README.md
In CDEager/nauf: Regression with NA Values in Unordered Factors
nauf 1.1.1
Installation
To install the nauf package, call:
install.packages("nauf")
Package use
It is often the case that a factor only makes sense in a subset of a dataset (i.e. for some observations a factor may simply not be meaningful or contrastive), or that with observational datasets there are no observations in some levels of an interaction term. There are also cases where a random effects grouping factor is only applicable in a subset the data, and it is desireable to model the noise introduced by the repeated measures on the group members within the subset of the data where the repeated measures exist. The nauf package allows unordered factors and random effects grouping factors to be coded as NA in the subsets of the data where they are not applicable or otherwise not contrastive. Sum contrasts are used for all unordered factors (using named_contr_sum in the standardize package), and then NA values are set to 0. This allows all of the data to be modeled together without creating collinearity or making the output difficult to interpret.
For example, in the fricatives dataset, the factor uvoi (underlying voicing) is not contrastive in Spanish, and in Catalan it can only be contrastive in for certain word positions, leading to an imbalanced distribution:
library(nauf)
#> Loading required package: standardize
#> Loading required package: lme4
#> Loading required package: Matrix
#> Loading required package: rstanarm
#> Loading required package: Rcpp
#> rstanarm (Version 2.15.3, packaged: 2017-04-29 06:18:44 UTC)
#> - Do not expect the default priors to remain the same in future rstanarm versions.
#> Thus, R scripts should specify priors explicitly, even if they are just the defaults.
#> - For execution on a local, multicore CPU with excess RAM we recommend calling
#> options(mc.cores = parallel::detectCores())
summary(fricatives)
#> dur pvoi lang wordpos
#> Min. : 29.71 Min. :0.0625 Catalan:974 Final :296
#> 1st Qu.: 71.14 1st Qu.:0.5263 Spanish:648 Initial:397
#> Median : 86.29 Median :0.6471 Medial :929
#> Mean : 90.35 Mean :0.6937
#> 3rd Qu.:105.53 3rd Qu.:0.9231
#> Max. :250.78 Max. :1.0000
#>
#> uvoi speaker
#> Neutralized: 160 s12 : 142
#> Voiced : 327 s09 : 141
#> Voiceless :1135 s10 : 102
#> s20 : 71
#> s36 : 70
#> s19 : 64
#> (Other):1032
dat <- fricatives
u <- unique(dat[, c("lang", "wordpos", "uvoi")])
u <- u[order(u$lang, u$wordpos, u$uvoi), ]
rownames(u) <- NULL
u
#> lang wordpos uvoi
#> 1 Catalan Final Neutralized
#> 2 Catalan Initial Voiceless
#> 3 Catalan Medial Voiced
#> 4 Catalan Medial Voiceless
#> 5 Spanish Final Voiceless
#> 6 Spanish Initial Voiceless
#> 7 Spanish Medial Voiceless
With nauf, we can code uvoi as NA when it is not contrastive, and include uvoi slopes only for speakers where it is contrastive by creating language-specific speaker columns set to NA for the opposite language:
u$uvoi[!(u$lang == "Catalan" & u$wordpos == "Medial")] <- NA
u
#> lang wordpos uvoi
#> 1 Catalan Final <NA>
#> 2 Catalan Initial <NA>
#> 3 Catalan Medial Voiced
#> 4 Catalan Medial Voiceless
#> 5 Spanish Final <NA>
#> 6 Spanish Initial <NA>
#> 7 Spanish Medial <NA>
dat$uvoi[!(dat$lang == "Catalan" & dat$wordpos == "Medial")] <- NA
dat$c_speaker <- dat$s_speaker <- dat$speaker
dat$c_speaker[dat$lang != "Catalan"] <- NA
dat$s_speaker[dat$lang != "Spanish"] <- NA
sobj <- standardize(pvoi ~ lang * wordpos + uvoi +
(1 + wordpos + uvoi | c_speaker) + (1 + wordpos | s_speaker),
dat)
mod <- nauf_lmer(sobj$formula, sobj$data)
summary(mod)
#> Linear mixed model fit by REML ['nauf.lmerMod']
#> Formula:
#> pvoi ~ lang * wordpos + uvoi + (1 + wordpos + uvoi | c_speaker) +
#> (1 + wordpos | s_speaker)
#> Data: sobj$data
#>
#> REML criterion at convergence: 3584.7
#>
#> Scaled residuals:
#> Min 1Q Median 3Q Max
#> -4.4721 -0.6148 0.0511 0.5195 3.1344
#>
#> Random effects:
#> Groups Name Variance Std.Dev. Corr
#> c_speaker (Intercept) 0.154115 0.39258
#> wordposFinal 0.042090 0.20516 -0.67
#> wordposInitial 0.034222 0.18499 0.36 -0.91
#> uvoiVoiced 0.028117 0.16768 -0.21 0.76 -0.73
#> s_speaker (Intercept) 0.123763 0.35180
#> wordposFinal 0.011092 0.10532 0.92
#> wordposInitial 0.006788 0.08239 -0.38 0.01
#> Residual 0.482529 0.69464
#> Number of obs: 1622, groups: c_speaker, 26; s_speaker, 16
#>
#> Fixed effects:
#> Estimate Std. Error t value
#> (Intercept) 0.02215 0.06339 0.349
#> langCatalan 0.28058 0.06339 4.426
#> wordposFinal 0.46883 0.04183 11.208
#> wordposInitial -0.40194 0.03791 -10.603
#> uvoiVoiced 0.64890 0.04716 13.761
#> langCatalan:wordposFinal 0.28252 0.04183 6.754
#> langCatalan:wordposInitial -0.39017 0.03791 -10.292
#>
#> Correlation of Fixed Effects:
#> (Intr) lngCtl wrdpsF wrdpsI uvoVcd lngC:F
#> langCatalan -0.150
#> wordposFinl 0.105 -0.416
#> wordposIntl 0.045 0.155 -0.669
#> uvoiVoiced -0.098 -0.098 0.282 -0.236
#> lngCtln:wrF -0.416 0.105 0.076 -0.184 0.282
#> lngCtln:wrI 0.155 0.045 -0.184 0.057 -0.236 -0.669
anova(mod, method = "S")
#> Mixed Model Anova Table (Type III tests, S-method)
#>
#> Model: pvoi ~ lang * wordpos + uvoi + (1 + wordpos + uvoi | c_speaker) +
#> Model: (1 + wordpos | s_speaker)
#> Data: $
#> Data: sobj
#> Data: data
#> num Df den Df F Pr(>F)
#> lang 1 29.382 19.591 0.0001215 ***
#> wordpos 2 30.293 71.535 3.349e-12 ***
#> uvoi 1 20.331 189.361 9.136e-12 ***
#> lang:wordpos 2 30.293 52.983 1.285e-10 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Predicted marginal means can be calculated for specific subsets where a factor is contrastive. For example, uvoi is only contrastive for word-medial Catalan fricatives, so we could call:
rg <- nauf_ref.grid(mod)
nauf_pmmeans(rg, "uvoi", pairwise = TRUE,
subset = list(lang = "Catalan", wordpos = "Medial")
)
#>
#> Predicted marginal means for 'uvoi'
#> NA not considered a level for: 'uvoi'
#>
#> Factors conditioned on: 'lang' 'wordpos'
#>
#> See the 'subset' element of the 'specs' attribute for subsetted groups
#>
#> $pmmeans
#> uvoi pmmean SE df lower.CL upper.CL
#> Voiced 0.9923974 0.1005598 25.74 0.7855905 1.19920437
#> Voiceless -0.3054125 0.1163456 23.48 -0.5446837 -0.06614136
#>
#> Confidence level used: 0.95
#>
#> $contrasts
#> contrast estimate SE df t.ratio p.value
#> Voiced - Voiceless 1.29781 0.09431177 20.33 13.761 <.0001
CDEager/nauf documentation built on May 6, 2019, 9:24 a.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
nauf 1.1.1
Installation
To install the nauf package, call:
install.packages("nauf")
Package use
It is often the case that a factor only makes sense in a subset of a dataset (i.e. for some observations a factor may simply not be meaningful or contrastive), or that with observational datasets there are no observations in some levels of an interaction term. There are also cases where a random effects grouping factor is only applicable in a subset the data, and it is desireable to model the noise introduced by the repeated measures on the group members within the subset of the data where the repeated measures exist. The nauf package allows unordered factors and random effects grouping factors to be coded as NA in the subsets of the data where they are not applicable or otherwise not contrastive. Sum contrasts are used for all unordered factors (using named_contr_sum in the standardize package), and then NA values are set to 0. This allows all of the data to be modeled together without creating collinearity or making the output difficult to interpret.
For example, in the fricatives dataset, the factor uvoi (underlying voicing) is not contrastive in Spanish, and in Catalan it can only be contrastive in for certain word positions, leading to an imbalanced distribution:
library(nauf)
#> Loading required package: standardize
#> Loading required package: lme4
#> Loading required package: Matrix
#> Loading required package: rstanarm
#> Loading required package: Rcpp
#> rstanarm (Version 2.15.3, packaged: 2017-04-29 06:18:44 UTC)
#> - Do not expect the default priors to remain the same in future rstanarm versions.
#> Thus, R scripts should specify priors explicitly, even if they are just the defaults.
#> - For execution on a local, multicore CPU with excess RAM we recommend calling
#> options(mc.cores = parallel::detectCores())
summary(fricatives)
#> dur pvoi lang wordpos
#> Min. : 29.71 Min. :0.0625 Catalan:974 Final :296
#> 1st Qu.: 71.14 1st Qu.:0.5263 Spanish:648 Initial:397
#> Median : 86.29 Median :0.6471 Medial :929
#> Mean : 90.35 Mean :0.6937
#> 3rd Qu.:105.53 3rd Qu.:0.9231
#> Max. :250.78 Max. :1.0000
#>
#> uvoi speaker
#> Neutralized: 160 s12 : 142
#> Voiced : 327 s09 : 141
#> Voiceless :1135 s10 : 102
#> s20 : 71
#> s36 : 70
#> s19 : 64
#> (Other):1032
dat <- fricatives
u <- unique(dat[, c("lang", "wordpos", "uvoi")])
u <- u[order(u$lang, u$wordpos, u$uvoi), ]
rownames(u) <- NULL
u
#> lang wordpos uvoi
#> 1 Catalan Final Neutralized
#> 2 Catalan Initial Voiceless
#> 3 Catalan Medial Voiced
#> 4 Catalan Medial Voiceless
#> 5 Spanish Final Voiceless
#> 6 Spanish Initial Voiceless
#> 7 Spanish Medial Voiceless
With nauf, we can code uvoi as NA when it is not contrastive, and include uvoi slopes only for speakers where it is contrastive by creating language-specific speaker columns set to NA for the opposite language:
u$uvoi[!(u$lang == "Catalan" & u$wordpos == "Medial")] <- NA
u
#> lang wordpos uvoi
#> 1 Catalan Final <NA>
#> 2 Catalan Initial <NA>
#> 3 Catalan Medial Voiced
#> 4 Catalan Medial Voiceless
#> 5 Spanish Final <NA>
#> 6 Spanish Initial <NA>
#> 7 Spanish Medial <NA>
dat$uvoi[!(dat$lang == "Catalan" & dat$wordpos == "Medial")] <- NA
dat$c_speaker <- dat$s_speaker <- dat$speaker
dat$c_speaker[dat$lang != "Catalan"] <- NA
dat$s_speaker[dat$lang != "Spanish"] <- NA
sobj <- standardize(pvoi ~ lang * wordpos + uvoi +
(1 + wordpos + uvoi | c_speaker) + (1 + wordpos | s_speaker),
dat)
mod <- nauf_lmer(sobj$formula, sobj$data)
summary(mod)
#> Linear mixed model fit by REML ['nauf.lmerMod']
#> Formula:
#> pvoi ~ lang * wordpos + uvoi + (1 + wordpos + uvoi | c_speaker) +
#> (1 + wordpos | s_speaker)
#> Data: sobj$data
#>
#> REML criterion at convergence: 3584.7
#>
#> Scaled residuals:
#> Min 1Q Median 3Q Max
#> -4.4721 -0.6148 0.0511 0.5195 3.1344
#>
#> Random effects:
#> Groups Name Variance Std.Dev. Corr
#> c_speaker (Intercept) 0.154115 0.39258
#> wordposFinal 0.042090 0.20516 -0.67
#> wordposInitial 0.034222 0.18499 0.36 -0.91
#> uvoiVoiced 0.028117 0.16768 -0.21 0.76 -0.73
#> s_speaker (Intercept) 0.123763 0.35180
#> wordposFinal 0.011092 0.10532 0.92
#> wordposInitial 0.006788 0.08239 -0.38 0.01
#> Residual 0.482529 0.69464
#> Number of obs: 1622, groups: c_speaker, 26; s_speaker, 16
#>
#> Fixed effects:
#> Estimate Std. Error t value
#> (Intercept) 0.02215 0.06339 0.349
#> langCatalan 0.28058 0.06339 4.426
#> wordposFinal 0.46883 0.04183 11.208
#> wordposInitial -0.40194 0.03791 -10.603
#> uvoiVoiced 0.64890 0.04716 13.761
#> langCatalan:wordposFinal 0.28252 0.04183 6.754
#> langCatalan:wordposInitial -0.39017 0.03791 -10.292
#>
#> Correlation of Fixed Effects:
#> (Intr) lngCtl wrdpsF wrdpsI uvoVcd lngC:F
#> langCatalan -0.150
#> wordposFinl 0.105 -0.416
#> wordposIntl 0.045 0.155 -0.669
#> uvoiVoiced -0.098 -0.098 0.282 -0.236
#> lngCtln:wrF -0.416 0.105 0.076 -0.184 0.282
#> lngCtln:wrI 0.155 0.045 -0.184 0.057 -0.236 -0.669
anova(mod, method = "S")
#> Mixed Model Anova Table (Type III tests, S-method)
#>
#> Model: pvoi ~ lang * wordpos + uvoi + (1 + wordpos + uvoi | c_speaker) +
#> Model: (1 + wordpos | s_speaker)
#> Data: $
#> Data: sobj
#> Data: data
#> num Df den Df F Pr(>F)
#> lang 1 29.382 19.591 0.0001215 ***
#> wordpos 2 30.293 71.535 3.349e-12 ***
#> uvoi 1 20.331 189.361 9.136e-12 ***
#> lang:wordpos 2 30.293 52.983 1.285e-10 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Predicted marginal means can be calculated for specific subsets where a factor is contrastive. For example, uvoi is only contrastive for word-medial Catalan fricatives, so we could call:
rg <- nauf_ref.grid(mod)
nauf_pmmeans(rg, "uvoi", pairwise = TRUE,
subset = list(lang = "Catalan", wordpos = "Medial")
)
#>
#> Predicted marginal means for 'uvoi'
#> NA not considered a level for: 'uvoi'
#>
#> Factors conditioned on: 'lang' 'wordpos'
#>
#> See the 'subset' element of the 'specs' attribute for subsetted groups
#>
#> $pmmeans
#> uvoi pmmean SE df lower.CL upper.CL
#> Voiced 0.9923974 0.1005598 25.74 0.7855905 1.19920437
#> Voiceless -0.3054125 0.1163456 23.48 -0.5446837 -0.06614136
#>
#> Confidence level used: 0.95
#>
#> $contrasts
#> contrast estimate SE df t.ratio p.value
#> Voiced - Voiceless 1.29781 0.09431177 20.33 13.761 <.0001
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