Print method for "percent" objects: e.g. fin_percent().

Usage

## S3 method for class 'percent'
print(x, ...)

Arguments

Value

• invisible

See Also

• fin_percent()

Examples

# Percent needed to return to original value after 10% off
fin_percent(-10)
# Percent needed to return to original value after 10% on
fin_percent(10)

# Percent needed to return to original value after 50% off 34.50
fin_percent(-50, value = 34.5)

Description

Print method for the reliability() function.

Usage

## S3 method for class 'reliability'
print(x, digits = 4, ...)

Arguments

Value

• invisible reliability object (x)

See Also

• print(), reliability(),

Examples

# treat vehicle aspects as items of a test
data(mtcars)
reliability(cov(mtcars))

Description

Print method for "RMSEA" objects: e.g. RMSEA().

Usage

## S3 method for class 'RMSEA'
print(x, ...)

Arguments

Value

• invisible

See Also

• RMSEA(), print()

Examples

## Not run: 
data(demoOneFactor)
manifests = names(demoOneFactor)

m1 = umxRAM("One Factor", data = demoOneFactor, type= "cov",
	umxPath("G", to = manifests),
	umxPath(var = manifests),
	umxPath(var = "G", fixedAt = 1.0)
)
tmp = summary(m1)
RMSEA(tmp)

## End(Not run)

Description

prolific.ac IDs and other columns like IP and lat/long might compromise subject anonymity when shared. prolific_anonymize replaces PIDs with a simple numeric sequence, preserving repeated measures in long data, and removing other columns. You can delete additional columns by adding them to extraColumns. It is ideal for use when sharing data to https://researchbox.org which enforces anonymization.

Usage

prolific_anonymize(
  df = NULL,
  PID = "PID",
  extraColumns = NA,
  baseOffset = 10000
)

Arguments

Value

• [data.frame]

References

• https://github.com/tbates/umx, https://tbates.github.io

See Also

• prolific_check_ID(), prolific_read_demog(), umx_merge_randomized_columns()

Other Data Functions: noNAs(), prolific_check_ID(), prolific_read_demog(), umx, umxFactor(), umxHetCor(), umx_as_numeric(), umx_cont_2_quantiles(), umx_lower2full(), umx_make_MR_data(), umx_make_TwinData(), umx_make_fake_data(), umx_make_raw_from_cov(), umx_merge_randomized_columns(), umx_polychoric(), umx_polypairwise(), umx_polytriowise(), umx_read_lower(), umx_rename(), umx_reorder(), umx_score_scale(), umx_select_valid(), umx_stack(), umx_strings2numeric()

Examples

## Not run: 
tmp = prolific_anonymize(df, PID = "PID")

## End(Not run)

Description

Participants may time-out on Prolific, but still complete on Qualtrics. This identifies them.

Usage

prolific_check_ID(IDs, df, IDcol = "PROLIFIC_PID")

Arguments

Value

- list of IDs in the dataframe

See Also

- [prolific_read_demog()], [prolific_anonymize()], [umx_merge_randomized_columns()] # [prolific_check_ID()]

Other Data Functions: noNAs(), prolific_anonymize(), prolific_read_demog(), umx, umxFactor(), umxHetCor(), umx_as_numeric(), umx_cont_2_quantiles(), umx_lower2full(), umx_make_MR_data(), umx_make_TwinData(), umx_make_fake_data(), umx_make_raw_from_cov(), umx_merge_randomized_columns(), umx_polychoric(), umx_polypairwise(), umx_polytriowise(), umx_read_lower(), umx_rename(), umx_reorder(), umx_score_scale(), umx_select_valid(), umx_stack(), umx_strings2numeric()

Examples

# IDs = c("59d0ec2446447f00011edb063","5a08c9a7f2e3460001edb063f0254")
# prolific_check_ID(IDs, df)

Description

prolific academic provides a demographics file. This reads it and merges with your data using PID and participant_id

Usage

prolific_read_demog(
  file,
  base = "",
  df = NULL,
  by.df = "PROLIFIC_PID",
  by.demog = "Participant.id",
  age = "age",
  sex = "Gender",
  vars = NULL,
  all.df = TRUE,
  all.demog = FALSE,
  verbose = FALSE
)

Arguments

Value

• [data.frame]

References

• https://github.com/tbates/umx, https://tbates.github.io

See Also

• prolific_check_ID(), prolific_anonymize(), umx_merge_randomized_columns()

Other Data Functions: noNAs(), prolific_anonymize(), prolific_check_ID(), umx, umxFactor(), umxHetCor(), umx_as_numeric(), umx_cont_2_quantiles(), umx_lower2full(), umx_make_MR_data(), umx_make_TwinData(), umx_make_fake_data(), umx_make_raw_from_cov(), umx_merge_randomized_columns(), umx_polychoric(), umx_polypairwise(), umx_polytriowise(), umx_read_lower(), umx_rename(), umx_reorder(), umx_score_scale(), umx_select_valid(), umx_stack(), umx_strings2numeric()

Examples

## Not run: 
fp = "~/Desktop/prolific_export_5f20c3e662e3b6407dcd37a5.csv"
df = prolific_read_demog(fp, sex = "Gender", age = "Age", df = df)
tmp = prolific_read_demog(fp, by.df = "PROLIFIC_PID", vars=c("Ethnicity.simplified"))

## End(Not run)

Description

Quickmatrix function

Usage

qm(..., rowMarker = "|")

Arguments

Value

- matrix

See Also

Other Miscellaneous Utility Functions: install.OpenMx(), libs(), umx, umxLav2RAM(), umxModelNames(), umxRAM2Lav(), umxVersion(), umx_array_shift(), umx_find_object(), umx_lower.tri(), umx_msg(), umx_open_CRAN_page(), umx_pad(), umx_print()

Examples

# simple example
qm(0, 1 |
   2, NA)
## Not run: 
# clever example
M1 = M2 = diag(2)
qm(M1,c(4,5) | c(1,2),M2 | t(1:3))

## End(Not run)

Description

Just a helper to multiply radians by 180 and divide by $\pi$ to get degrees.

note: R's trig functions, e.g. sin() use Radians for input!

There are $2\pi$ radians in a circle. 1 Rad = $180/\pi$ degrees = ~ 57.296 degrees.

Usage

rad2deg(rad)

Arguments

Value

• value in degrees

References

https://en.wikipedia.org/wiki/Radian

See Also

• deg2rad(), sin()

Other Miscellaneous Functions: deg2rad(), fin_JustifiedPE(), fin_NI(), fin_interest(), fin_percent(), fin_ticker(), fin_valuation(), umxBrownie()

Examples

rad2deg(pi) #180 degrees

Description

Compute and report Coefficient alpha (extracted from Rcmdr to avoid its dependencies)

Usage

reliability(S)

Arguments

Value

None

References

- <https://cran.r-project.org/package=Rcmdr>

See Also

- [umx::print.reliability()],

Other Miscellaneous Stats Functions: FishersMethod(), SE_from_p(), geometric_mean(), harmonic_mean(), oddsratio(), umx, umxCov2cor(), umxHetCor(), umxParan(), umxWeightedAIC(), umx_apply(), umx_cor(), umx_means(), umx_r_test(), umx_round(), umx_scale(), umx_var()

Examples

# treat car data as items of a test
data(mtcars)
reliability(cov(mtcars))

Description

Return the residuals() from an OpenMx RAM model. You can format these (with digits), and suppress small values.

Usage

## S3 method for class 'MxModel'
residuals(object, digits = 2, suppress = NULL, reorder = NULL, ...)

Arguments

Value

• matrix of residuals

References

• https://tbates.github.io, https://github.com/tbates/umx

See Also

Other Reporting functions: RMSEA(), RMSEA.MxModel(), RMSEA.summary.mxmodel(), extractAIC.MxModel(), loadings(), loadings.MxModel(), tmx_show(), tmx_show.MxMatrix(), umxCI(), umxCI_boot(), umxConfint(), umxExpCov(), umxExpMeans(), umxFitIndices(), umxRotate()

Examples

## Not run: 
require(umx)
data(demoOneFactor)
manifests = names(demoOneFactor)

m1 = umxRAM("One Factor", data = demoOneFactor, type = "cov",
	umxPath("G", to = manifests),
	umxPath(var = manifests),
	umxPath(var = "G", fixedAt = 1.0)
)

# ===================================
# = Show the residuals of the model =
# ===================================
residuals(m1)
# |   |x1   |x2    |x3   |x4    |x5 |
# |:--|:----|:-----|:----|:-----|:--|
# |x1 |.    |.     |0.01 |.     |.  |
# |x2 |.    |.     |0.01 |-0.01 |.  |
# |x3 |0.01 |0.01  |.    |.     |.  |
# |x4 |.    |-0.01 |.    |.     |.  |
# |x5 |.    |.     |.    |.     |.  |
# [1] "nb: You can zoom in on bad values with, e.g. suppress = .01, which
#      will hide values smaller than this. Use digits = to round"

residuals(m1, digits = 3)
residuals(m1, digits = 3, suppress = .005)
# residuals are returned as an invisible object you can capture in a variable
a = residuals(m1); a

## End(Not run)

Description

See RMSEA.MxModel() to access the RMSEA of MxModels

Usage

RMSEA(x, ci.lower, ci.upper, digits)

Arguments

Value

• RMSEA object containing value (and perhaps a CI)

See Also

Other Reporting functions: RMSEA.MxModel(), RMSEA.summary.mxmodel(), extractAIC.MxModel(), loadings(), loadings.MxModel(), residuals.MxModel(), tmx_show(), tmx_show.MxMatrix(), umxCI(), umxCI_boot(), umxConfint(), umxExpCov(), umxExpMeans(), umxFitIndices(), umxRotate()

Description

Return RMSEA and its confidence interval on a model. RMSEA(tmp, silent=TRUE)$RMSEA

Usage

## S3 method for class 'MxModel'
RMSEA(x, ci.lower = 0.025, ci.upper = 0.975, digits = 3)

Arguments

Value

• object containing the RMSEA, lower and upper bounds, and p-close

References

• https://github.com/tbates/umx

See Also

Other Reporting functions: RMSEA(), RMSEA.summary.mxmodel(), extractAIC.MxModel(), loadings(), loadings.MxModel(), residuals.MxModel(), tmx_show(), tmx_show.MxMatrix(), umxCI(), umxCI_boot(), umxConfint(), umxExpCov(), umxExpMeans(), umxFitIndices(), umxRotate()

Examples

## Not run: 
require(umx)
data(demoOneFactor)
manifests = names(demoOneFactor)

m1 = umxRAM("One Factor", data = demoOneFactor, type = "cov",
	umxPath("G", to = manifests),
	umxPath(var = manifests),
	umxPath(var = "G", fixedAt = 1)
)
RMSEA(m1)

x = RMSEA(m1)
x$RMSEA # 0.0309761

# Raw: needs to be run by umx to get RMSEA
m2 = umxRAM("One Factor", data = demoOneFactor,
	umxPath("G", to = manifests),
	umxPath(v.m. = manifests),
	umxPath(v1m0 = "G")
)
RMSEA(m2)

## End(Not run)

Description

Extract the RMSEA and confidence interval from a model summary and returns it as an RMSEA object. To report just the RMSEA, you can use RMSEA(model)$RMSEA

Usage

## S3 method for class 'summary.mxmodel'
RMSEA(x, ci.lower = 0.025, ci.upper = 0.975, digits = 3)

Arguments

Value

• object containing the RMSEA and lower and upper bounds

References

• https://github.com/simsem/semTools/wiki/Functions, https://github.com/tbates/umx

See Also

Other Reporting functions: RMSEA(), RMSEA.MxModel(), extractAIC.MxModel(), loadings(), loadings.MxModel(), residuals.MxModel(), tmx_show(), tmx_show.MxMatrix(), umxCI(), umxCI_boot(), umxConfint(), umxExpCov(), umxExpMeans(), umxFitIndices(), umxRotate()

Examples

## Not run: 
require(umx)
data(demoOneFactor)
manifests = names(demoOneFactor)

m1 = umxRAM("One Factor", data = demoOneFactor[1:100,], type = "cov",
	umxPath("G", to = manifests),
	umxPath(var = manifests),
	umxPath(var = "G", fixedAt = 1.0)
)
tmp = summary(m1)
RMSEA(tmp)

## End(Not run)

Description

SE_from_p takes beta and p, and returns an SE.

Usage

SE_from_p(beta = NULL, p = NULL, SE = NULL, lower = NULL, upper = NULL)

Arguments

Value

• Standard error

See Also

• umxAPA()

Other Miscellaneous Stats Functions: FishersMethod(), geometric_mean(), harmonic_mean(), oddsratio(), reliability(), umx, umxCov2cor(), umxHetCor(), umxParan(), umxWeightedAIC(), umx_apply(), umx_cor(), umx_means(), umx_r_test(), umx_round(), umx_scale(), umx_var()

Examples

SE_from_p(beta = .0020, p = .780)
SE_from_p(beta = .0020, p = .01)
SE_from_p(beta = .0020, SE = 0.01)
umxAPA(.0020, p = .01)

Description

tmx_genotypic_effect allows you to explore the concept of genotypic effect at a locus. With it, you can interactively explore the effects of allele frequency, additive variance, and dominance.

This function lets you explore the simplest two–allele system (B and b), with three possible genotypes, BB, Bb, and bb.

The point between the two homozygotes is m – the mean effect of the homozygous genotypes.

Parameter a is half the measured phenotypic difference between the homozygotes BB and bb. It corresponds to the additive effect of each additional B allele, relative to the bb phenotype.

Parameter d is the deviation of the heterozygote Bb phenotype from the homozygote mid-point m. It corresponds to the non-additive (dominance) effect of the B allele. The heterozygote phenotype may lie on either side of m and the sign of d will vary accordingly.

Old system from book ed 2:

Adapted from Mather and Jinks, 1977, p. 32). See book issue old-style nomenclature https://github.com/tbates/BGBook/issues/23

u = Frequency of the dominant allele (now = p). v = Frequency of the recessive allele (now = q).

m = midpoint between the two homozygotes d = half the difference between the two homozygote (now a)

h = deviation of the heterozygote from m (now = d)

New system:

u and v -> p and q

d and h -> a and d

See BGBook issue 23

Usage

tmx_genotypic_effect(p = 0.75, q = (1 - p), a = 0.5, d = 0, m = 0, show = TRUE)

Arguments

Value

• optional plot

References

• Neale, M. C. (2005). Quantitative Genetics. In Encyclopedia of Life Sciences. New York: John Wiley & Sons, Ltd. pdf

See Also

Other Teaching and testing Functions: tmx_is.identified(), umx

Examples

library(umx);


# =========================
# = Pure additivity: d= 0 =
# =========================
tmx_genotypic_effect(p = .5, a = 1, d = 0, m = 0, show = TRUE);

# =============================
# = Complete dominance: a=d=1 =
# =============================
tmx_genotypic_effect(p = .5, q =.5, a = 1, d = 1, m = 0, show = TRUE);

# ===========================
# = Over dominance: a< d =1 =
# ===========================
tmx_genotypic_effect(p = .5, q =.5, a =.5, d = 1, m = 0)

p = tmx_genotypic_effect(p = .5, q = .5, a = 1, d = .5, m = 0, show = TRUE); 
# p = p + ggplot2::geom_point() 
# p + ggplot2::geom_text(hjust = 0, nudge_x = 0.05, label= "x")
# ggsave(paste0(base, "c03_genotypic_effect_by_gene_dose.pdf"), width = 4.6, height = 4.6)

Description

Test if a factor model is identified by establishing if the number of variables is equal too or grater than the number of model parameters. See also OpenMx::mxCheckIdentification() for checking actual models.

Usage

tmx_is.identified(nVariables, nFactors)

Arguments

Value

• Binary

References

• https://github.com/tbates/umx, https://tbates.github.io

See Also

• OpenMx::mxCheckIdentification()

Other Teaching and testing Functions: tmx_genotypic_effect(), umx

Examples

tmx_is.identified(nVariables = 2, nFactors = 1) # FALSE
tmx_is.identified(nVariables = 3, nFactors = 1) # TRUE
tmx_is.identified(nVariables = 4, nFactors = 2) # FALSE
tmx_is.identified(nVariables = 5, nFactors = 2) # TRUE

Description

Show matrices of models in a easy-to-learn-from format.

Usage

tmx_show(
  x,
  what = c("values", "free", "labels", "nonzero_or_free"),
  show = c("free", "fixed", "all"),
  matrices = c("S", "A", "M"),
  digits = 2,
  report = c("html", "markdown"),
  na.print = "",
  zero.print = ".",
  html_font = NULL,
  style = c("paper", "material_dark", "classic", "classic_2", "minimal", "material"),
  bootstrap_options = c("hover", "bordered", "condensed", "responsive"),
  lightable_options = "striped",
  freeColor = c("black", "#AAAAAA")
)

Arguments

Value

None

See Also

Other Reporting functions: RMSEA(), RMSEA.MxModel(), RMSEA.summary.mxmodel(), extractAIC.MxModel(), loadings(), loadings.MxModel(), residuals.MxModel(), tmx_show.MxMatrix(), umxCI(), umxCI_boot(), umxConfint(), umxExpCov(), umxExpMeans(), umxFitIndices(), umxRotate()

Description

Show matrices of models in a easy-to-learn-from format.

Usage

## S3 method for class 'MxMatrix'
tmx_show(
  x,
  what = c("values", "free", "labels", "nonzero_or_free"),
  show = c("free", "fixed", "all"),
  matrices = c("S", "A", "M"),
  digits = 2,
  report = c("html", "markdown"),
  na.print = "",
  zero.print = ".",
  html_font = NULL,
  style = c("paper", "material_dark", "classic", "classic_2", "minimal", "material"),
  bootstrap_options = c("hover", "bordered", "condensed", "responsive"),
  lightable_options = "striped",
  freeColor = c("black", "#AAAAAA")
)

Arguments

Value

None

See Also

Other Reporting functions: RMSEA(), RMSEA.MxModel(), RMSEA.summary.mxmodel(), extractAIC.MxModel(), loadings(), loadings.MxModel(), residuals.MxModel(), tmx_show(), umxCI(), umxCI_boot(), umxConfint(), umxExpCov(), umxExpMeans(), umxFitIndices(), umxRotate()

Examples

## Not run: 
nameStr = c('x1', 'x2', 'g')
amat = mxMatrix(type='Full', name='demo', nrow=2, ncol=3,
  values=c(0, 0, 1,
           0, 0, 0),
  labels=c(NA   , NA, 'alice',
           "bob", NA, 'bob'),
  free=c(TRUE, FALSE, TRUE,
         TRUE, TRUE , TRUE),
  byrow=TRUE, dimnames=list(nameStr[1:2], nameStr)
)
amat
tmx_show(amat, freeColor = c("green","red")) #green=#26D71E red=#D7261E
tmx_show(amat, report = "markdown")
tmx_show(amat, "labels", report = "markdown")
tmx_show(amat, "labels", report = "markdown", show= "all")
tmx_show(amat, na.print = "NA")
tmx_show(amat, zero.print = "0.00", freeColor = c("green","red"))
tmx_show(amat, style = "classic_2")
tmx_show(amat, lightable_options = "hover")

## End(Not run)

Description

A great way to learn about models is to look at the matrix contents. tmx_show is designed to do this in a way that makes it easy to process for users: The matrix contents are formatted as tables, and can even be displayed as tables in a web browser.

Usage

## S3 method for class 'MxModel'
tmx_show(
  x,
  what = c("values", "free", "labels", "nonzero_or_free"),
  show = c("free", "fixed", "all"),
  matrices = c("S", "A", "M"),
  digits = 2,
  report = c("html", "markdown"),
  na.print = "",
  zero.print = ".",
  html_font = NULL,
  style = c("paper", "material_dark", "classic", "classic_2", "minimal", "material"),
  bootstrap_options = c("hover", "bordered", "condensed", "responsive"),
  lightable_options = "striped",
  freeColor = c("black", "#AAAAAA")
)