I have to run many CFAs and want to automate saving specific output values in a data frame so I can convert it to a latex table later.
Specifically I get my output something like this using lavaan:
model <- 'y =~ x1 + x2 + x3'
fit <- cfa(model, data)
sum <- summary(fit_os_bi, fit.measures=TRUE, standardized=T)
I managed to extract some values like this p_val <- sum$test$standard$pvalue but I couldn't figure out how to get to CFI, TLI, RMSEA, and SRMR. I think I'm even missing the right search terms to google that problem successfully.
How can I access these values from the summary-object? I'd be grateful if you could provide me with the right code or point me to a resource that explains it!
Here's an excerpt of the cfa summary:
lavaan 0.6-12 ended normally after 42 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 9
Number of observations 213
Model Test User Model:
Test statistic 1.625
Degrees of freedom 1
P-value (Chi-square) 0.202
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.997
Tucker-Lewis Index (TLI) 0.983
Root Mean Square Error of Approximation:
RMSEA 0.054
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.200
P-value RMSEA <= 0.05 0.315
Standardized Root Mean Square Residual:
SRMR 0.014
I looked at 'sum' in the environment inspector in R-Studio (where I found the location of the p-value) and searched the documentation of lavaan, but to no avail.
Since the values I'm looking for appear in the output I expect they must be stored somewhere in the summary-object.
lavaan has a number of helper functions to extract coefficients from the model object. In this case you can use fitMeasures():
fitMeasures(fit, c("pvalue", "cfi", "tli", "rmsea","srmr"))
Use broom::tidy() and broom::glance() to get model information in a data.frame object. I will use the standard example from lavaan package
library(lavaan)
#> This is lavaan 0.6-12
#> lavaan is FREE software! Please report any bugs.
HS.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
fit <- cfa(HS.model, data = HolzingerSwineford1939)
broom::tidy(fit)
#> # A tibble: 24 × 9
#> term op estimate std.e…¹ stati…² p.value std.lv std.all std.nox
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 visual =~ x1 =~ 1 0 NA NA 0.900 0.772 0.772
#> 2 visual =~ x2 =~ 0.554 0.0997 5.55 2.80e- 8 0.498 0.424 0.424
#> 3 visual =~ x3 =~ 0.729 0.109 6.68 2.31e-11 0.656 0.581 0.581
#> 4 textual =~ x4 =~ 1 0 NA NA 0.990 0.852 0.852
#> 5 textual =~ x5 =~ 1.11 0.0654 17.0 0 1.10 0.855 0.855
#> 6 textual =~ x6 =~ 0.926 0.0554 16.7 0 0.917 0.838 0.838
#> 7 speed =~ x7 =~ 1 0 NA NA 0.619 0.570 0.570
#> 8 speed =~ x8 =~ 1.18 0.165 7.15 8.56e-13 0.731 0.723 0.723
#> 9 speed =~ x9 =~ 1.08 0.151 7.15 8.40e-13 0.670 0.665 0.665
#> 10 x1 ~~ x1 ~~ 0.549 0.114 4.83 1.34e- 6 0.549 0.404 0.404
#> # … with 14 more rows, and abbreviated variable names ¹std.error, ²statistic
broom::glance(fit)
#> # A tibble: 1 × 17
#> agfi AIC BIC cfi chisq npar rmsea rmsea.conf.h…¹ srmr tli conve…²
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
#> 1 0.894 7517. 7595. 0.931 85.3 21 0.0921 0.114 0.0652 0.896 TRUE
#> # … with 6 more variables: estimator <chr>, ngroups <int>,
#> # missing_method <chr>, nobs <int>, norig <int>, nexcluded <int>, and
#> # abbreviated variable names ¹rmsea.conf.high, ²converged
Created on 2023-01-26 with reprex v2.0.2
Related
I am trying to write a function that will run multiple regressions and then store the outputs in a vector. What I want is for the function to pick the dependent variables from a list that I will provide, and then run the regressions on the same right hand-side variables. Not sure how to go about doing this. Any hints will be appreciated.
my_data <- data.frame(x1=(1:10) + rnorm(10, 3, 1.5), x2=25/3 + rnorm(10, 0, 1),
dep.var1=seq(5, 28, 2.5), dep.var2=seq(100, -20, -12.5),
dep.var3=seq(1, 25, 2.5))
## The following is a list that tells the function
dep.var <- list(dep.var1=my_data$dep.var1, dep.var2=my_data$dep.var2)
## which dependent variables to use from my_data
all_models <- function(dep.var){lm(dep.var ~ x1 + x2, data=my_data)}
model <- sapply(dep.var, all_models) ## The "sapply" here tells the function to
## take the dependent variables from the list dep.var.
I want the "model" list to have two objects: model1 with dep.var1 and model2 with dep.var2. Then as required, I will use summary(model#) to see the regression output.
I know that this in theory works when a vector is used (i.e., p):
p <- seq(0.25, 0.95, 0.05)
s <- function(p) {1 - pnorm(35, p*1*44, sqrt(44)*sqrt(p*(1 - p)))}
f <- sapply(p, s)
But I can't get the whole thing to work as required for my regression models. It works somewhat because you can run and check "model" and it will show you the two regression outputs - but it is horrible. And the "model" does not show the regression specification, i.e., dep.var1 ~ x1 + x2.
Consider reformulate to dynamically change model formulas using character values for lm calls:
# VECTOR OF COLUMN NAMES (NOT VALUES)
dep.vars <- c("dep.var1", "dep.var2")
# USER-DEFINED METHOD TO PROCESS DIFFERENT DEP VAR
run_model <- function(dep.var) {
fml <- reformulate(c("x1", "x2"), dep.var)
lm(fml, data=data)
}
# NAMED LIST OF MODELS
all_models <- sapply(dep.vars, run_model, simplify = FALSE)
# OUTPUT RESULTS
all_models$dep.var1
all_models$dep.var2
...
From there, you can run further extractions or processes across model objects:
# NAMED LIST OF MODEL SUMMARIES
all_summaries <- lapply(all_models, summary)
all_summaries$dep.var1
all_summaries$dep.var2
...
# NAMED LIST OF MODEL COEFFICIENTS
all_coefficients <- lapply(all_models, `[`, "coefficients")
all_coefficients$dep.var1
all_coefficients$dep.var2
...
You could sapply over the names of the dependent vars which you could nicely identify with grep. In lm use reformulate to build the formula.
sapply(grep('^dep', names(my_data), value=TRUE), \(x)
lm(reformulate(c('x1', 'x2'), x), my_data))
# dep.var1 dep.var2 dep.var3
# coefficients numeric,3 numeric,3 numeric,3
# residuals numeric,10 numeric,10 numeric,10
# effects numeric,10 numeric,10 numeric,10
# rank 3 3 3
# fitted.values numeric,10 numeric,10 numeric,10
# assign integer,3 integer,3 integer,3
# qr qr,5 qr,5 qr,5
# df.residual 7 7 7
# xlevels list,0 list,0 list,0
# call expression expression expression
# terms dep.var1 ~ x1 + x2 dep.var2 ~ x1 + x2 dep.var3 ~ x1 + x2
# model data.frame,3 data.frame,3 data.frame,3
The dep.var* appear nicely in the result.
However, you probably want to use lapply and pipe it into setNames() to get the list elements named. Instead of grep you may of course define the dependent variables manually. To get a clean formular call stored, we use a trick once #g-grothendieck taught me using do.call.
dv <- as.list(grep('^dep', names(my_data), value=TRUE)[1:2])
res <- lapply(dv, \(x) {
f <- reformulate(c('x1', 'x2'), x)
do.call('lm', list(f, quote(my_data)))
}) |>
setNames(dv)
res
# $dep.var1
#
# Call:
# lm(formula = dep.var1 ~ x1 + x2, data = my_data)
#
# Coefficients:
# (Intercept) x1 x2
# -4.7450 2.3398 0.2747
#
#
# $dep.var2
#
# Call:
# lm(formula = dep.var2 ~ x1 + x2, data = my_data)
#
# Coefficients:
# (Intercept) x1 x2
# 148.725 -11.699 -1.373
This allows you to get the summary() of the objects, which probably is what you want.
summary(res$dep.var1)
# Call:
# lm(formula = dep.var1 ~ x1 + x2, data = my_data)
#
# Residuals:
# Min 1Q Median 3Q Max
# -2.8830 -1.8345 -0.2326 1.4335 4.2452
#
# Coefficients:
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) -4.7450 7.2884 -0.651 0.536
# x1 2.3398 0.2836 8.251 7.48e-05 ***
# x2 0.2747 0.7526 0.365 0.726
# ---
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
# Residual standard error: 2.55 on 7 degrees of freedom
# Multiple R-squared: 0.9117, Adjusted R-squared: 0.8865
# F-statistic: 36.14 on 2 and 7 DF, p-value: 0.0002046
Finally you could wrap it in a function
calc_models <- \(dv) {
lapply(dv, \(x) {
f <- reformulate(c('x1', 'x2'), x)
do.call('lm', list(f, quote(my_data)))
}) |>
setNames(dv)
}
calc_models(list('dep.var1', 'dep.var2'))
Here is a way how you could iterate through your dataframe and apply the function to the group you define (here dep.var) and save the different models in a dataframe:
library(tidyverse)
library(broom)
my_data %>%
pivot_longer(
starts_with("dep"),
names_to = "group",
values_to = "dep.var"
) %>%
mutate(group = as.factor(group)) %>%
group_by(group) %>%
group_split() %>%
map_dfr(.f = function(df) {
lm(dep.var ~ x1 + x2, data = df) %>%
tidy() %>% # first output
#glance() %>% # second output
add_column(group = unique(df$group), .before=1)
})
dataframe output:
# A tibble: 9 x 6
group term estimate std.error statistic p.value
<fct> <chr> <dbl> <dbl> <dbl> <dbl>
1 dep.var1 (Intercept) -5.29 11.6 -0.456 0.662
2 dep.var1 x1 2.11 0.268 7.87 0.000101
3 dep.var1 x2 0.538 1.23 0.437 0.675
4 dep.var2 (Intercept) 151. 57.9 2.61 0.0347
5 dep.var2 x1 -10.6 1.34 -7.87 0.000101
6 dep.var2 x2 -2.69 6.15 -0.437 0.675
7 dep.var3 (Intercept) -9.29 11.6 -0.802 0.449
8 dep.var3 x1 2.11 0.268 7.87 0.000101
9 dep.var3 x2 0.538 1.23 0.437 0.675
list output:
[[1]]
# A tibble: 3 x 6
group term estimate std.error statistic p.value
<fct> <chr> <dbl> <dbl> <dbl> <dbl>
1 dep.var1 (Intercept) -5.29 11.6 -0.456 0.662
2 dep.var1 x1 2.11 0.268 7.87 0.000101
3 dep.var1 x2 0.538 1.23 0.437 0.675
[[2]]
# A tibble: 3 x 6
group term estimate std.error statistic p.value
<fct> <chr> <dbl> <dbl> <dbl> <dbl>
1 dep.var2 (Intercept) 151. 57.9 2.61 0.0347
2 dep.var2 x1 -10.6 1.34 -7.87 0.000101
3 dep.var2 x2 -2.69 6.15 -0.437 0.675
[[3]]
# A tibble: 3 x 6
group term estimate std.error statistic p.value
<fct> <chr> <dbl> <dbl> <dbl> <dbl>
1 dep.var3 (Intercept) -9.29 11.6 -0.802 0.449
2 dep.var3 x1 2.11 0.268 7.87 0.000101
3 dep.var3 x2 0.538 1.23 0.437 0.675
glance output:
group r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
<fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
1 dep.var1 0.927 0.906 2.32 44.3 0.000106 2 -20.8 49.7 50.9 37.8 7 10
2 dep.var2 0.927 0.906 11.6 44.3 0.000106 2 -36.9 81.9 83.1 944. 7 10
3 dep.var3 0.927 0.906 2.32 44.3 0.000106 2 -20.8 49.7 50.9 37.8 7 10
I want to calculate CI in mixed models, zero inflated negative binomial and hurdle model. My code for hurdle model looks like this (x1, x2 continuous, x3 categorical):
m1 <- glmmTMB(count~x1+x2+x3+(1|year/class),
data = bd, zi = ~x2+x3+(1|year/class), family = truncated_nbinom2,
)
I used confint, and I got these results:
ci <- confint(m1,parm="beta_")
ci
2.5 % 97.5 % Estimate
cond.(Intercept) 1.816255e-01 0.448860094 0.285524861
cond.x1 9.045278e-01 0.972083366 0.937697401
cond.x2 1.505770e+01 26.817439186 20.094998772
cond.x3high 1.190972e+00 1.492335046 1.333164894
cond.x3low 1.028147e+00 1.215828654 1.118056377
cond.x3reg 1.135515e+00 1.385833853 1.254445909
class:year.cond.Std.Dev.(Intercept)2.256324e+00 2.662976154 2.441845815
year.cond.Std.Dev.(Intercept) 1.051889e+00 1.523719169 1.157153015
zi.(Intercept) 1.234418e-04 0.001309705 0.000402085
zi.x2 2.868578e-02 0.166378014 0.069084606
zi.x3high 8.972025e-01 1.805832900 1.272869874
Am I calculating the intervals correctly? Why is there only one category in x3 for zi?
If possible, I would also like to know if it's possible to plot these CIs.
Thanks!
Data looks like this:
class id year count x1 x2 x3
956 5 3002 2002 3 15.6 47.9 high
957 5 4004 2002 3 14.3 47.9 low
958 5 6021 2002 3 14.2 47.9 high
959 4 2030 2002 3 10.5 46.3 high
960 4 2031 2002 3 15.3 46.3 high
961 4 2034 2002 3 15.2 46.3 reg
with x1 and x2 continuous, x3 three level categorical variable (factor)
Summary of the model:
summary(m1)
'giveCsparse' has been deprecated; setting 'repr = "T"' for you'giveCsparse' has been deprecated; setting 'repr = "T"' for you'giveCsparse' has been deprecated; setting 'repr = "T"' for you
Family: truncated_nbinom2 ( log )
Formula: count ~ x1 + x2 + x3 + (1 | year/class)
Zero inflation: ~x2 + x3 + (1 | year/class)
Data: bd
AIC BIC logLik deviance df.resid
37359.7 37479.7 -18663.8 37327.7 13323
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
class:year(Intercept) 0.79701 0.8928
year (Intercept) 0.02131 0.1460
Number of obs: 13339, groups: class:year, 345; year, 15
Zero-inflation model:
Groups Name Variance Std.Dev.
dpto:year (Intercept) 1.024e+02 1.012e+01
year (Intercept) 7.842e-07 8.856e-04
Number of obs: 13339, groups: class:year, 345; year, 15
Overdispersion parameter for truncated_nbinom2 family (): 1.02
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.25343 0.23081 -5.431 5.62e-08 ***
x1 -0.06433 0.01837 -3.501 0.000464 ***
x2 3.00047 0.14724 20.378 < 2e-16 ***
x3high 0.28756 0.05755 4.997 5.82e-07 ***
x3low 0.11159 0.04277 2.609 0.009083 **
x3reg 0.22669 0.05082 4.461 8.17e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Zero-inflation model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -7.8188 0.6025 -12.977 < 2e-16 ***
x2 -2.6724 0.4484 -5.959 2.53e-09 ***
x3high 0.2413 0.1784 1.352 0.17635
x3low -0.1325 0.1134 -1.169 0.24258
x3reg -0.3806 0.1436 -2.651 0.00802 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
CI with broom.mixed
> broom.mixed::tidy(m1, effects="fixed", conf.int=TRUE)
# A tibble: 12 x 9
effect component term estimate std.error statistic p.value conf.low conf.high
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 fixed cond (Intercept) -1.25 0.231 -5.43 5.62e- 8 -1.71 -0.801
2 fixed cond x1 -0.0643 0.0184 -3.50 4.64e- 4 -0.100 -0.0283
3 fixed cond x2 3.00 0.147 20.4 2.60e-92 2.71 3.29
4 fixed cond x3high 0.288 0.0575 5.00 5.82e- 7 0.175 0.400
5 fixed cond x3low 0.112 0.0428 2.61 9.08e- 3 0.0278 0.195
6 fixed cond x3reg 0.227 0.0508 4.46 8.17e- 6 0.127 0.326
7 fixed zi (Intercept) -9.88 1.32 -7.49 7.04e-14 -12.5 -7.30
8 fixed zi x1 0.214 0.120 1.79 7.38e- 2 -0.0206 0.448
9 fixed zi x2 -2.69 0.449 -6.00 2.01e- 9 -3.57 -1.81
10 fixed zi x3high 0.232 0.178 1.30 1.93e- 1 -0.117 0.582
11 fixed zi x3low -0.135 0.113 -1.19 2.36e- 1 -0.357 0.0878
12 fixed zi x4reg -0.382 0.144 -2.66 7.74e- 3 -0.664 -0.101
tl;dr as far as I can tell this is a bug in confint.glmmTMB (and probably in the internal function glmmTMB:::getParms). In the meantime, broom.mixed::tidy(m1, effects="fixed") should do what you want. (There's now a fix in progress in the development version on GitHub, should make it to CRAN sometime? soon ...)
Reproducible example:
set up data
set.seed(101)
n <- 1e3
bd <- data.frame(
year=factor(sample(2002:2018, size=n, replace=TRUE)),
class=factor(sample(1:20, size=n, replace=TRUE)),
x1 = rnorm(n),
x2 = rnorm(n),
x3 = factor(sample(c("low","reg","high"), size=n, replace=TRUE),
levels=c("low","reg","high")),
count = rnbinom(n, mu = 3, size=1))
fit
library(glmmTMB)
m1 <- glmmTMB(count~x1+x2+x3+(1|year/class),
data = bd, zi = ~x2+x3+(1|year/class), family = truncated_nbinom2,
)
confidence intervals
confint(m1, "beta_") ## wrong/ incomplete
broom.mixed::tidy(m1, effects="fixed", conf.int=TRUE) ## correct
You may want to think about which kind of confidence intervals you want:
Wald CIs (default) are much faster to compute and are generally OK as long as (1) your data set is large and (2) you aren't estimating any parameters on the log/logit scale that are near the boundaries
likelihood profile CIs are more accurate but much slower
I am using the mlogit function from the mlogit package to run a multinomial logit regression. I am not sure how to add interaction terms into my model. Here is a toy dataset and my attempt to add interactions:
library(mlogit)
data <- data.frame(y=sample(1:3, 24, replace = TRUE),
x1 = c(rep(1,12), rep(2,12)),
x2 = rep(c(rep(1,4), rep(2,4), rep(3,4)),2),
x3=rnorm(24),
z1 = sample(1:10, 24, replace = TRUE))
m0 <- mlogit(y ~ 0|x1 + x2 + x3 + z1, shape = "wide", data = data) #model with only main effects
m1 <- mlogit(y ~ 0|(x1 + x2 + x3 + z1)^2, shape = "wide", data = data) #model assuming with all possible 2-way interactions?
The output from summary(m1) shows:
Coefficients :
Estimate Std. Error z-value Pr(>|z|)
(Intercept):2 86.41088 164.93831 0.5239 0.6003
(Intercept):3 62.43859 163.57346 0.3817 0.7027
x1:2 -32.27065 82.62474 -0.3906 0.6961
x1:3 0.24661 84.07429 0.0029 0.9977
x2:2 -75.09247 81.36496 -0.9229 0.3561
x2:3 -85.16452 81.40983 -1.0461 0.2955
x3:2 113.11778 119.15990 0.9493 0.3425
x3:3 112.77622 117.74567 0.9578 0.3382
z1:2 11.18665 22.32508 0.5011 0.6163
z1:3 13.15552 22.26441 0.5909 0.5546
x1:2 34.01298 39.66983 0.8574 0.3912
x1:3 32.19141 39.48373 0.8153 0.4149
x1:2 -53.86747 59.75696 -0.9014 0.3674
x1:3 -47.97693 59.09055 -0.8119 0.4168
x1:2 -6.98799 11.29920 -0.6185 0.5363
x1:3 -10.41574 11.52313 -0.9039 0.3660
x2:2 0.59185 6.68807 0.0885 0.9295
x2:3 2.63458 4.94419 0.5329 0.5941
x2:2 0.80945 2.03769 0.3972 0.6912
x2:3 2.60383 2.21878 1.1735 0.2406
x3:2 -0.64112 1.64678 -0.3893 0.6970
x3:3 -2.14289 1.98436 -1.0799 0.2802
the first column is not quite clear to me what specific interactions were outputted. Any pointers will be greatly appreciated!
This might be a clearer way to do it:
library(dplyr)
library(broom)
library(nnet)
multinom(formula = y ~ (x1 + x2 + x3 + z1)^2, data = data) %>%
tidy()
# A tibble: 22 x 6
y.level term estimate std.error statistic p.value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 2 (Intercept) -158. 247. -0.640 0.522
2 2 x1 -388. 247. -1.57 0.116
3 2 x2 -13.4 248. -0.0543 0.957
4 2 x3 120. 334. 0.360 0.719
5 2 z1 173. 968. 0.179 0.858
6 2 x1:x2 337. 248. 1.36 0.174
7 2 x1:x3 40.2 334. 0.120 0.904
8 2 x1:z1 -53.8 968. -0.0555 0.956
9 2 x2:x3 -137. 1018. -0.135 0.893
10 2 x2:z1 -76.6 910. -0.0841 0.933
# … with 12 more rows
I am currently working on running a structural equation modelling analysis with a dataset and I am running into a few problems. Before running the full sem, I intended to run a CFA to replicate the psychometric testing done with this measure I am using. This measure has 24 items, which make up 5 subscales (latent variables), which in turn load onto an "total" higher order factor. In the literature they describe that "In all models, the items were constrained to load on one factor only, error terms were not allowed to correlate, and the variance of the factors was fixed to 1".
I've constraint items to load onto one factor, and set the variance of those factors to 1, but I am having trouble specifying in my model that the error terms are not allowed to correlate. Do they mean the error term of the items are not allowed to correlate? Is there an easy way to do this in lavaan or do I have to literally go "y1~~ 0y2","y1~~0y3".. and so on for every item?
Thank you in advance for the help.
By default the error terms do not correlate, the authors intended to mention that they did not use that kind of modification indices. It is usual to correlate items' residuals inside the same factor. Here is an example of a hierarchical model with three first-order factors, with factors variance fixed to one, and with no error terms correlated:
library(lavaan)
#> This is lavaan 0.6-7
#> lavaan is BETA software! Please report any bugs.
#>
HS.model3 <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9
higher =~ visual + textual + speed'
fit6 <- cfa(HS.model3, data = HolzingerSwineford1939, std.lv=T)
summary(fit6)
#> lavaan 0.6-7 ended normally after 36 iterations
#>
#> Estimator ML
#> Optimization method NLMINB
#> Number of free parameters 21
#>
#> Number of observations 301
#>
#> Model Test User Model:
#>
#> Test statistic 85.306
#> Degrees of freedom 24
#> P-value (Chi-square) 0.000
#>
#> Parameter Estimates:
#>
#> Standard errors Standard
#> Information Expected
#> Information saturated (h1) model Structured
#>
#> Latent Variables:
#> Estimate Std.Err z-value P(>|z|)
#> visual =~
#> x1 0.439 0.194 2.257 0.024
#> x2 0.243 0.108 2.253 0.024
#> x3 0.320 0.138 2.326 0.020
#> textual =~
#> x4 0.842 0.064 13.251 0.000
#> x5 0.937 0.071 13.293 0.000
#> x6 0.780 0.060 13.084 0.000
#> speed =~
#> x7 0.522 0.066 7.908 0.000
#> x8 0.616 0.067 9.129 0.000
#> x9 0.564 0.064 8.808 0.000
#> higher =~
#> visual 1.791 0.990 1.809 0.070
#> textual 0.617 0.129 4.798 0.000
#> speed 0.640 0.143 4.489 0.000
#>
#> Variances:
#> Estimate Std.Err z-value P(>|z|)
#> .x1 0.549 0.114 4.833 0.000
#> .x2 1.134 0.102 11.146 0.000
#> .x3 0.844 0.091 9.317 0.000
#> .x4 0.371 0.048 7.779 0.000
#> .x5 0.446 0.058 7.642 0.000
#> .x6 0.356 0.043 8.277 0.000
#> .x7 0.799 0.081 9.823 0.000
#> .x8 0.488 0.074 6.573 0.000
#> .x9 0.566 0.071 8.003 0.000
#> .visual 1.000 #fixed...
#> .textual 1.000 #fixed...
#> .speed 1.000 #fixed...
#> higher 1.000
Created on 2021-03-08 by the reprex package (v0.3.0)
As you can observe, no correlations, first-order and second-order factor with fixed variances to 1 (i.e. std.lv=T).
how do I create a data.table in r with coefficient, std.err and Pvlaues with rqpd regression type? It's easy with the coefficients using summary(myregression)[2] but don't know how to get std.err and Pval. Thanks
Try with broom:
library(broom)
library(dplyr)
#Model
mod <- lm(Sepal.Length~.,data=iris)
#Broom
summaryobj <- tidy(mod)
Output:
# A tibble: 6 x 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.17 0.280 7.76 1.43e-12
2 Sepal.Width 0.496 0.0861 5.76 4.87e- 8
3 Petal.Length 0.829 0.0685 12.1 1.07e-23
4 Petal.Width -0.315 0.151 -2.08 3.89e- 2
5 Speciesversicolor -0.724 0.240 -3.01 3.06e- 3
6 Speciesvirginica -1.02 0.334 -3.07 2.58e- 3
Found a solution that is working
summ <- summary(myregression, se = "boot")
summ
str(summ)
PValues <- summ$coefficients[,4]