Chapter 10 Assignments II
10.1 Andreas Flache
10.1.1 data
<- read.csv("https://stulp.gmw.rug.nl/Rworkshop/simulationResultsExpLevelAPolYouGov.csv") flache
library(skimr)
skim(flache)
10.1.2 visualisations
Andreas wrote: “association across country-elections of sdOpFin with r14Last at parameters sf=1, phi=1, eps=0.25, intergroupDistance=1”
library(tidyverse)
<- flache %>%
viz1_flache filter(sF == 0.1 & phi == 1 & eps0 == 0.2)
ggplot(viz1_flache, aes(x = as.numeric(sdOpFin), y = r14Last, fill = factor(intergroupDistance))) +
geom_point() +
geom_smooth(method = "lm", colour = "NA") +
scale_fill_viridis_d() +
facet_wrap(~intergroupDistance, nrow = 1) +
theme_minimal() +
theme(panel.grid.minor = element_blank()) +
guides(fill = "none")
Andreas wrote: “correlation sdOpFin with r14last by eps0 and intergroupDistance, for SF = 0.5 and phi = 0.5”
<- flache %>%
viz2_flache filter(sF == 0.6 & phi == 0.5) %>% # sd = 0.5 does not exist
select(eps0, intergroupDistance, sdOpFin, r14Last)
<- viz2_flache %>%
corr_flache group_by(eps0, intergroupDistance) %>%
summarise(cor = cor(as.numeric(sdOpFin), r14Last))
ggplot(corr_flache, aes(x = eps0, y = intergroupDistance, z = cor)) +
geom_contour_filled() +
theme_minimal()
10.1.3 running simulations in R
Andreas, people may tell you R is slow for simulations. Maybe, but code is relatively readable. A major benefit to me is that you can store output in the same ‘dataset’ as your parameters. Let’ see an example, a simple one. we’ll simulate from a normal distribution, but we vary the n, mean and sd.
library(tidyverse)
<- data.frame(n= c(10, 100, 1000), mean = c(0, 10, 100), sd = c(1, 10, 1000)) %>%
sims_params expand.grid() # this creates a dataframe with all combinations across parameters
head(sims_params)
## n mean sd
## 1 10 0 1
## 2 100 0 1
## 3 1000 0 1
## 4 10 10 1
## 5 100 10 1
## 6 1000 10 1
<- sims_params %>%
sims_params # add new column with list of results in a cell, can also be a dataframe itself
# mutate creates new column, results = name of column
# pmap is a function that iterates over multiple lists/columns
# and applies a function to it
mutate(results = pmap(list(n, mean, sd),
function(n, m, s) rnorm(n, m, s) ))
head(sims_params)
## n mean sd
## 1 10 0 1
## 2 100 0 1
## 3 1000 0 1
## 4 10 10 1
## 5 100 10 1
## 6 1000 10 1
## results
## 1 0.08253913, 1.14481207, 0.61831526, 1.18015927, 0.35906253, 0.29999311, 0.08924839, -1.30339310, 0.64521117, 0.00387676
## 2 -0.12644266, -0.04013385, 0.13424358, 1.20695279, -0.15156935, 1.04678696, 0.50579736, -0.81840117, 0.96306738, -1.17807379, 1.74996793, 0.42864712, -0.36396594, 1.44339283, -1.11322643, 1.12180678, -1.04389519, 0.27865356, -0.85391621, 1.85298574, 1.16956680, 0.53401295, -0.46036706, -0.35324319, -0.32126563, 1.96983864, 2.18008183, 1.23845062, -0.31279422, 0.52552422, -0.64684565, 0.41770540, -0.39400747, 0.14923564, 1.10360119, -1.34003486, 0.19757137, -0.95493648, 0.21989905, -0.50736179, 0.46187524, 0.26324753, 1.10621382, 1.27377736, -0.21249891, -0.31845505, -1.22989788, -0.55419864, 1.05263581, -0.01807382, 0.01833068, 0.89320320, -1.66979955, 1.02548722, -0.74200304, -0.65300053, -2.17902232, -0.91480198, -0.14677017, -0.22364239, -0.02706994, 1.30482752, 1.40766161, 1.91699640, -0.09802513, -0.25228937, 0.42523511, -0.16277811, -0.50154037, 1.62074865, 0.79388037, -0.13541047, -0.32751044, 0.31246948, 0.80302704, 0.87625488, 3.06640101, 0.87609350, -0.41518104, 0.99094651, 0.59648482, 1.50981952, 0.38972467, -0.56961156, 0.28778694, -0.78895699, -1.57267127, 0.86330951, -1.14378044, -0.67695734, -0.53470414, 1.42206841, 0.30570597, 0.24837952, -0.38606898, -0.59450738, -0.73452015, 1.62861694, 0.29532419, 0.34363503
## 3 1.284910137, -0.965990664, -0.965915614, 0.422348457, 0.622262678, 0.397786224, 0.583377426, -1.178682323, 0.751745401, 2.868632849, 1.098618573, 0.007233447, 1.081316166, -0.005687142, 0.400639874, -0.458316027, -1.391275001, -1.770166615, -2.220107671, -0.965280351, -1.139785062, -2.131144914, -1.752217516, 0.666518134, -1.036250936, -0.480407953, 1.508379702, 0.858227267, 0.259811847, -0.051592931, -0.092162070, 0.944555400, 1.467954092, -0.384510291, 2.502911605, 1.369809014, -0.657358314, -1.141062069, 0.821721302, -1.165849727, 0.287745011, -0.327174616, -0.968445173, -0.214079444, -0.083389597, -0.359611343, -0.579272353, -0.762652361, -0.590932997, -0.680623358, -1.163207024, 0.288362267, 0.044986007, -0.887025958, -0.391935497, -1.112575585, -0.466624259, 1.406108891, -0.644197845, 0.281443816, 0.143096997, -0.280480017, -0.231191228, 4.023802008, 0.088471252, -0.270096062, -1.718992429, -0.394437777, 1.289285818, 0.462217735, -0.243117770, -0.604123575, 0.573491039, 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## 4 8.766951, 9.916208, 10.681969, 10.219824, 9.645698, 8.456056, 9.358497, 9.758353, 8.364799, 9.109842
## 5 10.013276, 9.003234, 9.512473, 11.482904, 11.333754, 10.315594, 9.413221, 11.293851, 10.210573, 10.187081, 9.665132, 9.496611, 10.560666, 9.773229, 10.153726, 8.875782, 9.153158, 9.885102, 10.854091, 10.983281, 10.655535, 9.503668, 10.584081, 7.790547, 10.526621, 11.374476, 9.846156, 11.977505, 10.055137, 10.618027, 9.557403, 9.179893, 10.014511, 10.474342, 12.148060, 10.496651, 9.737687, 9.603052, 10.551177, 9.860411, 9.778223, 10.018564, 9.430931, 9.605745, 9.242329, 8.761382, 9.045070, 12.114589, 8.112512, 9.703065, 9.452200, 9.743365, 8.979015, 7.920891, 8.748625, 8.239042, 9.038123, 9.309724, 11.849483, 11.587624, 9.478112, 10.662735, 9.902571, 10.214461, 10.948827, 9.532308, 9.654804, 10.799529, 10.110343, 10.797373, 9.635561, 11.961176, 10.827048, 10.583447, 9.932555, 9.140787, 10.404370, 10.377485, 10.133418, 10.110438, 11.311388, 10.994017, 11.139376, 10.903522, 10.795338, 8.786192, 9.812807, 11.832230, 10.455167, 7.551468, 10.001574, 11.247531, 9.731868, 11.096129, 10.351853, 9.679640, 10.562622, 7.635083, 11.651947, 10.016915
## 6 9.477562, 9.977058, 9.974844, 10.008179, 11.608159, 8.374145, 11.025970, 10.467922, 10.118501, 9.279936, 9.962589, 8.683943, 9.662898, 8.854335, 12.082842, 11.528198, 10.467551, 10.110976, 8.608957, 9.239243, 10.561875, 8.423583, 9.583281, 9.796159, 10.623297, 9.184807, 9.846544, 11.691508, 10.299703, 8.526404, 10.895011, 10.637559, 6.873112, 10.878282, 10.667601, 10.505379, 10.785710, 9.480026, 8.585067, 10.806939, 8.954733, 9.392333, 9.756607, 10.356257, 10.797369, 9.103217, 9.457823, 10.826402, 9.584278, 11.003623, 8.607599, 8.807024, 8.461442, 10.469930, 11.145573, 9.062749, 11.278614, 12.919709, 7.796748, 9.067483, 11.695434, 9.680086, 10.495174, 10.215256, 10.227871, 9.803502, 9.701477, 12.647164, 9.265913, 8.890708, 9.835611, 11.063336, 10.297175, 9.738232, 10.264158, 9.716277, 10.908181, 8.388173, 11.448555, 9.000950, 11.301788, 10.781935, 9.718257, 9.815015, 10.605263, 9.550405, 10.367422, 9.817029, 9.064936, 11.030010, 11.267156, 10.099500, 11.293414, 7.220752, 9.966569, 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9.544052, 9.266302, 8.167258, 10.631599, 9.849817, 9.868850, 10.728361, 9.354710, 9.377939, 9.746100, 10.747862, 8.989380, 8.827305, 8.801978, 8.741015, 9.664542, 9.901717, 10.263631, 8.744049, 10.478964, 10.073194, 9.711056, 9.535023, 9.083965, 10.719378, 10.296844, 9.908362, 11.610282, 11.501248, 10.353716, 9.060216, 9.125652, 8.508152, 9.665984, 10.560817, 10.389446, 10.203933, 8.585846, 9.693076, 7.806456, 9.228678, 11.206045, 10.004210, 10.685133, 9.748971, 9.791968, 9.124395, 10.691426, 9.306298, 8.556914, 10.978633, 9.896028, 9.410492, 9.842168, 10.518184, 11.587089, 9.659977, 10.336470, 9.374351, 9.564199, 11.336254, 8.955792, 10.069912, 10.463644, 9.205037, 9.419180, 13.292354, 9.610713, 11.908050, 9.764769, 9.553787, 9.374184, 11.168603, 10.753956, 10.773290, 11.030094, 10.064411, 11.533500, 10.279346, 9.003859, 9.223373, 9.322732, 11.003284, 8.708577, 10.406839, 8.904925, 9.654805, 10.380845, 9.686316, 9.462291, 10.772549, 10.241606, 9.285133, 8.187873, 11.320723, 9.729517, 9.495353, 10.425702, 10.051868, 10.073336, 8.943924, 9.461441, 9.502669, 10.456348, 9.804450, 10.214987, 9.282813, 9.017329, 10.041324, 10.057059, 9.969523, 8.797695, 10.320788, 9.092741, 11.278159, 8.739611, 9.243788, 10.262926, 8.625632, 11.958114, 10.113955, 9.505054, 9.376113, 12.324662, 9.860883, 11.368752, 8.683030, 10.769603, 9.829992, 9.413043, 9.627115, 11.885099, 10.511566, 9.654177, 9.146754, 10.104255, 10.237020, 9.180521, 10.166853, 10.734459, 8.077281, 10.786672, 10.074116, 9.709398, 11.252524, 8.617186
10.2 Marieke de Haan
10.2.1 data
library(haven)
<- read_sav("https://stulp.gmw.rug.nl/Rworkshop/haan.sav") haan
library(skimr)
skim(haan)
10.2.2 accessing the labels from SPSS
This turns most variables into factors, also the ones you may not want to (likert scales)
<- as_factor(haan) haan
skim(haan)
library(tidyverse)
<- haan %>%
haan_summ group_by(Q8) %>%
# calculate sample size of each group
summarise(n = n()) %>%
# calculate total n + percentages per group
mutate(tot_n = sum(n),
perc = round(100 * n / tot_n)) %>%
# remove na
filter(!is.na(Q8))
ggplot(haan_summ, aes(x = Q8, y = n, fill = Q8)) +
geom_bar(stat = "identity") +
geom_text(aes(label = paste0(perc, "%")), hjust = 1, colour = "white", size = 8) +
theme_linedraw() +
scale_fill_viridis_d() +
coord_flip() +
guides(fill = "none") +
labs(x = NULL, title = "Vul jij meestal cursusevaluaties in?") +
theme(panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank())
10.2.3 reshaping data
# select three variables with same measurement
<- haan %>%
haan_sel select(Q13, Q14, Q15)
# go from wide to long
<- haan_sel %>%
haan_long pivot_longer(cols = c(Q13, Q14, Q15), names_to = "question", values_to = "answer") %>%
filter(!is.na(answer)) # exclude NA
ggplot(haan_long, aes(y = answer)) +
geom_bar(fill = "grey") +
theme_classic() +
facet_wrap(~question)
ggplot(haan_long, aes(x = question, fill = answer)) +
geom_bar(position = "fill") +
theme_classic() +
scale_fill_viridis_d() +
coord_flip()
10.3 Wietske de Vries
10.3.1 data
<- read_sav("https://stulp.gmw.rug.nl/Rworkshop/vries.sav") %>%
vries as_factor()
library(skimr)
skim(vries)
library(lme4)
# empty model, random intercept
<- lmer(Self_efficacy ~ 1 + (1 | sub_ID), data = vries)
null_mod summary(null_mod)
## Linear mixed model fit by REML ['lmerMod']
## Formula: Self_efficacy ~ 1 + (1 | sub_ID)
## Data: vries
##
## REML criterion at convergence: 1762.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.5184 -0.3095 0.0804 0.4563 2.8046
##
## Random effects:
## Groups Name Variance Std.Dev.
## sub_ID (Intercept) 8.170 2.858
## Residual 4.561 2.136
## Number of obs: 352, groups: sub_ID, 133
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 35.2133 0.2742 128.4
# List of fitted intercepts (estimate average self_efficacy for each respondent)
head(coef(null_mod)$sub_ID) # alternatively: ranef(null_mod)[["sub_ID"]]
## (Intercept)
## 1 38.40594
## 2 37.84386
## 3 32.50941
## 4 37.56282
## 5 31.66100
## 6 39.24905
# all fixed effectsl, random intercept
<- lmer(Self_efficacy ~ 1 + time + Group + Biomed_beliefs + (1 | sub_ID), data = vries)
mod_fe summary(mod_fe)
## Linear mixed model fit by REML ['lmerMod']
## Formula: Self_efficacy ~ 1 + time + Group + Biomed_beliefs + (1 | sub_ID)
## Data: vries
##
## REML criterion at convergence: 1749.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.1893 -0.3698 0.0683 0.4544 2.5142
##
## Random effects:
## Groups Name Variance Std.Dev.
## sub_ID (Intercept) 9.022 3.004
## Residual 4.095 2.024
## Number of obs: 352, groups: sub_ID, 133
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 33.71864 0.64287 52.450
## timepost-measure 0.34428 0.26948 1.278
## timefollow-up -0.49680 0.28350 -1.752
## Groupexperimental group -0.34269 0.58499 -0.586
## Biomed_beliefs 0.08559 0.03067 2.791
##
## Correlation of Fixed Effects:
## (Intr) tmpst- tmfll- Grpxpg
## timepst-msr 0.062
## timefollw-p -0.093 0.452
## Grpxprmntlg -0.235 0.082 0.040
## Biomed_blfs -0.755 -0.319 -0.108 -0.249
# all fixed effectsl, random intercept, random slope of Biomed_beliefs
<- lmer(Self_efficacy ~ 1 + time + Group + Biomed_beliefs + (1 + Biomed_beliefs | sub_ID), data = vries)
mod_rs summary(mod_rs)
## Linear mixed model fit by REML ['lmerMod']
## Formula:
## Self_efficacy ~ 1 + time + Group + Biomed_beliefs + (1 + Biomed_beliefs |
## sub_ID)
## Data: vries
##
## REML criterion at convergence: 1711.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8355 -0.3406 0.0413 0.4184 2.7887
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## sub_ID (Intercept) 35.90404 5.9920
## Biomed_beliefs 0.05607 0.2368 -0.88
## Residual 2.78444 1.6687
## Number of obs: 352, groups: sub_ID, 133
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 34.51437 0.78502 43.966
## timepost-measure 0.25599 0.23798 1.076
## timefollow-up -0.41104 0.25026 -1.642
## Groupexperimental group -0.05147 0.57267 -0.090
## Biomed_beliefs 0.03278 0.03723 0.880
##
## Correlation of Fixed Effects:
## (Intr) tmpst- tmfll- Grpxpg
## timepst-msr 0.032
## timefollw-p -0.085 0.477
## Grpxprmntlg -0.163 0.024 -0.001
## Biomed_blfs -0.848 -0.177 -0.034 -0.230
# save estimates per respondent of intercept and slope of Biomed_beliefs into dataframe
<- ranef(mod_rs)[["sub_ID"]]
df
ggplot(df, aes(x = `(Intercept)`, y = Biomed_beliefs)) +
geom_point() +
geom_smooth(method = "lm") +
theme_minimal() +
labs(
x = "per respondent fitted intercept of self-efficacy",
y = "per respondent fitted slope of\nbiomedical beliefs on self-efficacy",
title = "respondents that had higher levels of self-efficacy,\nhad lower effects of biomedical beliefs on self-efficacy"
)
10.4 Goda Perlaviciute
library(haven)
<- read_sav("https://stulp.gmw.rug.nl/Rworkshop/goda.sav") %>%
goda as_factor()
library(skimr)
skim(goda)
10.4.1 visualisation
library(tidyverse)
<- goda %>%
goda_sel select(Q1, Q9) %>%
rename(pre = "Q1",
post = "Q9") %>%
filter(!is.na(pre) & !is.na(post))
10.4.1.1 option 1
<- goda_sel %>%
goda_sel_summ group_by(pre, post) %>%
summarise(n = n()) %>%
group_by(pre) %>%
mutate(n_tot = sum(n),
prop = n / n_tot)
ggplot(goda_sel_summ, aes(x = pre, y = post, fill = prop)) +
geom_tile() +
geom_text(aes(label = paste0(round(100 * prop), "%")), colour = "white", size = 5 ) +
scale_fill_viridis_c() +
coord_fixed() +
scale_x_discrete(expand = c(0, 0)) +
labs(
x = "opinion prior to intervention",
y = "opinion post intervention"
+
) theme_minimal() +
theme(axis.text.x = element_text(angle = 25, hjust = 1),
panel.grid.major = element_blank())
#### option 2 See https://ggforce.data-imaginist.com/reference/geom_parallel_sets.html
library(ggforce)
<- reshape2::melt(Titanic)
temp <- gather_set_data(temp, 1:4)
temp
# select only relevant variables
<- goda_sel_summ %>%
goda_sankey select(pre, post, n)
# code from ggforce package
<- gather_set_data(goda_sankey, 1:2)
goda_sankey
ggplot(goda_sankey, aes(x, id = id, split = y, value = n)) +
geom_parallel_sets(aes(fill = pre), alpha = 0.3, axis.width = 0.1) +
geom_parallel_sets_axes(axis.width = 0.1) +
geom_parallel_sets_labels(colour = 'white', angle = 0, nudge_x = 0, hjust = 0.5) +
scale_fill_brewer(palette = "Set1") +
scale_x_continuous(limits = c(0.8, 2.2), expand = c(0, 0)) +
coord_cartesian(clip = "off") +
theme_void() +
guides(fill = "none") +
theme(panel.background = element_rect(colour = "grey77", fill = "grey77"))
10.5 Thomas de Lang
[I am absolutely not knowledgeable about structure equation modelling or confirmatory factor analyses. This was helpful to me: https://lavaan.ugent.be/tutorial/tutorial.pdf]
library(lavaan)
There is this dataset mpg
on cars in the ggplot package, let’s use it for a confirmatory factor analysis. There are three variables that measure efficiency of the car, hwy
, cty
, and displ
library(ggplot2)
<- mpg
data
# model specifications
<- ' efficiency =~ hwy + cty + displ'
cars_model_spec
# fit model
<- cfa(cars_model_spec, data = data)
fit summary(fit, fit.measures = TRUE)
## lavaan 0.6-12 ended normally after 82 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 6
##
## Number of observations 234
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 811.079
## Degrees of freedom 3
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1405.396
## Loglikelihood unrestricted model (H1) -1405.396
##
## Akaike (AIC) 2822.792
## Bayesian (BIC) 2843.524
## Sample-size adjusted Bayesian (BIC) 2824.506
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value RMSEA <= 0.05 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## efficiency =~
## hwy 1.000
## cty 0.745 0.019 39.903 0.000
## displ -0.181 0.010 -18.893 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .hwy 2.930 0.558 5.247 0.000
## .cty 0.064 0.271 0.234 0.815
## .displ 0.599 0.058 10.389 0.000
## efficiency 32.376 3.289 9.843 0.000